feat(benchmark): Add -P, --parallel-tasks option to allow running multiple tasks concurrently

* Add dependency `pytest-parallel` and indirect dependency `py` (pylib)
* Make `SingletonReportManager` thread safe

.github/CODEOWNERS

@@ -1,5 +1,5 @@
-.github/workflows/  @Significant-Gravitas/devops
-autogpts/autogpt/   @Significant-Gravitas/maintainers
-autogpts/forge/     @Significant-Gravitas/forge-maintainers
-benchmark/          @Significant-Gravitas/benchmark-maintainers
-frontend/           @Significant-Gravitas/frontend-maintainers
+.github/workflows/  @Significant-Gravitas/maintainers
+autogpts/autogpt/   @Pwuts
+benchmark/  @Significant-Gravitas/benchmarkers
+forge/      @Swiftyos
+frontend/   @hunteraraujo

.github/labeler.yml

@@ -1,23 +0,0 @@
-AutoGPT Agent:
-- changed-files:
-  - any-glob-to-any-file: autogpts/autogpt/**
-
-Forge:
-- changed-files:
-  - any-glob-to-any-file: autogpts/forge/**
-
-Benchmark:
-- changed-files:
-  - any-glob-to-any-file: benchmark/**
-
-Frontend:
-- changed-files:
-  - any-glob-to-any-file: frontend/**
-
-Arena:
-- changed-files:
-  - any-glob-to-any-file: arena/**
-
-documentation:
-- changed-files:
-  - any-glob-to-any-file: docs/**

.github/workflows/arena-intake.yml

@@ -1,169 +0,0 @@
-name: Arena intake
-
-on:
-  # We recommend `pull_request_target` so that github secrets are available.
-  # In `pull_request` we wouldn't be able to change labels of fork PRs
-  pull_request_target:
-    types: [ opened, synchronize ]
-    paths:
-      - 'arena/**'
-
-jobs:
-  check:
-    permissions:
-      pull-requests: write
-    runs-on: ubuntu-latest
-    steps:
-    - name: Checkout PR
-      uses: actions/checkout@v4
-      with:
-        ref: ${{ github.event.pull_request.head.sha }}
-
-    - name: Check Arena entry
-      uses: actions/github-script@v7
-      with:
-        script: |
-          console.log('⚙️ Setting up...');
-
-          const fs = require('fs');
-          const path = require('path');
-
-          const pr = context.payload.pull_request;
-          const isFork = pr.head.repo.fork;
-
-          console.log('🔄️ Fetching PR diff metadata...');
-          const prFilesChanged = (await github.rest.pulls.listFiles({
-            owner: context.repo.owner,
-            repo: context.repo.repo,
-            pull_number: pr.number,
-          })).data;
-          console.debug(prFilesChanged);
-          const arenaFilesChanged = prFilesChanged.filter(
-            ({ filename: file }) => file.startsWith('arena/') && file.endsWith('.json')
-          );
-          const hasChangesInAutogptsFolder = prFilesChanged.some(
-            ({ filename }) => filename.startsWith('autogpts/')
-          );
-
-          console.log(`🗒️ ${arenaFilesChanged.length} arena entries affected`);
-          console.debug(arenaFilesChanged);
-          if (arenaFilesChanged.length === 0) {
-            // If no files in `arena/` are changed, this job does not need to run.
-            return;
-          }
-
-          let close = false;
-          let flagForManualCheck = false;
-          let issues = [];
-
-          if (isFork) {
-            if (arenaFilesChanged.length > 1) {
-              // Impacting multiple entries in `arena/` is not allowed
-              issues.push('This pull request impacts multiple arena entries');
-            }
-            if (hasChangesInAutogptsFolder) {
-              // PRs that include the custom agent are generally not allowed
-              issues.push(
-                'This pull request includes changes in `autogpts/`.\n'
-                + 'Please make sure to only submit your arena entry (`arena/*.json`), '
-                + 'and not to accidentally include your custom agent itself.'
-              );
-            }
-          }
-
-          if (arenaFilesChanged.length === 1) {
-            const newArenaFile = arenaFilesChanged[0]
-            const newArenaFileName = path.basename(newArenaFile.filename)
-            console.log(`🗒️ Arena entry in PR: ${newArenaFile}`);
-
-            if (newArenaFile.status != 'added') {
-              flagForManualCheck = true;
-            }
-
-            if (pr.mergeable != false) {
-              const newArenaEntry = JSON.parse(fs.readFileSync(newArenaFile.filename));
-              const allArenaFiles = await (await glob.create('arena/*.json')).glob();
-              console.debug(newArenaEntry);
-
-              console.log(`➡️ Checking ${newArenaFileName} against existing entries...`);
-              for (const file of allArenaFiles) {
-                const existingEntryName = path.basename(file);
-
-                if (existingEntryName === newArenaFileName) {
-                  continue;
-                }
-
-                console.debug(`Checking against ${existingEntryName}...`);
-
-                const arenaEntry = JSON.parse(fs.readFileSync(file));
-                if (arenaEntry.github_repo_url === newArenaEntry.github_repo_url) {
-                  console.log(`⚠️ Duplicate detected: ${existingEntryName}`);
-                  issues.push(
-                    `The \`github_repo_url\` specified in __${newArenaFileName}__ `
-                    + `already exists in __${existingEntryName}__. `
-                    + `This PR will be closed as duplicate.`
-                  )
-                  close = true;
-                }
-              }
-            } else {
-              console.log('⚠️ PR has conflicts');
-              issues.push(
-                `__${newArenaFileName}__ conflicts with existing entry with the same name`
-              )
-              close = true;
-            }
-          }  // end if (arenaFilesChanged.length === 1)
-
-          console.log('🏁 Finished checking against existing entries');
-
-          if (issues.length == 0) {
-            console.log('✅ No issues detected');
-            if (flagForManualCheck) {
-              console.log('🤔 Requesting review from maintainers...');
-              await github.rest.pulls.requestReviewers({
-                owner: context.repo.owner,
-                repo: context.repo.repo,
-                pull_number: pr.number,
-                reviewers: ['Pwuts'],
-                // team_reviewers: ['maintainers'],  // doesn't work: https://stackoverflow.com/a/64977184/4751645
-              });
-            } else {
-              console.log('➡️ Approving PR...');
-              await github.rest.pulls.createReview({
-                owner: context.repo.owner,
-                repo: context.repo.repo,
-                pull_number: pr.number,
-                event: 'APPROVE',
-              });
-            }
-          } else {
-            console.log(`⚠️ ${issues.length} issues detected`);
-
-            console.log('➡️ Posting comment indicating issues...');
-            await github.rest.issues.createComment({
-              owner: context.repo.owner,
-              repo: context.repo.repo,
-              issue_number: pr.number,
-              body: `Our automation found one or more issues with this submission:\n`
-                + issues.map(i => `- ${i.replace('\n', '\n  ')}`).join('\n'),
-            });
-
-            console.log("➡️ Applying label 'invalid'...");
-            await github.rest.issues.addLabels({
-              owner: context.repo.owner,
-              repo: context.repo.repo,
-              issue_number: pr.number,
-              labels: ['invalid'],
-            });
-
-            if (close) {
-              console.log('➡️ Auto-closing PR...');
-              await github.rest.pulls.update({
-                owner: context.repo.owner,
-                repo: context.repo.repo,
-                pull_number: pr.number,
-                state: 'closed',
-              });
-            }
-          }

.github/workflows/autogpt-ci.yml

@@ -20,7 +20,6 @@ concurrency:
 
 defaults:
   run:
-    shell: bash
     working-directory: autogpts/autogpt
 
 jobs:
@@ -31,12 +30,12 @@ jobs:
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v4
+        uses: actions/checkout@v3
         with:
           fetch-depth: 0
 
       - name: Set up Python ${{ env.min-python-version }}
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@v4
         with:
           python-version: ${{ env.min-python-version }}
 
@@ -45,7 +44,7 @@ jobs:
         run: echo "date=$(date +'%Y-%m-%d')" >> $GITHUB_OUTPUT
 
       - name: Set up Python dependency cache
-        uses: actions/cache@v4
+        uses: actions/cache@v3
         with:
           path: ~/.cache/pypoetry
           key: ${{ runner.os }}-poetry-${{ hashFiles('autogpts/autogpt/pyproject.toml') }}-${{ steps.get_date.outputs.date }}
@@ -78,43 +77,24 @@ jobs:
   test:
     permissions:
       contents: read
+    runs-on: ubuntu-latest
     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' }}
+
+    services:
+      minio:
+        image: minio/minio:edge-cicd
+        ports:
+          - 9000:9000
+        options: >
+          --health-interval=10s --health-timeout=5s --health-retries=3
+          --health-cmd="curl -f http://localhost:9000/minio/health/live"
 
     steps:
-      # 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
+        uses: actions/checkout@v3
         with:
           fetch-depth: 0
           submodules: true
@@ -156,7 +136,7 @@ jobs:
           fi
 
       - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@v4
         with:
           python-version: ${{ matrix.python-version }}
 
@@ -165,34 +145,15 @@ jobs:
         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
+        uses: actions/cache@v3
         with:
-          path: ${{ runner.os == 'macOS' && '~/Library/Caches/pypoetry' || '~/.cache/pypoetry' }}
-          key: poetry-${{ runner.os }}-${{ hashFiles('autogpts/autogpt/poetry.lock') }}
-
-      - name: Install Poetry (Unix)
-        if: runner.os != 'Windows'
-        run: |
-          curl -sSL https://install.python-poetry.org | python3 -
-
-          if [ "${{ runner.os }}" = "macOS" ]; then
-            PATH="$HOME/.local/bin:$PATH"
-            echo "$HOME/.local/bin" >> $GITHUB_PATH
-          fi
-
-      - name: Install Poetry (Windows)
-        if: runner.os == 'Windows'
-        shell: pwsh
-        run: |
-          (Invoke-WebRequest -Uri https://install.python-poetry.org -UseBasicParsing).Content | python -
-
-          $env:PATH += ";$env:APPDATA\Python\Scripts"
-          echo "$env:APPDATA\Python\Scripts" >> $env:GITHUB_PATH
+          path: ~/.cache/pypoetry
+          key: ${{ runner.os }}-poetry-${{ hashFiles('autogpts/autogpt/pyproject.toml') }}-${{ steps.get_date.outputs.date }}
 
       - name: Install Python dependencies
-        run: poetry install
+        run: |
+          curl -sSL https://install.python-poetry.org | python3 -
+          poetry install
 
       - name: Run pytest with coverage
         run: |
@@ -204,15 +165,12 @@ jobs:
           CI: true
           PLAIN_OUTPUT: True
           OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
-          S3_ENDPOINT_URL: ${{ runner.os != 'Windows' && 'http://127.0.0.1:9000' || '' }}
+          S3_ENDPOINT_URL: http://localhost:9000
           AWS_ACCESS_KEY_ID: minioadmin
           AWS_SECRET_ACCESS_KEY: minioadmin
 
       - name: Upload coverage reports to Codecov
-        uses: codecov/codecov-action@v4
-        with:
-          token: ${{ secrets.CODECOV_TOKEN }}
-          flags: autogpt-agent,${{ runner.os }}
+        uses: codecov/codecov-action@v3
 
       - id: setup_git_auth
         name: Set up git token authentication
@@ -220,11 +178,7 @@ jobs:
         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
+          base64_pat=$(echo -n "pat:${{ secrets.PAT_REVIEW }}" | base64 -w0)
 
           git config "$config_key" \
             "Authorization: Basic $base64_pat"
@@ -285,12 +239,12 @@ jobs:
             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."
+            gh issue comment $PR_NUMBER --body "You changed AutoGPT's behaviour. 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
+        uses: actions/upload-artifact@v3
         with:
           name: test-logs
           path: autogpts/autogpt/logs/

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

@@ -16,14 +16,14 @@ jobs:
         build-type: [release, dev]
     steps:
     - name: Checkout repository
-      uses: actions/checkout@v4
+      uses: actions/checkout@v3
 
     - name: Set up Docker Buildx
-      uses: docker/setup-buildx-action@v3
+      uses: docker/setup-buildx-action@v2
 
     - id: build
       name: Build image
-      uses: docker/build-push-action@v5
+      uses: docker/build-push-action@v3
       with:
         context: autogpts/autogpt
         build-args: BUILD_TYPE=${{ matrix.build-type }}

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

@@ -24,7 +24,7 @@ defaults:
 
 env:
   IMAGE_NAME: auto-gpt
-  DEPLOY_IMAGE_NAME: ${{ secrets.DOCKER_USER && format('{0}/', secrets.DOCKER_USER) || '' }}auto-gpt
+  DEPLOY_IMAGE_NAME: ${{ secrets.DOCKER_USER }}/auto-gpt
   DEV_IMAGE_TAG: latest-dev
 
 jobs:
@@ -35,10 +35,10 @@ jobs:
         build-type: [release, dev]
     steps:
     - name: Checkout repository
-      uses: actions/checkout@v4
+      uses: actions/checkout@v3
 
     - name: Set up Docker Buildx
-      uses: docker/setup-buildx-action@v3
+      uses: docker/setup-buildx-action@v2
 
     - if: runner.debug
       run: |
@@ -47,12 +47,11 @@ jobs:
 
     - id: build
       name: Build image
-      uses: docker/build-push-action@v5
+      uses: docker/build-push-action@v3
       with:
         context: autogpts/autogpt
         build-args: BUILD_TYPE=${{ matrix.build-type }}
         tags: ${{ env.IMAGE_NAME }}
-        labels: GIT_REVISION=${{ github.sha }}
         load: true    # save to docker images
         # cache layers in GitHub Actions cache to speed up builds
         cache-from: type=gha,scope=autogpt-docker-${{ matrix.build-type }}
@@ -101,30 +100,28 @@ jobs:
 
     steps:
       - name: Check out repository
-        uses: actions/checkout@v4
+        uses: actions/checkout@v3
         with:
           submodules: true
 
-      - if: github.event_name == 'push'
-        name: Log in to Docker hub
-        uses: docker/login-action@v3
+      - name: Log in to Docker hub
+        uses: docker/login-action@v2
         with:
           username: ${{ secrets.DOCKER_USER }}
           password: ${{ secrets.DOCKER_PASSWORD }}
 
       - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
+        uses: docker/setup-buildx-action@v2
 
       - id: build
         name: Build image
-        uses: docker/build-push-action@v5
+        uses: docker/build-push-action@v3
         with:
           context: autogpts/autogpt
           build-args: BUILD_TYPE=dev  # include pytest
           tags: >
             ${{ env.IMAGE_NAME }},
             ${{ env.DEPLOY_IMAGE_NAME }}:${{ env.DEV_IMAGE_TAG }}
-          labels: GIT_REVISION=${{ github.sha }}
           load: true                  # save to docker images
           # cache layers in GitHub Actions cache to speed up builds
           cache-from: type=gha,scope=autogpt-docker-dev

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

@@ -24,16 +24,16 @@ jobs:
     runs-on: ubuntu-latest
     steps:
     - name: Checkout repository
-      uses: actions/checkout@v4
+      uses: actions/checkout@v3
 
     - name: Log in to Docker hub
-      uses: docker/login-action@v3
+      uses: docker/login-action@v2
       with:
         username: ${{ secrets.DOCKER_USER }}
         password: ${{ secrets.DOCKER_PASSWORD }}
 
     - name: Set up Docker Buildx
-      uses: docker/setup-buildx-action@v3
+      uses: docker/setup-buildx-action@v2
 
       # slashes are not allowed in image tags, but can appear in git branch or tag names
     - id: sanitize_tag
@@ -46,7 +46,7 @@ jobs:
 
     - id: build
       name: Build image
-      uses: docker/build-push-action@v5
+      uses: docker/build-push-action@v3
       with:
         context: autogpts/autogpt
         build-args: BUILD_TYPE=release
@@ -56,7 +56,6 @@ jobs:
           ${{ env.IMAGE_NAME }},
           ${{ env.DEPLOY_IMAGE_NAME }}:latest,
           ${{ env.DEPLOY_IMAGE_NAME }}:${{ steps.sanitize_tag.outputs.tag }}
-        labels: GIT_REVISION=${{ github.sha }}
 
         # cache layers in GitHub Actions cache to speed up builds
         cache-from: ${{ !inputs.no_cache && 'type=gha' || '' }},scope=autogpt-docker-release

.github/workflows/autogpts-benchmark.yml

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

.github/workflows/autogpts-ci.yml

@@ -37,13 +37,13 @@ jobs:
       min-python-version: '3.10'
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v4
+        uses: actions/checkout@v3
         with:
           fetch-depth: 0
           submodules: true
 
       - name: Set up Python ${{ env.min-python-version }}
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@v2
         with:
           python-version: ${{ env.min-python-version }}
 
@@ -56,14 +56,13 @@ jobs:
         run: |
           ./run agent start ${{ matrix.agent-name }}
           cd autogpts/${{ matrix.agent-name }}
-          poetry run agbenchmark --mock --test=BasicRetrieval --test=Battleship --test=WebArenaTask_0
+          poetry run agbenchmark --mock
           poetry run agbenchmark --test=WriteFile
         env:
           OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
           AGENT_NAME: ${{ matrix.agent-name }}
+          HELICONE_API_KEY: ${{ secrets.HELICONE_API_KEY }}
           REQUESTS_CA_BUNDLE: /etc/ssl/certs/ca-certificates.crt
           HELICONE_CACHE_ENABLED: false
           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' }}
+          REPORT_LOCATION: ${{ format('../../reports/{0}', matrix.agent-name) }}

.github/workflows/benchmark-ci.yml

@@ -23,12 +23,12 @@ jobs:
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v4
+        uses: actions/checkout@v3
         with:
           fetch-depth: 0
 
       - name: Set up Python ${{ env.min-python-version }}
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@v2
         with:
           python-version: ${{ env.min-python-version }}
 
@@ -78,13 +78,13 @@ jobs:
     timeout-minutes: 20
     steps:
       - name: Checkout repository
-        uses: actions/checkout@v4
+        uses: actions/checkout@v3
         with:
           fetch-depth: 0
           submodules: true
 
       - name: Set up Python ${{ env.min-python-version }}
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@v2
         with:
           python-version: ${{ env.min-python-version }}
 
@@ -96,10 +96,11 @@ jobs:
       - name: Run regression tests
         run: |
           ./run agent start ${{ matrix.agent-name }}
+          sleep 10
           cd autogpts/${{ matrix.agent-name }}
-
           set +e # Ignore non-zero exit codes and continue execution
           echo "Running the following command: poetry run agbenchmark --maintain --mock"
+
           poetry run agbenchmark --maintain --mock
           EXIT_CODE=$?
           set -e  # Stop ignoring non-zero exit codes
@@ -126,7 +127,7 @@ jobs:
 
           poetry run agbenchmark --mock
           poetry run pytest -vv -s tests
-
+          
           CHANGED=$(git diff --name-only | grep -E '(agbenchmark/challenges)|(../frontend/assets)') || echo "No diffs"
           if [ ! -z "$CHANGED" ]; then
             echo "There are unstaged changes please run agbenchmark and commit those changes since they are needed."
@@ -137,5 +138,3 @@ jobs:
           fi
         env:
           OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
-          TELEMETRY_ENVIRONMENT: autogpt-benchmark-ci
-          TELEMETRY_OPT_IN: ${{ github.ref_name == 'master' }}

.github/workflows/benchmark_publish_package.yml

@@ -10,13 +10,13 @@ jobs:
       contents: write
     steps:
     - name: Checkout repository
-      uses: actions/checkout@v4
+      uses: actions/checkout@v2
       with:
         submodules: true
         fetch-depth: 0
 
     - name: Set up Python
-      uses: actions/setup-python@v5
+      uses: actions/setup-python@v2
       with:
         python-version: 3.8
 

.github/workflows/build-frontend.yml

@@ -0,0 +1,46 @@
+name: Build and Commit Frontend
+
+on:
+  push:
+    branches:
+      - master
+      - development
+      - 'ci-test*' # This will match any branch that starts with "ci-test"
+    paths:
+      - 'frontend/**'
+
+jobs:
+  build:
+    permissions:
+      contents: write
+    runs-on: ubuntu-latest
+    steps:
+    - name: Checkout Repo
+      uses: actions/checkout@v2
+    - name: Setup Flutter
+      uses: subosito/flutter-action@v1
+      with:
+        flutter-version: '3.13.2'
+    - name: Build Flutter Web
+      run: |
+        cd frontend
+        flutter build web --base-href /app/
+    - name: Set branch name
+      id: vars
+      run: echo "::set-output name=branch::frontend_build_${GITHUB_SHA}"
+    - name: Commit and Push
+      run: |
+        git config --local user.email "action@github.com"
+        git config --local user.name "GitHub Action"
+        git add frontend/build/web
+        git commit -m "Update frontend build" -a
+        git checkout -b ${{ steps.vars.outputs.branch }}
+        echo "Commit hash: ${GITHUB_SHA}"
+        git push origin ${{ steps.vars.outputs.branch }}
+    # - name: Create Pull Request
+    #   uses: peter-evans/create-pull-request@v3
+    #   with:
+    #     title: "Update frontend build"
+    #     body: "This PR updates the frontend build."
+    #     branch: ${{ steps.vars.outputs.branch }}
+    #     base: "master"

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

@@ -11,7 +11,7 @@ jobs:
   stale:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/stale@v9
+      - uses: actions/stale@v8
         with:
           # operations-per-run: 5000
           stale-issue-message: >

.github/workflows/frontend-ci.yml

@@ -1,60 +0,0 @@
-name: Frontend CI/CD
-
-on:
-  push:
-    branches:
-      - master
-      - development
-      - 'ci-test*' # This will match any branch that starts with "ci-test"
-    paths:
-      - 'frontend/**'
-      - '.github/workflows/frontend-ci.yml'
-  pull_request:
-    paths:
-      - 'frontend/**'
-      - '.github/workflows/frontend-ci.yml'
-
-jobs:
-  build:
-    permissions:
-      contents: write
-      pull-requests: write
-    runs-on: ubuntu-latest
-    env:
-      BUILD_BRANCH: ${{ format('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 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 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@v6
-      with:
-        add-paths: 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/hackathon.yml

@@ -88,13 +88,13 @@ jobs:
         run: docker ps
 
       - name: Checkout repository
-        uses: actions/checkout@v4
+        uses: actions/checkout@v3
         with:
           fetch-depth: 0
           submodules: true
 
       - name: Set up Python ${{ env.min-python-version }}
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@v2
         with:
           python-version: ${{ env.min-python-version }}
 
@@ -107,7 +107,7 @@ jobs:
           curl -sSL https://install.python-poetry.org | python -
 
       - name: Install Node.js
-        uses: actions/setup-node@v4
+        uses: actions/setup-node@v1
         with:
           node-version: v18.15
 

.github/workflows/pr-label.yml

@@ -52,15 +52,6 @@ jobs:
           l_label: 'size/l'
           l_max_size: 500
           xl_label: 'size/xl'
-          message_if_xl:
-
-  scope:
-    if: ${{ github.event_name == 'pull_request_target' }}
-    permissions:
-      contents: read
-      pull-requests: write
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/labeler@v5
-        with:
-          sync-labels: true
+          message_if_xl: >
+            This PR exceeds the recommended size of 500 lines.
+            Please make sure you are NOT addressing multiple issues with one PR.

.pr_agent.toml

@@ -1,6 +0,0 @@
-[pr_reviewer]
-num_code_suggestions=0
-
-[pr_code_suggestions]
-commitable_code_suggestions=false
-num_code_suggestions=0

QUICKSTART.md

@@ -77,47 +77,37 @@ After executing the above commands, running `./run setup` should work successful
 
 #### 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 issues related to path translations and permissions 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:
 
-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)
+   ![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 a name of your choosing.
+    Now setup has been completed its time to create your agent template. 
+    Do so by running the `./run agent create YOUR_AGENT_NAME` replacing YOUR_AGENT_NAME with a name of your choice. Examples of valid names: swiftyosgpt or SwiftyosAgent or swiftyos_agent
 
-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
+   ![Create an Agent](docs/content/imgs/quickstart/007_create_agent.png)
 
-Examples: `SwiftyosAssistant`, `PwutsPRAgent`, `Narvis`, `evo.ninja`
+    Upon creating your agent its time to officially enter the Arena!
+    Do so by running `./run arena enter YOUR_AGENT_NAME`
 
-![Create an Agent](docs/content/imgs/quickstart/007_create_agent.png)
+   ![Enter the Arena](docs/content/imgs/quickstart/008_enter_arena.png)
 
-### Optional: Entering the Arena
-
-Entering the Arena is an optional step intended for those who wish to actively participate in the agent leaderboard. If you decide to participate, you can enter the Arena by running `./run arena enter YOUR_AGENT_NAME`. This step is not mandatory for the development or testing of your agent.
-
-Entries with names like `agent`, `ExampleAgent`, `test_agent` or `MyExampleGPT` will NOT be merged. We also don't accept copycat entries that use the name of other projects, like `AutoGPT` or `evo.ninja`.
-
-![Enter the Arena](docs/content/imgs/quickstart/008_enter_arena.png)
-
-> **Note**  
-> For advanced users, create a new branch and create a file called YOUR_AGENT_NAME.json in the arena directory. Then commit this and create a PR to merge into the main repo. Only single file entries will be permitted. The json file needs the following format:
-> ```json
-> {
->   "github_repo_url": "https://github.com/Swiftyos/YourAgentName",
->   "timestamp": "2023-09-18T10:03:38.051498",
->   "commit_hash_to_benchmark": "ac36f7bfc7f23ad8800339fa55943c1405d80d5e",
->   "branch_to_benchmark": "master"
-> }
-> ```
-> - `github_repo_url`: the url to your fork
-> - `timestamp`: timestamp of the last update of this file
-> - `commit_hash_to_benchmark`: the commit hash of your entry. You update each time you have an something ready to be officially entered into the hackathon
-> - `branch_to_benchmark`: the branch you are using to develop your agent on, default is master.
+   > Note: for advanced users, create a new branch and create a file called YOUR_AGENT_NAME.json in the arena directory. Then commit this and create a PR to merge into the main repo. Only single file entries will be permitted. The json file needs the following format. 
+   ```json
+   {
+    "github_repo_url": "https://github.com/Swiftyos/YourAgentName",
+    "timestamp": "2023-09-18T10:03:38.051498",
+    "commit_hash_to_benchmark": "ac36f7bfc7f23ad8800339fa55943c1405d80d5e",
+    "branch_to_benchmark": "master"
+   }
+   ```
+   - github_repo_url: the url to your fork
+   - timestamp: timestamp of the last update of this file
+   - commit_hash_to_benchmark: the commit hash of your entry. You update each time you have an something ready to be officially entered into the hackathon
+   - branch_to_benchmark: the branch you are using to develop your agent on, default is master.
 
 
 ## Running your Agent

README.md

@@ -102,11 +102,7 @@ To maintain a uniform standard and ensure seamless compatibility with many curre
 ---
 
 <p align="center">
-<a href="https://star-history.com/#Significant-Gravitas/AutoGPT">
-  <picture>
-    <source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=Significant-Gravitas/AutoGPT&type=Date&theme=dark" />
-    <source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/svg?repos=Significant-Gravitas/AutoGPT&type=Date" />
-    <img alt="Star History Chart" src="https://api.star-history.com/svg?repos=Significant-Gravitas/AutoGPT&type=Date" />
-  </picture>
-</a>
+  <a href="https://star-history.com/#Significant-Gravitas/AutoGPT&Date">
+    <img src="https://api.star-history.com/svg?repos=Significant-Gravitas/AutoGPT&type=Date" alt="Star History Chart">
+  </a>
 </p>

SECURITY.md

@@ -1,66 +0,0 @@
-# Security Policy
-
- - [**Using AutoGPT Securely**](#using-AutoGPT-securely)
-   - [Restrict Workspace](#restrict-workspace)
-   - [Untrusted inputs](#untrusted-inputs)
-   - [Data privacy](#data-privacy)
-   - [Untrusted environments or networks](#untrusted-environments-or-networks)
-   - [Multi-Tenant environments](#multi-tenant-environments)
- - [**Reporting a Vulnerability**](#reporting-a-vulnerability)
-
-## Using AutoGPT Securely
-
-### Restrict Workspace
-
-Since agents can read and write files, it is important to keep them restricted to a specific workspace. This happens by default *unless* RESTRICT_TO_WORKSPACE is set to False.
-
-Disabling RESTRICT_TO_WORKSPACE can increase security risks. However, if you still need to disable it, consider running AutoGPT inside a [sandbox](https://developers.google.com/code-sandboxing), to mitigate some of these risks.
-
-### Untrusted inputs
-
-When handling untrusted inputs, it's crucial to isolate the execution and carefully pre-process inputs to mitigate script injection risks.
-
-For maximum security when handling untrusted inputs, you may need to employ the following:
-
-* Sandboxing: Isolate the process.
-* Updates: Keep your libraries (including AutoGPT) updated with the latest security patches.
-* Input Sanitation: Before feeding data to the model, sanitize inputs rigorously. This involves techniques such as:
-    * Validation: Enforce strict rules on allowed characters and data types.
-    * Filtering: Remove potentially malicious scripts or code fragments.
-    * Encoding: Convert special characters into safe representations.
-    * Verification: Run tooling that identifies potential script injections (e.g. [models that detect prompt injection attempts](https://python.langchain.com/docs/guides/safety/hugging_face_prompt_injection)). 
-
-### Data privacy
-
-To protect sensitive data from potential leaks or unauthorized access, it is crucial to sandbox the agent execution. This means running it in a secure, isolated environment, which helps mitigate many attack vectors.
-
-### Untrusted environments or networks
-
-Since AutoGPT performs network calls to the OpenAI API, it is important to always run it with trusted environments and networks. Running it on untrusted environments can expose your API KEY to attackers.
-Additionally, running it on an untrusted network can expose your data to potential network attacks. 
-
-However, even when running on trusted networks, it is important to always encrypt sensitive data while sending it over the network.
-
-### Multi-Tenant environments
-
-If you intend to run multiple AutoGPT brains in parallel, it is your responsibility to ensure the models do not interact or access each other's data.
-
-The primary areas of concern are tenant isolation, resource allocation, model sharing and hardware attacks.
-
-- Tenant Isolation: you must make sure that the tenants run separately to prevent unwanted access to the data from other tenants. Keeping model network traffic separate is also important because you not only prevent unauthorized access to data, but also prevent malicious users or tenants sending prompts to execute under another tenant’s identity.
-
-- Resource Allocation: a denial of service caused by one tenant can affect the overall system health. Implement safeguards like rate limits, access controls, and health monitoring.
-
-- Data Sharing: in a multi-tenant design with data sharing, ensure tenants and users understand the security risks and sandbox agent execution to mitigate risks.
-
-- Hardware Attacks: the hardware (GPUs or TPUs) can also be attacked. [Research](https://scholar.google.com/scholar?q=gpu+side+channel) has shown that side channel attacks on GPUs are possible, which can make data leak from other brains or processes running on the same system at the same time.
-
-## Reporting a Vulnerability
-
-Beware that none of the topics under [Using AutoGPT Securely](#using-AutoGPT-securely) are considered vulnerabilities on AutoGPT.
-
-However, If you have discovered a security vulnerability in this project, please report it privately. **Do not disclose it as a public issue.** This gives us time to work with you to fix the issue before public exposure, reducing the chance that the exploit will be used before a patch is released.
-
-Please disclose it as a private [security advisory](https://github.com/Significant-Gravitas/AutoGPT/security/advisories/new).
-
-A team of volunteers on a reasonable-effort basis maintains this project. As such, please give us at least 90 days to work on a fix before public exposure.

autogpts/autogpt/.env.template

@@ -2,15 +2,8 @@
 ### AutoGPT - GENERAL SETTINGS
 ################################################################################
 
-## OPENAI_API_KEY - OpenAI API Key (Example: sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx)
-# OPENAI_API_KEY=
-
-## ANTHROPIC_API_KEY - Anthropic API Key (Example: sk-ant-api03-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx)
-# ANTHROPIC_API_KEY=
-
-## TELEMETRY_OPT_IN - Share telemetry on errors and other issues with the AutoGPT team, e.g. through Sentry.
-##   This helps us to spot and solve problems earlier & faster. (Default: DISABLED)
-# TELEMETRY_OPT_IN=true
+## OPENAI_API_KEY - OpenAI API Key (Example: my-openai-api-key)
+OPENAI_API_KEY=your-openai-api-key
 
 ## EXECUTE_LOCAL_COMMANDS - Allow local command execution (Default: False)
 # EXECUTE_LOCAL_COMMANDS=False
@@ -20,15 +13,15 @@
 ## RESTRICT_TO_WORKSPACE - Restrict file operations to workspace ./data/agents/<agent_id>/workspace (Default: True)
 # RESTRICT_TO_WORKSPACE=True
 
-## DISABLED_COMMANDS - The comma separated list of commands that are disabled (Default: None)
-# DISABLED_COMMANDS=
+## DISABLED_COMMAND_CATEGORIES - The list of categories of commands that are disabled (Default: None)
+# DISABLED_COMMAND_CATEGORIES=
 
-## FILE_STORAGE_BACKEND - Choose a storage backend for contents
+## WORKSPACE_BACKEND - Choose a storage backend for workspace contents
 ## Options: local, gcs, s3
-# FILE_STORAGE_BACKEND=local
+# WORKSPACE_BACKEND=local
 
-## STORAGE_BUCKET - GCS/S3 Bucket to store contents in
-# STORAGE_BUCKET=autogpt
+## WORKSPACE_STORAGE_BUCKET - GCS/S3 Bucket to store workspace contents in
+# WORKSPACE_STORAGE_BUCKET=autogpt
 
 ## GCS Credentials
 # see https://cloud.google.com/storage/docs/authentication#libauth
@@ -47,6 +40,9 @@
 ## AI_SETTINGS_FILE - Specifies which AI Settings file to use, relative to the AutoGPT root directory. (defaults to ai_settings.yaml)
 # AI_SETTINGS_FILE=ai_settings.yaml
 
+## PLUGINS_CONFIG_FILE - The path to the plugins_config.yaml file, relative to the AutoGPT root directory. (Default plugins_config.yaml)
+# PLUGINS_CONFIG_FILE=plugins_config.yaml
+
 ## PROMPT_SETTINGS_FILE - Specifies which Prompt Settings file to use, relative to the AutoGPT root directory. (defaults to prompt_settings.yaml)
 # PROMPT_SETTINGS_FILE=prompt_settings.yaml
 
@@ -90,14 +86,14 @@
 ### LLM MODELS
 ################################################################################
 
-## SMART_LLM - Smart language model (Default: gpt-4-turbo)
-# SMART_LLM=gpt-4-turbo
+## SMART_LLM - Smart language model (Default: gpt-4-0314)
+# SMART_LLM=gpt-4-0314
 
-## FAST_LLM - Fast language model (Default: gpt-3.5-turbo)
-# FAST_LLM=gpt-3.5-turbo
+## FAST_LLM - Fast language model (Default: gpt-3.5-turbo-16k)
+# FAST_LLM=gpt-3.5-turbo-16k
 
 ## EMBEDDING_MODEL - Model to use for creating embeddings
-# EMBEDDING_MODEL=text-embedding-3-small
+# EMBEDDING_MODEL=text-embedding-ada-002
 
 ################################################################################
 ### SHELL EXECUTION
@@ -232,8 +228,6 @@
 ### Agent Protocol Server Settings
 ################################################################################
 ## AP_SERVER_PORT - Specifies what port the agent protocol server will listen on. (Default: 8000)
-## AP_SERVER_DB_URL - Specifies what connection url the agent protocol database will connect to (Default: Internal SQLite)
-## AP_SERVER_CORS_ALLOWED_ORIGINS - Comma separated list of allowed origins for CORS. (Default: http://localhost:{AP_SERVER_PORT})
 # AP_SERVER_PORT=8000
+# # AP_SERVER_DB_URL - Specifies what connection url the agent protocol database will connect to (Default: Internal SQLite)
 # AP_SERVER_DB_URL=sqlite:///data/ap_server.db
-# AP_SERVER_CORS_ALLOWED_ORIGINS=

autogpts/autogpt/.pre-commit-config.yaml

@@ -22,11 +22,6 @@ repos:
       - id: black
         language_version: python3.10
 
-  - repo: https://github.com/PyCQA/flake8
-    rev: 7.0.0
-    hooks:
-      - id: flake8
-
   # - repo: https://github.com/pre-commit/mirrors-mypy
   #   rev: 'v1.3.0'
   #   hooks:

autogpts/autogpt/agbenchmark_config/benchmarks.py

@@ -3,12 +3,12 @@ import logging
 import sys
 from pathlib import Path
 
-from autogpt.agent_manager.agent_manager import AgentManager
 from autogpt.agents.agent import Agent, AgentConfiguration, AgentSettings
-from autogpt.app.main import _configure_llm_provider, run_interaction_loop
+from autogpt.app.main import _configure_openai_provider, run_interaction_loop
+from autogpt.commands import COMMAND_CATEGORIES
 from autogpt.config import AIProfile, ConfigBuilder
-from autogpt.file_storage import FileStorageBackendName, get_storage
 from autogpt.logs.config import configure_logging
+from autogpt.models.command_registry import CommandRegistry
 
 LOG_DIR = Path(__file__).parent / "logs"
 
@@ -19,27 +19,29 @@ def run_specific_agent(task: str, continuous_mode: bool = False) -> None:
 
 
 def bootstrap_agent(task: str, continuous_mode: bool) -> Agent:
-    configure_logging(
-        level=logging.DEBUG,
-        log_dir=LOG_DIR,
-        plain_console_output=True,
-    )
-
     config = ConfigBuilder.build_config_from_env()
+    config.logging.level = logging.DEBUG
+    config.logging.log_dir = LOG_DIR
+    config.logging.plain_console_output = True
+    configure_logging(**config.logging.dict())
+
     config.continuous_mode = continuous_mode
     config.continuous_limit = 20
     config.noninteractive_mode = True
     config.memory_backend = "no_memory"
 
+    command_registry = CommandRegistry.with_command_modules(COMMAND_CATEGORIES, config)
+
     ai_profile = AIProfile(
         ai_name="AutoGPT",
         ai_role="a multi-purpose AI assistant.",
         ai_goals=[task],
     )
 
+    agent_prompt_config = Agent.default_settings.prompt_config.copy(deep=True)
+    agent_prompt_config.use_functions_api = config.openai_functions
     agent_settings = AgentSettings(
         name=Agent.default_settings.name,
-        agent_id=AgentManager.generate_id("AutoGPT-benchmark"),
         description=Agent.default_settings.description,
         ai_profile=ai_profile,
         config=AgentConfiguration(
@@ -47,23 +49,19 @@ def bootstrap_agent(task: str, continuous_mode: bool) -> Agent:
             smart_llm=config.smart_llm,
             allow_fs_access=not config.restrict_to_workspace,
             use_functions_api=config.openai_functions,
+            plugins=config.plugins,
         ),
+        prompt_config=agent_prompt_config,
         history=Agent.default_settings.history.copy(deep=True),
     )
 
-    local = config.file_storage_backend == FileStorageBackendName.LOCAL
-    restrict_to_root = not local or config.restrict_to_workspace
-    file_storage = get_storage(
-        config.file_storage_backend, root_path="data", restrict_to_root=restrict_to_root
-    )
-    file_storage.initialize()
-
     agent = Agent(
         settings=agent_settings,
-        llm_provider=_configure_llm_provider(config),
-        file_storage=file_storage,
+        llm_provider=_configure_openai_provider(config),
+        command_registry=command_registry,
         legacy_config=config,
     )
+    agent.attach_fs(config.app_data_dir / "agents" / "AutoGPT-benchmark")  # HACK
     return agent
 
 

autogpts/autogpt/autogpt/agent_factory/configurators.py

@@ -1,17 +1,19 @@
 from typing import Optional
 
+from autogpt.agent_manager import AgentManager
 from autogpt.agents.agent import Agent, AgentConfiguration, AgentSettings
+from autogpt.commands import COMMAND_CATEGORIES
 from autogpt.config import AIDirectives, AIProfile, Config
 from autogpt.core.resource.model_providers import ChatModelProvider
-from autogpt.file_storage.base import FileStorage
+from autogpt.logs.config import configure_chat_plugins
+from autogpt.models.command_registry import CommandRegistry
+from autogpt.plugins import scan_plugins
 
 
 def create_agent(
-    agent_id: str,
     task: str,
     ai_profile: AIProfile,
     app_config: Config,
-    file_storage: FileStorage,
     llm_provider: ChatModelProvider,
     directives: Optional[AIDirectives] = None,
 ) -> Agent:
@@ -21,28 +23,26 @@ def create_agent(
         directives = AIDirectives.from_file(app_config.prompt_settings_file)
 
     agent = _configure_agent(
-        agent_id=agent_id,
         task=task,
         ai_profile=ai_profile,
         directives=directives,
         app_config=app_config,
-        file_storage=file_storage,
         llm_provider=llm_provider,
     )
 
+    agent.state.agent_id = AgentManager.generate_id(agent.ai_profile.ai_name)
+
     return agent
 
 
 def configure_agent_with_state(
     state: AgentSettings,
     app_config: Config,
-    file_storage: FileStorage,
     llm_provider: ChatModelProvider,
 ) -> Agent:
     return _configure_agent(
         state=state,
         app_config=app_config,
-        file_storage=file_storage,
         llm_provider=llm_provider,
     )
 
@@ -50,21 +50,26 @@ def configure_agent_with_state(
 def _configure_agent(
     app_config: Config,
     llm_provider: ChatModelProvider,
-    file_storage: FileStorage,
-    agent_id: str = "",
     task: str = "",
     ai_profile: Optional[AIProfile] = None,
     directives: Optional[AIDirectives] = None,
     state: Optional[AgentSettings] = None,
 ) -> Agent:
-    if not (state or agent_id and task and ai_profile and directives):
+    if not (state or task and ai_profile and directives):
         raise TypeError(
-            "Either (state) or (agent_id, task, ai_profile, directives)"
-            " must be specified"
+            "Either (state) or (task, ai_profile, directives) must be specified"
         )
 
+    app_config.plugins = scan_plugins(app_config)
+    configure_chat_plugins(app_config)
+
+    # Create a CommandRegistry instance and scan default folder
+    command_registry = CommandRegistry.with_command_modules(
+        modules=COMMAND_CATEGORIES,
+        config=app_config,
+    )
+
     agent_state = state or create_agent_state(
-        agent_id=agent_id,
         task=task,
         ai_profile=ai_profile,
         directives=directives,
@@ -76,20 +81,21 @@ def _configure_agent(
     return Agent(
         settings=agent_state,
         llm_provider=llm_provider,
-        file_storage=file_storage,
+        command_registry=command_registry,
         legacy_config=app_config,
     )
 
 
 def create_agent_state(
-    agent_id: str,
     task: str,
     ai_profile: AIProfile,
     directives: AIDirectives,
     app_config: Config,
 ) -> AgentSettings:
+    agent_prompt_config = Agent.default_settings.prompt_config.copy(deep=True)
+    agent_prompt_config.use_functions_api = app_config.openai_functions
+
     return AgentSettings(
-        agent_id=agent_id,
         name=Agent.default_settings.name,
         description=Agent.default_settings.description,
         task=task,
@@ -100,6 +106,8 @@ def create_agent_state(
             smart_llm=app_config.smart_llm,
             allow_fs_access=not app_config.restrict_to_workspace,
             use_functions_api=app_config.openai_functions,
+            plugins=app_config.plugins,
         ),
+        prompt_config=agent_prompt_config,
         history=Agent.default_settings.history.copy(deep=True),
     )

autogpts/autogpt/autogpt/agent_factory/generators.py

@@ -1,26 +1,21 @@
-from __future__ import annotations
-
 from typing import TYPE_CHECKING
 
-from autogpt.config.ai_directives import AIDirectives
-from autogpt.file_storage.base import FileStorage
-
-from .configurators import _configure_agent
-from .profile_generator import generate_agent_profile_for_task
-
 if TYPE_CHECKING:
     from autogpt.agents.agent import Agent
     from autogpt.config import Config
     from autogpt.core.resource.model_providers.schema import ChatModelProvider
 
+from autogpt.config.ai_directives import AIDirectives
+
+from .configurators import _configure_agent
+from .profile_generator import generate_agent_profile_for_task
+
 
 async def generate_agent_for_task(
-    agent_id: str,
     task: str,
-    app_config: Config,
-    file_storage: FileStorage,
-    llm_provider: ChatModelProvider,
-) -> Agent:
+    app_config: "Config",
+    llm_provider: "ChatModelProvider",
+) -> "Agent":
     base_directives = AIDirectives.from_file(app_config.prompt_settings_file)
     ai_profile, task_directives = await generate_agent_profile_for_task(
         task=task,
@@ -28,11 +23,9 @@ async def generate_agent_for_task(
         llm_provider=llm_provider,
     )
     return _configure_agent(
-        agent_id=agent_id,
         task=task,
         ai_profile=ai_profile,
         directives=base_directives + task_directives,
         app_config=app_config,
-        file_storage=file_storage,
         llm_provider=llm_provider,
     )

autogpts/autogpt/autogpt/agent_factory/profile_generator.py

@@ -8,6 +8,7 @@ from autogpt.core.prompting import (
     LanguageModelClassification,
     PromptStrategy,
 )
+from autogpt.core.prompting.utils import json_loads
 from autogpt.core.resource.model_providers.schema import (
     AssistantChatMessage,
     ChatMessage,
@@ -202,7 +203,9 @@ class AgentProfileGenerator(PromptStrategy):
                     f"LLM did not call {self._create_agent_function.name} function; "
                     "agent profile creation failed"
                 )
-            arguments: object = response_content.tool_calls[0].function.arguments
+            arguments: object = json_loads(
+                response_content.tool_calls[0].function.arguments
+            )
             ai_profile = AIProfile(
                 ai_name=arguments.get("name"),
                 ai_role=arguments.get("description"),
@@ -235,14 +238,18 @@ async def generate_agent_profile_for_task(
     prompt = agent_profile_generator.build_prompt(task)
 
     # Call LLM with the string as user input
-    output = await llm_provider.create_chat_completion(
-        prompt.messages,
-        model_name=app_config.smart_llm,
-        functions=prompt.functions,
-        completion_parser=agent_profile_generator.parse_response_content,
-    )
+    output = (
+        await llm_provider.create_chat_completion(
+            prompt.messages,
+            model_name=app_config.smart_llm,
+            functions=prompt.functions,
+        )
+    ).response
 
     # Debug LLM Output
-    logger.debug(f"AI Config Generator Raw Output: {output.response}")
+    logger.debug(f"AI Config Generator Raw Output: {output}")
 
-    return output.parsed_result
+    # Parse the output
+    ai_profile, ai_directives = agent_profile_generator.parse_response_content(output)
+
+    return ai_profile, ai_directives

autogpts/autogpt/autogpt/agent_manager/agent_manager.py

@@ -2,44 +2,47 @@ from __future__ import annotations
 
 import uuid
 from pathlib import Path
+from typing import TYPE_CHECKING
 
-from autogpt.agents.agent import AgentSettings
-from autogpt.file_storage.base import FileStorage
+if TYPE_CHECKING:
+    from autogpt.agents.agent import AgentSettings
+
+from autogpt.agents.utils.agent_file_manager import AgentFileManager
 
 
 class AgentManager:
-    def __init__(self, file_storage: FileStorage):
-        self.file_manager = file_storage.clone_with_subroot("agents")
+    def __init__(self, app_data_dir: Path):
+        self.agents_dir = app_data_dir / "agents"
+        if not self.agents_dir.exists():
+            self.agents_dir.mkdir()
 
     @staticmethod
     def generate_id(agent_name: str) -> str:
-        """Generate a unique ID for an agent given agent name."""
         unique_id = str(uuid.uuid4())[:8]
         return f"{agent_name}-{unique_id}"
 
     def list_agents(self) -> list[str]:
-        """Return all agent directories within storage."""
-        agent_dirs: list[str] = []
-        for file_path in self.file_manager.list_files():
-            if len(file_path.parts) == 2 and file_path.name == "state.json":
-                agent_dirs.append(file_path.parent.name)
-        return agent_dirs
+        return [
+            dir.name
+            for dir in self.agents_dir.iterdir()
+            if dir.is_dir() and AgentFileManager(dir).state_file_path.exists()
+        ]
 
-    def get_agent_dir(self, agent_id: str) -> Path:
-        """Return the directory of the agent with the given ID."""
+    def get_agent_dir(self, agent_id: str, must_exist: bool = False) -> Path:
         assert len(agent_id) > 0
-        agent_dir: Path | None = None
-        if self.file_manager.exists(agent_id):
-            agent_dir = self.file_manager.root / agent_id
-        else:
+        agent_dir = self.agents_dir / agent_id
+        if must_exist and not agent_dir.exists():
             raise FileNotFoundError(f"No agent with ID '{agent_id}'")
         return agent_dir
 
-    def load_agent_state(self, agent_id: str) -> AgentSettings:
-        """Load the state of the agent with the given ID."""
-        state_file_path = Path(agent_id) / "state.json"
-        if not self.file_manager.exists(state_file_path):
+    def retrieve_state(self, agent_id: str) -> AgentSettings:
+        from autogpt.agents.agent import AgentSettings
+
+        agent_dir = self.get_agent_dir(agent_id, True)
+        state_file = AgentFileManager(agent_dir).state_file_path
+        if not state_file.exists():
             raise FileNotFoundError(f"Agent with ID '{agent_id}' has no state.json")
 
-        state = self.file_manager.read_file(state_file_path)
-        return AgentSettings.parse_raw(state)
+        state = AgentSettings.load_from_json_file(state_file)
+        state.agent_data_dir = agent_dir
+        return state

autogpts/autogpt/autogpt/agents/README.md

@@ -1,37 +0,0 @@
-# 🤖 Agents
-
-Agent is composed of [🧩 Components](./components.md) and responsible for executing pipelines and some additional logic. The base class for all agents is `BaseAgent`, it has the necessary logic to collect components and execute protocols.
-
-## Important methods
-
-`BaseAgent` provides two abstract methods needed for any agent to work properly:
-1. `propose_action`: This method is responsible for proposing an action based on the current state of the agent, it returns `ThoughtProcessOutput`.
-2. `execute`: This method is responsible for executing the proposed action, returns `ActionResult`.
-
-## AutoGPT Agent
-
-`Agent` is the main agent provided by AutoGPT. It's a subclass of `BaseAgent`. It has all the [Built-in Components](./built-in-components.md). `Agent` implements the essential abstract methods from `BaseAgent`: `propose_action` and `execute`.
-
-## Building your own Agent
-
-The easiest way to build your own agent is to extend the `Agent` class and add additional components. By doing this you can reuse the existing components and the default logic for executing [⚙️ Protocols](./protocols.md).
-
-```py
-class MyComponent(AgentComponent):
-    pass
-
-class MyAgent(Agent):
-    def __init__(
-        self,
-        settings: AgentSettings,
-        llm_provider: ChatModelProvider,
-        file_storage: FileStorage,
-        legacy_config: Config,
-    ):
-        # Call the parent constructor to bring in the default components
-        super().__init__(settings, llm_provider, file_storage, legacy_config)
-        # Add your custom component
-        self.my_component = MyComponent()
-```
-
-For more customization, you can override the `propose_action` and `execute` or even subclass `BaseAgent` directly. This way you can have full control over the agent's components and behavior. Have a look at the [implementation of Agent](https://github.com/Significant-Gravitas/AutoGPT/tree/master/autogpts/autogpt/autogpt/agents/agent.py) for more details.

autogpts/autogpt/autogpt/agents/__init__.py

@@ -1,9 +1,4 @@
-from .agent import Agent, OneShotAgentActionProposal
-from .base import BaseAgent, BaseAgentActionProposal
+from .agent import Agent
+from .base import AgentThoughts, BaseAgent, CommandArgs, CommandName
 
-__all__ = [
-    "BaseAgent",
-    "Agent",
-    "BaseAgentActionProposal",
-    "OneShotAgentActionProposal",
-]
+__all__ = ["BaseAgent", "Agent", "CommandName", "CommandArgs", "AgentThoughts"]

autogpts/autogpt/autogpt/agents/agent.py

@@ -2,71 +2,56 @@ from __future__ import annotations
 
 import inspect
 import logging
+import time
 from datetime import datetime
 from typing import TYPE_CHECKING, Optional
 
-import sentry_sdk
+if TYPE_CHECKING:
+    from autogpt.config import Config
+    from autogpt.models.command_registry import CommandRegistry
+
 from pydantic import Field
 
-from autogpt.commands.execute_code import CodeExecutorComponent
-from autogpt.commands.git_operations import GitOperationsComponent
-from autogpt.commands.image_gen import ImageGeneratorComponent
-from autogpt.commands.system import SystemComponent
-from autogpt.commands.user_interaction import UserInteractionComponent
-from autogpt.commands.web_search import WebSearchComponent
-from autogpt.commands.web_selenium import WebSeleniumComponent
-from autogpt.components.event_history import EventHistoryComponent
 from autogpt.core.configuration import Configurable
 from autogpt.core.prompting import ChatPrompt
 from autogpt.core.resource.model_providers import (
-    AssistantFunctionCall,
+    AssistantChatMessage,
     ChatMessage,
     ChatModelProvider,
-    ChatModelResponse,
 )
-from autogpt.core.runner.client_lib.logging.helpers import dump_prompt
-from autogpt.file_storage.base import FileStorage
-from autogpt.llm.providers.openai import function_specs_from_commands
+from autogpt.llm.api_manager import ApiManager
 from autogpt.logs.log_cycle import (
     CURRENT_CONTEXT_FILE_NAME,
     NEXT_ACTION_FILE_NAME,
     USER_INPUT_FILE_NAME,
     LogCycleHandler,
 )
+from autogpt.logs.utils import fmt_kwargs
 from autogpt.models.action_history import (
+    Action,
     ActionErrorResult,
     ActionInterruptedByHuman,
     ActionResult,
     ActionSuccessResult,
-    EpisodicActionHistory,
 )
-from autogpt.models.command import Command, CommandOutput
-from autogpt.utils.exceptions import (
+from autogpt.models.command import CommandOutput
+from autogpt.models.context_item import ContextItem
+
+from .base import BaseAgent, BaseAgentConfiguration, BaseAgentSettings
+from .features.context import ContextMixin
+from .features.file_workspace import FileWorkspaceMixin
+from .features.watchdog import WatchdogMixin
+from .prompt_strategies.one_shot import (
+    OneShotAgentPromptConfiguration,
+    OneShotAgentPromptStrategy,
+)
+from .utils.exceptions import (
     AgentException,
     AgentTerminated,
     CommandExecutionError,
     UnknownCommandError,
 )
 
-from .base import BaseAgent, BaseAgentConfiguration, BaseAgentSettings
-from .features.agent_file_manager import FileManagerComponent
-from .features.context import ContextComponent
-from .features.watchdog import WatchdogComponent
-from .prompt_strategies.one_shot import (
-    OneShotAgentActionProposal,
-    OneShotAgentPromptStrategy,
-)
-from .protocols import (
-    AfterExecute,
-    AfterParse,
-    CommandProvider,
-    DirectiveProvider,
-    MessageProvider,
-)
-
-if TYPE_CHECKING:
-    from autogpt.config import Config
-
 logger = logging.getLogger(__name__)
 
 
@@ -76,64 +61,47 @@ class AgentConfiguration(BaseAgentConfiguration):
 
 class AgentSettings(BaseAgentSettings):
     config: AgentConfiguration = Field(default_factory=AgentConfiguration)
-
-    history: EpisodicActionHistory[OneShotAgentActionProposal] = Field(
-        default_factory=EpisodicActionHistory[OneShotAgentActionProposal]
+    prompt_config: OneShotAgentPromptConfiguration = Field(
+        default_factory=(
+            lambda: OneShotAgentPromptStrategy.default_configuration.copy(deep=True)
+        )
     )
-    """(STATE) The action history of the agent."""
 
 
-class Agent(BaseAgent, Configurable[AgentSettings]):
+class Agent(
+    ContextMixin,
+    FileWorkspaceMixin,
+    WatchdogMixin,
+    BaseAgent,
+    Configurable[AgentSettings],
+):
+    """AutoGPT's primary Agent; uses one-shot prompting."""
+
     default_settings: AgentSettings = AgentSettings(
         name="Agent",
-        description=__doc__ if __doc__ else "",
+        description=__doc__,
     )
 
+    prompt_strategy: OneShotAgentPromptStrategy
+
     def __init__(
         self,
         settings: AgentSettings,
         llm_provider: ChatModelProvider,
-        file_storage: FileStorage,
+        command_registry: CommandRegistry,
         legacy_config: Config,
     ):
-        super().__init__(settings)
-
-        self.llm_provider = llm_provider
-        self.ai_profile = settings.ai_profile
-        self.directives = settings.directives
-        prompt_config = OneShotAgentPromptStrategy.default_configuration.copy(deep=True)
-        prompt_config.use_functions_api = (
-            settings.config.use_functions_api
-            # Anthropic currently doesn't support tools + prefilling :(
-            and self.llm.provider_name != "anthropic"
+        prompt_strategy = OneShotAgentPromptStrategy(
+            configuration=settings.prompt_config,
+            logger=logger,
         )
-        self.prompt_strategy = OneShotAgentPromptStrategy(prompt_config, logger)
-        self.commands: list[Command] = []
-
-        # Components
-        self.system = SystemComponent(legacy_config, settings.ai_profile)
-        self.history = EventHistoryComponent(
-            settings.history,
-            self.send_token_limit,
-            lambda x: self.llm_provider.count_tokens(x, self.llm.name),
-            legacy_config,
-            llm_provider,
+        super().__init__(
+            settings=settings,
+            llm_provider=llm_provider,
+            prompt_strategy=prompt_strategy,
+            command_registry=command_registry,
+            legacy_config=legacy_config,
         )
-        self.user_interaction = UserInteractionComponent(legacy_config)
-        self.file_manager = FileManagerComponent(settings, file_storage)
-        self.code_executor = CodeExecutorComponent(
-            self.file_manager.workspace,
-            settings,
-            legacy_config,
-        )
-        self.git_ops = GitOperationsComponent(legacy_config)
-        self.image_gen = ImageGeneratorComponent(
-            self.file_manager.workspace, legacy_config
-        )
-        self.web_search = WebSearchComponent(legacy_config)
-        self.web_selenium = WebSeleniumComponent(legacy_config, llm_provider, self.llm)
-        self.context = ContextComponent(self.file_manager.workspace)
-        self.watchdog = WatchdogComponent(settings.config, settings.history)
 
         self.created_at = datetime.now().strftime("%Y%m%d_%H%M%S")
         """Timestamp the agent was created; only used for structured debug logging."""
@@ -141,153 +109,199 @@ class Agent(BaseAgent, Configurable[AgentSettings]):
         self.log_cycle_handler = LogCycleHandler()
         """LogCycleHandler for structured debug logging."""
 
-        self.event_history = settings.history
-        self.legacy_config = legacy_config
+    def build_prompt(
+        self,
+        *args,
+        extra_messages: Optional[list[ChatMessage]] = None,
+        include_os_info: Optional[bool] = None,
+        **kwargs,
+    ) -> ChatPrompt:
+        if not extra_messages:
+            extra_messages = []
 
-    async def propose_action(self) -> OneShotAgentActionProposal:
-        """Proposes the next action to execute, based on the task and current state.
-
-        Returns:
-            The command name and arguments, if any, and the agent's thoughts.
-        """
-        self.reset_trace()
-
-        # Get directives
-        resources = await self.run_pipeline(DirectiveProvider.get_resources)
-        constraints = await self.run_pipeline(DirectiveProvider.get_constraints)
-        best_practices = await self.run_pipeline(DirectiveProvider.get_best_practices)
-
-        directives = self.state.directives.copy(deep=True)
-        directives.resources += resources
-        directives.constraints += constraints
-        directives.best_practices += best_practices
-
-        # Get commands
-        self.commands = await self.run_pipeline(CommandProvider.get_commands)
-        self._remove_disabled_commands()
-
-        # Get messages
-        messages = await self.run_pipeline(MessageProvider.get_messages)
-
-        prompt: ChatPrompt = self.prompt_strategy.build_prompt(
-            messages=messages,
-            task=self.state.task,
-            ai_profile=self.state.ai_profile,
-            ai_directives=directives,
-            commands=function_specs_from_commands(self.commands),
-            include_os_info=self.legacy_config.execute_local_commands,
+        # Clock
+        extra_messages.append(
+            ChatMessage.system(f"The current time and date is {time.strftime('%c')}"),
         )
 
+        # Add budget information (if any) to prompt
+        api_manager = ApiManager()
+        if api_manager.get_total_budget() > 0.0:
+            remaining_budget = (
+                api_manager.get_total_budget() - api_manager.get_total_cost()
+            )
+            if remaining_budget < 0:
+                remaining_budget = 0
+
+            budget_msg = ChatMessage.system(
+                f"Your remaining API budget is ${remaining_budget:.3f}"
+                + (
+                    " BUDGET EXCEEDED! SHUT DOWN!\n\n"
+                    if remaining_budget == 0
+                    else " Budget very nearly exceeded! Shut down gracefully!\n\n"
+                    if remaining_budget < 0.005
+                    else " Budget nearly exceeded. Finish up.\n\n"
+                    if remaining_budget < 0.01
+                    else ""
+                ),
+            )
+            logger.debug(budget_msg)
+            extra_messages.append(budget_msg)
+
+        if include_os_info is None:
+            include_os_info = self.legacy_config.execute_local_commands
+
+        return super().build_prompt(
+            *args,
+            extra_messages=extra_messages,
+            include_os_info=include_os_info,
+            **kwargs,
+        )
+
+    def on_before_think(self, *args, **kwargs) -> ChatPrompt:
+        prompt = super().on_before_think(*args, **kwargs)
+
         self.log_cycle_handler.log_count_within_cycle = 0
         self.log_cycle_handler.log_cycle(
-            self.state.ai_profile.ai_name,
+            self.ai_profile.ai_name,
             self.created_at,
             self.config.cycle_count,
             prompt.raw(),
             CURRENT_CONTEXT_FILE_NAME,
         )
+        return prompt
 
-        logger.debug(f"Executing prompt:\n{dump_prompt(prompt)}")
-        output = await self.complete_and_parse(prompt)
-        self.config.cycle_count += 1
+    def parse_and_process_response(
+        self, llm_response: AssistantChatMessage, *args, **kwargs
+    ) -> Agent.ThoughtProcessOutput:
+        for plugin in self.config.plugins:
+            if not plugin.can_handle_post_planning():
+                continue
+            llm_response.content = plugin.post_planning(llm_response.content or "")
 
-        return output
-
-    async def complete_and_parse(
-        self, prompt: ChatPrompt, exception: Optional[Exception] = None
-    ) -> OneShotAgentActionProposal:
-        if exception:
-            prompt.messages.append(ChatMessage.system(f"Error: {exception}"))
-
-        response: ChatModelResponse[
-            OneShotAgentActionProposal
-        ] = await self.llm_provider.create_chat_completion(
-            prompt.messages,
-            model_name=self.llm.name,
-            completion_parser=self.prompt_strategy.parse_response_content,
-            functions=prompt.functions,
-            prefill_response=prompt.prefill_response,
-        )
-        result = response.parsed_result
+        (
+            command_name,
+            arguments,
+            assistant_reply_dict,
+        ) = self.prompt_strategy.parse_response_content(llm_response)
 
         self.log_cycle_handler.log_cycle(
-            self.state.ai_profile.ai_name,
+            self.ai_profile.ai_name,
             self.created_at,
             self.config.cycle_count,
-            result.thoughts.dict(),
+            assistant_reply_dict,
             NEXT_ACTION_FILE_NAME,
         )
 
-        await self.run_pipeline(AfterParse.after_parse, result)
+        if command_name:
+            self.event_history.register_action(
+                Action(
+                    name=command_name,
+                    args=arguments,
+                    reasoning=assistant_reply_dict["thoughts"]["reasoning"],
+                )
+            )
 
-        return result
+        return command_name, arguments, assistant_reply_dict
 
     async def execute(
         self,
-        proposal: OneShotAgentActionProposal,
-        user_feedback: str = "",
+        command_name: str,
+        command_args: dict[str, str] = {},
+        user_input: str = "",
     ) -> ActionResult:
-        tool = proposal.use_tool
+        result: ActionResult
 
-        # Get commands
-        self.commands = await self.run_pipeline(CommandProvider.get_commands)
-        self._remove_disabled_commands()
-
-        try:
-            return_value = await self._execute_tool(tool)
-
-            result = ActionSuccessResult(outputs=return_value)
-        except AgentTerminated:
-            raise
-        except AgentException as e:
-            result = ActionErrorResult.from_exception(e)
-            logger.warning(f"{tool} raised an error: {e}")
-            sentry_sdk.capture_exception(e)
-
-        result_tlength = self.llm_provider.count_tokens(str(result), self.llm.name)
-        if result_tlength > self.send_token_limit // 3:
-            result = ActionErrorResult(
-                reason=f"Command {tool.name} returned too much output. "
-                "Do not execute this command again with the same arguments."
+        if command_name == "human_feedback":
+            result = ActionInterruptedByHuman(feedback=user_input)
+            self.log_cycle_handler.log_cycle(
+                self.ai_profile.ai_name,
+                self.created_at,
+                self.config.cycle_count,
+                user_input,
+                USER_INPUT_FILE_NAME,
             )
 
-        await self.run_pipeline(AfterExecute.after_execute, result)
+        else:
+            for plugin in self.config.plugins:
+                if not plugin.can_handle_pre_command():
+                    continue
+                command_name, command_args = plugin.pre_command(
+                    command_name, command_args
+                )
 
-        logger.debug("\n".join(self.trace))
+            try:
+                return_value = await execute_command(
+                    command_name=command_name,
+                    arguments=command_args,
+                    agent=self,
+                )
+
+                # Intercept ContextItem if one is returned by the command
+                if type(return_value) is tuple and isinstance(
+                    return_value[1], ContextItem
+                ):
+                    context_item = return_value[1]
+                    return_value = return_value[0]
+                    logger.debug(
+                        f"Command {command_name} returned a ContextItem: {context_item}"
+                    )
+                    self.context.add(context_item)
+
+                result = ActionSuccessResult(outputs=return_value)
+            except AgentTerminated:
+                raise
+            except AgentException as e:
+                result = ActionErrorResult.from_exception(e)
+                logger.warning(
+                    f"{command_name}({fmt_kwargs(command_args)}) raised an error: {e}"
+                )
+
+            result_tlength = self.llm_provider.count_tokens(str(result), self.llm.name)
+            if result_tlength > self.send_token_limit // 3:
+                result = ActionErrorResult(
+                    reason=f"Command {command_name} returned too much output. "
+                    "Do not execute this command again with the same arguments."
+                )
+
+            for plugin in self.config.plugins:
+                if not plugin.can_handle_post_command():
+                    continue
+                if result.status == "success":
+                    result.outputs = plugin.post_command(command_name, result.outputs)
+                elif result.status == "error":
+                    result.reason = plugin.post_command(command_name, result.reason)
+
+        # Update action history
+        self.event_history.register_result(result)
 
         return result
 
-    async def do_not_execute(
-        self, denied_proposal: OneShotAgentActionProposal, user_feedback: str
-    ) -> ActionResult:
-        result = ActionInterruptedByHuman(feedback=user_feedback)
-        self.log_cycle_handler.log_cycle(
-            self.state.ai_profile.ai_name,
-            self.created_at,
-            self.config.cycle_count,
-            user_feedback,
-            USER_INPUT_FILE_NAME,
-        )
 
-        await self.run_pipeline(AfterExecute.after_execute, result)
+#############
+# Utilities #
+#############
 
-        logger.debug("\n".join(self.trace))
 
-        return result
+async def execute_command(
+    command_name: str,
+    arguments: dict[str, str],
+    agent: Agent,
+) -> CommandOutput:
+    """Execute the command and return the result
 
-    async def _execute_tool(self, tool_call: AssistantFunctionCall) -> CommandOutput:
-        """Execute the command and return the result
+    Args:
+        command_name (str): The name of the command to execute
+        arguments (dict): The arguments for the command
+        agent (Agent): The agent that is executing the command
 
-        Args:
-            tool_call (AssistantFunctionCall): The tool call to execute
-
-        Returns:
-            str: The execution result
-        """
-        # Execute a native command with the same name or alias, if it exists
-        command = self._get_command(tool_call.name)
+    Returns:
+        str: The result of the command
+    """
+    # Execute a native command with the same name or alias, if it exists
+    if command := agent.command_registry.get_command(command_name):
         try:
-            result = command(**tool_call.arguments)
+            result = command(**arguments, agent=agent)
             if inspect.isawaitable(result):
                 return await result
             return result
@@ -296,31 +310,20 @@ class Agent(BaseAgent, Configurable[AgentSettings]):
         except Exception as e:
             raise CommandExecutionError(str(e))
 
-    def _get_command(self, command_name: str) -> Command:
-        for command in reversed(self.commands):
-            if command_name in command.names:
-                return command
+    # Handle non-native commands (e.g. from plugins)
+    if agent._prompt_scratchpad:
+        for name, command in agent._prompt_scratchpad.commands.items():
+            if (
+                command_name == name
+                or command_name.lower() == command.description.lower()
+            ):
+                try:
+                    return command.method(**arguments)
+                except AgentException:
+                    raise
+                except Exception as e:
+                    raise CommandExecutionError(str(e))
 
-        raise UnknownCommandError(
-            f"Cannot execute command '{command_name}': unknown command."
-        )
-
-    def _remove_disabled_commands(self) -> None:
-        self.commands = [
-            command
-            for command in self.commands
-            if not any(
-                name in self.legacy_config.disabled_commands for name in command.names
-            )
-        ]
-
-    def find_obscured_commands(self) -> list[Command]:
-        seen_names = set()
-        obscured_commands = []
-        for command in reversed(self.commands):
-            # If all of the command's names have been seen, it's obscured
-            if seen_names.issuperset(command.names):
-                obscured_commands.append(command)
-            else:
-                seen_names.update(command.names)
-        return list(reversed(obscured_commands))
+    raise UnknownCommandError(
+        f"Cannot execute command '{command_name}': unknown command."
+    )

autogpts/autogpt/autogpt/agents/base.py

@@ -1,35 +1,25 @@
 from __future__ import annotations
 
-import copy
-import inspect
 import logging
-from abc import ABCMeta, abstractmethod
-from typing import (
-    TYPE_CHECKING,
-    Any,
-    Callable,
-    Iterator,
-    Optional,
-    ParamSpec,
-    TypeVar,
-    overload,
-)
+from abc import ABC, abstractmethod
+from pathlib import Path
+from typing import TYPE_CHECKING, Any, Optional
 
-from colorama import Fore
-from pydantic import BaseModel, Field, validator
+from auto_gpt_plugin_template import AutoGPTPluginTemplate
+from pydantic import Field, validator
 
 if TYPE_CHECKING:
+    from autogpt.config import Config
+    from autogpt.core.prompting.base import PromptStrategy
     from autogpt.core.resource.model_providers.schema import (
+        AssistantChatMessage,
         ChatModelInfo,
+        ChatModelProvider,
+        ChatModelResponse,
     )
-    from autogpt.models.action_history import ActionResult
+    from autogpt.models.command_registry import CommandRegistry
 
-from autogpt.agents import protocols as _protocols
-from autogpt.agents.components import (
-    AgentComponent,
-    ComponentEndpointError,
-    EndpointPipelineError,
-)
+from autogpt.agents.utils.prompt_scratchpad import PromptScratchpad
 from autogpt.config import ConfigBuilder
 from autogpt.config.ai_directives import AIDirectives
 from autogpt.config.ai_profile import AIProfile
@@ -39,26 +29,34 @@ from autogpt.core.configuration import (
     SystemSettings,
     UserConfigurable,
 )
-from autogpt.core.resource.model_providers import (
-    CHAT_MODELS,
-    AssistantFunctionCall,
-    ModelName,
+from autogpt.core.prompting.schema import (
+    ChatMessage,
+    ChatPrompt,
+    CompletionModelFunction,
 )
-from autogpt.core.resource.model_providers.openai import OpenAIModelName
-from autogpt.models.utils import ModelWithSummary
+from autogpt.core.resource.model_providers.openai import (
+    OPEN_AI_CHAT_MODELS,
+    OpenAIModelName,
+)
+from autogpt.core.runner.client_lib.logging.helpers import dump_prompt
+from autogpt.llm.providers.openai import get_openai_command_specs
+from autogpt.models.action_history import ActionResult, EpisodicActionHistory
 from autogpt.prompts.prompt import DEFAULT_TRIGGERING_PROMPT
 
+from .utils.agent_file_manager import AgentFileManager
+
 logger = logging.getLogger(__name__)
 
-T = TypeVar("T")
-P = ParamSpec("P")
+CommandName = str
+CommandArgs = dict[str, str]
+AgentThoughts = dict[str, Any]
 
 
 class BaseAgentConfiguration(SystemConfiguration):
     allow_fs_access: bool = UserConfigurable(default=False)
 
-    fast_llm: ModelName = UserConfigurable(default=OpenAIModelName.GPT3_16k)
-    smart_llm: ModelName = UserConfigurable(default=OpenAIModelName.GPT4)
+    fast_llm: OpenAIModelName = UserConfigurable(default=OpenAIModelName.GPT3_16k)
+    smart_llm: OpenAIModelName = UserConfigurable(default=OpenAIModelName.GPT4)
     use_functions_api: bool = UserConfigurable(default=False)
 
     default_cycle_instruction: str = DEFAULT_TRIGGERING_PROMPT
@@ -94,6 +92,21 @@ class BaseAgentConfiguration(SystemConfiguration):
     summary_max_tlength: Optional[int] = None
     # TODO: move to ActionHistoryConfiguration
 
+    plugins: list[AutoGPTPluginTemplate] = Field(default_factory=list, exclude=True)
+
+    class Config:
+        arbitrary_types_allowed = True  # Necessary for plugins
+
+    @validator("plugins", each_item=True)
+    def validate_plugins(cls, p: AutoGPTPluginTemplate | Any):
+        assert issubclass(
+            p.__class__, AutoGPTPluginTemplate
+        ), f"{p} does not subclass AutoGPTPluginTemplate"
+        assert (
+            p.__class__.__name__ != "AutoGPTPluginTemplate"
+        ), f"Plugins must subclass AutoGPTPluginTemplate; {p} is a template instance"
+        return p
+
     @validator("use_functions_api")
     def validate_openai_functions(cls, v: bool, values: dict[str, Any]):
         if v:
@@ -113,6 +126,7 @@ class BaseAgentConfiguration(SystemConfiguration):
 
 class BaseAgentSettings(SystemSettings):
     agent_id: str = ""
+    agent_data_dir: Optional[Path] = None
 
     ai_profile: AIProfile = Field(default_factory=lambda: AIProfile(ai_name="AutoGPT"))
     """The AI profile or "personality" of the agent."""
@@ -130,44 +144,79 @@ class BaseAgentSettings(SystemSettings):
     config: BaseAgentConfiguration = Field(default_factory=BaseAgentConfiguration)
     """The configuration for this BaseAgent subsystem instance."""
 
+    history: EpisodicActionHistory = Field(default_factory=EpisodicActionHistory)
+    """(STATE) The action history of the agent."""
 
-class AgentMeta(ABCMeta):
-    def __call__(cls, *args, **kwargs):
-        # Create instance of the class (Agent or BaseAgent)
-        instance = super().__call__(*args, **kwargs)
-        # Automatically collect modules after the instance is created
-        instance._collect_components()
-        return instance
+    def save_to_json_file(self, file_path: Path) -> None:
+        with file_path.open("w") as f:
+            f.write(self.json())
+
+    @classmethod
+    def load_from_json_file(cls, file_path: Path):
+        return cls.parse_file(file_path)
 
 
-class BaseAgentActionProposal(BaseModel):
-    thoughts: str | ModelWithSummary
-    use_tool: AssistantFunctionCall = None
+class BaseAgent(Configurable[BaseAgentSettings], ABC):
+    """Base class for all AutoGPT agent classes."""
 
-
-class BaseAgent(Configurable[BaseAgentSettings], metaclass=AgentMeta):
-    C = TypeVar("C", bound=AgentComponent)
+    ThoughtProcessOutput = tuple[CommandName, CommandArgs, AgentThoughts]
 
     default_settings = BaseAgentSettings(
         name="BaseAgent",
-        description=__doc__ if __doc__ else "",
+        description=__doc__,
     )
 
     def __init__(
         self,
         settings: BaseAgentSettings,
+        llm_provider: ChatModelProvider,
+        prompt_strategy: PromptStrategy,
+        command_registry: CommandRegistry,
+        legacy_config: Config,
     ):
         self.state = settings
-        self.components: list[AgentComponent] = []
         self.config = settings.config
-        # Execution data for debugging
-        self._trace: list[str] = []
+        self.ai_profile = settings.ai_profile
+        self.directives = settings.directives
+        self.event_history = settings.history
 
-        logger.debug(f"Created {__class__} '{self.state.ai_profile.ai_name}'")
+        self.legacy_config = legacy_config
+        """LEGACY: Monolithic application configuration."""
 
-    @property
-    def trace(self) -> list[str]:
-        return self._trace
+        self.file_manager: AgentFileManager = (
+            AgentFileManager(settings.agent_data_dir)
+            if settings.agent_data_dir
+            else None
+        )  # type: ignore
+
+        self.llm_provider = llm_provider
+
+        self.prompt_strategy = prompt_strategy
+
+        self.command_registry = command_registry
+        """The registry containing all commands available to the agent."""
+
+        self._prompt_scratchpad: PromptScratchpad | None = None
+
+        # Support multi-inheritance and mixins for subclasses
+        super(BaseAgent, self).__init__()
+
+        logger.debug(f"Created {__class__} '{self.ai_profile.ai_name}'")
+
+    def set_id(self, new_id: str, new_agent_dir: Optional[Path] = None):
+        self.state.agent_id = new_id
+        if self.state.agent_data_dir:
+            if not new_agent_dir:
+                raise ValueError(
+                    "new_agent_dir must be specified if one is currently configured"
+                )
+            self.attach_fs(new_agent_dir)
+
+    def attach_fs(self, agent_dir: Path) -> AgentFileManager:
+        self.file_manager = AgentFileManager(agent_dir)
+        self.file_manager.initialize()
+        self.state.agent_data_dir = agent_dir
+        return self.file_manager
 
     @property
     def llm(self) -> ChatModelInfo:
@@ -175,180 +224,208 @@ class BaseAgent(Configurable[BaseAgentSettings], metaclass=AgentMeta):
         llm_name = (
             self.config.smart_llm if self.config.big_brain else self.config.fast_llm
         )
-        return CHAT_MODELS[llm_name]
+        return OPEN_AI_CHAT_MODELS[llm_name]
 
     @property
     def send_token_limit(self) -> int:
         return self.config.send_token_limit or self.llm.max_tokens * 3 // 4
 
-    @abstractmethod
-    async def propose_action(self) -> BaseAgentActionProposal:
-        ...
+    async def propose_action(self) -> ThoughtProcessOutput:
+        """Proposes the next action to execute, based on the task and current state.
+
+        Returns:
+            The command name and arguments, if any, and the agent's thoughts.
+        """
+        assert self.file_manager, (
+            f"Agent has no FileManager: call {__class__.__name__}.attach_fs()"
+            " before trying to run the agent."
+        )
+
+        # Scratchpad as surrogate PromptGenerator for plugin hooks
+        self._prompt_scratchpad = PromptScratchpad()
+
+        prompt: ChatPrompt = self.build_prompt(scratchpad=self._prompt_scratchpad)
+        prompt = self.on_before_think(prompt, scratchpad=self._prompt_scratchpad)
+
+        logger.debug(f"Executing prompt:\n{dump_prompt(prompt)}")
+        response = await self.llm_provider.create_chat_completion(
+            prompt.messages,
+            functions=get_openai_command_specs(
+                self.command_registry.list_available_commands(self)
+            )
+            + list(self._prompt_scratchpad.commands.values())
+            if self.config.use_functions_api
+            else [],
+            model_name=self.llm.name,
+            completion_parser=lambda r: self.parse_and_process_response(
+                r,
+                prompt,
+                scratchpad=self._prompt_scratchpad,
+            ),
+        )
+        self.config.cycle_count += 1
+
+        return self.on_response(
+            llm_response=response,
+            prompt=prompt,
+            scratchpad=self._prompt_scratchpad,
+        )
 
     @abstractmethod
     async def execute(
         self,
-        proposal: BaseAgentActionProposal,
-        user_feedback: str = "",
+        command_name: str,
+        command_args: dict[str, str] = {},
+        user_input: str = "",
     ) -> ActionResult:
+        """Executes the given command, if any, and returns the agent's response.
+
+        Params:
+            command_name: The name of the command to execute, if any.
+            command_args: The arguments to pass to the command, if any.
+            user_input: The user's input, if any.
+
+        Returns:
+            ActionResult: An object representing the result(s) of the command.
+        """
         ...
 
+    def build_prompt(
+        self,
+        scratchpad: PromptScratchpad,
+        extra_commands: Optional[list[CompletionModelFunction]] = None,
+        extra_messages: Optional[list[ChatMessage]] = None,
+        **extras,
+    ) -> ChatPrompt:
+        """Constructs a prompt using `self.prompt_strategy`.
+
+        Params:
+            scratchpad: An object for plugins to write additional prompt elements to.
+                (E.g. commands, constraints, best practices)
+            extra_commands: Additional commands that the agent has access to.
+            extra_messages: Additional messages to include in the prompt.
+        """
+        if not extra_commands:
+            extra_commands = []
+        if not extra_messages:
+            extra_messages = []
+
+        # Apply additions from plugins
+        for plugin in self.config.plugins:
+            if not plugin.can_handle_post_prompt():
+                continue
+            plugin.post_prompt(scratchpad)
+        ai_directives = self.directives.copy(deep=True)
+        ai_directives.resources += scratchpad.resources
+        ai_directives.constraints += scratchpad.constraints
+        ai_directives.best_practices += scratchpad.best_practices
+        extra_commands += list(scratchpad.commands.values())
+
+        prompt = self.prompt_strategy.build_prompt(
+            task=self.state.task,
+            ai_profile=self.ai_profile,
+            ai_directives=ai_directives,
+            commands=get_openai_command_specs(
+                self.command_registry.list_available_commands(self)
+            )
+            + extra_commands,
+            event_history=self.event_history,
+            max_prompt_tokens=self.send_token_limit,
+            count_tokens=lambda x: self.llm_provider.count_tokens(x, self.llm.name),
+            count_message_tokens=lambda x: self.llm_provider.count_message_tokens(
+                x, self.llm.name
+            ),
+            extra_messages=extra_messages,
+            **extras,
+        )
+
+        return prompt
+
+    def on_before_think(
+        self,
+        prompt: ChatPrompt,
+        scratchpad: PromptScratchpad,
+    ) -> ChatPrompt:
+        """Called after constructing the prompt but before executing it.
+
+        Calls the `on_planning` hook of any enabled and capable plugins, adding their
+        output to the prompt.
+
+        Params:
+            prompt: The prompt that is about to be executed.
+            scratchpad: An object for plugins to write additional prompt elements to.
+                (E.g. commands, constraints, best practices)
+
+        Returns:
+            The prompt to execute
+        """
+        current_tokens_used = self.llm_provider.count_message_tokens(
+            prompt.messages, self.llm.name
+        )
+        plugin_count = len(self.config.plugins)
+        for i, plugin in enumerate(self.config.plugins):
+            if not plugin.can_handle_on_planning():
+                continue
+            plugin_response = plugin.on_planning(scratchpad, prompt.raw())
+            if not plugin_response or plugin_response == "":
+                continue
+            message_to_add = ChatMessage.system(plugin_response)
+            tokens_to_add = self.llm_provider.count_message_tokens(
+                message_to_add, self.llm.name
+            )
+            if current_tokens_used + tokens_to_add > self.send_token_limit:
+                logger.debug(f"Plugin response too long, skipping: {plugin_response}")
+                logger.debug(f"Plugins remaining at stop: {plugin_count - i}")
+                break
+            prompt.messages.insert(
+                -1, message_to_add
+            )  # HACK: assumes cycle instruction to be at the end
+            current_tokens_used += tokens_to_add
+        return prompt
+
+    def on_response(
+        self,
+        llm_response: ChatModelResponse,
+        prompt: ChatPrompt,
+        scratchpad: PromptScratchpad,
+    ) -> ThoughtProcessOutput:
+        """Called upon receiving a response from the chat model.
+
+        Calls `self.parse_and_process_response()`.
+
+        Params:
+            llm_response: The raw response from the chat model.
+            prompt: The prompt that was executed.
+            scratchpad: An object containing additional prompt elements from plugins.
+                (E.g. commands, constraints, best practices)
+
+        Returns:
+            The parsed command name and command args, if any, and the agent thoughts.
+        """
+
+        return llm_response.parsed_result
+
+        # TODO: update memory/context
+
     @abstractmethod
-    async def do_not_execute(
+    def parse_and_process_response(
         self,
-        denied_proposal: BaseAgentActionProposal,
-        user_feedback: str,
-    ) -> ActionResult:
-        ...
+        llm_response: AssistantChatMessage,
+        prompt: ChatPrompt,
+        scratchpad: PromptScratchpad,
+    ) -> ThoughtProcessOutput:
+        """Validate, parse & process the LLM's response.
 
-    def reset_trace(self):
-        self._trace = []
+        Must be implemented by derivative classes: no base implementation is provided,
+        since the implementation depends on the role of the derivative Agent.
 
-    @overload
-    async def run_pipeline(
-        self, protocol_method: Callable[P, Iterator[T]], *args, retry_limit: int = 3
-    ) -> list[T]:
-        ...
+        Params:
+            llm_response: The raw response from the chat model.
+            prompt: The prompt that was executed.
+            scratchpad: An object containing additional prompt elements from plugins.
+                (E.g. commands, constraints, best practices)
 
-    @overload
-    async def run_pipeline(
-        self, protocol_method: Callable[P, None], *args, retry_limit: int = 3
-    ) -> list[None]:
-        ...
-
-    async def run_pipeline(
-        self,
-        protocol_method: Callable[P, Iterator[T] | None],
-        *args,
-        retry_limit: int = 3,
-    ) -> list[T] | list[None]:
-        method_name = protocol_method.__name__
-        protocol_name = protocol_method.__qualname__.split(".")[0]
-        protocol_class = getattr(_protocols, protocol_name)
-        if not issubclass(protocol_class, AgentComponent):
-            raise TypeError(f"{repr(protocol_method)} is not a protocol method")
-
-        # Clone parameters to revert on failure
-        original_args = self._selective_copy(args)
-        pipeline_attempts = 0
-        method_result: list[T] = []
-        self._trace.append(f"⬇️  {Fore.BLUE}{method_name}{Fore.RESET}")
-
-        while pipeline_attempts < retry_limit:
-            try:
-                for component in self.components:
-                    # Skip other protocols
-                    if not isinstance(component, protocol_class):
-                        continue
-
-                    # Skip disabled components
-                    if not component.enabled:
-                        self._trace.append(
-                            f"   {Fore.LIGHTBLACK_EX}"
-                            f"{component.__class__.__name__}{Fore.RESET}"
-                        )
-                        continue
-
-                    method = getattr(component, method_name, None)
-                    if not callable(method):
-                        continue
-
-                    component_attempts = 0
-                    while component_attempts < retry_limit:
-                        try:
-                            component_args = self._selective_copy(args)
-                            if inspect.iscoroutinefunction(method):
-                                result = await method(*component_args)
-                            else:
-                                result = method(*component_args)
-                            if result is not None:
-                                method_result.extend(result)
-                            args = component_args
-                            self._trace.append(f"✅ {component.__class__.__name__}")
-
-                        except ComponentEndpointError:
-                            self._trace.append(
-                                f"❌ {Fore.YELLOW}{component.__class__.__name__}: "
-                                f"ComponentEndpointError{Fore.RESET}"
-                            )
-                            # Retry the same component on ComponentEndpointError
-                            component_attempts += 1
-                            continue
-                        # Successful component execution
-                        break
-                # Successful pipeline execution
-                break
-            except EndpointPipelineError:
-                self._trace.append(
-                    f"❌ {Fore.LIGHTRED_EX}{component.__class__.__name__}: "
-                    f"EndpointPipelineError{Fore.RESET}"
-                )
-                # Restart from the beginning on EndpointPipelineError
-                # Revert to original parameters
-                args = self._selective_copy(original_args)
-                pipeline_attempts += 1
-                continue  # Start the loop over
-            except Exception as e:
-                raise e
-        return method_result
-
-    def _collect_components(self):
-        components = [
-            getattr(self, attr)
-            for attr in dir(self)
-            if isinstance(getattr(self, attr), AgentComponent)
-        ]
-
-        if self.components:
-            # Check if any coponent is missed (added to Agent but not to components)
-            for component in components:
-                if component not in self.components:
-                    logger.warning(
-                        f"Component {component.__class__.__name__} "
-                        "is attached to an agent but not added to components list"
-                    )
-            # Skip collecting anf sorting and sort if ordering is explicit
-            return
-        self.components = self._topological_sort(components)
-
-    def _topological_sort(
-        self, components: list[AgentComponent]
-    ) -> list[AgentComponent]:
-        visited = set()
-        stack = []
-
-        def visit(node: AgentComponent):
-            if node in visited:
-                return
-            visited.add(node)
-            for neighbor_class in node.__class__.run_after:
-                # Find the instance of neighbor_class in components
-                neighbor = next(
-                    (m for m in components if isinstance(m, neighbor_class)), None
-                )
-                if neighbor:
-                    visit(neighbor)
-            stack.append(node)
-
-        for component in components:
-            visit(component)
-
-        return stack
-
-    def _selective_copy(self, args: tuple[Any, ...]) -> tuple[Any, ...]:
-        copied_args = []
-        for item in args:
-            if isinstance(item, list):
-                # Shallow copy for lists
-                copied_item = item[:]
-            elif isinstance(item, dict):
-                # Shallow copy for dicts
-                copied_item = item.copy()
-            elif isinstance(item, BaseModel):
-                # Deep copy for Pydantic models (deep=True to also copy nested models)
-                copied_item = item.copy(deep=True)
-            else:
-                # Deep copy for other objects
-                copied_item = copy.deepcopy(item)
-            copied_args.append(copied_item)
-        return tuple(copied_args)
+        Returns:
+            The parsed command name and command args, if any, and the agent thoughts.
+        """
+        pass

autogpts/autogpt/autogpt/agents/components.py

@@ -1,35 +0,0 @@
-from abc import ABC
-from typing import Callable
-
-
-class AgentComponent(ABC):
-    run_after: list[type["AgentComponent"]] = []
-    _enabled: Callable[[], bool] | bool = True
-    _disabled_reason: str = ""
-
-    @property
-    def enabled(self) -> bool:
-        if callable(self._enabled):
-            return self._enabled()
-        return self._enabled
-
-    @property
-    def disabled_reason(self) -> str:
-        return self._disabled_reason
-
-
-class ComponentEndpointError(Exception):
-    """Error of a single protocol method on a component."""
-
-    def __init__(self, message: str = ""):
-        self.message = message
-        super().__init__(message)
-
-
-class EndpointPipelineError(ComponentEndpointError):
-    """Error of an entire pipline of one endpoint."""
-
-
-class ComponentSystemError(EndpointPipelineError):
-    """Error of a group of pipelines;
-    multiple different enpoints."""

autogpts/autogpt/autogpt/agents/features/agent_file_manager.py

@@ -1,161 +0,0 @@
-import logging
-import os
-from pathlib import Path
-from typing import Iterator, Optional
-
-from autogpt.agents.protocols import CommandProvider, DirectiveProvider
-from autogpt.command_decorator import command
-from autogpt.core.utils.json_schema import JSONSchema
-from autogpt.file_storage.base import FileStorage
-from autogpt.models.command import Command
-from autogpt.utils.file_operations_utils import decode_textual_file
-
-from ..base import BaseAgentSettings
-
-logger = logging.getLogger(__name__)
-
-
-class FileManagerComponent(DirectiveProvider, CommandProvider):
-    """
-    Adds general file manager (e.g. Agent state),
-    workspace manager (e.g. Agent output files) support and
-    commands to perform operations on files and folders.
-    """
-
-    files: FileStorage
-    """Agent-related files, e.g. state, logs.
-    Use `workspace` to access the agent's workspace files."""
-
-    workspace: FileStorage
-    """Workspace that the agent has access to, e.g. for reading/writing files.
-    Use `files` to access agent-related files, e.g. state, logs."""
-
-    STATE_FILE = "state.json"
-    """The name of the file where the agent's state is stored."""
-
-    def __init__(self, state: BaseAgentSettings, file_storage: FileStorage):
-        self.state = state
-
-        if not state.agent_id:
-            raise ValueError("Agent must have an ID.")
-
-        self.files = file_storage.clone_with_subroot(f"agents/{state.agent_id}/")
-        self.workspace = file_storage.clone_with_subroot(
-            f"agents/{state.agent_id}/workspace"
-        )
-        self._file_storage = file_storage
-
-    async def save_state(self, save_as: Optional[str] = None) -> None:
-        """Save the agent's state to the state file."""
-        state: BaseAgentSettings = getattr(self, "state")
-        if save_as:
-            temp_id = state.agent_id
-            state.agent_id = save_as
-            self._file_storage.make_dir(f"agents/{save_as}")
-            # Save state
-            await self._file_storage.write_file(
-                f"agents/{save_as}/{self.STATE_FILE}", state.json()
-            )
-            # Copy workspace
-            self._file_storage.copy(
-                f"agents/{temp_id}/workspace",
-                f"agents/{save_as}/workspace",
-            )
-            state.agent_id = temp_id
-        else:
-            await self.files.write_file(self.files.root / self.STATE_FILE, state.json())
-
-    def change_agent_id(self, new_id: str):
-        """Change the agent's ID and update the file storage accordingly."""
-        state: BaseAgentSettings = getattr(self, "state")
-        # Rename the agent's files and workspace
-        self._file_storage.rename(f"agents/{state.agent_id}", f"agents/{new_id}")
-        # Update the file storage objects
-        self.files = self._file_storage.clone_with_subroot(f"agents/{new_id}/")
-        self.workspace = self._file_storage.clone_with_subroot(
-            f"agents/{new_id}/workspace"
-        )
-        state.agent_id = new_id
-
-    def get_resources(self) -> Iterator[str]:
-        yield "The ability to read and write files."
-
-    def get_commands(self) -> Iterator[Command]:
-        yield self.read_file
-        yield self.write_to_file
-        yield self.list_folder
-
-    @command(
-        parameters={
-            "filename": JSONSchema(
-                type=JSONSchema.Type.STRING,
-                description="The path of the file to read",
-                required=True,
-            )
-        },
-    )
-    def read_file(self, filename: str | Path) -> str:
-        """Read a file and return the contents
-
-        Args:
-            filename (str): The name of the file to read
-
-        Returns:
-            str: The contents of the file
-        """
-        file = self.workspace.open_file(filename, binary=True)
-        content = decode_textual_file(file, os.path.splitext(filename)[1], logger)
-
-        return content
-
-    @command(
-        ["write_file", "create_file"],
-        "Write a file, creating it if necessary. "
-        "If the file exists, it is overwritten.",
-        {
-            "filename": JSONSchema(
-                type=JSONSchema.Type.STRING,
-                description="The name of the file to write to",
-                required=True,
-            ),
-            "contents": JSONSchema(
-                type=JSONSchema.Type.STRING,
-                description="The contents to write to the file",
-                required=True,
-            ),
-        },
-    )
-    async def write_to_file(self, filename: str | Path, contents: str) -> str:
-        """Write contents to a file
-
-        Args:
-            filename (str): The name of the file to write to
-            contents (str): The contents to write to the file
-
-        Returns:
-            str: A message indicating success or failure
-        """
-        if directory := os.path.dirname(filename):
-            self.workspace.make_dir(directory)
-        await self.workspace.write_file(filename, contents)
-        return f"File {filename} has been written successfully."
-
-    @command(
-        parameters={
-            "folder": JSONSchema(
-                type=JSONSchema.Type.STRING,
-                description="The folder to list files in",
-                required=True,
-            )
-        },
-    )
-    def list_folder(self, folder: str | Path) -> list[str]:
-        """Lists files in a folder recursively
-
-        Args:
-            folder (str): The folder to search in
-
-        Returns:
-            list[str]: A list of files found in the folder
-        """
-        return [str(p) for p in self.workspace.list_files(folder)]

autogpts/autogpt/autogpt/agents/features/context.py

@@ -1,15 +1,14 @@
-import contextlib
-from pathlib import Path
-from typing import Iterator, Optional
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any, Optional
+
+if TYPE_CHECKING:
+    from autogpt.core.prompting import ChatPrompt
+    from autogpt.models.context_item import ContextItem
+
+    from ..base import BaseAgent
 
-from autogpt.agents.protocols import CommandProvider, MessageProvider
-from autogpt.command_decorator import command
 from autogpt.core.resource.model_providers import ChatMessage
-from autogpt.core.utils.json_schema import JSONSchema
-from autogpt.file_storage.base import FileStorage
-from autogpt.models.command import Command
-from autogpt.models.context_item import ContextItem, FileContextItem, FolderContextItem
-from autogpt.utils.exceptions import InvalidArgumentError
 
 
 class AgentContext:
@@ -33,129 +32,51 @@ class AgentContext:
     def clear(self) -> None:
         self.items.clear()
 
-    def format_numbered(self, workspace: FileStorage) -> str:
-        return "\n\n".join(
-            [f"{i}. {c.fmt(workspace)}" for i, c in enumerate(self.items, 1)]
-        )
+    def format_numbered(self) -> str:
+        return "\n\n".join([f"{i}. {c.fmt()}" for i, c in enumerate(self.items, 1)])
 
 
-class ContextComponent(MessageProvider, CommandProvider):
-    """Adds ability to keep files and folders open in the context (prompt)."""
+class ContextMixin:
+    """Mixin that adds context support to a BaseAgent subclass"""
 
-    def __init__(self, workspace: FileStorage):
+    context: AgentContext
+
+    def __init__(self, **kwargs: Any):
         self.context = AgentContext()
-        self.workspace = workspace
 
-    def get_messages(self) -> Iterator[ChatMessage]:
+        super(ContextMixin, self).__init__(**kwargs)
+
+    def build_prompt(
+        self,
+        *args: Any,
+        extra_messages: Optional[list[ChatMessage]] = None,
+        **kwargs: Any,
+    ) -> ChatPrompt:
+        if not extra_messages:
+            extra_messages = []
+
+        # Add context section to prompt
         if self.context:
-            yield ChatMessage.system(
-                "## Context\n"
-                f"{self.context.format_numbered(self.workspace)}\n\n"
-                "When a context item is no longer needed and you are not done yet, "
-                "you can hide the item by specifying its number in the list above "
-                "to `hide_context_item`.",
+            extra_messages.insert(
+                0,
+                ChatMessage.system(
+                    "## Context\n"
+                    f"{self.context.format_numbered()}\n\n"
+                    "When a context item is no longer needed and you are not done yet, "
+                    "you can hide the item by specifying its number in the list above "
+                    "to `hide_context_item`.",
+                ),
             )
 
-    def get_commands(self) -> Iterator[Command]:
-        yield self.open_file
-        yield self.open_folder
-        if self.context:
-            yield self.close_context_item
+        return super(ContextMixin, self).build_prompt(
+            *args,
+            extra_messages=extra_messages,
+            **kwargs,
+        )  # type: ignore
 
-    @command(
-        parameters={
-            "file_path": JSONSchema(
-                type=JSONSchema.Type.STRING,
-                description="The path of the file to open",
-                required=True,
-            )
-        }
-    )
-    async def open_file(self, file_path: str | Path) -> str:
-        """Opens a file for editing or continued viewing;
-        creates it if it does not exist yet.
-        Note: If you only need to read or write a file once,
-        use `write_to_file` instead.
 
-        Args:
-            file_path (str | Path): The path of the file to open
+def get_agent_context(agent: BaseAgent) -> AgentContext | None:
+    if isinstance(agent, ContextMixin):
+        return agent.context
 
-        Returns:
-            str: A status message indicating what happened
-        """
-        if not isinstance(file_path, Path):
-            file_path = Path(file_path)
-
-        created = False
-        if not self.workspace.exists(file_path):
-            await self.workspace.write_file(file_path, "")
-            created = True
-
-        # Try to make the file path relative
-        with contextlib.suppress(ValueError):
-            file_path = file_path.relative_to(self.workspace.root)
-
-        file = FileContextItem(path=file_path)
-        self.context.add(file)
-        return (
-            f"File {file_path}{' created,' if created else ''} has been opened"
-            " and added to the context ✅"
-        )
-
-    @command(
-        parameters={
-            "path": JSONSchema(
-                type=JSONSchema.Type.STRING,
-                description="The path of the folder to open",
-                required=True,
-            )
-        }
-    )
-    def open_folder(self, path: str | Path) -> str:
-        """Open a folder to keep track of its content
-
-        Args:
-            path (str | Path): The path of the folder to open
-
-        Returns:
-            str: A status message indicating what happened
-        """
-        if not isinstance(path, Path):
-            path = Path(path)
-
-        if not self.workspace.exists(path):
-            raise FileNotFoundError(
-                f"open_folder {path} failed: no such file or directory"
-            )
-
-        # Try to make the path relative
-        with contextlib.suppress(ValueError):
-            path = path.relative_to(self.workspace.root)
-
-        folder = FolderContextItem(path=path)
-        self.context.add(folder)
-        return f"Folder {path} has been opened and added to the context ✅"
-
-    @command(
-        parameters={
-            "number": JSONSchema(
-                type=JSONSchema.Type.INTEGER,
-                description="The 1-based index of the context item to hide",
-                required=True,
-            )
-        }
-    )
-    def close_context_item(self, number: int) -> str:
-        """Hide an open file, folder or other context item, to save tokens.
-
-        Args:
-            number (int): The 1-based index of the context item to hide
-
-        Returns:
-            str: A status message indicating what happened
-        """
-        if number > len(self.context.items) or number == 0:
-            raise InvalidArgumentError(f"Index {number} out of range")
-
-        self.context.close(number)
-        return f"Context item {number} hidden ✅"
+    return None

autogpts/autogpt/autogpt/agents/features/file_workspace.py

@@ -0,0 +1,65 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from pathlib import Path
+
+    from ..base import BaseAgent, Config
+
+from autogpt.file_workspace import (
+    FileWorkspace,
+    FileWorkspaceBackendName,
+    get_workspace,
+)
+
+from ..base import AgentFileManager, BaseAgentSettings
+
+
+class FileWorkspaceMixin:
+    """Mixin that adds workspace support to a class"""
+
+    workspace: FileWorkspace = None
+    """Workspace that the agent has access to, e.g. for reading/writing files."""
+
+    def __init__(self, **kwargs):
+        # Initialize other bases first, because we need the config from BaseAgent
+        super(FileWorkspaceMixin, self).__init__(**kwargs)
+
+        file_manager: AgentFileManager = getattr(self, "file_manager")
+        if not file_manager:
+            return
+
+        self._setup_workspace()
+
+    def attach_fs(self, agent_dir: Path):
+        res = super(FileWorkspaceMixin, self).attach_fs(agent_dir)
+
+        self._setup_workspace()
+
+        return res
+
+    def _setup_workspace(self) -> None:
+        settings: BaseAgentSettings = getattr(self, "state")
+        assert settings.agent_id, "Cannot attach workspace to anonymous agent"
+        app_config: Config = getattr(self, "legacy_config")
+        file_manager: AgentFileManager = getattr(self, "file_manager")
+
+        ws_backend = app_config.workspace_backend
+        local = ws_backend == FileWorkspaceBackendName.LOCAL
+        workspace = get_workspace(
+            backend=ws_backend,
+            id=settings.agent_id if not local else "",
+            root_path=file_manager.root / "workspace" if local else None,
+        )
+        if local and settings.config.allow_fs_access:
+            workspace._restrict_to_root = False  # type: ignore
+        workspace.initialize()
+        self.workspace = workspace
+
+
+def get_agent_workspace(agent: BaseAgent) -> FileWorkspace | None:
+    if isinstance(agent, FileWorkspaceMixin):
+        return agent.workspace
+
+    return None

autogpts/autogpt/autogpt/agents/features/watchdog.py

@@ -1,35 +1,41 @@
-import logging
+from __future__ import annotations
+
+import logging
+from contextlib import ExitStack
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    from ..base import BaseAgentConfiguration
 
-from autogpt.agents.base import BaseAgentActionProposal, BaseAgentConfiguration
-from autogpt.agents.components import ComponentSystemError
-from autogpt.agents.features.context import ContextComponent
-from autogpt.agents.protocols import AfterParse
 from autogpt.models.action_history import EpisodicActionHistory
 
+from ..base import BaseAgent
+
 logger = logging.getLogger(__name__)
 
 
-class WatchdogComponent(AfterParse):
+class WatchdogMixin:
     """
-    Adds a watchdog feature to an agent class. Whenever the agent starts
+    Mixin that adds a watchdog feature to an agent class. Whenever the agent starts
     looping, the watchdog will switch from the FAST_LLM to the SMART_LLM and re-think.
     """
 
-    run_after = [ContextComponent]
+    config: BaseAgentConfiguration
+    event_history: EpisodicActionHistory
 
-    def __init__(
-        self,
-        config: BaseAgentConfiguration,
-        event_history: EpisodicActionHistory[BaseAgentActionProposal],
-    ):
-        self.config = config
-        self.event_history = event_history
-        self.revert_big_brain = False
+    def __init__(self, **kwargs) -> None:
+        # Initialize other bases first, because we need the event_history from BaseAgent
+        super(WatchdogMixin, self).__init__(**kwargs)
 
-    def after_parse(self, result: BaseAgentActionProposal) -> None:
-        if self.revert_big_brain:
-            self.config.big_brain = False
-            self.revert_big_brain = False
+        if not isinstance(self, BaseAgent):
+            raise NotImplementedError(
+                f"{__class__.__name__} can only be applied to BaseAgent derivatives"
+            )
+
+    async def propose_action(self, *args, **kwargs) -> BaseAgent.ThoughtProcessOutput:
+        command_name, command_args, thoughts = await super(
+            WatchdogMixin, self
+        ).propose_action(*args, **kwargs)
 
         if not self.config.big_brain and self.config.fast_llm != self.config.smart_llm:
             previous_command, previous_command_args = None, None
@@ -38,23 +44,33 @@ class WatchdogComponent(AfterParse):
                 previous_cycle = self.event_history.episodes[
                     self.event_history.cursor - 1
                 ]
-                previous_command = previous_cycle.action.use_tool.name
-                previous_command_args = previous_cycle.action.use_tool.arguments
+                previous_command = previous_cycle.action.name
+                previous_command_args = previous_cycle.action.args
 
             rethink_reason = ""
 
-            if not result.use_tool:
+            if not command_name:
                 rethink_reason = "AI did not specify a command"
             elif (
-                result.use_tool.name == previous_command
-                and result.use_tool.arguments == previous_command_args
+                command_name == previous_command
+                and command_args == previous_command_args
             ):
-                rethink_reason = f"Repititive command detected ({result.use_tool.name})"
+                rethink_reason = f"Repititive command detected ({command_name})"
 
             if rethink_reason:
                 logger.info(f"{rethink_reason}, re-thinking with SMART_LLM...")
-                self.event_history.rewind()
-                self.big_brain = True
-                self.revert_big_brain = True
-                # Trigger retry of all pipelines prior to this component
-                raise ComponentSystemError()
+                with ExitStack() as stack:
+
+                    @stack.callback
+                    def restore_state() -> None:
+                        # Executed after exiting the ExitStack context
+                        self.config.big_brain = False
+
+                    # Remove partial record of current cycle
+                    self.event_history.rewind()
+
+                    # Switch to SMART_LLM and re-think
+                    self.big_brain = True
+                    return await self.propose_action(*args, **kwargs)
+
+        return command_name, command_args, thoughts

autogpts/autogpt/autogpt/agents/prompt_strategies/one_shot.py

@@ -4,11 +4,15 @@ import json
 import platform
 import re
 from logging import Logger
+from typing import TYPE_CHECKING, Callable, Optional
 
 import distro
-from pydantic import Field
 
-from autogpt.agents.base import BaseAgentActionProposal
+if TYPE_CHECKING:
+    from autogpt.agents.agent import Agent
+    from autogpt.models.action_history import Episode
+
+from autogpt.agents.utils.exceptions import InvalidAgentResponseError
 from autogpt.config import AIDirectives, AIProfile
 from autogpt.core.configuration.schema import SystemConfiguration, UserConfigurable
 from autogpt.core.prompting import (
@@ -22,32 +26,8 @@ from autogpt.core.resource.model_providers.schema import (
     CompletionModelFunction,
 )
 from autogpt.core.utils.json_schema import JSONSchema
-from autogpt.core.utils.json_utils import extract_dict_from_json
-from autogpt.models.utils import ModelWithSummary
-from autogpt.prompts.utils import format_numbered_list
-from autogpt.utils.exceptions import InvalidAgentResponseError
-
-_RESPONSE_INTERFACE_NAME = "AssistantResponse"
-
-
-class AssistantThoughts(ModelWithSummary):
-    observations: str = Field(
-        ..., description="Relevant observations from your last action (if any)"
-    )
-    text: str = Field(..., description="Thoughts")
-    reasoning: str = Field(..., description="Reasoning behind the thoughts")
-    self_criticism: str = Field(..., description="Constructive self-criticism")
-    plan: list[str] = Field(
-        ..., description="Short list that conveys the long-term plan"
-    )
-    speak: str = Field(..., description="Summary of thoughts, to say to user")
-
-    def summary(self) -> str:
-        return self.text
-
-
-class OneShotAgentActionProposal(BaseAgentActionProposal):
-    thoughts: AssistantThoughts
+from autogpt.json_utils.utilities import extract_dict_from_response
+from autogpt.prompts.utils import format_numbered_list, indent
 
 
 class OneShotAgentPromptConfiguration(SystemConfiguration):
@@ -75,7 +55,70 @@ class OneShotAgentPromptConfiguration(SystemConfiguration):
         "and respond using the JSON schema specified previously:"
     )
 
+    DEFAULT_RESPONSE_SCHEMA = JSONSchema(
+        type=JSONSchema.Type.OBJECT,
+        properties={
+            "thoughts": JSONSchema(
+                type=JSONSchema.Type.OBJECT,
+                required=True,
+                properties={
+                    "observations": JSONSchema(
+                        description=(
+                            "Relevant observations from your last action (if any)"
+                        ),
+                        type=JSONSchema.Type.STRING,
+                        required=False,
+                    ),
+                    "text": JSONSchema(
+                        description="Thoughts",
+                        type=JSONSchema.Type.STRING,
+                        required=True,
+                    ),
+                    "reasoning": JSONSchema(
+                        type=JSONSchema.Type.STRING,
+                        required=True,
+                    ),
+                    "self_criticism": JSONSchema(
+                        description="Constructive self-criticism",
+                        type=JSONSchema.Type.STRING,
+                        required=True,
+                    ),
+                    "plan": JSONSchema(
+                        description=(
+                            "Short markdown-style bullet list that conveys the "
+                            "long-term plan"
+                        ),
+                        type=JSONSchema.Type.STRING,
+                        required=True,
+                    ),
+                    "speak": JSONSchema(
+                        description="Summary of thoughts, to say to user",
+                        type=JSONSchema.Type.STRING,
+                        required=True,
+                    ),
+                },
+            ),
+            "command": JSONSchema(
+                type=JSONSchema.Type.OBJECT,
+                required=True,
+                properties={
+                    "name": JSONSchema(
+                        type=JSONSchema.Type.STRING,
+                        required=True,
+                    ),
+                    "args": JSONSchema(
+                        type=JSONSchema.Type.OBJECT,
+                        required=True,
+                    ),
+                },
+            ),
+        },
+    )
+
     body_template: str = UserConfigurable(default=DEFAULT_BODY_TEMPLATE)
+    response_schema: dict = UserConfigurable(
+        default_factory=DEFAULT_RESPONSE_SCHEMA.to_dict
+    )
     choose_action_instruction: str = UserConfigurable(
         default=DEFAULT_CHOOSE_ACTION_INSTRUCTION
     )
@@ -100,7 +143,7 @@ class OneShotAgentPromptStrategy(PromptStrategy):
         logger: Logger,
     ):
         self.config = configuration
-        self.response_schema = JSONSchema.from_dict(OneShotAgentActionProposal.schema())
+        self.response_schema = JSONSchema.from_dict(configuration.response_schema)
         self.logger = logger
 
     @property
@@ -110,55 +153,81 @@ class OneShotAgentPromptStrategy(PromptStrategy):
     def build_prompt(
         self,
         *,
-        messages: list[ChatMessage],
         task: str,
         ai_profile: AIProfile,
         ai_directives: AIDirectives,
         commands: list[CompletionModelFunction],
+        event_history: list[Episode],
         include_os_info: bool,
+        max_prompt_tokens: int,
+        count_tokens: Callable[[str], int],
+        count_message_tokens: Callable[[ChatMessage | list[ChatMessage]], int],
+        extra_messages: Optional[list[ChatMessage]] = None,
         **extras,
     ) -> ChatPrompt:
         """Constructs and returns a prompt with the following structure:
         1. System prompt
+        2. Message history of the agent, truncated & prepended with running summary
+            as needed
         3. `cycle_instruction`
         """
-        system_prompt, response_prefill = self.build_system_prompt(
+        if not extra_messages:
+            extra_messages = []
+
+        system_prompt = self.build_system_prompt(
             ai_profile=ai_profile,
             ai_directives=ai_directives,
             commands=commands,
             include_os_info=include_os_info,
         )
+        system_prompt_tlength = count_message_tokens(ChatMessage.system(system_prompt))
+
+        user_task = f'"""{task}"""'
+        user_task_tlength = count_message_tokens(ChatMessage.user(user_task))
+
+        response_format_instr = self.response_format_instruction(
+            self.config.use_functions_api
+        )
+        extra_messages.append(ChatMessage.system(response_format_instr))
 
         final_instruction_msg = ChatMessage.user(self.config.choose_action_instruction)
+        final_instruction_tlength = count_message_tokens(final_instruction_msg)
 
-        return ChatPrompt(
+        if event_history:
+            progress = self.compile_progress(
+                event_history,
+                count_tokens=count_tokens,
+                max_tokens=(
+                    max_prompt_tokens
+                    - system_prompt_tlength
+                    - user_task_tlength
+                    - final_instruction_tlength
+                    - count_message_tokens(extra_messages)
+                ),
+            )
+            extra_messages.insert(
+                0,
+                ChatMessage.system(f"## Progress\n\n{progress}"),
+            )
+
+        prompt = ChatPrompt(
             messages=[
                 ChatMessage.system(system_prompt),
-                ChatMessage.user(f'"""{task}"""'),
-                *messages,
+                ChatMessage.user(user_task),
+                *extra_messages,
                 final_instruction_msg,
             ],
-            prefill_response=response_prefill,
-            functions=commands if self.config.use_functions_api else [],
         )
 
+        return prompt
+
     def build_system_prompt(
         self,
         ai_profile: AIProfile,
         ai_directives: AIDirectives,
         commands: list[CompletionModelFunction],
         include_os_info: bool,
-    ) -> tuple[str, str]:
-        """
-        Builds the system prompt.
-
-        Returns:
-            str: The system prompt body
-            str: The desired start for the LLM's response; used to steer the output
-        """
-        response_fmt_instruction, response_prefill = self.response_format_instruction(
-            self.config.use_functions_api
-        )
+    ) -> str:
         system_prompt_parts = (
             self._generate_intro_prompt(ai_profile)
             + (self._generate_os_info() if include_os_info else [])
@@ -179,39 +248,82 @@ class OneShotAgentPromptStrategy(PromptStrategy):
                 " in the next message. Your job is to complete the task while following"
                 " your directives as given above, and terminate when your task is done."
             ]
-            + ["## RESPONSE FORMAT\n" + response_fmt_instruction]
         )
 
         # Join non-empty parts together into paragraph format
-        return (
-            "\n\n".join(filter(None, system_prompt_parts)).strip("\n"),
-            response_prefill,
-        )
+        return "\n\n".join(filter(None, system_prompt_parts)).strip("\n")
 
-    def response_format_instruction(self, use_functions_api: bool) -> tuple[str, str]:
+    def compile_progress(
+        self,
+        episode_history: list[Episode],
+        max_tokens: Optional[int] = None,
+        count_tokens: Optional[Callable[[str], int]] = None,
+    ) -> str:
+        if max_tokens and not count_tokens:
+            raise ValueError("count_tokens is required if max_tokens is set")
+
+        steps: list[str] = []
+        tokens: int = 0
+        # start: int = len(episode_history)
+
+        for i, c in reversed(list(enumerate(episode_history))):
+            step = f"### Step {i+1}: Executed `{c.action.format_call()}`\n"
+            step += f'- **Reasoning:** "{c.action.reasoning}"\n'
+            step += (
+                f"- **Status:** `{c.result.status if c.result else 'did_not_finish'}`\n"
+            )
+            if c.result:
+                if c.result.status == "success":
+                    result = str(c.result)
+                    result = "\n" + indent(result) if "\n" in result else result
+                    step += f"- **Output:** {result}"
+                elif c.result.status == "error":
+                    step += f"- **Reason:** {c.result.reason}\n"
+                    if c.result.error:
+                        step += f"- **Error:** {c.result.error}\n"
+                elif c.result.status == "interrupted_by_human":
+                    step += f"- **Feedback:** {c.result.feedback}\n"
+
+            if max_tokens and count_tokens:
+                step_tokens = count_tokens(step)
+                if tokens + step_tokens > max_tokens:
+                    break
+                tokens += step_tokens
+
+            steps.insert(0, step)
+        #     start = i
+
+        # # TODO: summarize remaining
+        # part = slice(0, start)
+
+        return "\n\n".join(steps)
+
+    def response_format_instruction(self, use_functions_api: bool) -> str:
         response_schema = self.response_schema.copy(deep=True)
         if (
             use_functions_api
             and response_schema.properties
-            and "use_tool" in response_schema.properties
+            and "command" in response_schema.properties
         ):
-            del response_schema.properties["use_tool"]
+            del response_schema.properties["command"]
 
         # Unindent for performance
         response_format = re.sub(
             r"\n\s+",
             "\n",
-            response_schema.to_typescript_object_interface(_RESPONSE_INTERFACE_NAME),
+            response_schema.to_typescript_object_interface("Response"),
+        )
+
+        instruction = (
+            "Respond with pure JSON containing your thoughts, " "and invoke a tool."
+            if use_functions_api
+            else "Respond with pure JSON."
         )
-        response_prefill = f'{{\n    "{list(response_schema.properties.keys())[0]}":'
 
         return (
-            (
-                f"YOU MUST ALWAYS RESPOND WITH A JSON OBJECT OF THE FOLLOWING TYPE:\n"
-                f"{response_format}"
-                + ("\n\nYOU MUST ALSO INVOKE A TOOL!" if use_functions_api else "")
-            ),
-            response_prefill,
+            f"{instruction} "
+            "The JSON object should be compatible with the TypeScript type `Response` "
+            f"from the following:\n{response_format}"
         )
 
     def _generate_intro_prompt(self, ai_profile: AIProfile) -> list[str]:
@@ -275,7 +387,7 @@ class OneShotAgentPromptStrategy(PromptStrategy):
     def parse_response_content(
         self,
         response: AssistantChatMessage,
-    ) -> OneShotAgentActionProposal:
+    ) -> Agent.ThoughtProcessOutput:
         if not response.content:
             raise InvalidAgentResponseError("Assistant response has no text content")
 
@@ -287,15 +399,86 @@ class OneShotAgentPromptStrategy(PromptStrategy):
                 else f" '{response.content}'"
             )
         )
-        assistant_reply_dict = extract_dict_from_json(response.content)
+        assistant_reply_dict = extract_dict_from_response(response.content)
         self.logger.debug(
-            "Parsing object extracted from LLM response:\n"
+            "Validating object extracted from LLM response:\n"
             f"{json.dumps(assistant_reply_dict, indent=4)}"
         )
 
-        parsed_response = OneShotAgentActionProposal.parse_obj(assistant_reply_dict)
-        if self.config.use_functions_api:
-            if not response.tool_calls:
-                raise InvalidAgentResponseError("Assistant did not use a tool")
-            parsed_response.use_tool = response.tool_calls[0].function
-        return parsed_response
+        _, errors = self.response_schema.validate_object(
+            object=assistant_reply_dict,
+            logger=self.logger,
+        )
+        if errors:
+            raise InvalidAgentResponseError(
+                "Validation of response failed:\n  "
+                + ";\n  ".join([str(e) for e in errors])
+            )
+
+        # Get command name and arguments
+        command_name, arguments = extract_command(
+            assistant_reply_dict, response, self.config.use_functions_api
+        )
+        return command_name, arguments, assistant_reply_dict
+
+
+#############
+# Utilities #
+#############
+
+
+def extract_command(
+    assistant_reply_json: dict,
+    assistant_reply: AssistantChatMessage,
+    use_openai_functions_api: bool,
+) -> tuple[str, dict[str, str]]:
+    """Parse the response and return the command name and arguments
+
+    Args:
+        assistant_reply_json (dict): The response object from the AI
+        assistant_reply (AssistantChatMessage): The model response from the AI
+        config (Config): The config object
+
+    Returns:
+        tuple: The command name and arguments
+
+    Raises:
+        json.decoder.JSONDecodeError: If the response is not valid JSON
+
+        Exception: If any other error occurs
+    """
+    if use_openai_functions_api:
+        if not assistant_reply.tool_calls:
+            raise InvalidAgentResponseError("No 'tool_calls' in assistant reply")
+        assistant_reply_json["command"] = {
+            "name": assistant_reply.tool_calls[0].function.name,
+            "args": json.loads(assistant_reply.tool_calls[0].function.arguments),
+        }
+    try:
+        if not isinstance(assistant_reply_json, dict):
+            raise InvalidAgentResponseError(
+                f"The previous message sent was not a dictionary {assistant_reply_json}"
+            )
+
+        if "command" not in assistant_reply_json:
+            raise InvalidAgentResponseError("Missing 'command' object in JSON")
+
+        command = assistant_reply_json["command"]
+        if not isinstance(command, dict):
+            raise InvalidAgentResponseError("'command' object is not a dictionary")
+
+        if "name" not in command:
+            raise InvalidAgentResponseError("Missing 'name' field in 'command' object")
+
+        command_name = command["name"]
+
+        # Use an empty dictionary if 'args' field is not present in 'command' object
+        arguments = command.get("args", {})
+
+        return command_name, arguments
+
+    except json.decoder.JSONDecodeError:
+        raise InvalidAgentResponseError("Invalid JSON")
+
+    except Exception as e:
+        raise InvalidAgentResponseError(str(e))

autogpts/autogpt/autogpt/agents/protocols.py

@@ -1,51 +0,0 @@
-from abc import abstractmethod
-from typing import TYPE_CHECKING, Iterator
-
-from autogpt.agents.components import AgentComponent
-
-if TYPE_CHECKING:
-    from autogpt.agents.base import BaseAgentActionProposal
-    from autogpt.core.resource.model_providers.schema import ChatMessage
-    from autogpt.models.action_history import ActionResult
-    from autogpt.models.command import Command
-
-
-class DirectiveProvider(AgentComponent):
-    def get_constraints(self) -> Iterator[str]:
-        return iter([])
-
-    def get_resources(self) -> Iterator[str]:
-        return iter([])
-
-    def get_best_practices(self) -> Iterator[str]:
-        return iter([])
-
-
-class CommandProvider(AgentComponent):
-    @abstractmethod
-    def get_commands(self) -> Iterator["Command"]:
-        ...
-
-
-class MessageProvider(AgentComponent):
-    @abstractmethod
-    def get_messages(self) -> Iterator["ChatMessage"]:
-        ...
-
-
-class AfterParse(AgentComponent):
-    @abstractmethod
-    def after_parse(self, result: "BaseAgentActionProposal") -> None:
-        ...
-
-
-class ExecutionFailure(AgentComponent):
-    @abstractmethod
-    def execution_failure(self, error: Exception) -> None:
-        ...
-
-
-class AfterExecute(AgentComponent):
-    @abstractmethod
-    def after_execute(self, result: "ActionResult") -> None:
-        ...

autogpts/autogpt/autogpt/agents/utils/agent_file_manager.py

@@ -0,0 +1,37 @@
+from __future__ import annotations
+
+import logging
+from pathlib import Path
+
+logger = logging.getLogger(__name__)
+
+
+class AgentFileManager:
+    """A class that represents a workspace for an AutoGPT agent."""
+
+    def __init__(self, agent_data_dir: Path):
+        self._root = agent_data_dir.resolve()
+
+    @property
+    def root(self) -> Path:
+        """The root directory of the workspace."""
+        return self._root
+
+    def initialize(self) -> None:
+        self.root.mkdir(exist_ok=True, parents=True)
+        self.init_file_ops_log(self.file_ops_log_path)
+
+    @property
+    def state_file_path(self) -> Path:
+        return self.root / "state.json"
+
+    @property
+    def file_ops_log_path(self) -> Path:
+        return self.root / "file_logger.log"
+
+    @staticmethod
+    def init_file_ops_log(file_logger_path: Path) -> Path:
+        if not file_logger_path.exists():
+            with file_logger_path.open(mode="w", encoding="utf-8") as f:
+                f.write("")
+        return file_logger_path

autogpts/autogpt/autogpt/agents/utils/exceptions.py

@@ -18,10 +18,6 @@ class AgentTerminated(AgentException):
     """The agent terminated or was terminated"""
 
 
-class AgentFinished(AgentTerminated):
-    """The agent self-terminated"""
-
-
 class ConfigurationError(AgentException):
     """Error caused by invalid, incompatible or otherwise incorrect configuration"""
 
@@ -36,6 +32,10 @@ class UnknownCommandError(AgentException):
     hint = "Do not try to use this command again."
 
 
+class DuplicateOperationError(AgentException):
+    """The proposed operation has already been executed"""
+
+
 class CommandExecutionError(AgentException):
     """An error occurred when trying to execute the command"""
 

autogpts/autogpt/autogpt/agents/utils/prompt_scratchpad.py

@@ -0,0 +1,108 @@
+import logging
+from typing import Callable
+
+from pydantic import BaseModel, Field
+
+from autogpt.core.resource.model_providers.schema import CompletionModelFunction
+from autogpt.core.utils.json_schema import JSONSchema
+
+logger = logging.getLogger("PromptScratchpad")
+
+
+class CallableCompletionModelFunction(CompletionModelFunction):
+    method: Callable
+
+
+class PromptScratchpad(BaseModel):
+    commands: dict[str, CallableCompletionModelFunction] = Field(default_factory=dict)
+    resources: list[str] = Field(default_factory=list)
+    constraints: list[str] = Field(default_factory=list)
+    best_practices: list[str] = Field(default_factory=list)
+
+    def add_constraint(self, constraint: str) -> None:
+        """
+        Add a constraint to the constraints list.
+
+        Params:
+            constraint (str): The constraint to be added.
+        """
+        if constraint not in self.constraints:
+            self.constraints.append(constraint)
+
+    def add_command(
+        self,
+        name: str,
+        description: str,
+        params: dict[str, str | dict],
+        function: Callable,
+    ) -> None:
+        """
+        Registers a command.
+
+        *Should only be used by plugins.* Native commands should be added
+        directly to the CommandRegistry.
+
+        Params:
+            name (str): The name of the command (e.g. `command_name`).
+            description (str): The description of the command.
+            params (dict, optional): A dictionary containing argument names and their
+              types. Defaults to an empty dictionary.
+            function (callable, optional): A callable function to be called when
+                the command is executed. Defaults to None.
+        """
+        for p, s in params.items():
+            invalid = False
+            if type(s) is str and s not in JSONSchema.Type._value2member_map_:
+                invalid = True
+                logger.warning(
+                    f"Cannot add command '{name}':"
+                    f" parameter '{p}' has invalid type '{s}'."
+                    f" Valid types are: {JSONSchema.Type._value2member_map_.keys()}"
+                )
+            elif isinstance(s, dict):
+                try:
+                    JSONSchema.from_dict(s)
+                except KeyError:
+                    invalid = True
+            if invalid:
+                return
+
+        command = CallableCompletionModelFunction(
+            name=name,
+            description=description,
+            parameters={
+                name: JSONSchema(type=JSONSchema.Type._value2member_map_[spec])
+                if type(spec) is str
+                else JSONSchema.from_dict(spec)
+                for name, spec in params.items()
+            },
+            method=function,
+        )
+
+        if name in self.commands:
+            if description == self.commands[name].description:
+                return
+            logger.warning(
+                f"Replacing command {self.commands[name]} with conflicting {command}"
+            )
+        self.commands[name] = command
+
+    def add_resource(self, resource: str) -> None:
+        """
+        Add a resource to the resources list.
+
+        Params:
+            resource (str): The resource to be added.
+        """
+        if resource not in self.resources:
+            self.resources.append(resource)
+
+    def add_best_practice(self, best_practice: str) -> None:
+        """
+        Add an item to the list of best practices.
+
+        Params:
+            best_practice (str): The best practice item to be added.
+        """
+        if best_practice not in self.best_practices:
+            self.best_practices.append(best_practice)

autogpts/autogpt/autogpt/app/agent_protocol_server.py

@@ -1,7 +1,6 @@
 import logging
 import os
 import pathlib
-from collections import defaultdict
 from io import BytesIO
 from uuid import uuid4
 
@@ -26,18 +25,23 @@ from forge.sdk.model import (
 from forge.sdk.routes.agent_protocol import base_router
 from hypercorn.asyncio import serve as hypercorn_serve
 from hypercorn.config import Config as HypercornConfig
-from sentry_sdk import set_user
 
 from autogpt.agent_factory.configurators import configure_agent_with_state
 from autogpt.agent_factory.generators import generate_agent_for_task
 from autogpt.agent_manager import AgentManager
-from autogpt.app.utils import is_port_free
+from autogpt.commands.system import finish
+from autogpt.commands.user_interaction import ask_user
 from autogpt.config import Config
-from autogpt.core.resource.model_providers import ChatModelProvider, ModelProviderBudget
-from autogpt.file_storage import FileStorage
+from autogpt.core.resource.model_providers import ChatModelProvider
+from autogpt.core.resource.model_providers.openai import OpenAIProvider
+from autogpt.core.resource.model_providers.schema import ModelProviderBudget
+from autogpt.file_workspace import (
+    FileWorkspace,
+    FileWorkspaceBackendName,
+    get_workspace,
+)
+from autogpt.logs.utils import fmt_kwargs
 from autogpt.models.action_history import ActionErrorResult, ActionSuccessResult
-from autogpt.utils.exceptions import AgentFinished
-from autogpt.utils.utils import DEFAULT_ASK_COMMAND, DEFAULT_FINISH_COMMAND
 
 logger = logging.getLogger(__name__)
 
@@ -49,27 +53,17 @@ class AgentProtocolServer:
         self,
         app_config: Config,
         database: AgentDB,
-        file_storage: FileStorage,
         llm_provider: ChatModelProvider,
     ):
         self.app_config = app_config
         self.db = database
-        self.file_storage = file_storage
         self.llm_provider = llm_provider
-        self.agent_manager = AgentManager(file_storage)
-        self._task_budgets = defaultdict(ModelProviderBudget)
+        self.agent_manager = AgentManager(app_data_dir=app_config.app_data_dir)
+        self._task_budgets = {}
 
     async def start(self, port: int = 8000, router: APIRouter = base_router):
         """Start the agent server."""
         logger.debug("Starting the agent server...")
-        if not is_port_free(port):
-            logger.error(f"Port {port} is already in use.")
-            logger.info(
-                "You can specify a port by either setting the AP_SERVER_PORT "
-                "environment variable or defining AP_SERVER_PORT in the .env file."
-            )
-            return
-
         config = HypercornConfig()
         config.bind = [f"localhost:{port}"]
         app = FastAPI(
@@ -79,14 +73,11 @@ class AgentProtocolServer:
             version="v0.4",
         )
 
-        # Configure CORS middleware
-        default_origins = [f"http://localhost:{port}"]  # Default only local access
-        configured_origins = [
-            origin
-            for origin in os.getenv("AP_SERVER_CORS_ALLOWED_ORIGINS", "").split(",")
-            if origin  # Empty list if not configured
+        # Add CORS middleware
+        origins = [
+            "*",
+            # Add any other origins you want to whitelist
         ]
-        origins = configured_origins or default_origins
 
         app.add_middleware(
             CORSMiddleware,
@@ -130,22 +121,22 @@ class AgentProtocolServer:
         """
         Create a task for the agent.
         """
-        if user_id := (task_request.additional_input or {}).get("user_id"):
-            set_user({"id": user_id})
-
         task = await self.db.create_task(
             input=task_request.input,
             additional_input=task_request.additional_input,
         )
         logger.debug(f"Creating agent for task: '{task.input}'")
         task_agent = await generate_agent_for_task(
-            agent_id=task_agent_id(task.task_id),
             task=task.input,
             app_config=self.app_config,
-            file_storage=self.file_storage,
             llm_provider=self._get_task_llm_provider(task),
         )
-        await task_agent.file_manager.save_state()
+
+        # Assign an ID and a folder to the Agent and persist it
+        agent_id = task_agent.state.agent_id = task_agent_id(task.task_id)
+        logger.debug(f"New agent ID: {agent_id}")
+        task_agent.attach_fs(self.app_config.app_data_dir / "agents" / agent_id)
+        task_agent.state.save_to_json_file(task_agent.file_manager.state_file_path)
 
         return task
 
@@ -184,21 +175,17 @@ class AgentProtocolServer:
         # Restore Agent instance
         task = await self.get_task(task_id)
         agent = configure_agent_with_state(
-            state=self.agent_manager.load_agent_state(task_agent_id(task_id)),
+            state=self.agent_manager.retrieve_state(task_agent_id(task_id)),
             app_config=self.app_config,
-            file_storage=self.file_storage,
             llm_provider=self._get_task_llm_provider(task),
         )
 
-        if user_id := (task.additional_input or {}).get("user_id"):
-            set_user({"id": user_id})
-
         # According to the Agent Protocol spec, the first execute_step request contains
         #  the same task input as the parent create_task request.
         # To prevent this from interfering with the agent's process, we ignore the input
         #  of this first step request, and just generate the first step proposal.
         is_init_step = not bool(agent.event_history)
-        last_proposal, tool_result = None, None
+        execute_command, execute_command_args, execute_result = None, None, None
         execute_approved = False
 
         # HACK: only for compatibility with AGBenchmark
@@ -212,11 +199,13 @@ class AgentProtocolServer:
             and agent.event_history.current_episode
             and not agent.event_history.current_episode.result
         ):
-            last_proposal = agent.event_history.current_episode.action
+            execute_command = agent.event_history.current_episode.action.name
+            execute_command_args = agent.event_history.current_episode.action.args
             execute_approved = not user_input
 
             logger.debug(
-                f"Agent proposed command {last_proposal.use_tool}."
+                f"Agent proposed command"
+                f" {execute_command}({fmt_kwargs(execute_command_args)})."
                 f" User input/feedback: {repr(user_input)}"
             )
 
@@ -224,62 +213,58 @@ class AgentProtocolServer:
         step = await self.db.create_step(
             task_id=task_id,
             input=step_request,
-            is_last=(
-                last_proposal is not None
-                and last_proposal.use_tool.name == DEFAULT_FINISH_COMMAND
-                and execute_approved
-            ),
+            is_last=execute_command == finish.__name__ and execute_approved,
         )
         agent.llm_provider = self._get_task_llm_provider(task, step.step_id)
 
         # Execute previously proposed action
-        if last_proposal:
-            agent.file_manager.workspace.on_write_file = (
-                lambda path: self._on_agent_write_file(
-                    task=task, step=step, relative_path=path
-                )
+        if execute_command:
+            assert execute_command_args is not None
+            agent.workspace.on_write_file = lambda path: self._on_agent_write_file(
+                task=task, step=step, relative_path=path
             )
 
-            if last_proposal.use_tool.name == DEFAULT_ASK_COMMAND:
-                tool_result = ActionSuccessResult(outputs=user_input)
-                agent.event_history.register_result(tool_result)
+            if step.is_last and execute_command == finish.__name__:
+                assert execute_command_args
+                step = await self.db.update_step(
+                    task_id=task_id,
+                    step_id=step.step_id,
+                    output=execute_command_args["reason"],
+                )
+                logger.info(
+                    f"Total LLM cost for task {task_id}: "
+                    f"${round(agent.llm_provider.get_incurred_cost(), 2)}"
+                )
+                return step
+
+            if execute_command == ask_user.__name__:  # HACK
+                execute_result = ActionSuccessResult(outputs=user_input)
+                agent.event_history.register_result(execute_result)
+            elif not execute_command:
+                execute_result = None
             elif execute_approved:
                 step = await self.db.update_step(
                     task_id=task_id,
                     step_id=step.step_id,
                     status="running",
                 )
-
-                try:
-                    # Execute previously proposed action
-                    tool_result = await agent.execute(last_proposal)
-                except AgentFinished:
-                    additional_output = {}
-                    task_total_cost = agent.llm_provider.get_incurred_cost()
-                    if task_total_cost > 0:
-                        additional_output["task_total_cost"] = task_total_cost
-                        logger.info(
-                            f"Total LLM cost for task {task_id}: "
-                            f"${round(task_total_cost, 2)}"
-                        )
-
-                    step = await self.db.update_step(
-                        task_id=task_id,
-                        step_id=step.step_id,
-                        output=last_proposal.use_tool.arguments["reason"],
-                        additional_output=additional_output,
-                    )
-                    await agent.file_manager.save_state()
-                    return step
+                # Execute previously proposed action
+                execute_result = await agent.execute(
+                    command_name=execute_command,
+                    command_args=execute_command_args,
+                )
             else:
                 assert user_input
-                tool_result = await agent.do_not_execute(last_proposal, user_input)
+                execute_result = await agent.execute(
+                    command_name="human_feedback",  # HACK
+                    command_args={},
+                    user_input=user_input,
+                )
 
         # Propose next action
         try:
-            assistant_response = await agent.propose_action()
-            next_tool_to_use = assistant_response.use_tool
-            logger.debug(f"AI output: {assistant_response.thoughts}")
+            next_command, next_command_args, raw_output = await agent.propose_action()
+            logger.debug(f"AI output: {raw_output}")
         except Exception as e:
             step = await self.db.update_step(
                 task_id=task_id,
@@ -292,54 +277,42 @@ class AgentProtocolServer:
         # Format step output
         output = (
             (
-                f"`{last_proposal.use_tool}` returned:"
-                + ("\n\n" if "\n" in str(tool_result) else " ")
-                + f"{tool_result}\n\n"
+                f"`{execute_command}({fmt_kwargs(execute_command_args)})` returned:"
+                + ("\n\n" if "\n" in str(execute_result) else " ")
+                + f"{execute_result}\n\n"
             )
-            if last_proposal and last_proposal.use_tool.name != DEFAULT_ASK_COMMAND
+            if execute_command_args and execute_command != ask_user.__name__
             else ""
         )
-        output += f"{assistant_response.thoughts.speak}\n\n"
+        output += f"{raw_output['thoughts']['speak']}\n\n"
         output += (
-            f"Next Command: {next_tool_to_use}"
-            if next_tool_to_use.name != DEFAULT_ASK_COMMAND
-            else next_tool_to_use.arguments["question"]
+            f"Next Command: {next_command}({fmt_kwargs(next_command_args)})"
+            if next_command != ask_user.__name__
+            else next_command_args["question"]
         )
 
         additional_output = {
             **(
                 {
                     "last_action": {
-                        "name": last_proposal.use_tool.name,
-                        "args": last_proposal.use_tool.arguments,
+                        "name": execute_command,
+                        "args": execute_command_args,
                         "result": (
-                            ""
-                            if tool_result is None
-                            else (
-                                orjson.loads(tool_result.json())
-                                if not isinstance(tool_result, ActionErrorResult)
-                                else {
-                                    "error": str(tool_result.error),
-                                    "reason": tool_result.reason,
-                                }
-                            )
+                            orjson.loads(execute_result.json())
+                            if not isinstance(execute_result, ActionErrorResult)
+                            else {
+                                "error": str(execute_result.error),
+                                "reason": execute_result.reason,
+                            }
                         ),
                     },
                 }
-                if last_proposal and tool_result
+                if not is_init_step
                 else {}
             ),
-            **assistant_response.dict(),
+            **raw_output,
         }
 
-        task_cumulative_cost = agent.llm_provider.get_incurred_cost()
-        if task_cumulative_cost > 0:
-            additional_output["task_cumulative_cost"] = task_cumulative_cost
-        logger.debug(
-            f"Running total LLM cost for task {task_id}: "
-            f"${round(task_cumulative_cost, 3)}"
-        )
-
         step = await self.db.update_step(
             task_id=task_id,
             step_id=step.step_id,
@@ -348,7 +321,11 @@ class AgentProtocolServer:
             additional_output=additional_output,
         )
 
-        await agent.file_manager.save_state()
+        logger.debug(
+            f"Running total LLM cost for task {task_id}: "
+            f"${round(agent.llm_provider.get_incurred_cost(), 3)}"
+        )
+        agent.state.save_to_json_file(agent.file_manager.state_file_path)
         return step
 
     async def _on_agent_write_file(
@@ -407,7 +384,7 @@ class AgentProtocolServer:
         else:
             file_path = os.path.join(relative_path, file_name)
 
-        workspace = self._get_task_agent_file_workspace(task_id)
+        workspace = self._get_task_agent_file_workspace(task_id, self.agent_manager)
         await workspace.write_file(file_path, data)
 
         artifact = await self.db.create_artifact(
@@ -423,12 +400,12 @@ class AgentProtocolServer:
         Download a task artifact by ID.
         """
         try:
-            workspace = self._get_task_agent_file_workspace(task_id)
             artifact = await self.db.get_artifact(artifact_id)
             if artifact.file_name not in artifact.relative_path:
                 file_path = os.path.join(artifact.relative_path, artifact.file_name)
             else:
                 file_path = artifact.relative_path
+            workspace = self._get_task_agent_file_workspace(task_id, self.agent_manager)
             retrieved_artifact = workspace.read_file(file_path, binary=True)
         except NotFoundError:
             raise
@@ -443,9 +420,28 @@ class AgentProtocolServer:
             },
         )
 
-    def _get_task_agent_file_workspace(self, task_id: str | int) -> FileStorage:
+    def _get_task_agent_file_workspace(
+        self,
+        task_id: str | int,
+        agent_manager: AgentManager,
+    ) -> FileWorkspace:
+        use_local_ws = (
+            self.app_config.workspace_backend == FileWorkspaceBackendName.LOCAL
+        )
         agent_id = task_agent_id(task_id)
-        return self.file_storage.clone_with_subroot(f"agents/{agent_id}/workspace")
+        workspace = get_workspace(
+            backend=self.app_config.workspace_backend,
+            id=agent_id if not use_local_ws else "",
+            root_path=agent_manager.get_agent_dir(
+                agent_id=agent_id,
+                must_exist=True,
+            )
+            / "workspace"
+            if use_local_ws
+            else None,
+        )
+        workspace.initialize()
+        return workspace
 
     def _get_task_llm_provider(
         self, task: Task, step_id: str = ""
@@ -453,7 +449,9 @@ class AgentProtocolServer:
         """
         Configures the LLM provider with headers to link outgoing requests to the task.
         """
-        task_llm_budget = self._task_budgets[task.task_id]
+        task_llm_budget = self._task_budgets.get(
+            task.task_id, self.llm_provider.default_settings.budget.copy(deep=True)
+        )
 
         task_llm_provider_config = self.llm_provider._configuration.copy(deep=True)
         _extra_request_headers = task_llm_provider_config.extra_request_headers
@@ -463,18 +461,20 @@ class AgentProtocolServer:
         if task.additional_input and (user_id := task.additional_input.get("user_id")):
             _extra_request_headers["AutoGPT-UserID"] = user_id
 
-        settings = self.llm_provider._settings.copy()
-        settings.budget = task_llm_budget
-        settings.configuration = task_llm_provider_config
-        task_llm_provider = self.llm_provider.__class__(
-            settings=settings,
-            logger=logger.getChild(
-                f"Task-{task.task_id}_{self.llm_provider.__class__.__name__}"
-            ),
-        )
-        self._task_budgets[task.task_id] = task_llm_provider._budget  # type: ignore
+        task_llm_provider = None
+        if isinstance(self.llm_provider, OpenAIProvider):
+            settings = self.llm_provider._settings.copy()
+            settings.budget = task_llm_budget
+            settings.configuration = task_llm_provider_config  # type: ignore
+            task_llm_provider = OpenAIProvider(
+                settings=settings,
+                logger=logger.getChild(f"Task-{task.task_id}_OpenAIProvider"),
+            )
 
-        return task_llm_provider
+        if task_llm_provider and task_llm_provider._budget:
+            self._task_budgets[task.task_id] = task_llm_provider._budget
+
+        return task_llm_provider or self.llm_provider
 
 
 def task_agent_id(task_id: str | int) -> str:

autogpts/autogpt/autogpt/app/cli.py

@@ -7,14 +7,10 @@ import click
 
 from autogpt.logs.config import LogFormatName
 
-from .telemetry import setup_telemetry
-
 
 @click.group(invoke_without_command=True)
 @click.pass_context
 def cli(ctx: click.Context):
-    setup_telemetry()
-
     # Invoke `run` by default
     if ctx.invoked_subcommand is None:
         ctx.invoke(run)

autogpts/autogpt/autogpt/app/configurator.py

@@ -3,28 +3,37 @@ from __future__ import annotations
 
 import logging
 from pathlib import Path
-from typing import Literal, Optional
+from typing import TYPE_CHECKING, Literal, Optional
 
 import click
 from colorama import Back, Fore, Style
 
+from autogpt import utils
 from autogpt.config import Config
 from autogpt.config.config import GPT_3_MODEL, GPT_4_MODEL
-from autogpt.core.resource.model_providers import ModelName, MultiProvider
+from autogpt.llm.api_manager import ApiManager
+from autogpt.logs.config import LogFormatName
 from autogpt.logs.helpers import request_user_double_check
 from autogpt.memory.vector import get_supported_memory_backends
-from autogpt.utils import utils
+
+if TYPE_CHECKING:
+    from autogpt.core.resource.model_providers.openai import OpenAICredentials
 
 logger = logging.getLogger(__name__)
 
 
-async def apply_overrides_to_config(
+def apply_overrides_to_config(
     config: Config,
     continuous: bool = False,
     continuous_limit: Optional[int] = None,
     ai_settings_file: Optional[Path] = None,
     prompt_settings_file: Optional[Path] = None,
     skip_reprompt: bool = False,
+    speak: bool = False,
+    debug: bool = False,
+    log_level: Optional[str] = None,
+    log_format: Optional[str] = None,
+    log_file_format: Optional[str] = None,
     gpt3only: bool = False,
     gpt4only: bool = False,
     memory_type: Optional[str] = None,
@@ -54,6 +63,19 @@ async def apply_overrides_to_config(
         skips_news (bool): Whether to suppress the output of latest news on startup.
     """
     config.continuous_mode = False
+    config.tts_config.speak_mode = False
+
+    # Set log level
+    if debug:
+        config.logging.level = logging.DEBUG
+    elif log_level and type(_level := logging.getLevelName(log_level.upper())) is int:
+        config.logging.level = _level
+
+    # Set log format
+    if log_format and log_format in LogFormatName._value2member_map_:
+        config.logging.log_format = LogFormatName(log_format)
+    if log_file_format and log_file_format in LogFormatName._value2member_map_:
+        config.logging.log_file_format = LogFormatName(log_file_format)
 
     if continuous:
         logger.warning(
@@ -70,6 +92,9 @@ async def apply_overrides_to_config(
     if continuous_limit and not continuous:
         raise click.UsageError("--continuous-limit can only be used with --continuous")
 
+    if speak:
+        config.tts_config.speak_mode = True
+
     # Set the default LLM models
     if gpt3only:
         # --gpt3only should always use gpt-3.5-turbo, despite user's FAST_LLM config
@@ -77,14 +102,23 @@ async def apply_overrides_to_config(
         config.smart_llm = GPT_3_MODEL
     elif (
         gpt4only
-        and (await check_model(GPT_4_MODEL, model_type="smart_llm")) == GPT_4_MODEL
+        and check_model(
+            GPT_4_MODEL,
+            model_type="smart_llm",
+            api_credentials=config.openai_credentials,
+        )
+        == GPT_4_MODEL
     ):
         # --gpt4only should always use gpt-4, despite user's SMART_LLM config
         config.fast_llm = GPT_4_MODEL
         config.smart_llm = GPT_4_MODEL
     else:
-        config.fast_llm = await check_model(config.fast_llm, "fast_llm")
-        config.smart_llm = await check_model(config.smart_llm, "smart_llm")
+        config.fast_llm = check_model(
+            config.fast_llm, "fast_llm", api_credentials=config.openai_credentials
+        )
+        config.smart_llm = check_model(
+            config.smart_llm, "smart_llm", api_credentials=config.openai_credentials
+        )
 
     if memory_type:
         supported_memory = get_supported_memory_backends()
@@ -149,17 +183,19 @@ async def apply_overrides_to_config(
         config.skip_news = True
 
 
-async def check_model(
-    model_name: ModelName, model_type: Literal["smart_llm", "fast_llm"]
-) -> ModelName:
+def check_model(
+    model_name: str,
+    model_type: Literal["smart_llm", "fast_llm"],
+    api_credentials: OpenAICredentials,
+) -> str:
     """Check if model is available for use. If not, return gpt-3.5-turbo."""
-    multi_provider = MultiProvider()
-    models = await multi_provider.get_available_models()
+    api_manager = ApiManager()
+    models = api_manager.get_models(api_credentials)
 
-    if any(model_name == m.name for m in models):
+    if any(model_name == m.id for m in models):
         return model_name
 
     logger.warning(
-        f"You don't have access to {model_name}. Setting {model_type} to {GPT_3_MODEL}."
+        f"You don't have access to {model_name}. Setting {model_type} to gpt-3.5-turbo."
     )
-    return GPT_3_MODEL
+    return "gpt-3.5-turbo"

autogpts/autogpt/autogpt/app/main.py

@@ -1,7 +1,6 @@
 """
 The application entry point. Can be invoked by a CLI or any other front end application.
 """
-
 import enum
 import logging
 import math
@@ -18,16 +17,12 @@ from forge.sdk.db import AgentDB
 
 if TYPE_CHECKING:
     from autogpt.agents.agent import Agent
-    from autogpt.agents.base import BaseAgentActionProposal
 
 from autogpt.agent_factory.configurators import configure_agent_with_state, create_agent
 from autogpt.agent_factory.profile_generator import generate_agent_profile_for_task
 from autogpt.agent_manager import AgentManager
-from autogpt.agents.prompt_strategies.one_shot import AssistantThoughts
-from autogpt.commands.execute_code import (
-    is_docker_available,
-    we_are_running_in_a_docker_container,
-)
+from autogpt.agents import AgentThoughts, CommandArgs, CommandName
+from autogpt.agents.utils.exceptions import AgentTerminated, InvalidAgentResponseError
 from autogpt.config import (
     AIDirectives,
     AIProfile,
@@ -35,15 +30,12 @@ from autogpt.config import (
     ConfigBuilder,
     assert_config_has_openai_api_key,
 )
-from autogpt.core.resource.model_providers import MultiProvider
+from autogpt.core.resource.model_providers.openai import OpenAIProvider
 from autogpt.core.runner.client_lib.utils import coroutine
-from autogpt.file_storage import FileStorageBackendName, get_storage
-from autogpt.logs.config import configure_logging
+from autogpt.logs.config import configure_chat_plugins, configure_logging
 from autogpt.logs.helpers import print_attribute, speak
-from autogpt.models.action_history import ActionInterruptedByHuman
-from autogpt.models.utils import ModelWithSummary
-from autogpt.utils.exceptions import AgentTerminated, InvalidAgentResponseError
-from autogpt.utils.utils import DEFAULT_FINISH_COMMAND
+from autogpt.plugins import scan_plugins
+from scripts.install_plugin_deps import install_plugin_dependencies
 
 from .configurator import apply_overrides_to_config
 from .setup import apply_overrides_to_ai_settings, interactively_revise_ai_settings
@@ -84,38 +76,23 @@ async def run_auto_gpt(
     best_practices: Optional[list[str]] = None,
     override_directives: bool = False,
 ):
-    # Set up configuration
     config = ConfigBuilder.build_config_from_env()
-    # Storage
-    local = config.file_storage_backend == FileStorageBackendName.LOCAL
-    restrict_to_root = not local or config.restrict_to_workspace
-    file_storage = get_storage(
-        config.file_storage_backend, root_path="data", restrict_to_root=restrict_to_root
-    )
-    file_storage.initialize()
-
-    # Set up logging module
-    if speak:
-        config.tts_config.speak_mode = True
-    configure_logging(
-        debug=debug,
-        level=log_level,
-        log_format=log_format,
-        log_file_format=log_file_format,
-        config=config.logging,
-        tts_config=config.tts_config,
-    )
 
     # TODO: fill in llm values here
     assert_config_has_openai_api_key(config)
 
-    await apply_overrides_to_config(
+    apply_overrides_to_config(
         config=config,
         continuous=continuous,
         continuous_limit=continuous_limit,
         ai_settings_file=ai_settings,
         prompt_settings_file=prompt_settings,
         skip_reprompt=skip_reprompt,
+        speak=speak,
+        debug=debug,
+        log_level=log_level,
+        log_format=log_format,
+        log_file_format=log_file_format,
         gpt3only=gpt3only,
         gpt4only=gpt4only,
         browser_name=browser_name,
@@ -123,7 +100,13 @@ async def run_auto_gpt(
         skip_news=skip_news,
     )
 
-    llm_provider = _configure_llm_provider(config)
+    # Set up logging module
+    configure_logging(
+        **config.logging.dict(),
+        tts_config=config.tts_config,
+    )
+
+    llm_provider = _configure_openai_provider(config)
 
     logger = logging.getLogger(__name__)
 
@@ -157,17 +140,15 @@ async def run_auto_gpt(
             print_attribute("Using Prompt Settings File", prompt_settings)
         if config.allow_downloads:
             print_attribute("Native Downloading", "ENABLED")
-        if we_are_running_in_a_docker_container() or is_docker_available():
-            print_attribute("Code Execution", "ENABLED")
-        else:
-            print_attribute(
-                "Code Execution",
-                "DISABLED (Docker unavailable)",
-                title_color=Fore.YELLOW,
-            )
+
+    if install_plugin_deps:
+        install_plugin_dependencies()
+
+    config.plugins = scan_plugins(config)
+    configure_chat_plugins(config)
 
     # Let user choose an existing agent to run
-    agent_manager = AgentManager(file_storage)
+    agent_manager = AgentManager(config.app_data_dir)
     existing_agents = agent_manager.list_agents()
     load_existing_agent = ""
     if existing_agents:
@@ -175,23 +156,15 @@ async def run_auto_gpt(
             "Existing agents\n---------------\n"
             + "\n".join(f"{i} - {id}" for i, id in enumerate(existing_agents, 1))
         )
-        load_existing_agent = clean_input(
+        load_existing_agent = await clean_input(
             config,
             "Enter the number or name of the agent to run,"
             " or hit enter to create a new one:",
         )
-        if re.match(r"^\d+$", load_existing_agent.strip()) and 0 < int(
-            load_existing_agent
-        ) <= len(existing_agents):
+        if re.match(r"^\d+$", load_existing_agent):
             load_existing_agent = existing_agents[int(load_existing_agent) - 1]
-
-        if load_existing_agent != "" and load_existing_agent not in existing_agents:
-            logger.info(
-                f"Unknown agent '{load_existing_agent}', "
-                f"creating a new one instead.",
-                extra={"color": Fore.YELLOW},
-            )
-            load_existing_agent = ""
+        elif load_existing_agent and load_existing_agent not in existing_agents:
+            raise ValueError(f"Unknown agent '{load_existing_agent}'")
 
     # Either load existing or set up new agent state
     agent = None
@@ -201,20 +174,21 @@ async def run_auto_gpt(
     # Resume an Existing Agent #
     ############################
     if load_existing_agent:
-        agent_state = None
+        agent_state = agent_manager.retrieve_state(load_existing_agent)
         while True:
-            answer = clean_input(config, "Resume? [Y/n]")
-            if answer == "" or answer.lower() == "y":
-                agent_state = agent_manager.load_agent_state(load_existing_agent)
+            answer = await clean_input(config, "Resume? [Y/n]")
+            if answer.lower() == "y":
                 break
             elif answer.lower() == "n":
+                agent_state = None
                 break
+            else:
+                print("Please respond with 'y' or 'n'")
 
     if agent_state:
         agent = configure_agent_with_state(
             state=agent_state,
             app_config=config,
-            file_storage=file_storage,
             llm_provider=llm_provider,
         )
         apply_overrides_to_ai_settings(
@@ -228,21 +202,6 @@ async def run_auto_gpt(
             replace_directives=override_directives,
         )
 
-        if (
-            (current_episode := agent.event_history.current_episode)
-            and current_episode.action.use_tool.name == DEFAULT_FINISH_COMMAND
-            and not current_episode.result
-        ):
-            # Agent was resumed after `finish` -> rewrite result of `finish` action
-            finish_reason = current_episode.action.use_tool.arguments["reason"]
-            print(f"Agent previously self-terminated; reason: '{finish_reason}'")
-            new_assignment = clean_input(
-                config, "Please give a follow-up question or assignment:"
-            )
-            agent.event_history.register_result(
-                ActionInterruptedByHuman(feedback=new_assignment)
-            )
-
         # If any of these are specified as arguments,
         #  assume the user doesn't want to revise them
         if not any(
@@ -266,14 +225,11 @@ async def run_auto_gpt(
     # Set up a new Agent #
     ######################
     if not agent:
-        task = ""
-        while task.strip() == "":
-            task = clean_input(
-                config,
-                "Enter the task that you want AutoGPT to execute,"
-                " with as much detail as possible:",
-            )
-
+        task = await clean_input(
+            config,
+            "Enter the task that you want AutoGPT to execute,"
+            " with as much detail as possible:",
+        )
         base_ai_directives = AIDirectives.from_file(config.prompt_settings_file)
 
         ai_profile, task_oriented_ai_directives = await generate_agent_profile_for_task(
@@ -313,22 +269,19 @@ async def run_auto_gpt(
             logger.info("AI config overrides specified through CLI; skipping revision")
 
         agent = create_agent(
-            agent_id=agent_manager.generate_id(ai_profile.ai_name),
             task=task,
             ai_profile=ai_profile,
             directives=ai_directives,
             app_config=config,
-            file_storage=file_storage,
             llm_provider=llm_provider,
         )
+        agent.attach_fs(agent_manager.get_agent_dir(agent.state.agent_id))
 
-        file_manager = agent.file_manager
-
-        if file_manager and not agent.config.allow_fs_access:
+        if not agent.config.allow_fs_access:
             logger.info(
                 f"{Fore.YELLOW}"
                 "NOTE: All files/directories created by this agent can be found "
-                f"inside its workspace at:{Fore.RESET} {file_manager.workspace.root}",
+                f"inside its workspace at:{Fore.RESET} {agent.workspace.root}",
                 extra={"preserve_color": True},
             )
 
@@ -342,15 +295,23 @@ async def run_auto_gpt(
         logger.info(f"Saving state of {agent_id}...")
 
         # Allow user to Save As other ID
-        save_as_id = clean_input(
-            config,
-            f"Press enter to save as '{agent_id}',"
-            " or enter a different ID to save to:",
-        )
-        # TODO: allow many-to-one relations of agents and workspaces
-        await agent.file_manager.save_state(
-            save_as_id.strip() if not save_as_id.isspace() else None
+        save_as_id = (
+            await clean_input(
+                config,
+                f"Press enter to save as '{agent_id}',"
+                " or enter a different ID to save to:",
+            )
+            or agent_id
         )
+        if save_as_id and save_as_id != agent_id:
+            agent.set_id(
+                new_id=save_as_id,
+                new_agent_dir=agent_manager.get_agent_dir(save_as_id),
+            )
+            # TODO: clone workspace if user wants that
+            # TODO: ... OR allow many-to-one relations of agents and workspaces
+
+        agent.state.save_to_json_file(agent.file_manager.state_file_path)
 
 
 @coroutine
@@ -369,37 +330,35 @@ async def run_auto_gpt_server(
     from .agent_protocol_server import AgentProtocolServer
 
     config = ConfigBuilder.build_config_from_env()
-    # Storage
-    local = config.file_storage_backend == FileStorageBackendName.LOCAL
-    restrict_to_root = not local or config.restrict_to_workspace
-    file_storage = get_storage(
-        config.file_storage_backend, root_path="data", restrict_to_root=restrict_to_root
-    )
-    file_storage.initialize()
-
-    # Set up logging module
-    configure_logging(
-        debug=debug,
-        level=log_level,
-        log_format=log_format,
-        log_file_format=log_file_format,
-        config=config.logging,
-        tts_config=config.tts_config,
-    )
 
     # TODO: fill in llm values here
     assert_config_has_openai_api_key(config)
 
-    await apply_overrides_to_config(
+    apply_overrides_to_config(
         config=config,
         prompt_settings_file=prompt_settings,
+        debug=debug,
+        log_level=log_level,
+        log_format=log_format,
+        log_file_format=log_file_format,
         gpt3only=gpt3only,
         gpt4only=gpt4only,
         browser_name=browser_name,
         allow_downloads=allow_downloads,
     )
 
-    llm_provider = _configure_llm_provider(config)
+    # Set up logging module
+    configure_logging(
+        **config.logging.dict(),
+        tts_config=config.tts_config,
+    )
+
+    llm_provider = _configure_openai_provider(config)
+
+    if install_plugin_deps:
+        install_plugin_dependencies()
+
+    config.plugins = scan_plugins(config)
 
     # Set up & start server
     database = AgentDB(
@@ -408,10 +367,7 @@ async def run_auto_gpt_server(
     )
     port: int = int(os.getenv("AP_SERVER_PORT", default=8000))
     server = AgentProtocolServer(
-        app_config=config,
-        database=database,
-        file_storage=file_storage,
-        llm_provider=llm_provider,
+        app_config=config, database=database, llm_provider=llm_provider
     )
     await server.start(port=port)
 
@@ -421,12 +377,24 @@ async def run_auto_gpt_server(
     )
 
 
-def _configure_llm_provider(config: Config) -> MultiProvider:
-    multi_provider = MultiProvider()
-    for model in [config.smart_llm, config.fast_llm]:
-        # Ensure model providers for configured LLMs are available
-        multi_provider.get_model_provider(model)
-    return multi_provider
+def _configure_openai_provider(config: Config) -> OpenAIProvider:
+    """Create a configured OpenAIProvider object.
+
+    Args:
+        config: The program's configuration.
+
+    Returns:
+        A configured OpenAIProvider object.
+    """
+    if config.openai_credentials is None:
+        raise RuntimeError("OpenAI key is not configured")
+
+    openai_settings = OpenAIProvider.default_settings.copy(deep=True)
+    openai_settings.credentials = config.openai_credentials
+    return OpenAIProvider(
+        settings=openai_settings,
+        logger=logging.getLogger("OpenAIProvider"),
+    )
 
 
 def _get_cycle_budget(continuous_mode: bool, continuous_limit: int) -> int | float:
@@ -520,7 +488,11 @@ async def run_interaction_loop(
         # Have the agent determine the next action to take.
         with spinner:
             try:
-                action_proposal = await agent.propose_action()
+                (
+                    command_name,
+                    command_args,
+                    assistant_reply_dict,
+                ) = await agent.propose_action()
             except InvalidAgentResponseError as e:
                 logger.warning(f"The agent's thoughts could not be parsed: {e}")
                 consecutive_failures += 1
@@ -543,7 +515,9 @@ async def run_interaction_loop(
         # Print the assistant's thoughts and the next command to the user.
         update_user(
             ai_profile,
-            action_proposal,
+            command_name,
+            command_args,
+            assistant_reply_dict,
             speak_mode=legacy_config.tts_config.speak_mode,
         )
 
@@ -552,12 +526,12 @@ async def run_interaction_loop(
         ##################
         handle_stop_signal()
         if cycles_remaining == 1:  # Last cycle
-            feedback_type, feedback, new_cycles_remaining = await get_user_feedback(
+            user_feedback, user_input, new_cycles_remaining = await get_user_feedback(
                 legacy_config,
                 ai_profile,
             )
 
-            if feedback_type == UserFeedback.AUTHORIZE:
+            if user_feedback == UserFeedback.AUTHORIZE:
                 if new_cycles_remaining is not None:
                     # Case 1: User is altering the cycle budget.
                     if cycle_budget > 1:
@@ -581,13 +555,13 @@ async def run_interaction_loop(
                     "-=-=-=-=-=-=-= COMMAND AUTHORISED BY USER -=-=-=-=-=-=-=",
                     extra={"color": Fore.MAGENTA},
                 )
-            elif feedback_type == UserFeedback.EXIT:
+            elif user_feedback == UserFeedback.EXIT:
                 logger.warning("Exiting...")
                 exit()
             else:  # user_feedback == UserFeedback.TEXT
-                pass
+                command_name = "human_feedback"
         else:
-            feedback = ""
+            user_input = ""
             # First log new-line so user can differentiate sections better in console
             print()
             if cycles_remaining != math.inf:
@@ -602,31 +576,33 @@ async def run_interaction_loop(
         # Decrement the cycle counter first to reduce the likelihood of a SIGINT
         # happening during command execution, setting the cycles remaining to 1,
         # and then having the decrement set it to 0, exiting the application.
-        if not feedback:
+        if command_name != "human_feedback":
             cycles_remaining -= 1
 
-        if not action_proposal.use_tool:
+        if not command_name:
             continue
 
         handle_stop_signal()
 
-        if not feedback:
-            result = await agent.execute(action_proposal)
-        else:
-            result = await agent.do_not_execute(action_proposal, feedback)
+        if command_name:
+            result = await agent.execute(command_name, command_args, user_input)
 
-        if result.status == "success":
-            logger.info(result, extra={"title": "SYSTEM:", "title_color": Fore.YELLOW})
-        elif result.status == "error":
-            logger.warning(
-                f"Command {action_proposal.use_tool.name} returned an error: "
-                f"{result.error or result.reason}"
-            )
+            if result.status == "success":
+                logger.info(
+                    result, extra={"title": "SYSTEM:", "title_color": Fore.YELLOW}
+                )
+            elif result.status == "error":
+                logger.warning(
+                    f"Command {command_name} returned an error: "
+                    f"{result.error or result.reason}"
+                )
 
 
 def update_user(
     ai_profile: AIProfile,
-    action_proposal: "BaseAgentActionProposal",
+    command_name: CommandName,
+    command_args: CommandArgs,
+    assistant_reply_dict: AgentThoughts,
     speak_mode: bool = False,
 ) -> None:
     """Prints the assistant's thoughts and the next command to the user.
@@ -642,19 +618,18 @@ def update_user(
 
     print_assistant_thoughts(
         ai_name=ai_profile.ai_name,
-        thoughts=action_proposal.thoughts,
+        assistant_reply_json_valid=assistant_reply_dict,
         speak_mode=speak_mode,
     )
 
     if speak_mode:
-        speak(f"I want to execute {action_proposal.use_tool.name}")
+        speak(f"I want to execute {command_name}")
 
     # First log new-line so user can differentiate sections better in console
     print()
-    safe_tool_name = remove_ansi_escape(action_proposal.use_tool.name)
     logger.info(
-        f"COMMAND = {Fore.CYAN}{safe_tool_name}{Style.RESET_ALL}  "
-        f"ARGUMENTS = {Fore.CYAN}{action_proposal.use_tool.arguments}{Style.RESET_ALL}",
+        f"COMMAND = {Fore.CYAN}{remove_ansi_escape(command_name)}{Style.RESET_ALL}  "
+        f"ARGUMENTS = {Fore.CYAN}{command_args}{Style.RESET_ALL}",
         extra={
             "title": "NEXT ACTION:",
             "title_color": Fore.CYAN,
@@ -695,7 +670,12 @@ async def get_user_feedback(
 
     while user_feedback is None:
         # Get input from user
-        console_input = clean_input(config, Fore.MAGENTA + "Input:" + Style.RESET_ALL)
+        if config.chat_messages_enabled:
+            console_input = await clean_input(config, "Waiting for your response...")
+        else:
+            console_input = await clean_input(
+                config, Fore.MAGENTA + "Input:" + Style.RESET_ALL
+            )
 
         # Parse user input
         if console_input.lower().strip() == config.authorise_key:
@@ -723,59 +703,56 @@ async def get_user_feedback(
 
 def print_assistant_thoughts(
     ai_name: str,
-    thoughts: str | ModelWithSummary | AssistantThoughts,
+    assistant_reply_json_valid: dict,
     speak_mode: bool = False,
 ) -> None:
     logger = logging.getLogger(__name__)
 
-    thoughts_text = remove_ansi_escape(
-        thoughts.text
-        if isinstance(thoughts, AssistantThoughts)
-        else thoughts.summary()
-        if isinstance(thoughts, ModelWithSummary)
-        else thoughts
-    )
+    assistant_thoughts_reasoning = None
+    assistant_thoughts_plan = None
+    assistant_thoughts_speak = None
+    assistant_thoughts_criticism = None
+
+    assistant_thoughts = assistant_reply_json_valid.get("thoughts", {})
+    assistant_thoughts_text = remove_ansi_escape(assistant_thoughts.get("text", ""))
+    if assistant_thoughts:
+        assistant_thoughts_reasoning = remove_ansi_escape(
+            assistant_thoughts.get("reasoning", "")
+        )
+        assistant_thoughts_plan = remove_ansi_escape(assistant_thoughts.get("plan", ""))
+        assistant_thoughts_criticism = remove_ansi_escape(
+            assistant_thoughts.get("self_criticism", "")
+        )
+        assistant_thoughts_speak = remove_ansi_escape(
+            assistant_thoughts.get("speak", "")
+        )
     print_attribute(
-        f"{ai_name.upper()} THOUGHTS", thoughts_text, title_color=Fore.YELLOW
+        f"{ai_name.upper()} THOUGHTS", assistant_thoughts_text, title_color=Fore.YELLOW
+    )
+    print_attribute("REASONING", assistant_thoughts_reasoning, title_color=Fore.YELLOW)
+    if assistant_thoughts_plan:
+        print_attribute("PLAN", "", title_color=Fore.YELLOW)
+        # If it's a list, join it into a string
+        if isinstance(assistant_thoughts_plan, list):
+            assistant_thoughts_plan = "\n".join(assistant_thoughts_plan)
+        elif isinstance(assistant_thoughts_plan, dict):
+            assistant_thoughts_plan = str(assistant_thoughts_plan)
+
+        # Split the input_string using the newline character and dashes
+        lines = assistant_thoughts_plan.split("\n")
+        for line in lines:
+            line = line.lstrip("- ")
+            logger.info(line.strip(), extra={"title": "- ", "title_color": Fore.GREEN})
+    print_attribute(
+        "CRITICISM", f"{assistant_thoughts_criticism}", title_color=Fore.YELLOW
     )
 
-    if isinstance(thoughts, AssistantThoughts):
-        print_attribute(
-            "REASONING", remove_ansi_escape(thoughts.reasoning), title_color=Fore.YELLOW
-        )
-        if assistant_thoughts_plan := remove_ansi_escape(
-            "\n".join(f"- {p}" for p in thoughts.plan)
-        ):
-            print_attribute("PLAN", "", title_color=Fore.YELLOW)
-            # If it's a list, join it into a string
-            if isinstance(assistant_thoughts_plan, list):
-                assistant_thoughts_plan = "\n".join(assistant_thoughts_plan)
-            elif isinstance(assistant_thoughts_plan, dict):
-                assistant_thoughts_plan = str(assistant_thoughts_plan)
-
-            # Split the input_string using the newline character and dashes
-            lines = assistant_thoughts_plan.split("\n")
-            for line in lines:
-                line = line.lstrip("- ")
-                logger.info(
-                    line.strip(), extra={"title": "- ", "title_color": Fore.GREEN}
-                )
-        print_attribute(
-            "CRITICISM",
-            remove_ansi_escape(thoughts.self_criticism),
-            title_color=Fore.YELLOW,
-        )
-
-        # Speak the assistant's thoughts
-        if assistant_thoughts_speak := remove_ansi_escape(thoughts.speak):
-            if speak_mode:
-                speak(assistant_thoughts_speak)
-            else:
-                print_attribute(
-                    "SPEAK", assistant_thoughts_speak, title_color=Fore.YELLOW
-                )
-    else:
-        speak(thoughts_text)
+    # Speak the assistant's thoughts
+    if assistant_thoughts_speak:
+        if speak_mode:
+            speak(assistant_thoughts_speak)
+        else:
+            print_attribute("SPEAK", assistant_thoughts_speak, title_color=Fore.YELLOW)
 
 
 def remove_ansi_escape(s: str) -> str:

autogpts/autogpt/autogpt/app/setup.py

@@ -69,48 +69,44 @@ async def interactively_revise_ai_settings(
         )
 
         if (
-            clean_input(app_config, "Continue with these settings? [Y/n]").lower()
+            await clean_input(app_config, "Continue with these settings? [Y/n]")
             or app_config.authorise_key
         ) == app_config.authorise_key:
             break
 
         # Ask for revised ai_profile
         ai_profile.ai_name = (
-            clean_input(app_config, "Enter AI name (or press enter to keep current):")
+            await clean_input(
+                app_config, "Enter AI name (or press enter to keep current):"
+            )
             or ai_profile.ai_name
         )
         ai_profile.ai_role = (
-            clean_input(
+            await clean_input(
                 app_config, "Enter new AI role (or press enter to keep current):"
             )
             or ai_profile.ai_role
         )
 
         # Revise constraints
-        i = 0
-        while i < len(directives.constraints):
-            constraint = directives.constraints[i]
+        for i, constraint in enumerate(directives.constraints):
             print_attribute(f"Constraint {i+1}:", f'"{constraint}"')
             new_constraint = (
-                clean_input(
+                await clean_input(
                     app_config,
                     f"Enter new constraint {i+1}"
                     " (press enter to keep current, or '-' to remove):",
                 )
                 or constraint
             )
-
             if new_constraint == "-":
                 directives.constraints.remove(constraint)
-                continue
             elif new_constraint:
                 directives.constraints[i] = new_constraint
 
-            i += 1
-
         # Add new constraints
         while True:
-            new_constraint = clean_input(
+            new_constraint = await clean_input(
                 app_config,
                 "Press enter to finish, or enter a constraint to add:",
             )
@@ -119,12 +115,10 @@ async def interactively_revise_ai_settings(
             directives.constraints.append(new_constraint)
 
         # Revise resources
-        i = 0
-        while i < len(directives.resources):
-            resource = directives.resources[i]
+        for i, resource in enumerate(directives.resources):
             print_attribute(f"Resource {i+1}:", f'"{resource}"')
             new_resource = (
-                clean_input(
+                await clean_input(
                     app_config,
                     f"Enter new resource {i+1}"
                     " (press enter to keep current, or '-' to remove):",
@@ -133,15 +127,12 @@ async def interactively_revise_ai_settings(
             )
             if new_resource == "-":
                 directives.resources.remove(resource)
-                continue
             elif new_resource:
                 directives.resources[i] = new_resource
 
-            i += 1
-
         # Add new resources
         while True:
-            new_resource = clean_input(
+            new_resource = await clean_input(
                 app_config,
                 "Press enter to finish, or enter a resource to add:",
             )
@@ -150,12 +141,10 @@ async def interactively_revise_ai_settings(
             directives.resources.append(new_resource)
 
         # Revise best practices
-        i = 0
-        while i < len(directives.best_practices):
-            best_practice = directives.best_practices[i]
+        for i, best_practice in enumerate(directives.best_practices):
             print_attribute(f"Best Practice {i+1}:", f'"{best_practice}"')
             new_best_practice = (
-                clean_input(
+                await clean_input(
                     app_config,
                     f"Enter new best practice {i+1}"
                     " (press enter to keep current, or '-' to remove):",
@@ -164,15 +153,12 @@ async def interactively_revise_ai_settings(
             )
             if new_best_practice == "-":
                 directives.best_practices.remove(best_practice)
-                continue
             elif new_best_practice:
                 directives.best_practices[i] = new_best_practice
 
-            i += 1
-
         # Add new best practices
         while True:
-            new_best_practice = clean_input(
+            new_best_practice = await clean_input(
                 app_config,
                 "Press enter to finish, or add a best practice to add:",
             )

autogpts/autogpt/autogpt/app/telemetry.py

@@ -1,64 +0,0 @@
-import os
-
-import click
-from colorama import Fore, Style
-
-from .utils import (
-    env_file_exists,
-    get_git_user_email,
-    set_env_config_value,
-    vcs_state_diverges_from_master,
-)
-
-
-def setup_telemetry() -> None:
-    if os.getenv("TELEMETRY_OPT_IN") is None:
-        # If no .env file is present, don't bother asking to enable telemetry,
-        # to prevent repeated asking in non-persistent environments.
-        if not env_file_exists():
-            return
-
-        allow_telemetry = click.prompt(
-            f"""
-{Style.BRIGHT}❓ Do you want to enable telemetry? ❓{Style.NORMAL}
-This means AutoGPT will send diagnostic data to the core development team when something
-goes wrong, and will help us to diagnose and fix problems earlier and faster. It also
-allows us to collect basic performance data, which helps us find bottlenecks and other
-things that slow down the application.
-
-By entering 'yes', you confirm that you have read and agree to our Privacy Policy,
-which is available here:
-https://www.notion.so/auto-gpt/Privacy-Policy-ab11c9c20dbd4de1a15dcffe84d77984
-
-Please enter 'yes' or 'no'""",
-            type=bool,
-        )
-        set_env_config_value("TELEMETRY_OPT_IN", "true" if allow_telemetry else "false")
-        click.echo(
-            f"❤️  Thank you! Telemetry is {Fore.GREEN}enabled{Fore.RESET}."
-            if allow_telemetry
-            else f"👍 Telemetry is {Fore.RED}disabled{Fore.RESET}."
-        )
-        click.echo(
-            "💡 If you ever change your mind, you can change 'TELEMETRY_OPT_IN' in .env"
-        )
-        click.echo()
-
-    if os.getenv("TELEMETRY_OPT_IN", "").lower() == "true":
-        _setup_sentry()
-
-
-def _setup_sentry() -> None:
-    import sentry_sdk
-
-    sentry_sdk.init(
-        dsn="https://dc266f2f7a2381194d1c0fa36dff67d8@o4505260022104064.ingest.sentry.io/4506739844710400",  # noqa
-        enable_tracing=True,
-        environment=os.getenv(
-            "TELEMETRY_ENVIRONMENT",
-            "production" if not vcs_state_diverges_from_master() else "dev",
-        ),
-    )
-
-    # Allow Sentry to distinguish between users
-    sentry_sdk.set_user({"email": get_git_user_email(), "ip_address": "{{auto}}"})

autogpts/autogpt/autogpt/app/utils.py

@@ -1,31 +1,58 @@
-import contextlib
 import logging
 import os
 import re
-import socket
 import sys
-from pathlib import Path
-from typing import TYPE_CHECKING
 
-import click
 import requests
 from colorama import Fore, Style
 from git import InvalidGitRepositoryError, Repo
+from prompt_toolkit import ANSI, PromptSession
+from prompt_toolkit.history import InMemoryHistory
 
-if TYPE_CHECKING:
-    from autogpt.config import Config
+from autogpt.config import Config
 
 logger = logging.getLogger(__name__)
+session = PromptSession(history=InMemoryHistory())
 
 
-def clean_input(config: "Config", prompt: str = ""):
+async def clean_input(config: Config, prompt: str = ""):
     try:
+        if config.chat_messages_enabled:
+            for plugin in config.plugins:
+                if not hasattr(plugin, "can_handle_user_input"):
+                    continue
+                if not plugin.can_handle_user_input(user_input=prompt):
+                    continue
+                plugin_response = plugin.user_input(user_input=prompt)
+                if not plugin_response:
+                    continue
+                if plugin_response.lower() in [
+                    "yes",
+                    "yeah",
+                    "y",
+                    "ok",
+                    "okay",
+                    "sure",
+                    "alright",
+                ]:
+                    return config.authorise_key
+                elif plugin_response.lower() in [
+                    "no",
+                    "nope",
+                    "n",
+                    "negative",
+                ]:
+                    return config.exit_key
+                return plugin_response
+
         # ask for input, default when just pressing Enter is y
         logger.debug("Asking user via keyboard...")
 
-        return click.prompt(
-            text=prompt, prompt_suffix=" ", default="", show_default=False
-        )
+        # handle_sigint must be set to False, so the signal handler in the
+        # autogpt/main.py could be employed properly. This referes to
+        # https://github.com/Significant-Gravitas/AutoGPT/pull/4799/files/3966cdfd694c2a80c0333823c3bc3da090f85ed3#r1264278776
+        answer = await session.prompt_async(ANSI(prompt + " "), handle_sigint=False)
+        return answer
     except KeyboardInterrupt:
         logger.info("You interrupted AutoGPT")
         logger.info("Quitting...")
@@ -54,58 +81,6 @@ def get_current_git_branch() -> str:
         return ""
 
 
-def vcs_state_diverges_from_master() -> bool:
-    """
-    Returns whether a git repo is present and contains changes that are not in `master`.
-    """
-    paths_we_care_about = "autogpts/autogpt/autogpt/**/*.py"
-    try:
-        repo = Repo(search_parent_directories=True)
-
-        # Check for uncommitted changes in the specified path
-        uncommitted_changes = repo.index.diff(None, paths=paths_we_care_about)
-        if uncommitted_changes:
-            return True
-
-        # Find OG AutoGPT remote
-        for remote in repo.remotes:
-            if remote.url.endswith(
-                tuple(
-                    # All permutations of old/new repo name and HTTP(S)/Git URLs
-                    f"{prefix}{path}"
-                    for prefix in ("://github.com/", "git@github.com:")
-                    for path in (
-                        f"Significant-Gravitas/{n}.git" for n in ("AutoGPT", "Auto-GPT")
-                    )
-                )
-            ):
-                og_remote = remote
-                break
-        else:
-            # Original AutoGPT remote is not configured: assume local codebase diverges
-            return True
-
-        master_branch = og_remote.refs.master
-        with contextlib.suppress(StopIteration):
-            next(repo.iter_commits(f"HEAD..{master_branch}", paths=paths_we_care_about))
-            # Local repo is one or more commits ahead of OG AutoGPT master branch
-            return True
-
-        # Relevant part of the codebase is on master
-        return False
-    except InvalidGitRepositoryError:
-        # No git repo present: assume codebase is a clean download
-        return False
-
-
-def get_git_user_email() -> str:
-    try:
-        repo = Repo(search_parent_directories=True)
-        return repo.config_reader().get_value("user", "email", default="")
-    except InvalidGitRepositoryError:
-        return ""
-
-
 def get_latest_bulletin() -> tuple[str, bool]:
     exists = os.path.exists("data/CURRENT_BULLETIN.md")
     current_bulletin = ""
@@ -174,7 +149,7 @@ By using the System, you agree to indemnify, defend, and hold harmless the Proje
     return legal_text
 
 
-def print_motd(config: "Config", logger: logging.Logger):
+def print_motd(config: Config, logger: logging.Logger):
     motd, is_new_motd = get_latest_bulletin()
     if motd:
         motd = markdown_to_ansi_style(motd)
@@ -187,7 +162,7 @@ def print_motd(config: "Config", logger: logging.Logger):
                 },
                 msg=motd_line,
             )
-        if is_new_motd:
+        if is_new_motd and not config.chat_messages_enabled:
             input(
                 Fore.MAGENTA
                 + Style.BRIGHT
@@ -213,40 +188,3 @@ def print_python_version_info(logger: logging.Logger):
             "parts of AutoGPT with this version. "
             "Please consider upgrading to Python 3.10 or higher.",
         )
-
-
-ENV_FILE_PATH = Path(__file__).parent.parent.parent / ".env"
-
-
-def env_file_exists() -> bool:
-    return ENV_FILE_PATH.is_file()
-
-
-def set_env_config_value(key: str, value: str) -> None:
-    """Sets the specified env variable and updates it in .env as well"""
-    os.environ[key] = value
-
-    with ENV_FILE_PATH.open("r+") as file:
-        lines = file.readlines()
-        file.seek(0)
-        key_already_in_file = False
-        for line in lines:
-            if re.match(rf"^(?:# )?{key}=.*$", line):
-                file.write(f"{key}={value}\n")
-                key_already_in_file = True
-            else:
-                file.write(line)
-
-        if not key_already_in_file:
-            file.write(f"{key}={value}\n")
-
-        file.truncate()
-
-
-def is_port_free(port: int, host: str = "127.0.0.1"):
-    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
-        try:
-            s.bind((host, port))  # Try to bind to the port
-            return True  # If successful, the port is free
-        except OSError:
-            return False  # If failed, the port is likely in use

autogpts/autogpt/autogpt/command_decorator.py

@@ -1,5 +1,12 @@
-import re
-from typing import Callable, Optional, ParamSpec, TypeVar
+from __future__ import annotations
+
+import functools
+import inspect
+from typing import TYPE_CHECKING, Any, Callable, Literal, Optional, ParamSpec, TypeVar
+
+if TYPE_CHECKING:
+    from autogpt.agents.base import BaseAgent
+    from autogpt.config import Config
 
 from autogpt.core.utils.json_schema import JSONSchema
 from autogpt.models.command import Command, CommandOutput, CommandParameter
@@ -12,35 +19,19 @@ CO = TypeVar("CO", bound=CommandOutput)
 
 
 def command(
-    names: list[str] = [],
-    description: Optional[str] = None,
-    parameters: dict[str, JSONSchema] = {},
-) -> Callable[[Callable[P, CommandOutput]], Command]:
+    name: str,
+    description: str,
+    parameters: dict[str, JSONSchema],
+    enabled: Literal[True] | Callable[[Config], bool] = True,
+    disabled_reason: Optional[str] = None,
+    aliases: list[str] = [],
+    available: Literal[True] | Callable[[BaseAgent], bool] = True,
+) -> Callable[[Callable[P, CO]], Callable[P, CO]]:
     """
-    The command decorator is used to make a Command from a function.
-
-    Args:
-        names (list[str]): The names of the command.
-            If not provided, the function name will be used.
-        description (str): A brief description of what the command does.
-            If not provided, the docstring until double line break will be used
-            (or entire docstring if no double line break is found)
-        parameters (dict[str, JSONSchema]): The parameters of the function
-            that the command executes.
+    The command decorator is used to create Command objects from ordinary functions.
     """
 
-    def decorator(func: Callable[P, CO]) -> Command:
-        doc = func.__doc__ or ""
-        # If names is not provided, use the function name
-        command_names = names or [func.__name__]
-        # If description is not provided, use the first part of the docstring
-        if not (command_description := description):
-            if not func.__doc__:
-                raise ValueError("Description is required if function has no docstring")
-            # Return the part of the docstring before double line break or everything
-            command_description = re.sub(r"\s+", " ", doc.split("\n\n")[0].strip())
-
-        # Parameters
+    def decorator(func: Callable[P, CO]) -> Callable[P, CO]:
         typed_parameters = [
             CommandParameter(
                 name=param_name,
@@ -48,15 +39,32 @@ def command(
             )
             for param_name, spec in parameters.items()
         ]
-
-        # Wrap func with Command
-        command = Command(
-            names=command_names,
-            description=command_description,
+        cmd = Command(
+            name=name,
+            description=description,
             method=func,
             parameters=typed_parameters,
+            enabled=enabled,
+            disabled_reason=disabled_reason,
+            aliases=aliases,
+            available=available,
         )
 
-        return command
+        if inspect.iscoroutinefunction(func):
+
+            @functools.wraps(func)
+            async def wrapper(*args: P.args, **kwargs: P.kwargs) -> Any:
+                return await func(*args, **kwargs)
+
+        else:
+
+            @functools.wraps(func)
+            def wrapper(*args: P.args, **kwargs: P.kwargs) -> Any:
+                return func(*args, **kwargs)
+
+        setattr(wrapper, "command", cmd)
+        setattr(wrapper, AUTO_GPT_COMMAND_IDENTIFIER, True)
+
+        return wrapper
 
     return decorator

autogpts/autogpt/autogpt/commands/README.md

@@ -1,128 +0,0 @@
-# 🧩 Components
-
-Components are the building blocks of [🤖 Agents](./agents.md). They are classes inheriting `AgentComponent` or implementing one or more [⚙️ Protocols](./protocols.md) that give agent additional abilities or processing. 
-
-Components can be used to implement various functionalities like providing messages to the prompt, executing code, or interacting with external services.
-They can be enabled or disabled, ordered, and can rely on each other.
-
-Components assigned in the agent's `__init__` via `self` are automatically detected upon the agent's instantiation.
-For example inside `__init__`: `self.my_component = MyComponent()`.
-You can use any valid Python variable name, what matters for the component to be detected is its type (`AgentComponent` or any protocol inheriting from it).
-
-Visit [Built-in Components](./built-in-components.md) to see what components are available out of the box.
-
-```py
-from autogpt.agents import Agent
-from autogpt.agents.components import AgentComponent
-
-class HelloComponent(AgentComponent):
-    pass
-
-class SomeComponent(AgentComponent):
-    def __init__(self, hello_component: HelloComponent):
-        self.hello_component = hello_component
-
-class MyAgent(Agent):
-    def __init__(self):
-        # These components will be automatically discovered and used
-        self.hello_component = HelloComponent()
-        # We pass HelloComponent to SomeComponent
-        self.some_component = SomeComponent(self.hello_component)
-```
-
-## Ordering components
-
-The execution order of components is important because the latter ones may depend on the results of the former ones.
-
-### Implicit order
-
-Components can be ordered implicitly by the agent; each component can set `run_after` list to specify which components should run before it. This is useful when components rely on each other or need to be executed in a specific order. Otherwise, the order of components is alphabetical.
-
-```py
-# This component will run after HelloComponent
-class CalculatorComponent(AgentComponent):
-    run_after = [HelloComponent]
-```
-
-### Explicit order
-
-Sometimes it may be easier to order components explicitly by setting `self.components` list in the agent's `__init__` method. This way you can also ensure there's no circular dependencies and `run_after` is ignored.
-
-!!! warning
-    Be sure to include all components - by setting `self.components` list, you're overriding the default behavior of discovering components automatically. Since it's usually not intended agent will inform you in the terminal if some components were skipped.
-
-```py
-class MyAgent(Agent):
-    def __init__(self):
-        self.hello_component = HelloComponent()
-        self.calculator_component = CalculatorComponent(self.hello_component)
-        # Explicitly set components list
-        self.components = [self.hello_component, self.calculator_component]
-```
-
-## Disabling components
-
-You can control which components are enabled by setting their `_enabled` attribute.
-Either provide a `bool` value or a `Callable[[], bool]`, will be checked each time
-the component is about to be executed. This way you can dynamically enable or disable
-components based on some conditions.
-You can also provide a reason for disabling the component by setting `_disabled_reason`.
-The reason will be visible in the debug information.
-
-```py
-class DisabledComponent(MessageProvider):
-    def __init__(self):
-        # Disable this component
-        self._enabled = False
-        self._disabled_reason = "This component is disabled because of reasons."
-
-        # Or disable based on some condition, either statically...:
-        self._enabled = self.some_property is not None
-        # ... or dynamically:
-        self._enabled = lambda: self.some_property is not None
-
-    # This method will never be called
-    def get_messages(self) -> Iterator[ChatMessage]:
-        yield ChatMessage.user("This message won't be seen!")
-
-    def some_condition(self) -> bool:
-        return False
-```
-
-If you don't want the component at all, you can just remove it from the agent's `__init__` method. If you want to remove components you inherit from the parent class you can set the relevant attribute to `None`:
-
-!!! Warning
-    Be careful when removing components that are required by other components. This may lead to errors and unexpected behavior.
-
-```py
-class MyAgent(Agent):
-    def __init__(self):
-        super().__init__(...)
-        # Disable WatchdogComponent that is in the parent class
-        self.watchdog = None
-
-```
-
-## Exceptions
-
-Custom errors are provided which can be used to control the execution flow in case something went wrong. All those errors can be raised in protocol methods and will be caught by the agent.  
-By default agent will retry three times and then re-raise an exception if it's still not resolved. All passed arguments are automatically handled and the values are reverted when needed.
-All errors accept an optional `str` message. There are following errors ordered by increasing broadness:
-
-1. `ComponentEndpointError`: A single endpoint method failed to execute. Agent will retry the execution of this endpoint on the component.
-2. `EndpointPipelineError`: A pipeline failed to execute. Agent will retry the execution of the endpoint for all components.
-3. `ComponentSystemError`: Multiple pipelines failed.
-
-**Example**
-
-```py
-from autogpt.agents.components import ComponentEndpointError
-from autogpt.agents.protocols import MessageProvider
-
-# Example of raising an error
-class MyComponent(MessageProvider):
-    def get_messages(self) -> Iterator[ChatMessage]:
-        # This will cause the component to always fail 
-        # and retry 3 times before re-raising the exception
-        raise ComponentEndpointError("Endpoint error!")
-```

autogpts/autogpt/autogpt/commands/__init__.py

@@ -0,0 +1,9 @@
+COMMAND_CATEGORIES = [
+    "autogpt.commands.execute_code",
+    "autogpt.commands.file_operations",
+    "autogpt.commands.user_interaction",
+    "autogpt.commands.web_search",
+    "autogpt.commands.web_selenium",
+    "autogpt.commands.system",
+    "autogpt.commands.image_gen",
+]

autogpts/autogpt/autogpt/commands/decorators.py

@@ -0,0 +1,82 @@
+import functools
+import logging
+import re
+from pathlib import Path
+from typing import Callable, ParamSpec, TypeVar
+
+from autogpt.agents.agent import Agent
+
+P = ParamSpec("P")
+T = TypeVar("T")
+
+logger = logging.getLogger(__name__)
+
+
+def sanitize_path_arg(
+    arg_name: str, make_relative: bool = False
+) -> Callable[[Callable[P, T]], Callable[P, T]]:
+    """Sanitizes the specified path (str | Path) argument, resolving it to a Path"""
+
+    def decorator(func: Callable) -> Callable:
+        # Get position of path parameter, in case it is passed as a positional argument
+        try:
+            arg_index = list(func.__annotations__.keys()).index(arg_name)
+        except ValueError:
+            raise TypeError(
+                f"Sanitized parameter '{arg_name}' absent or not annotated"
+                f" on function '{func.__name__}'"
+            )
+
+        # Get position of agent parameter, in case it is passed as a positional argument
+        try:
+            agent_arg_index = list(func.__annotations__.keys()).index("agent")
+        except ValueError:
+            raise TypeError(
+                f"Parameter 'agent' absent or not annotated"
+                f" on function '{func.__name__}'"
+            )
+
+        @functools.wraps(func)
+        def wrapper(*args, **kwargs):
+            logger.debug(f"Sanitizing arg '{arg_name}' on function '{func.__name__}'")
+
+            # Get Agent from the called function's arguments
+            agent = kwargs.get(
+                "agent", len(args) > agent_arg_index and args[agent_arg_index]
+            )
+            if not isinstance(agent, Agent):
+                raise RuntimeError("Could not get Agent from decorated command's args")
+
+            # Sanitize the specified path argument, if one is given
+            given_path: str | Path | None = kwargs.get(
+                arg_name, len(args) > arg_index and args[arg_index] or None
+            )
+            if given_path:
+                if type(given_path) is str:
+                    # Fix workspace path from output in docker environment
+                    given_path = re.sub(r"^\/workspace", ".", given_path)
+
+                if given_path in {"", "/", "."}:
+                    sanitized_path = agent.workspace.root
+                else:
+                    sanitized_path = agent.workspace.get_path(given_path)
+
+                # Make path relative if possible
+                if make_relative and sanitized_path.is_relative_to(
+                    agent.workspace.root
+                ):
+                    sanitized_path = sanitized_path.relative_to(agent.workspace.root)
+
+                if arg_name in kwargs:
+                    kwargs[arg_name] = sanitized_path
+                else:
+                    # args is an immutable tuple; must be converted to a list to update
+                    arg_list = list(args)
+                    arg_list[arg_index] = sanitized_path
+                    args = tuple(arg_list)
+
+            return func(*args, **kwargs)
+
+        return wrapper
+
+    return decorator

autogpts/autogpt/autogpt/commands/execute_code.py

@@ -1,28 +1,31 @@
+"""Commands to execute code"""
+
 import logging
 import os
-import shlex
 import subprocess
 from pathlib import Path
 from tempfile import NamedTemporaryFile
-from typing import Iterator
 
 import docker
 from docker.errors import DockerException, ImageNotFound, NotFound
 from docker.models.containers import Container as DockerContainer
 
-from autogpt.agents.base import BaseAgentSettings
-from autogpt.agents.protocols import CommandProvider
-from autogpt.command_decorator import command
-from autogpt.config import Config
-from autogpt.core.utils.json_schema import JSONSchema
-from autogpt.file_storage.base import FileStorage
-from autogpt.models.command import Command
-from autogpt.utils.exceptions import (
+from autogpt.agents.agent import Agent
+from autogpt.agents.utils.exceptions import (
     CodeExecutionError,
     CommandExecutionError,
     InvalidArgumentError,
     OperationNotAllowedError,
 )
+from autogpt.command_decorator import command
+from autogpt.config import Config
+from autogpt.core.utils.json_schema import JSONSchema
+
+from .decorators import sanitize_path_arg
+
+COMMAND_CATEGORY = "execute_code"
+COMMAND_CATEGORY_TITLE = "Execute Code"
+
 
 logger = logging.getLogger(__name__)
 
@@ -30,6 +33,313 @@ ALLOWLIST_CONTROL = "allowlist"
 DENYLIST_CONTROL = "denylist"
 
 
+@command(
+    "execute_python_code",
+    "Executes the given Python code inside a single-use Docker container"
+    " with access to your workspace folder",
+    {
+        "code": JSONSchema(
+            type=JSONSchema.Type.STRING,
+            description="The Python code to run",
+            required=True,
+        ),
+    },
+)
+def execute_python_code(code: str, agent: Agent) -> str:
+    """
+    Create and execute a Python file in a Docker container and return the STDOUT of the
+    executed code.
+
+    If the code generates any data that needs to be captured, use a print statement.
+
+    Args:
+        code (str): The Python code to run.
+        agent (Agent): The Agent executing the command.
+
+    Returns:
+        str: The STDOUT captured from the code when it ran.
+    """
+
+    tmp_code_file = NamedTemporaryFile(
+        "w", dir=agent.workspace.root, suffix=".py", encoding="utf-8"
+    )
+    tmp_code_file.write(code)
+    tmp_code_file.flush()
+
+    try:
+        return execute_python_file(tmp_code_file.name, agent)  # type: ignore
+    except Exception as e:
+        raise CommandExecutionError(*e.args)
+    finally:
+        tmp_code_file.close()
+
+
+@command(
+    "execute_python_file",
+    "Execute an existing Python file inside a single-use Docker container"
+    " with access to your workspace folder",
+    {
+        "filename": JSONSchema(
+            type=JSONSchema.Type.STRING,
+            description="The name of the file to execute",
+            required=True,
+        ),
+        "args": JSONSchema(
+            type=JSONSchema.Type.ARRAY,
+            description="The (command line) arguments to pass to the script",
+            required=False,
+            items=JSONSchema(type=JSONSchema.Type.STRING),
+        ),
+    },
+)
+@sanitize_path_arg("filename")
+def execute_python_file(
+    filename: Path, agent: Agent, args: list[str] | str = []
+) -> str:
+    """Execute a Python file in a Docker container and return the output
+
+    Args:
+        filename (Path): The name of the file to execute
+        args (list, optional): The arguments with which to run the python script
+
+    Returns:
+        str: The output of the file
+    """
+    logger.info(
+        f"Executing python file '{filename}' "
+        f"in working directory '{agent.workspace.root}'"
+    )
+
+    if isinstance(args, str):
+        args = args.split()  # Convert space-separated string to a list
+
+    if not str(filename).endswith(".py"):
+        raise InvalidArgumentError("Invalid file type. Only .py files are allowed.")
+
+    file_path = filename
+    if not file_path.is_file():
+        # Mimic the response that you get from the command line to make it
+        # intuitively understandable for the LLM
+        raise FileNotFoundError(
+            f"python: can't open file '{filename}': [Errno 2] No such file or directory"
+        )
+
+    if we_are_running_in_a_docker_container():
+        logger.debug(
+            "AutoGPT is running in a Docker container; "
+            f"executing {file_path} directly..."
+        )
+        result = subprocess.run(
+            ["python", "-B", str(file_path)] + args,
+            capture_output=True,
+            encoding="utf8",
+            cwd=str(agent.workspace.root),
+        )
+        if result.returncode == 0:
+            return result.stdout
+        else:
+            raise CodeExecutionError(result.stderr)
+
+    logger.debug("AutoGPT is not running in a Docker container")
+    try:
+        assert agent.state.agent_id, "Need Agent ID to attach Docker container"
+
+        client = docker.from_env()
+        # You can replace this with the desired Python image/version
+        # You can find available Python images on Docker Hub:
+        # https://hub.docker.com/_/python
+        image_name = "python:3-alpine"
+        container_is_fresh = False
+        container_name = f"{agent.state.agent_id}_sandbox"
+        try:
+            container: DockerContainer = client.containers.get(
+                container_name
+            )  # type: ignore
+        except NotFound:
+            try:
+                client.images.get(image_name)
+                logger.debug(f"Image '{image_name}' found locally")
+            except ImageNotFound:
+                logger.info(
+                    f"Image '{image_name}' not found locally,"
+                    " pulling from Docker Hub..."
+                )
+                # Use the low-level API to stream the pull response
+                low_level_client = docker.APIClient()
+                for line in low_level_client.pull(image_name, stream=True, decode=True):
+                    # Print the status and progress, if available
+                    status = line.get("status")
+                    progress = line.get("progress")
+                    if status and progress:
+                        logger.info(f"{status}: {progress}")
+                    elif status:
+                        logger.info(status)
+
+            logger.debug(f"Creating new {image_name} container...")
+            container: DockerContainer = client.containers.run(
+                image_name,
+                ["sleep", "60"],  # Max 60 seconds to prevent permanent hangs
+                volumes={
+                    str(agent.workspace.root): {
+                        "bind": "/workspace",
+                        "mode": "rw",
+                    }
+                },
+                working_dir="/workspace",
+                stderr=True,
+                stdout=True,
+                detach=True,
+                name=container_name,
+            )  # type: ignore
+            container_is_fresh = True
+
+        if not container.status == "running":
+            container.start()
+        elif not container_is_fresh:
+            container.restart()
+
+        logger.debug(f"Running {file_path} in container {container.name}...")
+        exec_result = container.exec_run(
+            [
+                "python",
+                "-B",
+                file_path.relative_to(agent.workspace.root).as_posix(),
+            ]
+            + args,
+            stderr=True,
+            stdout=True,
+        )
+
+        if exec_result.exit_code != 0:
+            raise CodeExecutionError(exec_result.output.decode("utf-8"))
+
+        return exec_result.output.decode("utf-8")
+
+    except DockerException as e:
+        logger.warning(
+            "Could not run the script in a container. "
+            "If you haven't already, please install Docker: "
+            "https://docs.docker.com/get-docker/"
+        )
+        raise CommandExecutionError(f"Could not run the script in a container: {e}")
+
+
+def validate_command(command: str, config: Config) -> bool:
+    """Validate a command to ensure it is allowed
+
+    Args:
+        command (str): The command to validate
+        config (Config): The config to use to validate the command
+
+    Returns:
+        bool: True if the command is allowed, False otherwise
+    """
+    if not command:
+        return False
+
+    command_name = command.split()[0]
+
+    if config.shell_command_control == ALLOWLIST_CONTROL:
+        return command_name in config.shell_allowlist
+    else:
+        return command_name not in config.shell_denylist
+
+
+@command(
+    "execute_shell",
+    "Execute a Shell Command, non-interactive commands only",
+    {
+        "command_line": JSONSchema(
+            type=JSONSchema.Type.STRING,
+            description="The command line to execute",
+            required=True,
+        )
+    },
+    enabled=lambda config: config.execute_local_commands,
+    disabled_reason="You are not allowed to run local shell commands. To execute"
+    " shell commands, EXECUTE_LOCAL_COMMANDS must be set to 'True' "
+    "in your config file: .env - do not attempt to bypass the restriction.",
+)
+def execute_shell(command_line: str, agent: Agent) -> str:
+    """Execute a shell command and return the output
+
+    Args:
+        command_line (str): The command line to execute
+
+    Returns:
+        str: The output of the command
+    """
+    if not validate_command(command_line, agent.legacy_config):
+        logger.info(f"Command '{command_line}' not allowed")
+        raise OperationNotAllowedError("This shell command is not allowed.")
+
+    current_dir = Path.cwd()
+    # Change dir into workspace if necessary
+    if not current_dir.is_relative_to(agent.workspace.root):
+        os.chdir(agent.workspace.root)
+
+    logger.info(
+        f"Executing command '{command_line}' in working directory '{os.getcwd()}'"
+    )
+
+    result = subprocess.run(command_line, capture_output=True, shell=True)
+    output = f"STDOUT:\n{result.stdout.decode()}\nSTDERR:\n{result.stderr.decode()}"
+
+    # Change back to whatever the prior working dir was
+    os.chdir(current_dir)
+
+    return output
+
+
+@command(
+    "execute_shell_popen",
+    "Execute a Shell Command, non-interactive commands only",
+    {
+        "command_line": JSONSchema(
+            type=JSONSchema.Type.STRING,
+            description="The command line to execute",
+            required=True,
+        )
+    },
+    lambda config: config.execute_local_commands,
+    "You are not allowed to run local shell commands. To execute"
+    " shell commands, EXECUTE_LOCAL_COMMANDS must be set to 'True' "
+    "in your config. Do not attempt to bypass the restriction.",
+)
+def execute_shell_popen(command_line: str, agent: Agent) -> str:
+    """Execute a shell command with Popen and returns an english description
+    of the event and the process id
+
+    Args:
+        command_line (str): The command line to execute
+
+    Returns:
+        str: Description of the fact that the process started and its id
+    """
+    if not validate_command(command_line, agent.legacy_config):
+        logger.info(f"Command '{command_line}' not allowed")
+        raise OperationNotAllowedError("This shell command is not allowed.")
+
+    current_dir = Path.cwd()
+    # Change dir into workspace if necessary
+    if not current_dir.is_relative_to(agent.workspace.root):
+        os.chdir(agent.workspace.root)
+
+    logger.info(
+        f"Executing command '{command_line}' in working directory '{os.getcwd()}'"
+    )
+
+    do_not_show_output = subprocess.DEVNULL
+    process = subprocess.Popen(
+        command_line, shell=True, stdout=do_not_show_output, stderr=do_not_show_output
+    )
+
+    # Change back to whatever the prior working dir was
+    os.chdir(current_dir)
+
+    return f"Subprocess started with PID:'{str(process.pid)}'"
+
+
 def we_are_running_in_a_docker_container() -> bool:
     """Check if we are running in a Docker container
 
@@ -37,360 +347,3 @@ def we_are_running_in_a_docker_container() -> bool:
         bool: True if we are running in a Docker container, False otherwise
     """
     return os.path.exists("/.dockerenv")
-
-
-def is_docker_available() -> bool:
-    """Check if Docker is available and supports Linux containers
-
-    Returns:
-        bool: True if Docker is available and supports Linux containers, False otherwise
-    """
-    try:
-        client = docker.from_env()
-        docker_info = client.info()
-        return docker_info["OSType"] == "linux"
-    except Exception:
-        return False
-
-
-class CodeExecutorComponent(CommandProvider):
-    """Provides commands to execute Python code and shell commands."""
-
-    def __init__(
-        self, workspace: FileStorage, state: BaseAgentSettings, config: Config
-    ):
-        self.workspace = workspace
-        self.state = state
-        self.legacy_config = config
-
-        if not we_are_running_in_a_docker_container() and not is_docker_available():
-            logger.info(
-                "Docker is not available or does not support Linux containers. "
-                "The code execution commands will not be available."
-            )
-
-        if not self.legacy_config.execute_local_commands:
-            logger.info(
-                "Local shell commands are disabled. To enable them,"
-                " set EXECUTE_LOCAL_COMMANDS to 'True' in your config file."
-            )
-
-    def get_commands(self) -> Iterator[Command]:
-        if we_are_running_in_a_docker_container() or is_docker_available():
-            yield self.execute_python_code
-            yield self.execute_python_file
-
-        if self.legacy_config.execute_local_commands:
-            yield self.execute_shell
-            yield self.execute_shell_popen
-
-    @command(
-        ["execute_python_code"],
-        "Executes the given Python code inside a single-use Docker container"
-        " with access to your workspace folder",
-        {
-            "code": JSONSchema(
-                type=JSONSchema.Type.STRING,
-                description="The Python code to run",
-                required=True,
-            ),
-        },
-    )
-    def execute_python_code(self, code: str) -> str:
-        """
-        Create and execute a Python file in a Docker container
-        and return the STDOUT of the executed code.
-
-        If the code generates any data that needs to be captured,
-        use a print statement.
-
-        Args:
-            code (str): The Python code to run.
-            agent (Agent): The Agent executing the command.
-
-        Returns:
-            str: The STDOUT captured from the code when it ran.
-        """
-
-        tmp_code_file = NamedTemporaryFile(
-            "w", dir=self.workspace.root, suffix=".py", encoding="utf-8"
-        )
-        tmp_code_file.write(code)
-        tmp_code_file.flush()
-
-        try:
-            return self.execute_python_file(tmp_code_file.name)
-        except Exception as e:
-            raise CommandExecutionError(*e.args)
-        finally:
-            tmp_code_file.close()
-
-    @command(
-        ["execute_python_file"],
-        "Execute an existing Python file inside a single-use Docker container"
-        " with access to your workspace folder",
-        {
-            "filename": JSONSchema(
-                type=JSONSchema.Type.STRING,
-                description="The name of the file to execute",
-                required=True,
-            ),
-            "args": JSONSchema(
-                type=JSONSchema.Type.ARRAY,
-                description="The (command line) arguments to pass to the script",
-                required=False,
-                items=JSONSchema(type=JSONSchema.Type.STRING),
-            ),
-        },
-    )
-    def execute_python_file(self, filename: str, args: list[str] | str = []) -> str:
-        """Execute a Python file in a Docker container and return the output
-
-        Args:
-            filename (Path): The name of the file to execute
-            args (list, optional): The arguments with which to run the python script
-
-        Returns:
-            str: The output of the file
-        """
-        logger.info(
-            f"Executing python file '{filename}' "
-            f"in working directory '{self.workspace.root}'"
-        )
-
-        if isinstance(args, str):
-            args = args.split()  # Convert space-separated string to a list
-
-        if not str(filename).endswith(".py"):
-            raise InvalidArgumentError("Invalid file type. Only .py files are allowed.")
-
-        file_path = self.workspace.get_path(filename)
-        if not self.workspace.exists(file_path):
-            # Mimic the response that you get from the command line to make it
-            # intuitively understandable for the LLM
-            raise FileNotFoundError(
-                f"python: can't open file '{filename}': "
-                f"[Errno 2] No such file or directory"
-            )
-
-        if we_are_running_in_a_docker_container():
-            logger.debug(
-                "AutoGPT is running in a Docker container; "
-                f"executing {file_path} directly..."
-            )
-            result = subprocess.run(
-                ["python", "-B", str(file_path)] + args,
-                capture_output=True,
-                encoding="utf8",
-                cwd=str(self.workspace.root),
-            )
-            if result.returncode == 0:
-                return result.stdout
-            else:
-                raise CodeExecutionError(result.stderr)
-
-        logger.debug("AutoGPT is not running in a Docker container")
-        try:
-            assert self.state.agent_id, "Need Agent ID to attach Docker container"
-
-            client = docker.from_env()
-            image_name = "python:3-alpine"
-            container_is_fresh = False
-            container_name = f"{self.state.agent_id}_sandbox"
-            try:
-                container: DockerContainer = client.containers.get(
-                    container_name
-                )  # type: ignore
-            except NotFound:
-                try:
-                    client.images.get(image_name)
-                    logger.debug(f"Image '{image_name}' found locally")
-                except ImageNotFound:
-                    logger.info(
-                        f"Image '{image_name}' not found locally,"
-                        " pulling from Docker Hub..."
-                    )
-                    # Use the low-level API to stream the pull response
-                    low_level_client = docker.APIClient()
-                    for line in low_level_client.pull(
-                        image_name, stream=True, decode=True
-                    ):
-                        # Print the status and progress, if available
-                        status = line.get("status")
-                        progress = line.get("progress")
-                        if status and progress:
-                            logger.info(f"{status}: {progress}")
-                        elif status:
-                            logger.info(status)
-
-                logger.debug(f"Creating new {image_name} container...")
-                container: DockerContainer = client.containers.run(
-                    image_name,
-                    ["sleep", "60"],  # Max 60 seconds to prevent permanent hangs
-                    volumes={
-                        str(self.workspace.root): {
-                            "bind": "/workspace",
-                            "mode": "rw",
-                        }
-                    },
-                    working_dir="/workspace",
-                    stderr=True,
-                    stdout=True,
-                    detach=True,
-                    name=container_name,
-                )  # type: ignore
-                container_is_fresh = True
-
-            if not container.status == "running":
-                container.start()
-            elif not container_is_fresh:
-                container.restart()
-
-            logger.debug(f"Running {file_path} in container {container.name}...")
-            exec_result = container.exec_run(
-                [
-                    "python",
-                    "-B",
-                    file_path.relative_to(self.workspace.root).as_posix(),
-                ]
-                + args,
-                stderr=True,
-                stdout=True,
-            )
-
-            if exec_result.exit_code != 0:
-                raise CodeExecutionError(exec_result.output.decode("utf-8"))
-
-            return exec_result.output.decode("utf-8")
-
-        except DockerException as e:
-            logger.warning(
-                "Could not run the script in a container. "
-                "If you haven't already, please install Docker: "
-                "https://docs.docker.com/get-docker/"
-            )
-            raise CommandExecutionError(f"Could not run the script in a container: {e}")
-
-    def validate_command(self, command_line: str, config: Config) -> tuple[bool, bool]:
-        """Check whether a command is allowed and whether it may be executed in a shell.
-
-        If shell command control is enabled, we disallow executing in a shell, because
-        otherwise the model could circumvent the command filter using shell features.
-
-        Args:
-            command_line (str): The command line to validate
-            config (Config): The app config including shell command control settings
-
-        Returns:
-            bool: True if the command is allowed, False otherwise
-            bool: True if the command may be executed in a shell, False otherwise
-        """
-        if not command_line:
-            return False, False
-
-        command_name = shlex.split(command_line)[0]
-
-        if config.shell_command_control == ALLOWLIST_CONTROL:
-            return command_name in config.shell_allowlist, False
-        elif config.shell_command_control == DENYLIST_CONTROL:
-            return command_name not in config.shell_denylist, False
-        else:
-            return True, True
-
-    @command(
-        ["execute_shell"],
-        "Execute a Shell Command, non-interactive commands only",
-        {
-            "command_line": JSONSchema(
-                type=JSONSchema.Type.STRING,
-                description="The command line to execute",
-                required=True,
-            )
-        },
-    )
-    def execute_shell(self, command_line: str) -> str:
-        """Execute a shell command and return the output
-
-        Args:
-            command_line (str): The command line to execute
-
-        Returns:
-            str: The output of the command
-        """
-        allow_execute, allow_shell = self.validate_command(
-            command_line, self.legacy_config
-        )
-        if not allow_execute:
-            logger.info(f"Command '{command_line}' not allowed")
-            raise OperationNotAllowedError("This shell command is not allowed.")
-
-        current_dir = Path.cwd()
-        # Change dir into workspace if necessary
-        if not current_dir.is_relative_to(self.workspace.root):
-            os.chdir(self.workspace.root)
-
-        logger.info(
-            f"Executing command '{command_line}' in working directory '{os.getcwd()}'"
-        )
-
-        result = subprocess.run(
-            command_line if allow_shell else shlex.split(command_line),
-            capture_output=True,
-            shell=allow_shell,
-        )
-        output = f"STDOUT:\n{result.stdout.decode()}\nSTDERR:\n{result.stderr.decode()}"
-
-        # Change back to whatever the prior working dir was
-        os.chdir(current_dir)
-
-        return output
-
-    @command(
-        ["execute_shell_popen"],
-        "Execute a Shell Command, non-interactive commands only",
-        {
-            "command_line": JSONSchema(
-                type=JSONSchema.Type.STRING,
-                description="The command line to execute",
-                required=True,
-            )
-        },
-    )
-    def execute_shell_popen(self, command_line: str) -> str:
-        """Execute a shell command with Popen and returns an english description
-        of the event and the process id
-
-        Args:
-            command_line (str): The command line to execute
-
-        Returns:
-            str: Description of the fact that the process started and its id
-        """
-        allow_execute, allow_shell = self.validate_command(
-            command_line, self.legacy_config
-        )
-        if not allow_execute:
-            logger.info(f"Command '{command_line}' not allowed")
-            raise OperationNotAllowedError("This shell command is not allowed.")
-
-        current_dir = Path.cwd()
-        # Change dir into workspace if necessary
-        if not current_dir.is_relative_to(self.workspace.root):
-            os.chdir(self.workspace.root)
-
-        logger.info(
-            f"Executing command '{command_line}' in working directory '{os.getcwd()}'"
-        )
-
-        do_not_show_output = subprocess.DEVNULL
-        process = subprocess.Popen(
-            command_line if allow_shell else shlex.split(command_line),
-            shell=allow_shell,
-            stdout=do_not_show_output,
-            stderr=do_not_show_output,
-        )
-
-        # Change back to whatever the prior working dir was
-        os.chdir(current_dir)
-
-        return f"Subprocess started with PID:'{str(process.pid)}'"

autogpts/autogpt/autogpt/commands/file_context.py

@@ -0,0 +1,131 @@
+"""Commands to perform operations on files"""
+
+from __future__ import annotations
+
+import contextlib
+from pathlib import Path
+from typing import TYPE_CHECKING
+
+from autogpt.agents.features.context import ContextMixin, get_agent_context
+from autogpt.agents.utils.exceptions import (
+    CommandExecutionError,
+    DuplicateOperationError,
+)
+from autogpt.command_decorator import command
+from autogpt.core.utils.json_schema import JSONSchema
+from autogpt.models.context_item import FileContextItem, FolderContextItem
+
+from .decorators import sanitize_path_arg
+
+COMMAND_CATEGORY = "file_operations"
+COMMAND_CATEGORY_TITLE = "File Operations"
+
+
+if TYPE_CHECKING:
+    from autogpt.agents import Agent, BaseAgent
+
+
+def agent_implements_context(agent: BaseAgent) -> bool:
+    return isinstance(agent, ContextMixin)
+
+
+@command(
+    "open_file",
+    "Opens a file for editing or continued viewing;"
+    " creates it if it does not exist yet. "
+    "Note: If you only need to read or write a file once, use `write_to_file` instead.",
+    {
+        "file_path": JSONSchema(
+            type=JSONSchema.Type.STRING,
+            description="The path of the file to open",
+            required=True,
+        )
+    },
+    available=agent_implements_context,
+)
+@sanitize_path_arg("file_path")
+def open_file(file_path: Path, agent: Agent) -> tuple[str, FileContextItem]:
+    """Open a file and return a context item
+
+    Args:
+        file_path (Path): The path of the file to open
+
+    Returns:
+        str: A status message indicating what happened
+        FileContextItem: A ContextItem representing the opened file
+    """
+    # Try to make the file path relative
+    relative_file_path = None
+    with contextlib.suppress(ValueError):
+        relative_file_path = file_path.relative_to(agent.workspace.root)
+
+    assert (agent_context := get_agent_context(agent)) is not None
+
+    created = False
+    if not file_path.exists():
+        file_path.touch()
+        created = True
+    elif not file_path.is_file():
+        raise CommandExecutionError(f"{file_path} exists but is not a file")
+
+    file_path = relative_file_path or file_path
+
+    file = FileContextItem(
+        file_path_in_workspace=file_path,
+        workspace_path=agent.workspace.root,
+    )
+    if file in agent_context:
+        raise DuplicateOperationError(f"The file {file_path} is already open")
+
+    return (
+        f"File {file_path}{' created,' if created else ''} has been opened"
+        " and added to the context ✅",
+        file,
+    )
+
+
+@command(
+    "open_folder",
+    "Open a folder to keep track of its content",
+    {
+        "path": JSONSchema(
+            type=JSONSchema.Type.STRING,
+            description="The path of the folder to open",
+            required=True,
+        )
+    },
+    available=agent_implements_context,
+)
+@sanitize_path_arg("path")
+def open_folder(path: Path, agent: Agent) -> tuple[str, FolderContextItem]:
+    """Open a folder and return a context item
+
+    Args:
+        path (Path): The path of the folder to open
+
+    Returns:
+        str: A status message indicating what happened
+        FolderContextItem: A ContextItem representing the opened folder
+    """
+    # Try to make the path relative
+    relative_path = None
+    with contextlib.suppress(ValueError):
+        relative_path = path.relative_to(agent.workspace.root)
+
+    assert (agent_context := get_agent_context(agent)) is not None
+
+    if not path.exists():
+        raise FileNotFoundError(f"open_folder {path} failed: no such file or directory")
+    elif not path.is_dir():
+        raise CommandExecutionError(f"{path} exists but is not a folder")
+
+    path = relative_path or path
+
+    folder = FolderContextItem(
+        path_in_workspace=path,
+        workspace_path=agent.workspace.root,
+    )
+    if folder in agent_context:
+        raise DuplicateOperationError(f"The folder {path} is already open")
+
+    return f"Folder {path} has been opened and added to the context ✅", folder

autogpts/autogpt/autogpt/commands/file_operations.py

@@ -0,0 +1,268 @@
+"""Commands to perform operations on files"""
+
+from __future__ import annotations
+
+import hashlib
+import logging
+import os
+import os.path
+from pathlib import Path
+from typing import Iterator, Literal
+
+from autogpt.agents.agent import Agent
+from autogpt.agents.utils.exceptions import DuplicateOperationError
+from autogpt.command_decorator import command
+from autogpt.core.utils.json_schema import JSONSchema
+from autogpt.memory.vector import MemoryItemFactory, VectorMemory
+
+from .decorators import sanitize_path_arg
+from .file_operations_utils import decode_textual_file
+
+COMMAND_CATEGORY = "file_operations"
+COMMAND_CATEGORY_TITLE = "File Operations"
+
+
+from .file_context import open_file, open_folder  # NOQA
+
+logger = logging.getLogger(__name__)
+
+Operation = Literal["write", "append", "delete"]
+
+
+def text_checksum(text: str) -> str:
+    """Get the hex checksum for the given text."""
+    return hashlib.md5(text.encode("utf-8")).hexdigest()
+
+
+def operations_from_log(
+    log_path: str | Path,
+) -> Iterator[
+    tuple[Literal["write", "append"], str, str] | tuple[Literal["delete"], str, None]
+]:
+    """Parse the file operations log and return a tuple containing the log entries"""
+    try:
+        log = open(log_path, "r", encoding="utf-8")
+    except FileNotFoundError:
+        return
+
+    for line in log:
+        line = line.replace("File Operation Logger", "").strip()
+        if not line:
+            continue
+        operation, tail = line.split(": ", maxsplit=1)
+        operation = operation.strip()
+        if operation in ("write", "append"):
+            path, checksum = (x.strip() for x in tail.rsplit(" #", maxsplit=1))
+            yield (operation, path, checksum)
+        elif operation == "delete":
+            yield (operation, tail.strip(), None)
+
+    log.close()
+
+
+def file_operations_state(log_path: str | Path) -> dict[str, str]:
+    """Iterates over the operations log and returns the expected state.
+
+    Parses a log file at file_manager.file_ops_log_path to construct a dictionary
+    that maps each file path written or appended to its checksum. Deleted files are
+    removed from the dictionary.
+
+    Returns:
+        A dictionary mapping file paths to their checksums.
+
+    Raises:
+        FileNotFoundError: If file_manager.file_ops_log_path is not found.
+        ValueError: If the log file content is not in the expected format.
+    """
+    state = {}
+    for operation, path, checksum in operations_from_log(log_path):
+        if operation in ("write", "append"):
+            state[path] = checksum
+        elif operation == "delete":
+            del state[path]
+    return state
+
+
+@sanitize_path_arg("file_path", make_relative=True)
+def is_duplicate_operation(
+    operation: Operation, file_path: Path, agent: Agent, checksum: str | None = None
+) -> bool:
+    """Check if the operation has already been performed
+
+    Args:
+        operation: The operation to check for
+        file_path: The name of the file to check for
+        agent: The agent
+        checksum: The checksum of the contents to be written
+
+    Returns:
+        True if the operation has already been performed on the file
+    """
+    state = file_operations_state(agent.file_manager.file_ops_log_path)
+    if operation == "delete" and str(file_path) not in state:
+        return True
+    if operation == "write" and state.get(str(file_path)) == checksum:
+        return True
+    return False
+
+
+@sanitize_path_arg("file_path", make_relative=True)
+def log_operation(
+    operation: Operation,
+    file_path: str | Path,
+    agent: Agent,
+    checksum: str | None = None,
+) -> None:
+    """Log the file operation to the file_logger.log
+
+    Args:
+        operation: The operation to log
+        file_path: The name of the file the operation was performed on
+        checksum: The checksum of the contents to be written
+    """
+    log_entry = f"{operation}: {file_path}"
+    if checksum is not None:
+        log_entry += f" #{checksum}"
+    logger.debug(f"Logging file operation: {log_entry}")
+    append_to_file(
+        agent.file_manager.file_ops_log_path, f"{log_entry}\n", agent, should_log=False
+    )
+
+
+@command(
+    "read_file",
+    "Read an existing file",
+    {
+        "filename": JSONSchema(
+            type=JSONSchema.Type.STRING,
+            description="The path of the file to read",
+            required=True,
+        )
+    },
+)
+def read_file(filename: str | Path, agent: Agent) -> str:
+    """Read a file and return the contents
+
+    Args:
+        filename (Path): The name of the file to read
+
+    Returns:
+        str: The contents of the file
+    """
+    file = agent.workspace.open_file(filename, binary=True)
+    content = decode_textual_file(file, os.path.splitext(filename)[1], logger)
+
+    # # TODO: invalidate/update memory when file is edited
+    # file_memory = MemoryItem.from_text_file(content, str(filename), agent.config)
+    # if len(file_memory.chunks) > 1:
+    #     return file_memory.summary
+
+    return content
+
+
+def ingest_file(
+    filename: str,
+    memory: VectorMemory,
+) -> None:
+    """
+    Ingest a file by reading its content, splitting it into chunks with a specified
+    maximum length and overlap, and adding the chunks to the memory storage.
+
+    Args:
+        filename: The name of the file to ingest
+        memory: An object with an add() method to store the chunks in memory
+    """
+    try:
+        logger.info(f"Ingesting file {filename}")
+        content = read_file(filename)
+
+        # TODO: differentiate between different types of files
+        file_memory = MemoryItemFactory.from_text_file(content, filename)
+        logger.debug(f"Created memory: {file_memory.dump(True)}")
+        memory.add(file_memory)
+
+        logger.info(f"Ingested {len(file_memory.e_chunks)} chunks from {filename}")
+    except Exception as err:
+        logger.warning(f"Error while ingesting file '{filename}': {err}")
+
+
+@command(
+    "write_file",
+    "Write a file, creating it if necessary. If the file exists, it is overwritten.",
+    {
+        "filename": JSONSchema(
+            type=JSONSchema.Type.STRING,
+            description="The name of the file to write to",
+            required=True,
+        ),
+        "contents": JSONSchema(
+            type=JSONSchema.Type.STRING,
+            description="The contents to write to the file",
+            required=True,
+        ),
+    },
+    aliases=["create_file"],
+)
+async def write_to_file(filename: str | Path, contents: str, agent: Agent) -> str:
+    """Write contents to a file
+
+    Args:
+        filename (Path): The name of the file to write to
+        contents (str): The contents to write to the file
+
+    Returns:
+        str: A message indicating success or failure
+    """
+    checksum = text_checksum(contents)
+    if is_duplicate_operation("write", Path(filename), agent, checksum):
+        raise DuplicateOperationError(f"File {filename} has already been updated.")
+
+    if directory := os.path.dirname(filename):
+        agent.workspace.get_path(directory).mkdir(exist_ok=True)
+    await agent.workspace.write_file(filename, contents)
+    log_operation("write", filename, agent, checksum)
+    return f"File {filename} has been written successfully."
+
+
+def append_to_file(
+    filename: Path, text: str, agent: Agent, should_log: bool = True
+) -> None:
+    """Append text to a file
+
+    Args:
+        filename (Path): The name of the file to append to
+        text (str): The text to append to the file
+        should_log (bool): Should log output
+    """
+    directory = os.path.dirname(filename)
+    os.makedirs(directory, exist_ok=True)
+    with open(filename, "a") as f:
+        f.write(text)
+
+    if should_log:
+        with open(filename, "r") as f:
+            checksum = text_checksum(f.read())
+        log_operation("append", filename, agent, checksum=checksum)
+
+
+@command(
+    "list_folder",
+    "List the items in a folder",
+    {
+        "folder": JSONSchema(
+            type=JSONSchema.Type.STRING,
+            description="The folder to list files in",
+            required=True,
+        )
+    },
+)
+def list_folder(folder: str | Path, agent: Agent) -> list[str]:
+    """Lists files in a folder recursively
+
+    Args:
+        folder (Path): The folder to search in
+
+    Returns:
+        list[str]: A list of files found in the folder
+    """
+    return [str(p) for p in agent.workspace.list(folder)]

autogpts/autogpt/autogpt/commands/file_operations_utils.py

@@ -68,7 +68,7 @@ class XMLParser(ParserStrategy):
 # Reading as dictionary and returning string format
 class YAMLParser(ParserStrategy):
     def read(self, file: BinaryIO) -> str:
-        data = yaml.load(file, Loader=yaml.SafeLoader)
+        data = yaml.load(file, Loader=yaml.FullLoader)
         text = str(data)
         return text
 

autogpts/autogpt/autogpt/commands/git_operations.py

@@ -1,61 +1,58 @@
+"""Commands to perform Git operations"""
+
 from pathlib import Path
-from typing import Iterator
 
 from git.repo import Repo
 
-from autogpt.agents.protocols import CommandProvider
+from autogpt.agents.agent import Agent
+from autogpt.agents.utils.exceptions import CommandExecutionError
 from autogpt.command_decorator import command
-from autogpt.config.config import Config
 from autogpt.core.utils.json_schema import JSONSchema
-from autogpt.models.command import Command
 from autogpt.url_utils.validators import validate_url
-from autogpt.utils.exceptions import CommandExecutionError
+
+from .decorators import sanitize_path_arg
+
+COMMAND_CATEGORY = "git_operations"
+COMMAND_CATEGORY_TITLE = "Git Operations"
 
 
-class GitOperationsComponent(CommandProvider):
-    """Provides commands to perform Git operations."""
+@command(
+    "clone_repository",
+    "Clones a Repository",
+    {
+        "url": JSONSchema(
+            type=JSONSchema.Type.STRING,
+            description="The URL of the repository to clone",
+            required=True,
+        ),
+        "clone_path": JSONSchema(
+            type=JSONSchema.Type.STRING,
+            description="The path to clone the repository to",
+            required=True,
+        ),
+    },
+    lambda config: bool(config.github_username and config.github_api_key),
+    "Configure github_username and github_api_key.",
+)
+@sanitize_path_arg("clone_path")
+@validate_url
+def clone_repository(url: str, clone_path: Path, agent: Agent) -> str:
+    """Clone a GitHub repository locally.
 
-    def __init__(self, config: Config):
-        self._enabled = bool(config.github_username and config.github_api_key)
-        self._disabled_reason = "Configure github_username and github_api_key."
-        self.legacy_config = config
+    Args:
+        url (str): The URL of the repository to clone.
+        clone_path (Path): The path to clone the repository to.
 
-    def get_commands(self) -> Iterator[Command]:
-        yield self.clone_repository
-
-    @command(
-        parameters={
-            "url": JSONSchema(
-                type=JSONSchema.Type.STRING,
-                description="The URL of the repository to clone",
-                required=True,
-            ),
-            "clone_path": JSONSchema(
-                type=JSONSchema.Type.STRING,
-                description="The path to clone the repository to",
-                required=True,
-            ),
-        },
+    Returns:
+        str: The result of the clone operation.
+    """
+    split_url = url.split("//")
+    auth_repo_url = f"//{agent.legacy_config.github_username}:{agent.legacy_config.github_api_key}@".join(  # noqa: E501
+        split_url
     )
-    @validate_url
-    def clone_repository(self, url: str, clone_path: Path) -> str:
-        """Clone a GitHub repository locally.
+    try:
+        Repo.clone_from(url=auth_repo_url, to_path=clone_path)
+    except Exception as e:
+        raise CommandExecutionError(f"Could not clone repo: {e}")
 
-        Args:
-            url (str): The URL of the repository to clone.
-            clone_path (Path): The path to clone the repository to.
-
-        Returns:
-            str: The result of the clone operation.
-        """
-        split_url = url.split("//")
-        auth_repo_url = (
-            f"//{self.legacy_config.github_username}:"
-            f"{self.legacy_config.github_api_key}@".join(split_url)
-        )
-        try:
-            Repo.clone_from(url=auth_repo_url, to_path=clone_path)
-        except Exception as e:
-            raise CommandExecutionError(f"Could not clone repo: {e}")
-
-        return f"""Cloned {url} to {clone_path}"""
+    return f"""Cloned {url} to {clone_path}"""

autogpts/autogpt/autogpt/commands/image_gen.py

@@ -7,216 +7,206 @@ import time
 import uuid
 from base64 import b64decode
 from pathlib import Path
-from typing import Iterator
 
 import requests
 from openai import OpenAI
 from PIL import Image
 
-from autogpt.agents.protocols import CommandProvider
+from autogpt.agents.agent import Agent
 from autogpt.command_decorator import command
-from autogpt.config.config import Config
 from autogpt.core.utils.json_schema import JSONSchema
-from autogpt.file_storage.base import FileStorage
-from autogpt.models.command import Command
+
+COMMAND_CATEGORY = "text_to_image"
+COMMAND_CATEGORY_TITLE = "Text to Image"
+
 
 logger = logging.getLogger(__name__)
 
 
-class ImageGeneratorComponent(CommandProvider):
-    """A component that provides commands to generate images from text prompts."""
+@command(
+    "generate_image",
+    "Generates an Image",
+    {
+        "prompt": JSONSchema(
+            type=JSONSchema.Type.STRING,
+            description="The prompt used to generate the image",
+            required=True,
+        ),
+    },
+    lambda config: bool(config.image_provider),
+    "Requires a image provider to be set.",
+)
+def generate_image(prompt: str, agent: Agent, size: int = 256) -> str:
+    """Generate an image from a prompt.
 
-    def __init__(self, workspace: FileStorage, config: Config):
-        self._enabled = bool(config.image_provider)
-        self._disabled_reason = "No image provider set."
-        self.workspace = workspace
-        self.legacy_config = config
+    Args:
+        prompt (str): The prompt to use
+        size (int, optional): The size of the image. Defaults to 256.
+            Not supported by HuggingFace.
 
-    def get_commands(self) -> Iterator[Command]:
-        yield self.generate_image
+    Returns:
+        str: The filename of the image
+    """
+    filename = agent.workspace.root / f"{str(uuid.uuid4())}.jpg"
 
-    @command(
-        parameters={
-            "prompt": JSONSchema(
-                type=JSONSchema.Type.STRING,
-                description="The prompt used to generate the image",
-                required=True,
-            ),
-            "size": JSONSchema(
-                type=JSONSchema.Type.INTEGER,
-                description="The size of the image",
-                required=False,
-            ),
-        },
-    )
-    def generate_image(self, prompt: str, size: int) -> str:
-        """Generate an image from a prompt.
+    # DALL-E
+    if agent.legacy_config.image_provider == "dalle":
+        return generate_image_with_dalle(prompt, filename, size, agent)
+    # HuggingFace
+    elif agent.legacy_config.image_provider == "huggingface":
+        return generate_image_with_hf(prompt, filename, agent)
+    # SD WebUI
+    elif agent.legacy_config.image_provider == "sdwebui":
+        return generate_image_with_sd_webui(prompt, filename, agent, size)
+    return "No Image Provider Set"
 
-        Args:
-            prompt (str): The prompt to use
-            size (int, optional): The size of the image. Defaults to 256.
-                Not supported by HuggingFace.
 
-        Returns:
-            str: The filename of the image
-        """
-        filename = self.workspace.root / f"{str(uuid.uuid4())}.jpg"
+def generate_image_with_hf(prompt: str, output_file: Path, agent: Agent) -> str:
+    """Generate an image with HuggingFace's API.
 
-        # DALL-E
-        if self.legacy_config.image_provider == "dalle":
-            return self.generate_image_with_dalle(prompt, filename, size)
-        # HuggingFace
-        elif self.legacy_config.image_provider == "huggingface":
-            return self.generate_image_with_hf(prompt, filename)
-        # SD WebUI
-        elif self.legacy_config.image_provider == "sdwebui":
-            return self.generate_image_with_sd_webui(prompt, filename, size)
-        return "No Image Provider Set"
+    Args:
+        prompt (str): The prompt to use
+        filename (Path): The filename to save the image to
 
-    def generate_image_with_hf(self, prompt: str, output_file: Path) -> str:
-        """Generate an image with HuggingFace's API.
-
-        Args:
-            prompt (str): The prompt to use
-            filename (Path): The filename to save the image to
-
-        Returns:
-            str: The filename of the image
-        """
-        API_URL = f"https://api-inference.huggingface.co/models/{self.legacy_config.huggingface_image_model}"  # noqa: E501
-        if self.legacy_config.huggingface_api_token is None:
-            raise ValueError(
-                "You need to set your Hugging Face API token in the config file."
-            )
-        headers = {
-            "Authorization": f"Bearer {self.legacy_config.huggingface_api_token}",
-            "X-Use-Cache": "false",
-        }
-
-        retry_count = 0
-        while retry_count < 10:
-            response = requests.post(
-                API_URL,
-                headers=headers,
-                json={
-                    "inputs": prompt,
-                },
-            )
-
-            if response.ok:
-                try:
-                    image = Image.open(io.BytesIO(response.content))
-                    logger.info(f"Image Generated for prompt:{prompt}")
-                    image.save(output_file)
-                    return f"Saved to disk: {output_file}"
-                except Exception as e:
-                    logger.error(e)
-                    break
-            else:
-                try:
-                    error = json.loads(response.text)
-                    if "estimated_time" in error:
-                        delay = error["estimated_time"]
-                        logger.debug(response.text)
-                        logger.info("Retrying in", delay)
-                        time.sleep(delay)
-                    else:
-                        break
-                except Exception as e:
-                    logger.error(e)
-                    break
-
-            retry_count += 1
-
-        return "Error creating image."
-
-    def generate_image_with_dalle(
-        self, prompt: str, output_file: Path, size: int
-    ) -> str:
-        """Generate an image with DALL-E.
-
-        Args:
-            prompt (str): The prompt to use
-            filename (Path): The filename to save the image to
-            size (int): The size of the image
-
-        Returns:
-            str: The filename of the image
-        """
-
-        # Check for supported image sizes
-        if size not in [256, 512, 1024]:
-            closest = min([256, 512, 1024], key=lambda x: abs(x - size))
-            logger.info(
-                "DALL-E only supports image sizes of 256x256, 512x512, or 1024x1024. "
-                f"Setting to {closest}, was {size}."
-            )
-            size = closest
-
-        response = OpenAI(
-            api_key=self.legacy_config.openai_credentials.api_key.get_secret_value()
-        ).images.generate(
-            prompt=prompt,
-            n=1,
-            size=f"{size}x{size}",
-            response_format="b64_json",
+    Returns:
+        str: The filename of the image
+    """
+    API_URL = f"https://api-inference.huggingface.co/models/{agent.legacy_config.huggingface_image_model}"  # noqa: E501
+    if agent.legacy_config.huggingface_api_token is None:
+        raise ValueError(
+            "You need to set your Hugging Face API token in the config file."
         )
+    headers = {
+        "Authorization": f"Bearer {agent.legacy_config.huggingface_api_token}",
+        "X-Use-Cache": "false",
+    }
 
-        logger.info(f"Image Generated for prompt:{prompt}")
-
-        image_data = b64decode(response.data[0].b64_json)
-
-        with open(output_file, mode="wb") as png:
-            png.write(image_data)
-
-        return f"Saved to disk: {output_file}"
-
-    def generate_image_with_sd_webui(
-        self,
-        prompt: str,
-        output_file: Path,
-        size: int = 512,
-        negative_prompt: str = "",
-        extra: dict = {},
-    ) -> str:
-        """Generate an image with Stable Diffusion webui.
-        Args:
-            prompt (str): The prompt to use
-            filename (str): The filename to save the image to
-            size (int, optional): The size of the image. Defaults to 256.
-            negative_prompt (str, optional): The negative prompt to use. Defaults to "".
-            extra (dict, optional): Extra parameters to pass to the API. Defaults to {}.
-        Returns:
-            str: The filename of the image
-        """
-        # Create a session and set the basic auth if needed
-        s = requests.Session()
-        if self.legacy_config.sd_webui_auth:
-            username, password = self.legacy_config.sd_webui_auth.split(":")
-            s.auth = (username, password or "")
-
-        # Generate the images
+    retry_count = 0
+    while retry_count < 10:
         response = requests.post(
-            f"{self.legacy_config.sd_webui_url}/sdapi/v1/txt2img",
+            API_URL,
+            headers=headers,
             json={
-                "prompt": prompt,
-                "negative_prompt": negative_prompt,
-                "sampler_index": "DDIM",
-                "steps": 20,
-                "config_scale": 7.0,
-                "width": size,
-                "height": size,
-                "n_iter": 1,
-                **extra,
+                "inputs": prompt,
             },
         )
 
-        logger.info(f"Image Generated for prompt: '{prompt}'")
+        if response.ok:
+            try:
+                image = Image.open(io.BytesIO(response.content))
+                logger.info(f"Image Generated for prompt:{prompt}")
+                image.save(output_file)
+                return f"Saved to disk: {output_file}"
+            except Exception as e:
+                logger.error(e)
+                break
+        else:
+            try:
+                error = json.loads(response.text)
+                if "estimated_time" in error:
+                    delay = error["estimated_time"]
+                    logger.debug(response.text)
+                    logger.info("Retrying in", delay)
+                    time.sleep(delay)
+                else:
+                    break
+            except Exception as e:
+                logger.error(e)
+                break
 
-        # Save the image to disk
-        response = response.json()
-        b64 = b64decode(response["images"][0].split(",", 1)[0])
-        image = Image.open(io.BytesIO(b64))
-        image.save(output_file)
+        retry_count += 1
 
-        return f"Saved to disk: {output_file}"
+    return "Error creating image."
+
+
+def generate_image_with_dalle(
+    prompt: str, output_file: Path, size: int, agent: Agent
+) -> str:
+    """Generate an image with DALL-E.
+
+    Args:
+        prompt (str): The prompt to use
+        filename (Path): The filename to save the image to
+        size (int): The size of the image
+
+    Returns:
+        str: The filename of the image
+    """
+
+    # Check for supported image sizes
+    if size not in [256, 512, 1024]:
+        closest = min([256, 512, 1024], key=lambda x: abs(x - size))
+        logger.info(
+            "DALL-E only supports image sizes of 256x256, 512x512, or 1024x1024. "
+            f"Setting to {closest}, was {size}."
+        )
+        size = closest
+
+    response = OpenAI(
+        api_key=agent.legacy_config.openai_credentials.api_key.get_secret_value()
+    ).images.generate(
+        prompt=prompt,
+        n=1,
+        size=f"{size}x{size}",
+        response_format="b64_json",
+    )
+
+    logger.info(f"Image Generated for prompt:{prompt}")
+
+    image_data = b64decode(response.data[0].b64_json)
+
+    with open(output_file, mode="wb") as png:
+        png.write(image_data)
+
+    return f"Saved to disk: {output_file}"
+
+
+def generate_image_with_sd_webui(
+    prompt: str,
+    output_file: Path,
+    agent: Agent,
+    size: int = 512,
+    negative_prompt: str = "",
+    extra: dict = {},
+) -> str:
+    """Generate an image with Stable Diffusion webui.
+    Args:
+        prompt (str): The prompt to use
+        filename (str): The filename to save the image to
+        size (int, optional): The size of the image. Defaults to 256.
+        negative_prompt (str, optional): The negative prompt to use. Defaults to "".
+        extra (dict, optional): Extra parameters to pass to the API. Defaults to {}.
+    Returns:
+        str: The filename of the image
+    """
+    # Create a session and set the basic auth if needed
+    s = requests.Session()
+    if agent.legacy_config.sd_webui_auth:
+        username, password = agent.legacy_config.sd_webui_auth.split(":")
+        s.auth = (username, password or "")
+
+    # Generate the images
+    response = requests.post(
+        f"{agent.legacy_config.sd_webui_url}/sdapi/v1/txt2img",
+        json={
+            "prompt": prompt,
+            "negative_prompt": negative_prompt,
+            "sampler_index": "DDIM",
+            "steps": 20,
+            "config_scale": 7.0,
+            "width": size,
+            "height": size,
+            "n_iter": 1,
+            **extra,
+        },
+    )
+
+    logger.info(f"Image Generated for prompt: '{prompt}'")
+
+    # Save the image to disk
+    response = response.json()
+    b64 = b64decode(response["images"][0].split(",", 1)[0])
+    image = Image.open(io.BytesIO(b64))
+    image.save(output_file)
+
+    return f"Saved to disk: {output_file}"

autogpts/autogpt/autogpt/commands/system.py

@@ -1,55 +1,69 @@
-import logging
-import time
-from typing import Iterator
+"""Commands to control the internal state of the program"""
 
-from autogpt.agents.protocols import CommandProvider, DirectiveProvider, MessageProvider
+from __future__ import annotations
+
+import logging
+from typing import TYPE_CHECKING
+
+from autogpt.agents.features.context import get_agent_context
+from autogpt.agents.utils.exceptions import AgentTerminated, InvalidArgumentError
 from autogpt.command_decorator import command
-from autogpt.config.ai_profile import AIProfile
-from autogpt.config.config import Config
-from autogpt.core.resource.model_providers.schema import ChatMessage
 from autogpt.core.utils.json_schema import JSONSchema
-from autogpt.models.command import Command
-from autogpt.utils.exceptions import AgentFinished
-from autogpt.utils.utils import DEFAULT_FINISH_COMMAND
+
+COMMAND_CATEGORY = "system"
+COMMAND_CATEGORY_TITLE = "System"
+
+
+if TYPE_CHECKING:
+    from autogpt.agents.agent import Agent
+
 
 logger = logging.getLogger(__name__)
 
 
-class SystemComponent(DirectiveProvider, MessageProvider, CommandProvider):
-    """Component for system messages and commands."""
-
-    def __init__(self, config: Config, profile: AIProfile):
-        self.legacy_config = config
-        self.profile = profile
-
-    def get_constraints(self) -> Iterator[str]:
-        if self.profile.api_budget > 0.0:
-            yield (
-                f"It takes money to let you run. "
-                f"Your API budget is ${self.profile.api_budget:.3f}"
-            )
-
-    def get_messages(self) -> Iterator[ChatMessage]:
-        # Clock
-        yield ChatMessage.system(
-            f"## Clock\nThe current time and date is {time.strftime('%c')}"
+@command(
+    "finish",
+    "Use this to shut down once you have completed your task,"
+    " or when there are insurmountable problems that make it impossible"
+    " for you to finish your task.",
+    {
+        "reason": JSONSchema(
+            type=JSONSchema.Type.STRING,
+            description="A summary to the user of how the goals were accomplished",
+            required=True,
         )
+    },
+)
+def finish(reason: str, agent: Agent) -> None:
+    """
+    A function that takes in a string and exits the program
 
-    def get_commands(self) -> Iterator[Command]:
-        yield self.finish
+    Parameters:
+        reason (str): A summary to the user of how the goals were accomplished.
+    Returns:
+        A result string from create chat completion. A list of suggestions to
+            improve the code.
+    """
+    raise AgentTerminated(reason)
 
-    @command(
-        names=[DEFAULT_FINISH_COMMAND],
-        parameters={
-            "reason": JSONSchema(
-                type=JSONSchema.Type.STRING,
-                description="A summary to the user of how the goals were accomplished",
-                required=True,
-            ),
-        },
-    )
-    def finish(self, reason: str):
-        """Use this to shut down once you have completed your task,
-        or when there are insurmountable problems that make it impossible
-        for you to finish your task."""
-        raise AgentFinished(reason)
+
+@command(
+    "hide_context_item",
+    "Hide an open file, folder or other context item, to save memory.",
+    {
+        "number": JSONSchema(
+            type=JSONSchema.Type.INTEGER,
+            description="The 1-based index of the context item to hide",
+            required=True,
+        )
+    },
+    available=lambda a: bool(get_agent_context(a)),
+)
+def close_context_item(number: int, agent: Agent) -> str:
+    assert (context := get_agent_context(agent)) is not None
+
+    if number > len(context.items) or number == 0:
+        raise InvalidArgumentError(f"Index {number} out of range")
+
+    context.close(number)
+    return f"Context item {number} hidden ✅"

autogpts/autogpt/autogpt/commands/times.py

@@ -0,0 +1,10 @@
+from datetime import datetime
+
+
+def get_datetime() -> str:
+    """Return the current date and time
+
+    Returns:
+        str: The current date and time
+    """
+    return "Current date and time: " + datetime.now().strftime("%Y-%m-%d %H:%M:%S")

autogpts/autogpt/autogpt/commands/user_interaction.py

@@ -1,37 +1,32 @@
-from typing import Iterator
+"""Commands to interact with the user"""
 
-from autogpt.agents.protocols import CommandProvider
+from __future__ import annotations
+
+from autogpt.agents.agent import Agent
 from autogpt.app.utils import clean_input
 from autogpt.command_decorator import command
-from autogpt.config.config import Config
 from autogpt.core.utils.json_schema import JSONSchema
-from autogpt.models.command import Command
-from autogpt.utils.utils import DEFAULT_ASK_COMMAND
+
+COMMAND_CATEGORY = "user_interaction"
+COMMAND_CATEGORY_TITLE = "User Interaction"
 
 
-class UserInteractionComponent(CommandProvider):
-    """Provides commands to interact with the user."""
-
-    def __init__(self, config: Config):
-        self.config = config
-        self._enabled = not config.noninteractive_mode
-
-    def get_commands(self) -> Iterator[Command]:
-        yield self.ask_user
-
-    @command(
-        names=[DEFAULT_ASK_COMMAND],
-        parameters={
-            "question": JSONSchema(
-                type=JSONSchema.Type.STRING,
-                description="The question or prompt to the user",
-                required=True,
-            )
-        },
-    )
-    def ask_user(self, question: str) -> str:
-        """If you need more details or information regarding the given goals,
-        you can ask the user for input."""
-        print(f"\nQ: {question}")
-        resp = clean_input(self.config, "A:")
-        return f"The user's answer: '{resp}'"
+@command(
+    "ask_user",
+    (
+        "If you need more details or information regarding the given goals,"
+        " you can ask the user for input"
+    ),
+    {
+        "question": JSONSchema(
+            type=JSONSchema.Type.STRING,
+            description="The question or prompt to the user",
+            required=True,
+        )
+    },
+    enabled=lambda config: not config.noninteractive_mode,
+)
+async def ask_user(question: str, agent: Agent) -> str:
+    print(f"\nQ: {question}")
+    resp = await clean_input(agent.legacy_config, "A:")
+    return f"The user's answer: '{resp}'"

autogpts/autogpt/autogpt/commands/web_search.py

@@ -1,195 +1,171 @@
+"""Commands to search the web with"""
+
+from __future__ import annotations
+
 import json
-import logging
 import time
-from typing import Iterator
+from itertools import islice
 
 from duckduckgo_search import DDGS
 
-from autogpt.agents.protocols import CommandProvider, DirectiveProvider
+from autogpt.agents.agent import Agent
+from autogpt.agents.utils.exceptions import ConfigurationError
 from autogpt.command_decorator import command
-from autogpt.config.config import Config
 from autogpt.core.utils.json_schema import JSONSchema
-from autogpt.models.command import Command
-from autogpt.utils.exceptions import ConfigurationError
+
+COMMAND_CATEGORY = "web_search"
+COMMAND_CATEGORY_TITLE = "Web Search"
+
 
 DUCKDUCKGO_MAX_ATTEMPTS = 3
 
-logger = logging.getLogger(__name__)
 
-
-class WebSearchComponent(DirectiveProvider, CommandProvider):
-    """Provides commands to search the web."""
-
-    def __init__(self, config: Config):
-        self.legacy_config = config
-
-        if (
-            not self.legacy_config.google_api_key
-            or not self.legacy_config.google_custom_search_engine_id
-        ):
-            logger.info(
-                "Configure google_api_key and custom_search_engine_id "
-                "to use Google API search."
-            )
-
-    def get_resources(self) -> Iterator[str]:
-        yield "Internet access for searches and information gathering."
-
-    def get_commands(self) -> Iterator[Command]:
-        yield self.web_search
-
-        if (
-            self.legacy_config.google_api_key
-            and self.legacy_config.google_custom_search_engine_id
-        ):
-            yield self.google
-
-    @command(
-        ["web_search", "search"],
-        "Searches the web",
-        {
-            "query": JSONSchema(
-                type=JSONSchema.Type.STRING,
-                description="The search query",
-                required=True,
-            ),
-            "num_results": JSONSchema(
-                type=JSONSchema.Type.INTEGER,
-                description="The number of results to return",
-                minimum=1,
-                maximum=10,
-                required=False,
-            ),
-        },
-    )
-    def web_search(self, query: str, num_results: int = 8) -> str:
-        """Return the results of a Google search
-
-        Args:
-            query (str): The search query.
-            num_results (int): The number of results to return.
-
-        Returns:
-            str: The results of the search.
-        """
-        search_results = []
-        attempts = 0
-
-        while attempts < DUCKDUCKGO_MAX_ATTEMPTS:
-            if not query:
-                return json.dumps(search_results)
-
-            search_results = DDGS().text(query, max_results=num_results)
-
-            if search_results:
-                break
-
-            time.sleep(1)
-            attempts += 1
-
-        search_results = [
-            {
-                "title": r["title"],
-                "url": r["href"],
-                **({"exerpt": r["body"]} if r.get("body") else {}),
-            }
-            for r in search_results
-        ]
-
-        results = ("## Search results\n") + "\n\n".join(
-            f"### \"{r['title']}\"\n"
-            f"**URL:** {r['url']}  \n"
-            "**Excerpt:** " + (f'"{exerpt}"' if (exerpt := r.get("exerpt")) else "N/A")
-            for r in search_results
+@command(
+    "web_search",
+    "Searches the web",
+    {
+        "query": JSONSchema(
+            type=JSONSchema.Type.STRING,
+            description="The search query",
+            required=True,
         )
-        return self.safe_google_results(results)
+    },
+    aliases=["search"],
+)
+def web_search(query: str, agent: Agent, num_results: int = 8) -> str:
+    """Return the results of a Google search
 
-    @command(
-        ["google"],
-        "Google Search",
+    Args:
+        query (str): The search query.
+        num_results (int): The number of results to return.
+
+    Returns:
+        str: The results of the search.
+    """
+    search_results = []
+    attempts = 0
+
+    while attempts < DUCKDUCKGO_MAX_ATTEMPTS:
+        if not query:
+            return json.dumps(search_results)
+
+        results = DDGS().text(query)
+        search_results = list(islice(results, num_results))
+
+        if search_results:
+            break
+
+        time.sleep(1)
+        attempts += 1
+
+    search_results = [
         {
-            "query": JSONSchema(
-                type=JSONSchema.Type.STRING,
-                description="The search query",
-                required=True,
-            ),
-            "num_results": JSONSchema(
-                type=JSONSchema.Type.INTEGER,
-                description="The number of results to return",
-                minimum=1,
-                maximum=10,
-                required=False,
-            ),
-        },
+            "title": r["title"],
+            "url": r["href"],
+            **({"exerpt": r["body"]} if r.get("body") else {}),
+        }
+        for r in search_results
+    ]
+
+    results = (
+        "## Search results\n"
+        # "Read these results carefully."
+        # " Extract the information you need for your task from the list of results"
+        # " if possible. Otherwise, choose a webpage from the list to read entirely."
+        # "\n\n"
+    ) + "\n\n".join(
+        f"### \"{r['title']}\"\n"
+        f"**URL:** {r['url']}  \n"
+        "**Excerpt:** " + (f'"{exerpt}"' if (exerpt := r.get("exerpt")) else "N/A")
+        for r in search_results
     )
-    def google(self, query: str, num_results: int = 8) -> str | list[str]:
-        """Return the results of a Google search using the official Google API
+    return safe_google_results(results)
 
-        Args:
-            query (str): The search query.
-            num_results (int): The number of results to return.
 
-        Returns:
-            str: The results of the search.
-        """
+@command(
+    "google",
+    "Google Search",
+    {
+        "query": JSONSchema(
+            type=JSONSchema.Type.STRING,
+            description="The search query",
+            required=True,
+        )
+    },
+    lambda config: bool(config.google_api_key)
+    and bool(config.google_custom_search_engine_id),
+    "Configure google_api_key and custom_search_engine_id.",
+    aliases=["search"],
+)
+def google(query: str, agent: Agent, num_results: int = 8) -> str | list[str]:
+    """Return the results of a Google search using the official Google API
 
-        from googleapiclient.discovery import build
-        from googleapiclient.errors import HttpError
+    Args:
+        query (str): The search query.
+        num_results (int): The number of results to return.
 
-        try:
-            # Get the Google API key and Custom Search Engine ID from the config file
-            api_key = self.legacy_config.google_api_key
-            custom_search_engine_id = self.legacy_config.google_custom_search_engine_id
+    Returns:
+        str: The results of the search.
+    """
 
-            # Initialize the Custom Search API service
-            service = build("customsearch", "v1", developerKey=api_key)
+    from googleapiclient.discovery import build
+    from googleapiclient.errors import HttpError
 
-            # Send the search query and retrieve the results
-            result = (
-                service.cse()
-                .list(q=query, cx=custom_search_engine_id, num=num_results)
-                .execute()
+    try:
+        # Get the Google API key and Custom Search Engine ID from the config file
+        api_key = agent.legacy_config.google_api_key
+        custom_search_engine_id = agent.legacy_config.google_custom_search_engine_id
+
+        # Initialize the Custom Search API service
+        service = build("customsearch", "v1", developerKey=api_key)
+
+        # Send the search query and retrieve the results
+        result = (
+            service.cse()
+            .list(q=query, cx=custom_search_engine_id, num=num_results)
+            .execute()
+        )
+
+        # Extract the search result items from the response
+        search_results = result.get("items", [])
+
+        # Create a list of only the URLs from the search results
+        search_results_links = [item["link"] for item in search_results]
+
+    except HttpError as e:
+        # Handle errors in the API call
+        error_details = json.loads(e.content.decode())
+
+        # Check if the error is related to an invalid or missing API key
+        if error_details.get("error", {}).get(
+            "code"
+        ) == 403 and "invalid API key" in error_details.get("error", {}).get(
+            "message", ""
+        ):
+            raise ConfigurationError(
+                "The provided Google API key is invalid or missing."
             )
+        raise
+    # google_result can be a list or a string depending on the search results
 
-            # Extract the search result items from the response
-            search_results = result.get("items", [])
+    # Return the list of search result URLs
+    return safe_google_results(search_results_links)
 
-            # Create a list of only the URLs from the search results
-            search_results_links = [item["link"] for item in search_results]
 
-        except HttpError as e:
-            # Handle errors in the API call
-            error_details = json.loads(e.content.decode())
+def safe_google_results(results: str | list) -> str:
+    """
+        Return the results of a Google search in a safe format.
 
-            # Check if the error is related to an invalid or missing API key
-            if error_details.get("error", {}).get(
-                "code"
-            ) == 403 and "invalid API key" in error_details.get("error", {}).get(
-                "message", ""
-            ):
-                raise ConfigurationError(
-                    "The provided Google API key is invalid or missing."
-                )
-            raise
-        # google_result can be a list or a string depending on the search results
+    Args:
+        results (str | list): The search results.
 
-        # Return the list of search result URLs
-        return self.safe_google_results(search_results_links)
-
-    def safe_google_results(self, results: str | list) -> str:
-        """
-            Return the results of a Google search in a safe format.
-
-        Args:
-            results (str | list): The search results.
-
-        Returns:
-            str: The results of the search.
-        """
-        if isinstance(results, list):
-            safe_message = json.dumps(
-                [result.encode("utf-8", "ignore").decode("utf-8") for result in results]
-            )
-        else:
-            safe_message = results.encode("utf-8", "ignore").decode("utf-8")
-        return safe_message
+    Returns:
+        str: The results of the search.
+    """
+    if isinstance(results, list):
+        safe_message = json.dumps(
+            [result.encode("utf-8", "ignore").decode("utf-8") for result in results]
+        )
+    else:
+        safe_message = results.encode("utf-8", "ignore").decode("utf-8")
+    return safe_message

autogpts/autogpt/autogpt/commands/web_selenium.py

@@ -1,10 +1,12 @@
-import asyncio
+"""Commands for browsing a website"""
+
+from __future__ import annotations
+
 import logging
 import re
 from pathlib import Path
 from sys import platform
-from typing import Iterator, Type
-from urllib.request import urlretrieve
+from typing import TYPE_CHECKING, Optional, Type
 
 from bs4 import BeautifulSoup
 from selenium.common.exceptions import WebDriverException
@@ -28,24 +30,26 @@ from webdriver_manager.chrome import ChromeDriverManager
 from webdriver_manager.firefox import GeckoDriverManager
 from webdriver_manager.microsoft import EdgeChromiumDriverManager as EdgeDriverManager
 
-from autogpt.agents.protocols import CommandProvider, DirectiveProvider
+from autogpt.agents.utils.exceptions import CommandExecutionError
 from autogpt.command_decorator import command
-from autogpt.config import Config
-from autogpt.core.resource.model_providers.schema import (
-    ChatModelInfo,
-    ChatModelProvider,
-)
 from autogpt.core.utils.json_schema import JSONSchema
-from autogpt.models.command import Command
 from autogpt.processing.html import extract_hyperlinks, format_hyperlinks
-from autogpt.processing.text import extract_information, summarize_text
+from autogpt.processing.text import summarize_text
 from autogpt.url_utils.validators import validate_url
-from autogpt.utils.exceptions import CommandExecutionError, TooMuchOutputError
+
+COMMAND_CATEGORY = "web_browse"
+COMMAND_CATEGORY_TITLE = "Web Browsing"
+
+
+if TYPE_CHECKING:
+    from autogpt.agents.agent import Agent
+    from autogpt.config import Config
+
 
 logger = logging.getLogger(__name__)
 
 FILE_DIR = Path(__file__).parent.parent
-MAX_RAW_CONTENT_LENGTH = 500
+TOKENS_TO_TRIGGER_SUMMARY = 50
 LINKS_TO_RETURN = 20
 
 
@@ -53,324 +57,248 @@ class BrowsingError(CommandExecutionError):
     """An error occurred while trying to browse the page"""
 
 
-class WebSeleniumComponent(DirectiveProvider, CommandProvider):
-    """Provides commands to browse the web using Selenium."""
-
-    def __init__(
-        self,
-        config: Config,
-        llm_provider: ChatModelProvider,
-        model_info: ChatModelInfo,
-    ):
-        self.legacy_config = config
-        self.llm_provider = llm_provider
-        self.model_info = model_info
-
-    def get_resources(self) -> Iterator[str]:
-        yield "Ability to read websites."
-
-    def get_commands(self) -> Iterator[Command]:
-        yield self.read_webpage
-
-    @command(
-        ["read_webpage"],
-        (
-            "Read a webpage, and extract specific information from it."
-            " You must specify either topics_of_interest,"
-            " a question, or get_raw_content."
+@command(
+    "read_webpage",
+    (
+        "Read a webpage, and extract specific information from it"
+        " if a question is specified."
+        " If you are looking to extract specific information from the webpage,"
+        " you should specify a question."
+    ),
+    {
+        "url": JSONSchema(
+            type=JSONSchema.Type.STRING,
+            description="The URL to visit",
+            required=True,
         ),
-        {
-            "url": JSONSchema(
-                type=JSONSchema.Type.STRING,
-                description="The URL to visit",
-                required=True,
+        "question": JSONSchema(
+            type=JSONSchema.Type.STRING,
+            description=(
+                "A question that you want to answer using the content of the webpage."
             ),
-            "topics_of_interest": JSONSchema(
-                type=JSONSchema.Type.ARRAY,
-                items=JSONSchema(type=JSONSchema.Type.STRING),
-                description=(
-                    "A list of topics about which you want to extract information "
-                    "from the page."
-                ),
-                required=False,
-            ),
-            "question": JSONSchema(
-                type=JSONSchema.Type.STRING,
-                description=(
-                    "A question you want to answer using the content of the webpage."
-                ),
-                required=False,
-            ),
-            "get_raw_content": JSONSchema(
-                type=JSONSchema.Type.BOOLEAN,
-                description=(
-                    "If true, the unprocessed content of the webpage will be returned. "
-                    "This consumes a lot of tokens, so use it with caution."
-                ),
-                required=False,
-            ),
-        },
-    )
-    @validate_url
-    async def read_webpage(
-        self,
-        url: str,
-        *,
-        topics_of_interest: list[str] = [],
-        get_raw_content: bool = False,
-        question: str = "",
-    ) -> str:
-        """Browse a website and return the answer and links to the user
+            required=False,
+        ),
+    },
+)
+@validate_url
+async def read_webpage(url: str, agent: Agent, question: str = "") -> str:
+    """Browse a website and return the answer and links to the user
 
-        Args:
-            url (str): The url of the website to browse
-            question (str): The question to answer using the content of the webpage
+    Args:
+        url (str): The url of the website to browse
+        question (str): The question to answer using the content of the webpage
 
-        Returns:
-            str: The answer and links to the user and the webdriver
-        """
-        driver = None
-        try:
-            driver = await self.open_page_in_browser(url, self.legacy_config)
+    Returns:
+        str: The answer and links to the user and the webdriver
+    """
+    driver = None
+    try:
+        # FIXME: agent.config -> something else
+        driver = open_page_in_browser(url, agent.legacy_config)
 
-            text = self.scrape_text_with_selenium(driver)
-            links = self.scrape_links_with_selenium(driver, url)
+        text = scrape_text_with_selenium(driver)
+        links = scrape_links_with_selenium(driver, url)
 
-            return_literal_content = True
-            summarized = False
-            if not text:
-                return f"Website did not contain any text.\n\nLinks: {links}"
-            elif get_raw_content:
-                if (
-                    output_tokens := self.llm_provider.count_tokens(
-                        text, self.model_info.name
-                    )
-                ) > MAX_RAW_CONTENT_LENGTH:
-                    oversize_factor = round(output_tokens / MAX_RAW_CONTENT_LENGTH, 1)
-                    raise TooMuchOutputError(
-                        f"Page content is {oversize_factor}x the allowed length "
-                        "for `get_raw_content=true`"
-                    )
-                return text + (f"\n\nLinks: {links}" if links else "")
-            else:
-                text = await self.summarize_webpage(
-                    text, question or None, topics_of_interest
-                )
-                return_literal_content = bool(question)
-                summarized = True
-
-            # Limit links to LINKS_TO_RETURN
-            if len(links) > LINKS_TO_RETURN:
-                links = links[:LINKS_TO_RETURN]
-
-            text_fmt = f"'''{text}'''" if "\n" in text else f"'{text}'"
-            links_fmt = "\n".join(f"- {link}" for link in links)
-            return (
-                f"Page content{' (summary)' if summarized else ''}:"
-                if return_literal_content
-                else "Answer gathered from webpage:"
-            ) + f" {text_fmt}\n\nLinks:\n{links_fmt}"
-
-        except WebDriverException as e:
-            # These errors are often quite long and include lots of context.
-            # Just grab the first line.
-            msg = e.msg.split("\n")[0] if e.msg else str(e)
-            if "net::" in msg:
-                raise BrowsingError(
-                    "A networking error occurred while trying to load the page: %s"
-                    % re.sub(r"^unknown error: ", "", msg)
-                )
-            raise CommandExecutionError(msg)
-        finally:
-            if driver:
-                driver.close()
-
-    def scrape_text_with_selenium(self, driver: WebDriver) -> str:
-        """Scrape text from a browser window using selenium
-
-        Args:
-            driver (WebDriver): A driver object representing
-            the browser window to scrape
-
-        Returns:
-            str: the text scraped from the website
-        """
-
-        # Get the HTML content directly from the browser's DOM
-        page_source = driver.execute_script("return document.body.outerHTML;")
-        soup = BeautifulSoup(page_source, "html.parser")
-
-        for script in soup(["script", "style"]):
-            script.extract()
-
-        text = soup.get_text()
-        lines = (line.strip() for line in text.splitlines())
-        chunks = (phrase.strip() for line in lines for phrase in line.split("  "))
-        text = "\n".join(chunk for chunk in chunks if chunk)
-        return text
-
-    def scrape_links_with_selenium(self, driver: WebDriver, base_url: str) -> list[str]:
-        """Scrape links from a website using selenium
-
-        Args:
-            driver (WebDriver): A driver object representing
-            the browser window to scrape
-            base_url (str): The base URL to use for resolving relative links
-
-        Returns:
-            List[str]: The links scraped from the website
-        """
-        page_source = driver.page_source
-        soup = BeautifulSoup(page_source, "html.parser")
-
-        for script in soup(["script", "style"]):
-            script.extract()
-
-        hyperlinks = extract_hyperlinks(soup, base_url)
-
-        return format_hyperlinks(hyperlinks)
-
-    async def open_page_in_browser(self, url: str, config: Config) -> WebDriver:
-        """Open a browser window and load a web page using Selenium
-
-        Params:
-            url (str): The URL of the page to load
-            config (Config): The applicable application configuration
-
-        Returns:
-            driver (WebDriver): A driver object representing
-            the browser window to scrape
-        """
-        logging.getLogger("selenium").setLevel(logging.CRITICAL)
-
-        options_available: dict[str, Type[BrowserOptions]] = {
-            "chrome": ChromeOptions,
-            "edge": EdgeOptions,
-            "firefox": FirefoxOptions,
-            "safari": SafariOptions,
-        }
-
-        options: BrowserOptions = options_available[config.selenium_web_browser]()
-        options.add_argument(f"user-agent={config.user_agent}")
-
-        if isinstance(options, FirefoxOptions):
-            if config.selenium_headless:
-                options.headless = True
-                options.add_argument("--disable-gpu")
-            driver = FirefoxDriver(
-                service=GeckoDriverService(GeckoDriverManager().install()),
-                options=options,
-            )
-        elif isinstance(options, EdgeOptions):
-            driver = EdgeDriver(
-                service=EdgeDriverService(EdgeDriverManager().install()),
-                options=options,
-            )
-        elif isinstance(options, SafariOptions):
-            # Requires a bit more setup on the users end.
-            # See https://developer.apple.com/documentation/webkit/testing_with_webdriver_in_safari  # noqa: E501
-            driver = SafariDriver(options=options)
-        elif isinstance(options, ChromeOptions):
-            if platform == "linux" or platform == "linux2":
-                options.add_argument("--disable-dev-shm-usage")
-                options.add_argument("--remote-debugging-port=9222")
-
-            options.add_argument("--no-sandbox")
-            if config.selenium_headless:
-                options.add_argument("--headless=new")
-                options.add_argument("--disable-gpu")
-
-            self._sideload_chrome_extensions(
-                options, config.app_data_dir / "assets" / "crx"
-            )
-
-            if (chromium_driver_path := Path("/usr/bin/chromedriver")).exists():
-                chrome_service = ChromeDriverService(str(chromium_driver_path))
-            else:
-                try:
-                    chrome_driver = ChromeDriverManager().install()
-                except AttributeError as e:
-                    if "'NoneType' object has no attribute 'split'" in str(e):
-                        # https://github.com/SergeyPirogov/webdriver_manager/issues/649
-                        logger.critical(
-                            "Connecting to browser failed:"
-                            " is Chrome or Chromium installed?"
-                        )
-                    raise
-                chrome_service = ChromeDriverService(chrome_driver)
-            driver = ChromeDriver(service=chrome_service, options=options)
-
-        driver.get(url)
-
-        # Wait for page to be ready, sleep 2 seconds, wait again until page ready.
-        # This allows the cookiewall squasher time to get rid of cookie walls.
-        WebDriverWait(driver, 10).until(
-            EC.presence_of_element_located((By.TAG_NAME, "body"))
-        )
-        await asyncio.sleep(2)
-        WebDriverWait(driver, 10).until(
-            EC.presence_of_element_located((By.TAG_NAME, "body"))
-        )
-
-        return driver
-
-    def _sideload_chrome_extensions(
-        self, options: ChromeOptions, dl_folder: Path
-    ) -> None:
-        crx_download_url_template = "https://clients2.google.com/service/update2/crx?response=redirect&prodversion=49.0&acceptformat=crx3&x=id%3D{crx_id}%26installsource%3Dondemand%26uc"  # noqa
-        cookiewall_squasher_crx_id = "edibdbjcniadpccecjdfdjjppcpchdlm"
-        adblocker_crx_id = "cjpalhdlnbpafiamejdnhcphjbkeiagm"
-
-        # Make sure the target folder exists
-        dl_folder.mkdir(parents=True, exist_ok=True)
-
-        for crx_id in (cookiewall_squasher_crx_id, adblocker_crx_id):
-            crx_path = dl_folder / f"{crx_id}.crx"
-            if not crx_path.exists():
-                logger.debug(f"Downloading CRX {crx_id}...")
-                crx_download_url = crx_download_url_template.format(crx_id=crx_id)
-                urlretrieve(crx_download_url, crx_path)
-                logger.debug(f"Downloaded {crx_path.name}")
-            options.add_extension(str(crx_path))
-
-    async def summarize_webpage(
-        self,
-        text: str,
-        question: str | None,
-        topics_of_interest: list[str],
-    ) -> str:
-        """Summarize text using the OpenAI API
-
-        Args:
-            url (str): The url of the text
-            text (str): The text to summarize
-            question (str): The question to ask the model
-            driver (WebDriver): The webdriver to use to scroll the page
-
-        Returns:
-            str: The summary of the text
-        """
+        return_literal_content = True
+        summarized = False
         if not text:
-            raise ValueError("No text to summarize")
-
-        text_length = len(text)
-        logger.debug(f"Web page content length: {text_length} characters")
-
-        result = None
-        information = None
-        if topics_of_interest:
-            information = await extract_information(
-                text,
-                topics_of_interest=topics_of_interest,
-                llm_provider=self.llm_provider,
-                config=self.legacy_config,
+            return f"Website did not contain any text.\n\nLinks: {links}"
+        elif (
+            agent.llm_provider.count_tokens(text, agent.llm.name)
+            > TOKENS_TO_TRIGGER_SUMMARY
+        ):
+            text = await summarize_memorize_webpage(
+                url, text, question or None, agent, driver
             )
-            return "\n".join(f"* {i}" for i in information)
-        else:
-            result, _ = await summarize_text(
-                text,
-                question=question,
-                llm_provider=self.llm_provider,
-                config=self.legacy_config,
+            return_literal_content = bool(question)
+            summarized = True
+
+        # Limit links to LINKS_TO_RETURN
+        if len(links) > LINKS_TO_RETURN:
+            links = links[:LINKS_TO_RETURN]
+
+        text_fmt = f"'''{text}'''" if "\n" in text else f"'{text}'"
+        links_fmt = "\n".join(f"- {link}" for link in links)
+        return (
+            f"Page content{' (summary)' if summarized else ''}:"
+            if return_literal_content
+            else "Answer gathered from webpage:"
+        ) + f" {text_fmt}\n\nLinks:\n{links_fmt}"
+
+    except WebDriverException as e:
+        # These errors are often quite long and include lots of context.
+        # Just grab the first line.
+        msg = e.msg.split("\n")[0]
+        if "net::" in msg:
+            raise BrowsingError(
+                "A networking error occurred while trying to load the page: %s"
+                % re.sub(r"^unknown error: ", "", msg)
             )
-            return result
+        raise CommandExecutionError(msg)
+    finally:
+        if driver:
+            close_browser(driver)
+
+
+def scrape_text_with_selenium(driver: WebDriver) -> str:
+    """Scrape text from a browser window using selenium
+
+    Args:
+        driver (WebDriver): A driver object representing the browser window to scrape
+
+    Returns:
+        str: the text scraped from the website
+    """
+
+    # Get the HTML content directly from the browser's DOM
+    page_source = driver.execute_script("return document.body.outerHTML;")
+    soup = BeautifulSoup(page_source, "html.parser")
+
+    for script in soup(["script", "style"]):
+        script.extract()
+
+    text = soup.get_text()
+    lines = (line.strip() for line in text.splitlines())
+    chunks = (phrase.strip() for line in lines for phrase in line.split("  "))
+    text = "\n".join(chunk for chunk in chunks if chunk)
+    return text
+
+
+def scrape_links_with_selenium(driver: WebDriver, base_url: str) -> list[str]:
+    """Scrape links from a website using selenium
+
+    Args:
+        driver (WebDriver): A driver object representing the browser window to scrape
+        base_url (str): The base URL to use for resolving relative links
+
+    Returns:
+        List[str]: The links scraped from the website
+    """
+    page_source = driver.page_source
+    soup = BeautifulSoup(page_source, "html.parser")
+
+    for script in soup(["script", "style"]):
+        script.extract()
+
+    hyperlinks = extract_hyperlinks(soup, base_url)
+
+    return format_hyperlinks(hyperlinks)
+
+
+def open_page_in_browser(url: str, config: Config) -> WebDriver:
+    """Open a browser window and load a web page using Selenium
+
+    Params:
+        url (str): The URL of the page to load
+        config (Config): The applicable application configuration
+
+    Returns:
+        driver (WebDriver): A driver object representing the browser window to scrape
+    """
+    logging.getLogger("selenium").setLevel(logging.CRITICAL)
+
+    options_available: dict[str, Type[BrowserOptions]] = {
+        "chrome": ChromeOptions,
+        "edge": EdgeOptions,
+        "firefox": FirefoxOptions,
+        "safari": SafariOptions,
+    }
+
+    options: BrowserOptions = options_available[config.selenium_web_browser]()
+    options.add_argument(f"user-agent={config.user_agent}")
+
+    if config.selenium_web_browser == "firefox":
+        if config.selenium_headless:
+            options.headless = True
+            options.add_argument("--disable-gpu")
+        driver = FirefoxDriver(
+            service=GeckoDriverService(GeckoDriverManager().install()), options=options
+        )
+    elif config.selenium_web_browser == "edge":
+        driver = EdgeDriver(
+            service=EdgeDriverService(EdgeDriverManager().install()), options=options
+        )
+    elif config.selenium_web_browser == "safari":
+        # Requires a bit more setup on the users end.
+        # See https://developer.apple.com/documentation/webkit/testing_with_webdriver_in_safari  # noqa: E501
+        driver = SafariDriver(options=options)
+    else:
+        if platform == "linux" or platform == "linux2":
+            options.add_argument("--disable-dev-shm-usage")
+            options.add_argument("--remote-debugging-port=9222")
+
+        options.add_argument("--no-sandbox")
+        if config.selenium_headless:
+            options.add_argument("--headless=new")
+            options.add_argument("--disable-gpu")
+
+        chromium_driver_path = Path("/usr/bin/chromedriver")
+
+        driver = ChromeDriver(
+            service=ChromeDriverService(str(chromium_driver_path))
+            if chromium_driver_path.exists()
+            else ChromeDriverService(ChromeDriverManager().install()),
+            options=options,
+        )
+    driver.get(url)
+
+    WebDriverWait(driver, 10).until(
+        EC.presence_of_element_located((By.TAG_NAME, "body"))
+    )
+
+    return driver
+
+
+def close_browser(driver: WebDriver) -> None:
+    """Close the browser
+
+    Args:
+        driver (WebDriver): The webdriver to close
+
+    Returns:
+        None
+    """
+    driver.quit()
+
+
+async def summarize_memorize_webpage(
+    url: str,
+    text: str,
+    question: str | None,
+    agent: Agent,
+    driver: Optional[WebDriver] = None,
+) -> str:
+    """Summarize text using the OpenAI API
+
+    Args:
+        url (str): The url of the text
+        text (str): The text to summarize
+        question (str): The question to ask the model
+        driver (WebDriver): The webdriver to use to scroll the page
+
+    Returns:
+        str: The summary of the text
+    """
+    if not text:
+        raise ValueError("No text to summarize")
+
+    text_length = len(text)
+    logger.info(f"Text length: {text_length} characters")
+
+    # memory = get_memory(agent.legacy_config)
+
+    # new_memory = MemoryItem.from_webpage(
+    #     content=text,
+    #     url=url,
+    #     config=agent.legacy_config,
+    #     question=question,
+    # )
+    # memory.add(new_memory)
+
+    summary, _ = await summarize_text(
+        text,
+        question=question,
+        llm_provider=agent.llm_provider,
+        config=agent.legacy_config,  # FIXME
+    )
+    return summary

autogpts/autogpt/autogpt/components/event_history.py

@@ -1,82 +0,0 @@
-from typing import Callable, Generic, Iterator, Optional
-
-from autogpt.agents.features.watchdog import WatchdogComponent
-from autogpt.agents.protocols import AfterExecute, AfterParse, MessageProvider
-from autogpt.config.config import Config
-from autogpt.core.resource.model_providers.schema import ChatMessage, ChatModelProvider
-from autogpt.models.action_history import (
-    AP,
-    ActionResult,
-    Episode,
-    EpisodicActionHistory,
-)
-from autogpt.prompts.utils import indent
-
-
-class EventHistoryComponent(MessageProvider, AfterParse, AfterExecute, Generic[AP]):
-    """Keeps track of the event history and provides a summary of the steps."""
-
-    run_after = [WatchdogComponent]
-
-    def __init__(
-        self,
-        event_history: EpisodicActionHistory[AP],
-        max_tokens: int,
-        count_tokens: Callable[[str], int],
-        legacy_config: Config,
-        llm_provider: ChatModelProvider,
-    ) -> None:
-        self.event_history = event_history
-        self.max_tokens = max_tokens
-        self.count_tokens = count_tokens
-        self.legacy_config = legacy_config
-        self.llm_provider = llm_provider
-
-    def get_messages(self) -> Iterator[ChatMessage]:
-        if progress := self._compile_progress(
-            self.event_history.episodes,
-            self.max_tokens,
-            self.count_tokens,
-        ):
-            yield ChatMessage.system(f"## Progress on your Task so far\n\n{progress}")
-
-    def after_parse(self, result: AP) -> None:
-        self.event_history.register_action(result)
-
-    async def after_execute(self, result: ActionResult) -> None:
-        self.event_history.register_result(result)
-        await self.event_history.handle_compression(
-            self.llm_provider, self.legacy_config
-        )
-
-    def _compile_progress(
-        self,
-        episode_history: list[Episode],
-        max_tokens: Optional[int] = None,
-        count_tokens: Optional[Callable[[str], int]] = None,
-    ) -> str:
-        if max_tokens and not count_tokens:
-            raise ValueError("count_tokens is required if max_tokens is set")
-
-        steps: list[str] = []
-        tokens: int = 0
-        n_episodes = len(episode_history)
-
-        for i, episode in enumerate(reversed(episode_history)):
-            # Use full format for the latest 4 steps, summary or format for older steps
-            if i < 4 or episode.summary is None:
-                step_content = indent(episode.format(), 2).strip()
-            else:
-                step_content = episode.summary
-
-            step = f"* Step {n_episodes - i}: {step_content}"
-
-            if max_tokens and count_tokens:
-                step_tokens = count_tokens(step)
-                if tokens + step_tokens > max_tokens:
-                    break
-                tokens += step_tokens
-
-            steps.insert(0, step)
-
-        return "\n\n".join(steps)

autogpts/autogpt/autogpt/config/ai_directives.py

@@ -5,7 +5,7 @@ import yaml
 from pydantic import BaseModel, Field
 
 from autogpt.logs.helpers import request_user_double_check
-from autogpt.utils.utils import validate_yaml_file
+from autogpt.utils import validate_yaml_file
 
 logger = logging.getLogger(__name__)
 
@@ -32,7 +32,7 @@ class AIDirectives(BaseModel):
             raise RuntimeError(f"File validation failed: {message}")
 
         with open(prompt_settings_file, encoding="utf-8") as file:
-            config_params = yaml.load(file, Loader=yaml.SafeLoader)
+            config_params = yaml.load(file, Loader=yaml.FullLoader)
 
         return AIDirectives(
             constraints=config_params.get("constraints", []),

autogpts/autogpt/autogpt/config/ai_profile.py

@@ -35,7 +35,7 @@ class AIProfile(BaseModel):
 
         try:
             with open(ai_settings_file, encoding="utf-8") as file:
-                config_params = yaml.load(file, Loader=yaml.SafeLoader) or {}
+                config_params = yaml.load(file, Loader=yaml.FullLoader) or {}
         except FileNotFoundError:
             config_params = {}
 

autogpts/autogpt/autogpt/config/config.py

@@ -1,40 +1,38 @@
 """Configuration class to store the state of bools for different scripts access."""
 from __future__ import annotations
 
-import logging
 import os
 import re
 from pathlib import Path
 from typing import Any, Optional, Union
 
+from auto_gpt_plugin_template import AutoGPTPluginTemplate
 from colorama import Fore
-from pydantic import SecretStr, validator
+from pydantic import Field, SecretStr, validator
 
 import autogpt
-from autogpt.app.utils import clean_input
 from autogpt.core.configuration.schema import (
     Configurable,
     SystemSettings,
     UserConfigurable,
 )
-from autogpt.core.resource.model_providers import CHAT_MODELS, ModelName
 from autogpt.core.resource.model_providers.openai import (
+    OPEN_AI_CHAT_MODELS,
     OpenAICredentials,
-    OpenAIModelName,
 )
-from autogpt.file_storage import FileStorageBackendName
+from autogpt.file_workspace import FileWorkspaceBackendName
 from autogpt.logs.config import LoggingConfig
+from autogpt.plugins.plugins_config import PluginsConfig
 from autogpt.speech import TTSConfig
 
-logger = logging.getLogger(__name__)
-
 PROJECT_ROOT = Path(autogpt.__file__).parent.parent
 AI_SETTINGS_FILE = Path("ai_settings.yaml")
 AZURE_CONFIG_FILE = Path("azure.yaml")
+PLUGINS_CONFIG_FILE = Path("plugins_config.yaml")
 PROMPT_SETTINGS_FILE = Path("prompt_settings.yaml")
 
-GPT_4_MODEL = OpenAIModelName.GPT4
-GPT_3_MODEL = OpenAIModelName.GPT3
+GPT_4_MODEL = "gpt-4"
+GPT_3_MODEL = "gpt-3.5-turbo"
 
 
 class Config(SystemSettings, arbitrary_types_allowed=True):
@@ -51,14 +49,20 @@ class Config(SystemSettings, arbitrary_types_allowed=True):
     authorise_key: str = UserConfigurable(default="y", from_env="AUTHORISE_COMMAND_KEY")
     exit_key: str = UserConfigurable(default="n", from_env="EXIT_KEY")
     noninteractive_mode: bool = False
+    chat_messages_enabled: bool = UserConfigurable(
+        default=True, from_env=lambda: os.getenv("CHAT_MESSAGES_ENABLED") == "True"
+    )
 
     # TTS configuration
-    logging: LoggingConfig = LoggingConfig()
     tts_config: TTSConfig = TTSConfig()
+    logging: LoggingConfig = LoggingConfig()
 
-    # File storage
-    file_storage_backend: FileStorageBackendName = UserConfigurable(
-        default=FileStorageBackendName.LOCAL, from_env="FILE_STORAGE_BACKEND"
+    # Workspace
+    workspace_backend: FileWorkspaceBackendName = UserConfigurable(
+        default=FileWorkspaceBackendName.LOCAL,
+        from_env=lambda: FileWorkspaceBackendName(v)
+        if (v := os.getenv("WORKSPACE_BACKEND"))
+        else None,
     )
 
     ##########################
@@ -66,28 +70,32 @@ class Config(SystemSettings, arbitrary_types_allowed=True):
     ##########################
     # Paths
     ai_settings_file: Path = UserConfigurable(
-        default=AI_SETTINGS_FILE, from_env="AI_SETTINGS_FILE"
+        default=AI_SETTINGS_FILE,
+        from_env=lambda: Path(f) if (f := os.getenv("AI_SETTINGS_FILE")) else None,
     )
     prompt_settings_file: Path = UserConfigurable(
         default=PROMPT_SETTINGS_FILE,
-        from_env="PROMPT_SETTINGS_FILE",
+        from_env=lambda: Path(f) if (f := os.getenv("PROMPT_SETTINGS_FILE")) else None,
     )
 
     # Model configuration
-    fast_llm: ModelName = UserConfigurable(
-        default=OpenAIModelName.GPT3,
-        from_env="FAST_LLM",
+    fast_llm: str = UserConfigurable(
+        default="gpt-3.5-turbo-16k",
+        from_env=lambda: os.getenv("FAST_LLM"),
     )
-    smart_llm: ModelName = UserConfigurable(
-        default=OpenAIModelName.GPT4_TURBO,
-        from_env="SMART_LLM",
+    smart_llm: str = UserConfigurable(
+        default="gpt-4",
+        from_env=lambda: os.getenv("SMART_LLM"),
+    )
+    temperature: float = UserConfigurable(
+        default=0,
+        from_env=lambda: float(v) if (v := os.getenv("TEMPERATURE")) else None,
     )
-    temperature: float = UserConfigurable(default=0, from_env="TEMPERATURE")
     openai_functions: bool = UserConfigurable(
         default=False, from_env=lambda: os.getenv("OPENAI_FUNCTIONS", "False") == "True"
     )
     embedding_model: str = UserConfigurable(
-        default="text-embedding-3-small", from_env="EMBEDDING_MODEL"
+        default="text-embedding-ada-002", from_env="EMBEDDING_MODEL"
     )
     browse_spacy_language_model: str = UserConfigurable(
         default="en_core_web_sm", from_env="BROWSE_SPACY_LANGUAGE_MODEL"
@@ -103,7 +111,10 @@ class Config(SystemSettings, arbitrary_types_allowed=True):
     memory_backend: str = UserConfigurable("json_file", from_env="MEMORY_BACKEND")
     memory_index: str = UserConfigurable("auto-gpt-memory", from_env="MEMORY_INDEX")
     redis_host: str = UserConfigurable("localhost", from_env="REDIS_HOST")
-    redis_port: int = UserConfigurable(default=6379, from_env="REDIS_PORT")
+    redis_port: int = UserConfigurable(
+        default=6379,
+        from_env=lambda: int(v) if (v := os.getenv("REDIS_PORT")) else None,
+    )
     redis_password: str = UserConfigurable("", from_env="REDIS_PASSWORD")
     wipe_redis_on_start: bool = UserConfigurable(
         default=True,
@@ -114,9 +125,9 @@ class Config(SystemSettings, arbitrary_types_allowed=True):
     # Commands #
     ############
     # General
-    disabled_commands: list[str] = UserConfigurable(
+    disabled_command_categories: list[str] = UserConfigurable(
         default_factory=list,
-        from_env=lambda: _safe_split(os.getenv("DISABLED_COMMANDS")),
+        from_env=lambda: _safe_split(os.getenv("DISABLED_COMMAND_CATEGORIES")),
     )
 
     # File ops
@@ -155,7 +166,10 @@ class Config(SystemSettings, arbitrary_types_allowed=True):
     sd_webui_url: Optional[str] = UserConfigurable(
         default="http://localhost:7860", from_env="SD_WEBUI_URL"
     )
-    image_size: int = UserConfigurable(default=256, from_env="IMAGE_SIZE")
+    image_size: int = UserConfigurable(
+        default=256,
+        from_env=lambda: int(v) if (v := os.getenv("IMAGE_SIZE")) else None,
+    )
 
     # Audio to text
     audio_to_text_provider: str = UserConfigurable(
@@ -175,13 +189,38 @@ class Config(SystemSettings, arbitrary_types_allowed=True):
         from_env="USER_AGENT",
     )
 
+    ###################
+    # Plugin Settings #
+    ###################
+    plugins_dir: str = UserConfigurable("plugins", from_env="PLUGINS_DIR")
+    plugins_config_file: Path = UserConfigurable(
+        default=PLUGINS_CONFIG_FILE,
+        from_env=lambda: Path(f) if (f := os.getenv("PLUGINS_CONFIG_FILE")) else None,
+    )
+    plugins_config: PluginsConfig = Field(
+        default_factory=lambda: PluginsConfig(plugins={})
+    )
+    plugins: list[AutoGPTPluginTemplate] = Field(default_factory=list, exclude=True)
+    plugins_allowlist: list[str] = UserConfigurable(
+        default_factory=list,
+        from_env=lambda: _safe_split(os.getenv("ALLOWLISTED_PLUGINS")),
+    )
+    plugins_denylist: list[str] = UserConfigurable(
+        default_factory=list,
+        from_env=lambda: _safe_split(os.getenv("DENYLISTED_PLUGINS")),
+    )
+    plugins_openai: list[str] = UserConfigurable(
+        default_factory=list, from_env=lambda: _safe_split(os.getenv("OPENAI_PLUGINS"))
+    )
+
     ###############
     # Credentials #
     ###############
     # OpenAI
     openai_credentials: Optional[OpenAICredentials] = None
     azure_config_file: Optional[Path] = UserConfigurable(
-        default=AZURE_CONFIG_FILE, from_env="AZURE_CONFIG_FILE"
+        default=AZURE_CONFIG_FILE,
+        from_env=lambda: Path(f) if (f := os.getenv("AZURE_CONFIG_FILE")) else None,
     )
 
     # Github
@@ -191,7 +230,7 @@ class Config(SystemSettings, arbitrary_types_allowed=True):
     # Google
     google_api_key: Optional[str] = UserConfigurable(from_env="GOOGLE_API_KEY")
     google_custom_search_engine_id: Optional[str] = UserConfigurable(
-        from_env="GOOGLE_CUSTOM_SEARCH_ENGINE_ID",
+        from_env=lambda: os.getenv("GOOGLE_CUSTOM_SEARCH_ENGINE_ID"),
     )
 
     # Huggingface
@@ -202,12 +241,22 @@ class Config(SystemSettings, arbitrary_types_allowed=True):
     # Stable Diffusion
     sd_webui_auth: Optional[str] = UserConfigurable(from_env="SD_WEBUI_AUTH")
 
+    @validator("plugins", each_item=True)
+    def validate_plugins(cls, p: AutoGPTPluginTemplate | Any):
+        assert issubclass(
+            p.__class__, AutoGPTPluginTemplate
+        ), f"{p} does not subclass AutoGPTPluginTemplate"
+        assert (
+            p.__class__.__name__ != "AutoGPTPluginTemplate"
+        ), f"Plugins must subclass AutoGPTPluginTemplate; {p} is a template instance"
+        return p
+
     @validator("openai_functions")
     def validate_openai_functions(cls, v: bool, values: dict[str, Any]):
         if v:
             smart_llm = values["smart_llm"]
-            assert CHAT_MODELS[smart_llm].has_function_call_api, (
-                f"Model {smart_llm} does not support tool calling. "
+            assert OPEN_AI_CHAT_MODELS[smart_llm].has_function_call_api, (
+                f"Model {smart_llm} does not support OpenAI Functions. "
                 "Please disable OPENAI_FUNCTIONS or choose a suitable model."
             )
         return v
@@ -227,6 +276,7 @@ class ConfigBuilder(Configurable[Config]):
         for k in {
             "ai_settings_file",  # TODO: deprecate or repurpose
             "prompt_settings_file",  # TODO: deprecate or repurpose
+            "plugins_config_file",  # TODO: move from project root
             "azure_config_file",  # TODO: move from project root
         }:
             setattr(config, k, project_root / getattr(config, k))
@@ -238,56 +288,45 @@ class ConfigBuilder(Configurable[Config]):
         ):
             config.openai_credentials.load_azure_config(config_file)
 
+        config.plugins_config = PluginsConfig.load_config(
+            config.plugins_config_file,
+            config.plugins_denylist,
+            config.plugins_allowlist,
+        )
+
         return config
 
 
 def assert_config_has_openai_api_key(config: Config) -> None:
     """Check if the OpenAI API key is set in config.py or as an environment variable."""
-    key_pattern = r"^sk-(proj-)?\w{48}"
-    openai_api_key = (
-        config.openai_credentials.api_key.get_secret_value()
-        if config.openai_credentials
-        else ""
-    )
-
-    # If there's no credentials or empty API key, prompt the user to set it
-    if not openai_api_key:
-        logger.error(
-            "Please set your OpenAI API key in .env or as an environment variable."
+    if not config.openai_credentials:
+        print(
+            Fore.RED
+            + "Please set your OpenAI API key in .env or as an environment variable."
+            + Fore.RESET
         )
-        logger.info(
-            "You can get your key from https://platform.openai.com/account/api-keys"
-        )
-        openai_api_key = clean_input(
-            config, "Please enter your OpenAI API key if you have it:"
+        print("You can get your key from https://platform.openai.com/account/api-keys")
+        openai_api_key = input(
+            "If you do have the key, please enter your OpenAI API key now:\n"
         )
+        key_pattern = r"^sk-\w{48}"
         openai_api_key = openai_api_key.strip()
         if re.search(key_pattern, openai_api_key):
             os.environ["OPENAI_API_KEY"] = openai_api_key
-            if config.openai_credentials:
-                config.openai_credentials.api_key = SecretStr(openai_api_key)
-            else:
-                config.openai_credentials = OpenAICredentials(
-                    api_key=SecretStr(openai_api_key)
-                )
-            print("OpenAI API key successfully set!")
+            config.openai_credentials = OpenAICredentials(
+                api_key=SecretStr(openai_api_key)
+            )
             print(
-                f"{Fore.YELLOW}NOTE: The API key you've set is only temporary. "
-                f"For longer sessions, please set it in the .env file{Fore.RESET}"
+                Fore.GREEN
+                + "OpenAI API key successfully set!\n"
+                + Fore.YELLOW
+                + "NOTE: The API key you've set is only temporary.\n"
+                + "For longer sessions, please set it in .env file"
+                + Fore.RESET
             )
         else:
-            print(f"{Fore.RED}Invalid OpenAI API key{Fore.RESET}")
+            print("Invalid OpenAI API key!")
             exit(1)
-    # If key is set, but it looks invalid
-    elif not re.search(key_pattern, openai_api_key):
-        logger.error(
-            "Invalid OpenAI API key! "
-            "Please set your OpenAI API key in .env or as an environment variable."
-        )
-        logger.info(
-            "You can get your key from https://platform.openai.com/account/api-keys"
-        )
-        exit(1)
 
 
 def _safe_split(s: Union[str, None], sep: str = ",") -> list[str]:

autogpts/autogpt/autogpt/core/planning/prompt_strategies/initial_plan.py

@@ -4,7 +4,7 @@ from autogpt.core.configuration import SystemConfiguration, UserConfigurable
 from autogpt.core.planning.schema import Task, TaskType
 from autogpt.core.prompting import PromptStrategy
 from autogpt.core.prompting.schema import ChatPrompt, LanguageModelClassification
-from autogpt.core.prompting.utils import to_numbered_list
+from autogpt.core.prompting.utils import json_loads, to_numbered_list
 from autogpt.core.resource.model_providers import (
     AssistantChatMessage,
     ChatMessage,
@@ -194,7 +194,9 @@ class InitialPlan(PromptStrategy):
                     f"LLM did not call {self._create_plan_function.name} function; "
                     "plan creation failed"
                 )
-            parsed_response: object = response_content.tool_calls[0].function.arguments
+            parsed_response: object = json_loads(
+                response_content.tool_calls[0].function.arguments
+            )
             parsed_response["task_list"] = [
                 Task.parse_obj(task) for task in parsed_response["task_list"]
             ]

autogpts/autogpt/autogpt/core/planning/prompt_strategies/name_and_goals.py

@@ -3,6 +3,7 @@ import logging
 from autogpt.core.configuration import SystemConfiguration, UserConfigurable
 from autogpt.core.prompting import PromptStrategy
 from autogpt.core.prompting.schema import ChatPrompt, LanguageModelClassification
+from autogpt.core.prompting.utils import json_loads
 from autogpt.core.resource.model_providers import (
     AssistantChatMessage,
     ChatMessage,
@@ -140,7 +141,9 @@ class NameAndGoals(PromptStrategy):
                     f"LLM did not call {self._create_agent_function} function; "
                     "agent profile creation failed"
                 )
-            parsed_response = response_content.tool_calls[0].function.arguments
+            parsed_response = json_loads(
+                response_content.tool_calls[0].function.arguments
+            )
         except KeyError:
             logger.debug(f"Failed to parse this response content: {response_content}")
             raise

autogpts/autogpt/autogpt/core/planning/prompt_strategies/next_ability.py

@@ -4,7 +4,7 @@ from autogpt.core.configuration import SystemConfiguration, UserConfigurable
 from autogpt.core.planning.schema import Task
 from autogpt.core.prompting import PromptStrategy
 from autogpt.core.prompting.schema import ChatPrompt, LanguageModelClassification
-from autogpt.core.prompting.utils import to_numbered_list
+from autogpt.core.prompting.utils import json_loads, to_numbered_list
 from autogpt.core.resource.model_providers import (
     AssistantChatMessage,
     ChatMessage,
@@ -187,7 +187,9 @@ class NextAbility(PromptStrategy):
                 raise ValueError("LLM did not call any function")
 
             function_name = response_content.tool_calls[0].function.name
-            function_arguments = response_content.tool_calls[0].function.arguments
+            function_arguments = json_loads(
+                response_content.tool_calls[0].function.arguments
+            )
             parsed_response = {
                 "motivation": function_arguments.pop("motivation"),
                 "self_criticism": function_arguments.pop("self_criticism"),

autogpts/autogpt/autogpt/core/prompting/schema.py

@@ -24,7 +24,6 @@ class LanguageModelClassification(str, enum.Enum):
 class ChatPrompt(BaseModel):
     messages: list[ChatMessage]
     functions: list[CompletionModelFunction] = Field(default_factory=list)
-    prefill_response: str = ""
 
     def raw(self) -> list[ChatMessageDict]:
         return [m.dict() for m in self.messages]

autogpts/autogpt/autogpt/core/prompting/utils.py

@@ -1,3 +1,7 @@
+import ast
+import json
+
+
 def to_numbered_list(
     items: list[str], no_items_response: str = "", **template_args
 ) -> str:
@@ -7,3 +11,19 @@ def to_numbered_list(
         )
     else:
         return no_items_response
+
+
+def json_loads(json_str: str):
+    # TODO: this is a hack function for now. We'll see what errors show up in testing.
+    #   Can hopefully just replace with a call to ast.literal_eval.
+    # Can't use json.loads because the function API still sometimes returns json strings
+    #   with minor issues like trailing commas.
+    try:
+        json_str = json_str[json_str.index("{") : json_str.rindex("}") + 1]
+        return ast.literal_eval(json_str)
+    except json.decoder.JSONDecodeError as e:
+        try:
+            print(f"json decode error {e}. trying literal eval")
+            return ast.literal_eval(json_str)
+        except Exception:
+            breakpoint()

autogpts/autogpt/autogpt/core/resource/model_providers/__init__.py

@@ -1,4 +1,3 @@
-from .multi import CHAT_MODELS, ModelName, MultiProvider
 from .openai import (
     OPEN_AI_CHAT_MODELS,
     OPEN_AI_EMBEDDING_MODELS,
@@ -43,13 +42,11 @@ __all__ = [
     "ChatModelProvider",
     "ChatModelResponse",
     "CompletionModelFunction",
-    "CHAT_MODELS",
     "Embedding",
     "EmbeddingModelInfo",
     "EmbeddingModelProvider",
     "EmbeddingModelResponse",
     "ModelInfo",
-    "ModelName",
     "ModelProvider",
     "ModelProviderBudget",
     "ModelProviderCredentials",
@@ -59,7 +56,6 @@ __all__ = [
     "ModelProviderUsage",
     "ModelResponse",
     "ModelTokenizer",
-    "MultiProvider",
     "OPEN_AI_MODELS",
     "OPEN_AI_CHAT_MODELS",
     "OPEN_AI_EMBEDDING_MODELS",

autogpts/autogpt/autogpt/core/resource/model_providers/anthropic.py

@@ -1,495 +0,0 @@
-from __future__ import annotations
-
-import enum
-import logging
-from typing import TYPE_CHECKING, Callable, Optional, ParamSpec, TypeVar
-
-import sentry_sdk
-import tenacity
-import tiktoken
-from anthropic import APIConnectionError, APIStatusError
-from pydantic import SecretStr
-
-from autogpt.core.configuration import Configurable, UserConfigurable
-from autogpt.core.resource.model_providers.schema import (
-    AssistantChatMessage,
-    AssistantFunctionCall,
-    AssistantToolCall,
-    ChatMessage,
-    ChatModelInfo,
-    ChatModelProvider,
-    ChatModelResponse,
-    CompletionModelFunction,
-    ModelProviderBudget,
-    ModelProviderConfiguration,
-    ModelProviderCredentials,
-    ModelProviderName,
-    ModelProviderSettings,
-    ModelTokenizer,
-    ToolResultMessage,
-)
-
-from .utils import validate_tool_calls
-
-if TYPE_CHECKING:
-    from anthropic.types.beta.tools import MessageCreateParams
-    from anthropic.types.beta.tools import ToolsBetaMessage as Message
-    from anthropic.types.beta.tools import ToolsBetaMessageParam as MessageParam
-
-_T = TypeVar("_T")
-_P = ParamSpec("_P")
-
-
-class AnthropicModelName(str, enum.Enum):
-    CLAUDE3_OPUS_v1 = "claude-3-opus-20240229"
-    CLAUDE3_SONNET_v1 = "claude-3-sonnet-20240229"
-    CLAUDE3_HAIKU_v1 = "claude-3-haiku-20240307"
-
-
-ANTHROPIC_CHAT_MODELS = {
-    info.name: info
-    for info in [
-        ChatModelInfo(
-            name=AnthropicModelName.CLAUDE3_OPUS_v1,
-            provider_name=ModelProviderName.ANTHROPIC,
-            prompt_token_cost=15 / 1e6,
-            completion_token_cost=75 / 1e6,
-            max_tokens=200000,
-            has_function_call_api=True,
-        ),
-        ChatModelInfo(
-            name=AnthropicModelName.CLAUDE3_SONNET_v1,
-            provider_name=ModelProviderName.ANTHROPIC,
-            prompt_token_cost=3 / 1e6,
-            completion_token_cost=15 / 1e6,
-            max_tokens=200000,
-            has_function_call_api=True,
-        ),
-        ChatModelInfo(
-            name=AnthropicModelName.CLAUDE3_HAIKU_v1,
-            provider_name=ModelProviderName.ANTHROPIC,
-            prompt_token_cost=0.25 / 1e6,
-            completion_token_cost=1.25 / 1e6,
-            max_tokens=200000,
-            has_function_call_api=True,
-        ),
-    ]
-}
-
-
-class AnthropicConfiguration(ModelProviderConfiguration):
-    fix_failed_parse_tries: int = UserConfigurable(3)
-
-
-class AnthropicCredentials(ModelProviderCredentials):
-    """Credentials for Anthropic."""
-
-    api_key: SecretStr = UserConfigurable(from_env="ANTHROPIC_API_KEY")
-    api_base: Optional[SecretStr] = UserConfigurable(
-        default=None, from_env="ANTHROPIC_API_BASE_URL"
-    )
-
-    def get_api_access_kwargs(self) -> dict[str, str]:
-        return {
-            k: (v.get_secret_value() if type(v) is SecretStr else v)
-            for k, v in {
-                "api_key": self.api_key,
-                "base_url": self.api_base,
-            }.items()
-            if v is not None
-        }
-
-
-class AnthropicSettings(ModelProviderSettings):
-    configuration: AnthropicConfiguration
-    credentials: Optional[AnthropicCredentials]
-    budget: ModelProviderBudget
-
-
-class AnthropicProvider(Configurable[AnthropicSettings], ChatModelProvider):
-    default_settings = AnthropicSettings(
-        name="anthropic_provider",
-        description="Provides access to Anthropic's API.",
-        configuration=AnthropicConfiguration(
-            retries_per_request=7,
-        ),
-        credentials=None,
-        budget=ModelProviderBudget(),
-    )
-
-    _settings: AnthropicSettings
-    _configuration: AnthropicConfiguration
-    _credentials: AnthropicCredentials
-    _budget: ModelProviderBudget
-
-    def __init__(
-        self,
-        settings: Optional[AnthropicSettings] = None,
-        logger: Optional[logging.Logger] = None,
-    ):
-        if not settings:
-            settings = self.default_settings.copy(deep=True)
-        if not settings.credentials:
-            settings.credentials = AnthropicCredentials.from_env()
-
-        super(AnthropicProvider, self).__init__(settings=settings, logger=logger)
-
-        from anthropic import AsyncAnthropic
-
-        self._client = AsyncAnthropic(**self._credentials.get_api_access_kwargs())
-
-    async def get_available_models(self) -> list[ChatModelInfo]:
-        return list(ANTHROPIC_CHAT_MODELS.values())
-
-    def get_token_limit(self, model_name: str) -> int:
-        """Get the token limit for a given model."""
-        return ANTHROPIC_CHAT_MODELS[model_name].max_tokens
-
-    @classmethod
-    def get_tokenizer(cls, model_name: AnthropicModelName) -> ModelTokenizer:
-        # HACK: No official tokenizer is available for Claude 3
-        return tiktoken.encoding_for_model(model_name)
-
-    @classmethod
-    def count_tokens(cls, text: str, model_name: AnthropicModelName) -> int:
-        return 0  # HACK: No official tokenizer is available for Claude 3
-
-    @classmethod
-    def count_message_tokens(
-        cls,
-        messages: ChatMessage | list[ChatMessage],
-        model_name: AnthropicModelName,
-    ) -> int:
-        return 0  # HACK: No official tokenizer is available for Claude 3
-
-    async def create_chat_completion(
-        self,
-        model_prompt: list[ChatMessage],
-        model_name: AnthropicModelName,
-        completion_parser: Callable[[AssistantChatMessage], _T] = lambda _: None,
-        functions: Optional[list[CompletionModelFunction]] = None,
-        max_output_tokens: Optional[int] = None,
-        prefill_response: str = "",
-        **kwargs,
-    ) -> ChatModelResponse[_T]:
-        """Create a completion using the Anthropic API."""
-        anthropic_messages, completion_kwargs = self._get_chat_completion_args(
-            prompt_messages=model_prompt,
-            model=model_name,
-            functions=functions,
-            max_output_tokens=max_output_tokens,
-            **kwargs,
-        )
-
-        total_cost = 0.0
-        attempts = 0
-        while True:
-            completion_kwargs["messages"] = anthropic_messages.copy()
-            if prefill_response:
-                completion_kwargs["messages"].append(
-                    {"role": "assistant", "content": prefill_response}
-                )
-
-            (
-                _assistant_msg,
-                cost,
-                t_input,
-                t_output,
-            ) = await self._create_chat_completion(completion_kwargs)
-            total_cost += cost
-            self._logger.debug(
-                f"Completion usage: {t_input} input, {t_output} output "
-                f"- ${round(cost, 5)}"
-            )
-
-            # Merge prefill into generated response
-            if prefill_response:
-                first_text_block = next(
-                    b for b in _assistant_msg.content if b.type == "text"
-                )
-                first_text_block.text = prefill_response + first_text_block.text
-
-            assistant_msg = AssistantChatMessage(
-                content="\n\n".join(
-                    b.text for b in _assistant_msg.content if b.type == "text"
-                ),
-                tool_calls=self._parse_assistant_tool_calls(_assistant_msg),
-            )
-
-            # If parsing the response fails, append the error to the prompt, and let the
-            # LLM fix its mistake(s).
-            attempts += 1
-            tool_call_errors = []
-            try:
-                # Validate tool calls
-                if assistant_msg.tool_calls and functions:
-                    tool_call_errors = validate_tool_calls(
-                        assistant_msg.tool_calls, functions
-                    )
-                    if tool_call_errors:
-                        raise ValueError(
-                            "Invalid tool use(s):\n"
-                            + "\n".join(str(e) for e in tool_call_errors)
-                        )
-
-                parsed_result = completion_parser(assistant_msg)
-                break
-            except Exception as e:
-                self._logger.debug(
-                    f"Parsing failed on response: '''{_assistant_msg}'''"
-                )
-                self._logger.warning(f"Parsing attempt #{attempts} failed: {e}")
-                sentry_sdk.capture_exception(
-                    error=e,
-                    extras={"assistant_msg": _assistant_msg, "i_attempt": attempts},
-                )
-                if attempts < self._configuration.fix_failed_parse_tries:
-                    anthropic_messages.append(
-                        _assistant_msg.dict(include={"role", "content"})
-                    )
-                    anthropic_messages.append(
-                        {
-                            "role": "user",
-                            "content": [
-                                *(
-                                    # tool_result is required if last assistant message
-                                    # had tool_use block(s)
-                                    {
-                                        "type": "tool_result",
-                                        "tool_use_id": tc.id,
-                                        "is_error": True,
-                                        "content": [
-                                            {
-                                                "type": "text",
-                                                "text": "Not executed because parsing "
-                                                "of your last message failed"
-                                                if not tool_call_errors
-                                                else str(e)
-                                                if (
-                                                    e := next(
-                                                        (
-                                                            tce
-                                                            for tce in tool_call_errors
-                                                            if tce.name
-                                                            == tc.function.name
-                                                        ),
-                                                        None,
-                                                    )
-                                                )
-                                                else "Not executed because validation "
-                                                "of tool input failed",
-                                            }
-                                        ],
-                                    }
-                                    for tc in assistant_msg.tool_calls or []
-                                ),
-                                {
-                                    "type": "text",
-                                    "text": (
-                                        "ERROR PARSING YOUR RESPONSE:\n\n"
-                                        f"{e.__class__.__name__}: {e}"
-                                    ),
-                                },
-                            ],
-                        }
-                    )
-                else:
-                    raise
-
-        if attempts > 1:
-            self._logger.debug(
-                f"Total cost for {attempts} attempts: ${round(total_cost, 5)}"
-            )
-
-        return ChatModelResponse(
-            response=assistant_msg,
-            parsed_result=parsed_result,
-            model_info=ANTHROPIC_CHAT_MODELS[model_name],
-            prompt_tokens_used=t_input,
-            completion_tokens_used=t_output,
-        )
-
-    def _get_chat_completion_args(
-        self,
-        prompt_messages: list[ChatMessage],
-        model: AnthropicModelName,
-        functions: Optional[list[CompletionModelFunction]] = None,
-        max_output_tokens: Optional[int] = None,
-        **kwargs,
-    ) -> tuple[list[MessageParam], MessageCreateParams]:
-        """Prepare arguments for message completion API call.
-
-        Args:
-            prompt_messages: List of ChatMessages.
-            model: The model to use.
-            functions: Optional list of functions available to the LLM.
-            kwargs: Additional keyword arguments.
-
-        Returns:
-            list[MessageParam]: Prompt messages for the Anthropic call
-            dict[str, Any]: Any other kwargs for the Anthropic call
-        """
-        kwargs["model"] = model
-
-        if functions:
-            kwargs["tools"] = [
-                {
-                    "name": f.name,
-                    "description": f.description,
-                    "input_schema": {
-                        "type": "object",
-                        "properties": {
-                            name: param.to_dict()
-                            for name, param in f.parameters.items()
-                        },
-                        "required": [
-                            name
-                            for name, param in f.parameters.items()
-                            if param.required
-                        ],
-                    },
-                }
-                for f in functions
-            ]
-
-        kwargs["max_tokens"] = max_output_tokens or 4096
-
-        if extra_headers := self._configuration.extra_request_headers:
-            kwargs["extra_headers"] = kwargs.get("extra_headers", {})
-            kwargs["extra_headers"].update(extra_headers.copy())
-
-        system_messages = [
-            m for m in prompt_messages if m.role == ChatMessage.Role.SYSTEM
-        ]
-        if (_n := len(system_messages)) > 1:
-            self._logger.warning(
-                f"Prompt has {_n} system messages; Anthropic supports only 1. "
-                "They will be merged, and removed from the rest of the prompt."
-            )
-        kwargs["system"] = "\n\n".join(sm.content for sm in system_messages)
-
-        messages: list[MessageParam] = []
-        for message in prompt_messages:
-            if message.role == ChatMessage.Role.SYSTEM:
-                continue
-            elif message.role == ChatMessage.Role.USER:
-                # Merge subsequent user messages
-                if messages and (prev_msg := messages[-1])["role"] == "user":
-                    if isinstance(prev_msg["content"], str):
-                        prev_msg["content"] += f"\n\n{message.content}"
-                    else:
-                        assert isinstance(prev_msg["content"], list)
-                        prev_msg["content"].append(
-                            {"type": "text", "text": message.content}
-                        )
-                else:
-                    messages.append({"role": "user", "content": message.content})
-                # TODO: add support for image blocks
-            elif message.role == ChatMessage.Role.ASSISTANT:
-                if isinstance(message, AssistantChatMessage) and message.tool_calls:
-                    messages.append(
-                        {
-                            "role": "assistant",
-                            "content": [
-                                *(
-                                    [{"type": "text", "text": message.content}]
-                                    if message.content
-                                    else []
-                                ),
-                                *(
-                                    {
-                                        "type": "tool_use",
-                                        "id": tc.id,
-                                        "name": tc.function.name,
-                                        "input": tc.function.arguments,
-                                    }
-                                    for tc in message.tool_calls
-                                ),
-                            ],
-                        }
-                    )
-                elif message.content:
-                    messages.append(
-                        {
-                            "role": "assistant",
-                            "content": message.content,
-                        }
-                    )
-            elif isinstance(message, ToolResultMessage):
-                messages.append(
-                    {
-                        "role": "user",
-                        "content": [
-                            {
-                                "type": "tool_result",
-                                "tool_use_id": message.tool_call_id,
-                                "content": [{"type": "text", "text": message.content}],
-                                "is_error": message.is_error,
-                            }
-                        ],
-                    }
-                )
-
-        return messages, kwargs  # type: ignore
-
-    async def _create_chat_completion(
-        self, completion_kwargs: MessageCreateParams
-    ) -> tuple[Message, float, int, int]:
-        """
-        Create a chat completion using the Anthropic API with retry handling.
-
-        Params:
-            completion_kwargs: Keyword arguments for an Anthropic Messages API call
-
-        Returns:
-            Message: The message completion object
-            float: The cost ($) of this completion
-            int: Number of input tokens used
-            int: Number of output tokens used
-        """
-
-        @self._retry_api_request
-        async def _create_chat_completion_with_retry(
-            completion_kwargs: MessageCreateParams,
-        ) -> Message:
-            return await self._client.beta.tools.messages.create(
-                **completion_kwargs  # type: ignore
-            )
-
-        response = await _create_chat_completion_with_retry(completion_kwargs)
-
-        cost = self._budget.update_usage_and_cost(
-            model_info=ANTHROPIC_CHAT_MODELS[completion_kwargs["model"]],
-            input_tokens_used=response.usage.input_tokens,
-            output_tokens_used=response.usage.output_tokens,
-        )
-        return response, cost, response.usage.input_tokens, response.usage.output_tokens
-
-    def _parse_assistant_tool_calls(
-        self, assistant_message: Message
-    ) -> list[AssistantToolCall]:
-        return [
-            AssistantToolCall(
-                id=c.id,
-                type="function",
-                function=AssistantFunctionCall(name=c.name, arguments=c.input),
-            )
-            for c in assistant_message.content
-            if c.type == "tool_use"
-        ]
-
-    def _retry_api_request(self, func: Callable[_P, _T]) -> Callable[_P, _T]:
-        return tenacity.retry(
-            retry=(
-                tenacity.retry_if_exception_type(APIConnectionError)
-                | tenacity.retry_if_exception(
-                    lambda e: isinstance(e, APIStatusError) and e.status_code >= 500
-                )
-            ),
-            wait=tenacity.wait_exponential(),
-            stop=tenacity.stop_after_attempt(self._configuration.retries_per_request),
-            after=tenacity.after_log(self._logger, logging.DEBUG),
-        )(func)
-
-    def __repr__(self):
-        return "AnthropicProvider()"

autogpts/autogpt/autogpt/core/resource/model_providers/multi.py

@@ -1,162 +0,0 @@
-from __future__ import annotations
-
-import logging
-from typing import Callable, Iterator, Optional, TypeVar
-
-from pydantic import ValidationError
-
-from autogpt.core.configuration import Configurable
-
-from .anthropic import ANTHROPIC_CHAT_MODELS, AnthropicModelName, AnthropicProvider
-from .openai import OPEN_AI_CHAT_MODELS, OpenAIModelName, OpenAIProvider
-from .schema import (
-    AssistantChatMessage,
-    ChatMessage,
-    ChatModelInfo,
-    ChatModelProvider,
-    ChatModelResponse,
-    CompletionModelFunction,
-    ModelProviderBudget,
-    ModelProviderConfiguration,
-    ModelProviderName,
-    ModelProviderSettings,
-    ModelTokenizer,
-)
-
-_T = TypeVar("_T")
-
-ModelName = AnthropicModelName | OpenAIModelName
-
-CHAT_MODELS = {**ANTHROPIC_CHAT_MODELS, **OPEN_AI_CHAT_MODELS}
-
-
-class MultiProvider(Configurable[ModelProviderSettings], ChatModelProvider):
-    default_settings = ModelProviderSettings(
-        name="multi_provider",
-        description=(
-            "Provides access to all of the available models, regardless of provider."
-        ),
-        configuration=ModelProviderConfiguration(
-            retries_per_request=7,
-        ),
-        budget=ModelProviderBudget(),
-    )
-
-    _budget: ModelProviderBudget
-
-    _provider_instances: dict[ModelProviderName, ChatModelProvider]
-
-    def __init__(
-        self,
-        settings: Optional[ModelProviderSettings] = None,
-        logger: Optional[logging.Logger] = None,
-    ):
-        super(MultiProvider, self).__init__(settings=settings, logger=logger)
-        self._budget = self._settings.budget or ModelProviderBudget()
-
-        self._provider_instances = {}
-
-    async def get_available_models(self) -> list[ChatModelInfo]:
-        models = []
-        for provider in self.get_available_providers():
-            models.extend(await provider.get_available_models())
-        return models
-
-    def get_token_limit(self, model_name: ModelName) -> int:
-        """Get the token limit for a given model."""
-        return self.get_model_provider(model_name).get_token_limit(model_name)
-
-    @classmethod
-    def get_tokenizer(cls, model_name: ModelName) -> ModelTokenizer:
-        return cls._get_model_provider_class(model_name).get_tokenizer(model_name)
-
-    @classmethod
-    def count_tokens(cls, text: str, model_name: ModelName) -> int:
-        return cls._get_model_provider_class(model_name).count_tokens(
-            text=text, model_name=model_name
-        )
-
-    @classmethod
-    def count_message_tokens(
-        cls, messages: ChatMessage | list[ChatMessage], model_name: ModelName
-    ) -> int:
-        return cls._get_model_provider_class(model_name).count_message_tokens(
-            messages=messages, model_name=model_name
-        )
-
-    async def create_chat_completion(
-        self,
-        model_prompt: list[ChatMessage],
-        model_name: ModelName,
-        completion_parser: Callable[[AssistantChatMessage], _T] = lambda _: None,
-        functions: Optional[list[CompletionModelFunction]] = None,
-        max_output_tokens: Optional[int] = None,
-        prefill_response: str = "",
-        **kwargs,
-    ) -> ChatModelResponse[_T]:
-        """Create a completion using the Anthropic API."""
-        return await self.get_model_provider(model_name).create_chat_completion(
-            model_prompt=model_prompt,
-            model_name=model_name,
-            completion_parser=completion_parser,
-            functions=functions,
-            max_output_tokens=max_output_tokens,
-            prefill_response=prefill_response,
-            **kwargs,
-        )
-
-    def get_model_provider(self, model: ModelName) -> ChatModelProvider:
-        model_info = CHAT_MODELS[model]
-        return self._get_provider(model_info.provider_name)
-
-    def get_available_providers(self) -> Iterator[ChatModelProvider]:
-        for provider_name in ModelProviderName:
-            try:
-                yield self._get_provider(provider_name)
-            except Exception:
-                pass
-
-    def _get_provider(self, provider_name: ModelProviderName) -> ChatModelProvider:
-        _provider = self._provider_instances.get(provider_name)
-        if not _provider:
-            Provider = self._get_provider_class(provider_name)
-            settings = Provider.default_settings.copy(deep=True)
-            settings.budget = self._budget
-            settings.configuration.extra_request_headers.update(
-                self._settings.configuration.extra_request_headers
-            )
-            if settings.credentials is None:
-                try:
-                    Credentials = settings.__fields__["credentials"].type_
-                    settings.credentials = Credentials.from_env()
-                except ValidationError as e:
-                    raise ValueError(
-                        f"{provider_name} is unavailable: can't load credentials"
-                    ) from e
-
-            self._provider_instances[provider_name] = _provider = Provider(
-                settings=settings, logger=self._logger
-            )
-            _provider._budget = self._budget  # Object binding not preserved by Pydantic
-        return _provider
-
-    @classmethod
-    def _get_model_provider_class(
-        cls, model_name: ModelName
-    ) -> type[AnthropicProvider | OpenAIProvider]:
-        return cls._get_provider_class(CHAT_MODELS[model_name].provider_name)
-
-    @classmethod
-    def _get_provider_class(
-        cls, provider_name: ModelProviderName
-    ) -> type[AnthropicProvider | OpenAIProvider]:
-        try:
-            return {
-                ModelProviderName.ANTHROPIC: AnthropicProvider,
-                ModelProviderName.OPENAI: OpenAIProvider,
-            }[provider_name]
-        except KeyError:
-            raise ValueError(f"{provider_name} is not a known provider") from None
-
-    def __repr__(self):
-        return f"{self.__class__.__name__}()"

autogpts/autogpt/autogpt/core/resource/model_providers/openai.py

@@ -1,26 +1,21 @@
 import enum
 import logging
+import math
 import os
 from pathlib import Path
-from typing import Any, Callable, Coroutine, Iterator, Optional, ParamSpec, TypeVar
+from typing import Callable, Coroutine, Iterator, Optional, ParamSpec, TypeVar
 
-import sentry_sdk
 import tenacity
 import tiktoken
 import yaml
 from openai._exceptions import APIStatusError, RateLimitError
 from openai.types import CreateEmbeddingResponse
-from openai.types.chat import (
-    ChatCompletion,
-    ChatCompletionMessage,
-    ChatCompletionMessageParam,
-)
+from openai.types.chat import ChatCompletion
 from pydantic import SecretStr
 
 from autogpt.core.configuration import Configurable, UserConfigurable
 from autogpt.core.resource.model_providers.schema import (
     AssistantChatMessage,
-    AssistantFunctionCall,
     AssistantToolCall,
     AssistantToolCallDict,
     ChatMessage,
@@ -36,30 +31,27 @@ from autogpt.core.resource.model_providers.schema import (
     ModelProviderConfiguration,
     ModelProviderCredentials,
     ModelProviderName,
+    ModelProviderService,
     ModelProviderSettings,
+    ModelProviderUsage,
     ModelTokenizer,
 )
 from autogpt.core.utils.json_schema import JSONSchema
-from autogpt.core.utils.json_utils import json_loads
-
-from .utils import validate_tool_calls
 
 _T = TypeVar("_T")
 _P = ParamSpec("_P")
 
 OpenAIEmbeddingParser = Callable[[Embedding], Embedding]
+OpenAIChatParser = Callable[[str], dict]
 
 
 class OpenAIModelName(str, enum.Enum):
-    EMBEDDING_v2 = "text-embedding-ada-002"
-    EMBEDDING_v3_S = "text-embedding-3-small"
-    EMBEDDING_v3_L = "text-embedding-3-large"
+    ADA = "text-embedding-ada-002"
 
     GPT3_v1 = "gpt-3.5-turbo-0301"
     GPT3_v2 = "gpt-3.5-turbo-0613"
     GPT3_v2_16k = "gpt-3.5-turbo-16k-0613"
     GPT3_v3 = "gpt-3.5-turbo-1106"
-    GPT3_v4 = "gpt-3.5-turbo-0125"
     GPT3_ROLLING = "gpt-3.5-turbo"
     GPT3_ROLLING_16k = "gpt-3.5-turbo-16k"
     GPT3 = GPT3_ROLLING
@@ -70,43 +62,24 @@ class OpenAIModelName(str, enum.Enum):
     GPT4_v2 = "gpt-4-0613"
     GPT4_v2_32k = "gpt-4-32k-0613"
     GPT4_v3 = "gpt-4-1106-preview"
-    GPT4_v3_VISION = "gpt-4-1106-vision-preview"
     GPT4_v4 = "gpt-4-0125-preview"
-    GPT4_v5 = "gpt-4-turbo-2024-04-09"
     GPT4_ROLLING = "gpt-4"
     GPT4_ROLLING_32k = "gpt-4-32k"
-    GPT4_TURBO = "gpt-4-turbo"
-    GPT4_TURBO_PREVIEW = "gpt-4-turbo-preview"
+    GPT4_TURBO = "gpt-4-turbo-preview"
     GPT4_VISION = "gpt-4-vision-preview"
     GPT4 = GPT4_ROLLING
     GPT4_32k = GPT4_ROLLING_32k
 
 
 OPEN_AI_EMBEDDING_MODELS = {
-    info.name: info
-    for info in [
-        EmbeddingModelInfo(
-            name=OpenAIModelName.EMBEDDING_v2,
-            provider_name=ModelProviderName.OPENAI,
-            prompt_token_cost=0.0001 / 1000,
-            max_tokens=8191,
-            embedding_dimensions=1536,
-        ),
-        EmbeddingModelInfo(
-            name=OpenAIModelName.EMBEDDING_v3_S,
-            provider_name=ModelProviderName.OPENAI,
-            prompt_token_cost=0.00002 / 1000,
-            max_tokens=8191,
-            embedding_dimensions=1536,
-        ),
-        EmbeddingModelInfo(
-            name=OpenAIModelName.EMBEDDING_v3_L,
-            provider_name=ModelProviderName.OPENAI,
-            prompt_token_cost=0.00013 / 1000,
-            max_tokens=8191,
-            embedding_dimensions=3072,
-        ),
-    ]
+    OpenAIModelName.ADA: EmbeddingModelInfo(
+        name=OpenAIModelName.ADA,
+        service=ModelProviderService.EMBEDDING,
+        provider_name=ModelProviderName.OPENAI,
+        prompt_token_cost=0.0001 / 1000,
+        max_tokens=8191,
+        embedding_dimensions=1536,
+    ),
 }
 
 
@@ -114,7 +87,8 @@ OPEN_AI_CHAT_MODELS = {
     info.name: info
     for info in [
         ChatModelInfo(
-            name=OpenAIModelName.GPT3_v1,
+            name=OpenAIModelName.GPT3,
+            service=ModelProviderService.CHAT,
             provider_name=ModelProviderName.OPENAI,
             prompt_token_cost=0.0015 / 1000,
             completion_token_cost=0.002 / 1000,
@@ -122,7 +96,8 @@ OPEN_AI_CHAT_MODELS = {
             has_function_call_api=True,
         ),
         ChatModelInfo(
-            name=OpenAIModelName.GPT3_v2_16k,
+            name=OpenAIModelName.GPT3_16k,
+            service=ModelProviderService.CHAT,
             provider_name=ModelProviderName.OPENAI,
             prompt_token_cost=0.003 / 1000,
             completion_token_cost=0.004 / 1000,
@@ -131,6 +106,7 @@ OPEN_AI_CHAT_MODELS = {
         ),
         ChatModelInfo(
             name=OpenAIModelName.GPT3_v3,
+            service=ModelProviderService.CHAT,
             provider_name=ModelProviderName.OPENAI,
             prompt_token_cost=0.001 / 1000,
             completion_token_cost=0.002 / 1000,
@@ -138,15 +114,8 @@ OPEN_AI_CHAT_MODELS = {
             has_function_call_api=True,
         ),
         ChatModelInfo(
-            name=OpenAIModelName.GPT3_v4,
-            provider_name=ModelProviderName.OPENAI,
-            prompt_token_cost=0.0005 / 1000,
-            completion_token_cost=0.0015 / 1000,
-            max_tokens=16384,
-            has_function_call_api=True,
-        ),
-        ChatModelInfo(
-            name=OpenAIModelName.GPT4_v1,
+            name=OpenAIModelName.GPT4,
+            service=ModelProviderService.CHAT,
             provider_name=ModelProviderName.OPENAI,
             prompt_token_cost=0.03 / 1000,
             completion_token_cost=0.06 / 1000,
@@ -154,7 +123,8 @@ OPEN_AI_CHAT_MODELS = {
             has_function_call_api=True,
         ),
         ChatModelInfo(
-            name=OpenAIModelName.GPT4_v1_32k,
+            name=OpenAIModelName.GPT4_32k,
+            service=ModelProviderService.CHAT,
             provider_name=ModelProviderName.OPENAI,
             prompt_token_cost=0.06 / 1000,
             completion_token_cost=0.12 / 1000,
@@ -163,6 +133,7 @@ OPEN_AI_CHAT_MODELS = {
         ),
         ChatModelInfo(
             name=OpenAIModelName.GPT4_TURBO,
+            service=ModelProviderService.CHAT,
             provider_name=ModelProviderName.OPENAI,
             prompt_token_cost=0.01 / 1000,
             completion_token_cost=0.03 / 1000,
@@ -173,26 +144,19 @@ OPEN_AI_CHAT_MODELS = {
 }
 # Copy entries for models with equivalent specs
 chat_model_mapping = {
-    OpenAIModelName.GPT3_v1: [OpenAIModelName.GPT3_v2],
-    OpenAIModelName.GPT3_v2_16k: [OpenAIModelName.GPT3_16k],
-    OpenAIModelName.GPT3_v4: [OpenAIModelName.GPT3_ROLLING],
-    OpenAIModelName.GPT4_v1: [OpenAIModelName.GPT4_v2, OpenAIModelName.GPT4_ROLLING],
-    OpenAIModelName.GPT4_v1_32k: [
+    OpenAIModelName.GPT3: [OpenAIModelName.GPT3_v1, OpenAIModelName.GPT3_v2],
+    OpenAIModelName.GPT3_16k: [OpenAIModelName.GPT3_v2_16k],
+    OpenAIModelName.GPT4: [OpenAIModelName.GPT4_v1, OpenAIModelName.GPT4_v2],
+    OpenAIModelName.GPT4_32k: [
+        OpenAIModelName.GPT4_v1_32k,
         OpenAIModelName.GPT4_v2_32k,
-        OpenAIModelName.GPT4_32k,
-    ],
-    OpenAIModelName.GPT4_TURBO: [
-        OpenAIModelName.GPT4_v3,
-        OpenAIModelName.GPT4_v3_VISION,
-        OpenAIModelName.GPT4_VISION,
-        OpenAIModelName.GPT4_v4,
-        OpenAIModelName.GPT4_TURBO_PREVIEW,
-        OpenAIModelName.GPT4_v5,
     ],
+    OpenAIModelName.GPT4_TURBO: [OpenAIModelName.GPT4_v3, OpenAIModelName.GPT4_v4],
 }
 for base, copies in chat_model_mapping.items():
     for copy in copies:
-        copy_info = OPEN_AI_CHAT_MODELS[base].copy(update={"name": copy})
+        copy_info = ChatModelInfo(**OPEN_AI_CHAT_MODELS[base].__dict__)
+        copy_info.name = copy
         OPEN_AI_CHAT_MODELS[copy] = copy_info
         if copy.endswith(("-0301", "-0314")):
             copy_info.has_function_call_api = False
@@ -241,8 +205,7 @@ class OpenAICredentials(ModelProviderCredentials):
         }
         if self.api_type == "azure":
             kwargs["api_version"] = self.api_version
-            assert self.azure_endpoint, "Azure endpoint not configured"
-            kwargs["azure_endpoint"] = self.azure_endpoint.get_secret_value()
+            kwargs["azure_endpoint"] = self.azure_endpoint
         return kwargs
 
     def get_model_access_kwargs(self, model: str) -> dict[str, str]:
@@ -254,7 +217,7 @@ class OpenAICredentials(ModelProviderCredentials):
 
     def load_azure_config(self, config_file: Path) -> None:
         with open(config_file) as file:
-            config_params = yaml.load(file, Loader=yaml.SafeLoader) or {}
+            config_params = yaml.load(file, Loader=yaml.FullLoader) or {}
 
         try:
             assert config_params.get(
@@ -294,28 +257,35 @@ class OpenAIProvider(
         name="openai_provider",
         description="Provides access to OpenAI's API.",
         configuration=OpenAIConfiguration(
-            retries_per_request=7,
+            retries_per_request=10,
         ),
         credentials=None,
-        budget=ModelProviderBudget(),
+        budget=ModelProviderBudget(
+            total_budget=math.inf,
+            total_cost=0.0,
+            remaining_budget=math.inf,
+            usage=ModelProviderUsage(
+                prompt_tokens=0,
+                completion_tokens=0,
+                total_tokens=0,
+            ),
+        ),
     )
 
-    _settings: OpenAISettings
-    _configuration: OpenAIConfiguration
-    _credentials: OpenAICredentials
     _budget: ModelProviderBudget
+    _configuration: OpenAIConfiguration
 
     def __init__(
         self,
-        settings: Optional[OpenAISettings] = None,
-        logger: Optional[logging.Logger] = None,
+        settings: OpenAISettings,
+        logger: logging.Logger,
     ):
-        if not settings:
-            settings = self.default_settings.copy(deep=True)
-        if not settings.credentials:
-            settings.credentials = OpenAICredentials.from_env()
+        self._settings = settings
 
-        super(OpenAIProvider, self).__init__(settings=settings, logger=logger)
+        assert settings.credentials, "Cannot create OpenAIProvider without credentials"
+        self._configuration = settings.configuration
+        self._credentials = settings.credentials
+        self._budget = settings.budget
 
         if self._credentials.api_type == "azure":
             from openai import AsyncAzureOpenAI
@@ -328,9 +298,7 @@ class OpenAIProvider(
 
             self._client = AsyncOpenAI(**self._credentials.get_api_access_kwargs())
 
-    async def get_available_models(self) -> list[ChatModelInfo]:
-        _models = (await self._client.models.list()).data
-        return [OPEN_AI_MODELS[m.id] for m in _models if m.id in OPEN_AI_MODELS]
+        self._logger = logger
 
     def get_token_limit(self, model_name: str) -> int:
         """Get the token limit for a given model."""
@@ -394,104 +362,79 @@ class OpenAIProvider(
         model_name: OpenAIModelName,
         completion_parser: Callable[[AssistantChatMessage], _T] = lambda _: None,
         functions: Optional[list[CompletionModelFunction]] = None,
-        max_output_tokens: Optional[int] = None,
-        prefill_response: str = "",  # not supported by OpenAI
         **kwargs,
     ) -> ChatModelResponse[_T]:
-        """Create a completion using the OpenAI API and parse it."""
+        """Create a completion using the OpenAI API."""
 
-        openai_messages, completion_kwargs = self._get_chat_completion_args(
-            model_prompt=model_prompt,
-            model_name=model_name,
-            functions=functions,
-            max_tokens=max_output_tokens,
-            **kwargs,
-        )
-        tool_calls_compat_mode = bool(functions and "tools" not in completion_kwargs)
+        completion_kwargs = self._get_completion_kwargs(model_name, functions, **kwargs)
+        tool_calls_compat_mode = functions and "tools" not in completion_kwargs
+        if "messages" in completion_kwargs:
+            model_prompt += completion_kwargs["messages"]
+            del completion_kwargs["messages"]
 
-        total_cost = 0.0
+        cost = 0.0
         attempts = 0
         while True:
-            _response, _cost, t_input, t_output = await self._create_chat_completion(
-                messages=openai_messages,
+            _response = await self._create_chat_completion(
+                messages=model_prompt,
                 **completion_kwargs,
             )
-            total_cost += _cost
+
+            _assistant_msg = _response.choices[0].message
+            assistant_msg = AssistantChatMessage(
+                content=_assistant_msg.content,
+                tool_calls=(
+                    [AssistantToolCall(**tc.dict()) for tc in _assistant_msg.tool_calls]
+                    if _assistant_msg.tool_calls
+                    else None
+                ),
+            )
+            response = ChatModelResponse(
+                response=assistant_msg,
+                model_info=OPEN_AI_CHAT_MODELS[model_name],
+                prompt_tokens_used=(
+                    _response.usage.prompt_tokens if _response.usage else 0
+                ),
+                completion_tokens_used=(
+                    _response.usage.completion_tokens if _response.usage else 0
+                ),
+            )
+            cost += self._budget.update_usage_and_cost(response)
+            self._logger.debug(
+                f"Completion usage: {response.prompt_tokens_used} input, "
+                f"{response.completion_tokens_used} output - ${round(cost, 5)}"
+            )
 
             # If parsing the response fails, append the error to the prompt, and let the
             # LLM fix its mistake(s).
-            attempts += 1
-            parse_errors: list[Exception] = []
+            try:
+                attempts += 1
 
-            _assistant_msg = _response.choices[0].message
-
-            tool_calls, _errors = self._parse_assistant_tool_calls(
-                _assistant_msg, tool_calls_compat_mode
-            )
-            parse_errors += _errors
-
-            # Validate tool calls
-            if not parse_errors and tool_calls and functions:
-                parse_errors += validate_tool_calls(tool_calls, functions)
-
-            assistant_msg = AssistantChatMessage(
-                content=_assistant_msg.content,
-                tool_calls=tool_calls or None,
-            )
-
-            parsed_result: _T = None  # type: ignore
-            if not parse_errors:
-                try:
-                    parsed_result = completion_parser(assistant_msg)
-                except Exception as e:
-                    parse_errors.append(e)
-
-            if not parse_errors:
-                if attempts > 1:
-                    self._logger.debug(
-                        f"Total cost for {attempts} attempts: ${round(total_cost, 5)}"
-                    )
-
-                return ChatModelResponse(
-                    response=AssistantChatMessage(
-                        content=_assistant_msg.content,
-                        tool_calls=tool_calls or None,
-                    ),
-                    parsed_result=parsed_result,
-                    model_info=OPEN_AI_CHAT_MODELS[model_name],
-                    prompt_tokens_used=t_input,
-                    completion_tokens_used=t_output,
-                )
-
-            else:
-                self._logger.debug(
-                    f"Parsing failed on response: '''{_assistant_msg}'''"
-                )
-                parse_errors_fmt = "\n\n".join(
-                    f"{e.__class__.__name__}: {e}" for e in parse_errors
-                )
-                self._logger.warning(
-                    f"Parsing attempt #{attempts} failed: {parse_errors_fmt}"
-                )
-                for e in parse_errors:
-                    sentry_sdk.capture_exception(
-                        error=e,
-                        extras={"assistant_msg": _assistant_msg, "i_attempt": attempts},
+                if (
+                    tool_calls_compat_mode
+                    and assistant_msg.content
+                    and not assistant_msg.tool_calls
+                ):
+                    assistant_msg.tool_calls = list(
+                        _tool_calls_compat_extract_calls(assistant_msg.content)
                     )
 
+                response.parsed_result = completion_parser(assistant_msg)
+                break
+            except Exception as e:
+                self._logger.warning(f"Parsing attempt #{attempts} failed: {e}")
+                self._logger.debug(f"Parsing failed on response: '''{assistant_msg}'''")
                 if attempts < self._configuration.fix_failed_parse_tries:
-                    openai_messages.append(_assistant_msg.dict(exclude_none=True))
-                    openai_messages.append(
-                        {
-                            "role": "system",
-                            "content": (
-                                f"ERROR PARSING YOUR RESPONSE:\n\n{parse_errors_fmt}"
-                            ),
-                        }
+                    model_prompt.append(
+                        ChatMessage.system(f"ERROR PARSING YOUR RESPONSE:\n\n{e}")
                     )
-                    continue
                 else:
-                    raise parse_errors[0]
+                    raise
+
+        if attempts > 1:
+            self._logger.debug(f"Total cost for {attempts} attempts: ${round(cost, 5)}")
+
+        return response
 
     async def create_embedding(
         self,
@@ -513,24 +456,21 @@ class OpenAIProvider(
         self._budget.update_usage_and_cost(response)
         return response
 
-    def _get_chat_completion_args(
+    def _get_completion_kwargs(
         self,
-        model_prompt: list[ChatMessage],
         model_name: OpenAIModelName,
         functions: Optional[list[CompletionModelFunction]] = None,
         **kwargs,
-    ) -> tuple[list[ChatCompletionMessageParam], dict[str, Any]]:
-        """Prepare chat completion arguments and keyword arguments for API call.
+    ) -> dict:
+        """Get kwargs for completion API call.
 
         Args:
-            model_prompt: List of ChatMessages.
-            model_name: The model to use.
-            functions: Optional list of functions available to the LLM.
-            kwargs: Additional keyword arguments.
+            model: The model to use.
+            kwargs: Keyword arguments to override the default values.
 
         Returns:
-            list[ChatCompletionMessageParam]: Prompt messages for the OpenAI call
-            dict[str, Any]: Any other kwargs for the OpenAI call
+            The kwargs for the chat API call.
+
         """
         kwargs.update(self._credentials.get_model_access_kwargs(model_name))
 
@@ -550,22 +490,11 @@ class OpenAIProvider(
                 _functions_compat_fix_kwargs(functions, kwargs)
 
         if extra_headers := self._configuration.extra_request_headers:
-            kwargs["extra_headers"] = kwargs.get("extra_headers", {})
-            kwargs["extra_headers"].update(extra_headers.copy())
-
-        if "messages" in kwargs:
-            model_prompt += kwargs["messages"]
-            del kwargs["messages"]
-
-        openai_messages: list[ChatCompletionMessageParam] = [
-            message.dict(
-                include={"role", "content", "tool_calls", "name"},
-                exclude_none=True,
+            kwargs["extra_headers"] = kwargs.get("extra_headers", {}).update(
+                extra_headers.copy()
             )
-            for message in model_prompt
-        ]
 
-        return openai_messages, kwargs
+        return kwargs
 
     def _get_embedding_kwargs(
         self,
@@ -585,108 +514,31 @@ class OpenAIProvider(
         kwargs.update(self._credentials.get_model_access_kwargs(model_name))
 
         if extra_headers := self._configuration.extra_request_headers:
-            kwargs["extra_headers"] = kwargs.get("extra_headers", {})
-            kwargs["extra_headers"].update(extra_headers.copy())
+            kwargs["extra_headers"] = kwargs.get("extra_headers", {}).update(
+                extra_headers.copy()
+            )
 
         return kwargs
 
-    async def _create_chat_completion(
-        self,
-        messages: list[ChatCompletionMessageParam],
-        model: OpenAIModelName,
-        *_,
-        **kwargs,
-    ) -> tuple[ChatCompletion, float, int, int]:
-        """
-        Create a chat completion using the OpenAI API with retry handling.
-
-        Params:
-            openai_messages: List of OpenAI-consumable message dict objects
-            model: The model to use for the completion
-
-        Returns:
-            ChatCompletion: The chat completion response object
-            float: The cost ($) of this completion
-            int: Number of prompt tokens used
-            int: Number of completion tokens used
-        """
+    def _create_chat_completion(
+        self, messages: list[ChatMessage], *_, **kwargs
+    ) -> Coroutine[None, None, ChatCompletion]:
+        """Create a chat completion using the OpenAI API with retry handling."""
 
         @self._retry_api_request
         async def _create_chat_completion_with_retry(
-            messages: list[ChatCompletionMessageParam], **kwargs
+            messages: list[ChatMessage], *_, **kwargs
         ) -> ChatCompletion:
+            raw_messages = [
+                message.dict(include={"role", "content", "tool_calls", "name"})
+                for message in messages
+            ]
             return await self._client.chat.completions.create(
-                messages=messages,  # type: ignore
+                messages=raw_messages,  # type: ignore
                 **kwargs,
             )
 
-        completion = await _create_chat_completion_with_retry(
-            messages, model=model, **kwargs
-        )
-
-        if completion.usage:
-            prompt_tokens_used = completion.usage.prompt_tokens
-            completion_tokens_used = completion.usage.completion_tokens
-        else:
-            prompt_tokens_used = completion_tokens_used = 0
-
-        cost = self._budget.update_usage_and_cost(
-            model_info=OPEN_AI_CHAT_MODELS[model],
-            input_tokens_used=prompt_tokens_used,
-            output_tokens_used=completion_tokens_used,
-        )
-        self._logger.debug(
-            f"Completion usage: {prompt_tokens_used} input, "
-            f"{completion_tokens_used} output - ${round(cost, 5)}"
-        )
-        return completion, cost, prompt_tokens_used, completion_tokens_used
-
-    def _parse_assistant_tool_calls(
-        self, assistant_message: ChatCompletionMessage, compat_mode: bool = False
-    ):
-        tool_calls: list[AssistantToolCall] = []
-        parse_errors: list[Exception] = []
-
-        if assistant_message.tool_calls:
-            for _tc in assistant_message.tool_calls:
-                try:
-                    parsed_arguments = json_loads(_tc.function.arguments)
-                except Exception as e:
-                    err_message = (
-                        f"Decoding arguments for {_tc.function.name} failed: "
-                        + str(e.args[0])
-                    )
-                    parse_errors.append(
-                        type(e)(err_message, *e.args[1:]).with_traceback(
-                            e.__traceback__
-                        )
-                    )
-                    continue
-
-                tool_calls.append(
-                    AssistantToolCall(
-                        id=_tc.id,
-                        type=_tc.type,
-                        function=AssistantFunctionCall(
-                            name=_tc.function.name,
-                            arguments=parsed_arguments,
-                        ),
-                    )
-                )
-
-            # If parsing of all tool calls succeeds in the end, we ignore any issues
-            if len(tool_calls) == len(assistant_message.tool_calls):
-                parse_errors = []
-
-        elif compat_mode and assistant_message.content:
-            try:
-                tool_calls = list(
-                    _tool_calls_compat_extract_calls(assistant_message.content)
-                )
-            except Exception as e:
-                parse_errors.append(e)
-
-        return tool_calls, parse_errors
+        return _create_chat_completion_with_retry(messages, *_, **kwargs)
 
     def _create_embedding(
         self, text: str, *_, **kwargs
@@ -861,21 +713,20 @@ def _functions_compat_fix_kwargs(
 
 
 def _tool_calls_compat_extract_calls(response: str) -> Iterator[AssistantToolCall]:
+    import json
     import re
-    import uuid
 
     logging.debug(f"Trying to extract tool calls from response:\n{response}")
 
     if response[0] == "[":
-        tool_calls: list[AssistantToolCallDict] = json_loads(response)
+        tool_calls: list[AssistantToolCallDict] = json.loads(response)
     else:
         block = re.search(r"```(?:tool_calls)?\n(.*)\n```\s*$", response, re.DOTALL)
         if not block:
             raise ValueError("Could not find tool_calls block in response")
-        tool_calls: list[AssistantToolCallDict] = json_loads(block.group(1))
+        tool_calls: list[AssistantToolCallDict] = json.loads(block.group(1))
 
     for t in tool_calls:
-        t["id"] = str(uuid.uuid4())
         t["function"]["arguments"] = str(t["function"]["arguments"])  # HACK
 
         yield AssistantToolCall.parse_obj(t)

autogpts/autogpt/autogpt/core/resource/model_providers/schema.py

@@ -1,11 +1,7 @@
 import abc
 import enum
-import logging
 import math
-from collections import defaultdict
 from typing import (
-    TYPE_CHECKING,
-    Any,
     Callable,
     ClassVar,
     Generic,
@@ -28,10 +24,6 @@ from autogpt.core.resource.schema import (
     ResourceType,
 )
 from autogpt.core.utils.json_schema import JSONSchema
-from autogpt.logs.utils import fmt_kwargs
-
-if TYPE_CHECKING:
-    from jsonschema import ValidationError
 
 
 class ModelProviderService(str, enum.Enum):
@@ -44,7 +36,6 @@ class ModelProviderService(str, enum.Enum):
 
 class ModelProviderName(str, enum.Enum):
     OPENAI = "openai"
-    ANTHROPIC = "anthropic"
 
 
 class ChatMessage(BaseModel):
@@ -53,14 +44,16 @@ class ChatMessage(BaseModel):
         SYSTEM = "system"
         ASSISTANT = "assistant"
 
-        TOOL = "tool"
-        """May be used for the result of tool calls"""
         FUNCTION = "function"
         """May be used for the return value of function calls"""
 
     role: Role
     content: str
 
+    @staticmethod
+    def assistant(content: str) -> "ChatMessage":
+        return ChatMessage(role=ChatMessage.Role.ASSISTANT, content=content)
+
     @staticmethod
     def user(content: str) -> "ChatMessage":
         return ChatMessage(role=ChatMessage.Role.USER, content=content)
@@ -77,39 +70,30 @@ class ChatMessageDict(TypedDict):
 
 class AssistantFunctionCall(BaseModel):
     name: str
-    arguments: dict[str, Any]
-
-    def __str__(self) -> str:
-        return f"{self.name}({fmt_kwargs(self.arguments)})"
+    arguments: str
 
 
 class AssistantFunctionCallDict(TypedDict):
     name: str
-    arguments: dict[str, Any]
+    arguments: str
 
 
 class AssistantToolCall(BaseModel):
-    id: str
+    # id: str
     type: Literal["function"]
     function: AssistantFunctionCall
 
 
 class AssistantToolCallDict(TypedDict):
-    id: str
+    # id: str
     type: Literal["function"]
     function: AssistantFunctionCallDict
 
 
 class AssistantChatMessage(ChatMessage):
-    role: Literal[ChatMessage.Role.ASSISTANT] = ChatMessage.Role.ASSISTANT
+    role: Literal["assistant"] = "assistant"
     content: Optional[str]
-    tool_calls: Optional[list[AssistantToolCall]] = None
-
-
-class ToolResultMessage(ChatMessage):
-    role: Literal[ChatMessage.Role.TOOL] = ChatMessage.Role.TOOL
-    is_error: bool = False
-    tool_call_id: str
+    tool_calls: Optional[list[AssistantToolCall]]
 
 
 class AssistantChatMessageDict(TypedDict, total=False):
@@ -153,35 +137,10 @@ class CompletionModelFunction(BaseModel):
 
     def fmt_line(self) -> str:
         params = ", ".join(
-            f"{name}{'?' if not p.required else ''}: " f"{p.typescript_type}"
-            for name, p in self.parameters.items()
+            f"{name}: {p.type.value}" for name, p in self.parameters.items()
         )
         return f"{self.name}: {self.description}. Params: ({params})"
 
-    def validate_call(
-        self, function_call: AssistantFunctionCall
-    ) -> tuple[bool, list["ValidationError"]]:
-        """
-        Validates the given function call against the function's parameter specs
-
-        Returns:
-            bool: Whether the given set of arguments is valid for this command
-            list[ValidationError]: Issues with the set of arguments (if any)
-
-        Raises:
-            ValueError: If the function_call doesn't call this function
-        """
-        if function_call.name != self.name:
-            raise ValueError(
-                f"Can't validate {function_call.name} call using {self.name} spec"
-            )
-
-        params_schema = JSONSchema(
-            type=JSONSchema.Type.OBJECT,
-            properties={name: spec for name, spec in self.parameters.items()},
-        )
-        return params_schema.validate_object(function_call.arguments)
-
 
 class ModelInfo(BaseModel):
     """Struct for model information.
@@ -228,34 +187,39 @@ class ModelProviderUsage(ProviderUsage):
 
     completion_tokens: int = 0
     prompt_tokens: int = 0
+    total_tokens: int = 0
 
     def update_usage(
         self,
-        input_tokens_used: int,
-        output_tokens_used: int = 0,
+        model_response: ModelResponse,
     ) -> None:
-        self.prompt_tokens += input_tokens_used
-        self.completion_tokens += output_tokens_used
+        self.completion_tokens += model_response.completion_tokens_used
+        self.prompt_tokens += model_response.prompt_tokens_used
+        self.total_tokens += (
+            model_response.completion_tokens_used + model_response.prompt_tokens_used
+        )
 
 
 class ModelProviderBudget(ProviderBudget):
-    usage: defaultdict[str, ModelProviderUsage] = defaultdict(ModelProviderUsage)
+    total_budget: float = UserConfigurable()
+    total_cost: float
+    remaining_budget: float
+    usage: ModelProviderUsage
 
     def update_usage_and_cost(
         self,
-        model_info: ModelInfo,
-        input_tokens_used: int,
-        output_tokens_used: int = 0,
+        model_response: ModelResponse,
     ) -> float:
         """Update the usage and cost of the provider.
 
         Returns:
             float: The (calculated) cost of the given model response.
         """
-        self.usage[model_info.name].update_usage(input_tokens_used, output_tokens_used)
+        model_info = model_response.model_info
+        self.usage.update_usage(model_response)
         incurred_cost = (
-            output_tokens_used * model_info.completion_token_cost
-            + input_tokens_used * model_info.prompt_token_cost
+            model_response.completion_tokens_used * model_info.completion_token_cost
+            + model_response.prompt_tokens_used * model_info.prompt_token_cost
         )
         self.total_cost += incurred_cost
         self.remaining_budget -= incurred_cost
@@ -265,8 +229,8 @@ class ModelProviderBudget(ProviderBudget):
 class ModelProviderSettings(ProviderSettings):
     resource_type: ResourceType = ResourceType.MODEL
     configuration: ModelProviderConfiguration
-    credentials: Optional[ModelProviderCredentials] = None
-    budget: Optional[ModelProviderBudget] = None
+    credentials: ModelProviderCredentials
+    budget: ModelProviderBudget
 
 
 class ModelProvider(abc.ABC):
@@ -274,27 +238,8 @@ class ModelProvider(abc.ABC):
 
     default_settings: ClassVar[ModelProviderSettings]
 
-    _settings: ModelProviderSettings
+    _budget: Optional[ModelProviderBudget]
     _configuration: ModelProviderConfiguration
-    _credentials: Optional[ModelProviderCredentials] = None
-    _budget: Optional[ModelProviderBudget] = None
-
-    _logger: logging.Logger
-
-    def __init__(
-        self,
-        settings: Optional[ModelProviderSettings] = None,
-        logger: Optional[logging.Logger] = None,
-    ):
-        if not settings:
-            settings = self.default_settings.copy(deep=True)
-
-        self._settings = settings
-        self._configuration = settings.configuration
-        self._credentials = settings.credentials
-        self._budget = settings.budget
-
-        self._logger = logger or logging.getLogger(self.__module__)
 
     @abc.abstractmethod
     def count_tokens(self, text: str, model_name: str) -> int:
@@ -339,7 +284,7 @@ class ModelTokenizer(Protocol):
 class EmbeddingModelInfo(ModelInfo):
     """Struct for embedding model information."""
 
-    service: Literal[ModelProviderService.EMBEDDING] = ModelProviderService.EMBEDDING
+    llm_service = ModelProviderService.EMBEDDING
     max_tokens: int
     embedding_dimensions: int
 
@@ -377,7 +322,7 @@ class EmbeddingModelProvider(ModelProvider):
 class ChatModelInfo(ModelInfo):
     """Struct for language model information."""
 
-    service: Literal[ModelProviderService.CHAT] = ModelProviderService.CHAT
+    llm_service = ModelProviderService.CHAT
     max_tokens: int
     has_function_call_api: bool = False
 
@@ -393,10 +338,6 @@ class ChatModelResponse(ModelResponse, Generic[_T]):
 
 
 class ChatModelProvider(ModelProvider):
-    @abc.abstractmethod
-    async def get_available_models(self) -> list[ChatModelInfo]:
-        ...
-
     @abc.abstractmethod
     def count_message_tokens(
         self,
@@ -412,8 +353,6 @@ class ChatModelProvider(ModelProvider):
         model_name: str,
         completion_parser: Callable[[AssistantChatMessage], _T] = lambda _: None,
         functions: Optional[list[CompletionModelFunction]] = None,
-        max_output_tokens: Optional[int] = None,
-        prefill_response: str = "",
         **kwargs,
     ) -> ChatModelResponse[_T]:
         ...

autogpts/autogpt/autogpt/core/resource/model_providers/utils.py

@@ -1,71 +0,0 @@
-from typing import Any
-
-from .schema import AssistantToolCall, CompletionModelFunction
-
-
-class InvalidFunctionCallError(Exception):
-    def __init__(self, name: str, arguments: dict[str, Any], message: str):
-        self.message = message
-        self.name = name
-        self.arguments = arguments
-        super().__init__(message)
-
-    def __str__(self) -> str:
-        return f"Invalid function call for {self.name}: {self.message}"
-
-
-def validate_tool_calls(
-    tool_calls: list[AssistantToolCall], functions: list[CompletionModelFunction]
-) -> list[InvalidFunctionCallError]:
-    """
-    Validates a list of tool calls against a list of functions.
-
-    1. Tries to find a function matching each tool call
-    2. If a matching function is found, validates the tool call's arguments,
-    reporting any resulting errors
-    2. If no matching function is found, an error "Unknown function X" is reported
-    3. A list of all errors encountered during validation is returned
-
-    Params:
-        tool_calls: A list of tool calls to validate.
-        functions: A list of functions to validate against.
-
-    Returns:
-        list[InvalidFunctionCallError]: All errors encountered during validation.
-    """
-    errors: list[InvalidFunctionCallError] = []
-    for tool_call in tool_calls:
-        function_call = tool_call.function
-
-        if function := next(
-            (f for f in functions if f.name == function_call.name),
-            None,
-        ):
-            is_valid, validation_errors = function.validate_call(function_call)
-            if not is_valid:
-                fmt_errors = [
-                    f"{'.'.join(str(p) for p in f.path)}: {f.message}"
-                    if f.path
-                    else f.message
-                    for f in validation_errors
-                ]
-                errors.append(
-                    InvalidFunctionCallError(
-                        name=function_call.name,
-                        arguments=function_call.arguments,
-                        message=(
-                            "The set of arguments supplied is invalid:\n"
-                            + "\n".join(fmt_errors)
-                        ),
-                    )
-                )
-        else:
-            errors.append(
-                InvalidFunctionCallError(
-                    name=function_call.name,
-                    arguments=function_call.arguments,
-                    message=f"Unknown function {function_call.name}",
-                )
-            )
-
-    return errors

autogpts/autogpt/autogpt/core/resource/schema.py

@@ -1,6 +1,5 @@
 import abc
 import enum
-import math
 
 from pydantic import BaseModel, SecretBytes, SecretField, SecretStr
 
@@ -26,9 +25,9 @@ class ProviderUsage(SystemConfiguration, abc.ABC):
 
 
 class ProviderBudget(SystemConfiguration):
-    total_budget: float = UserConfigurable(math.inf)
-    total_cost: float = 0
-    remaining_budget: float = math.inf
+    total_budget: float = UserConfigurable()
+    total_cost: float
+    remaining_budget: float
     usage: ProviderUsage
 
     @abc.abstractmethod

autogpts/autogpt/autogpt/core/runner/cli_web_app/server/api.py

@@ -4,8 +4,10 @@ from agent_protocol import StepHandler, StepResult
 
 from autogpt.agents import Agent
 from autogpt.app.main import UserFeedback
+from autogpt.commands import COMMAND_CATEGORIES
 from autogpt.config import AIProfile, ConfigBuilder
 from autogpt.logs.helpers import user_friendly_output
+from autogpt.models.command_registry import CommandRegistry
 from autogpt.prompts.prompt import DEFAULT_TRIGGERING_PROMPT
 
 
@@ -80,16 +82,16 @@ def bootstrap_agent(task, continuous_mode) -> Agent:
     config.logging.plain_console_output = True
     config.continuous_mode = continuous_mode
     config.temperature = 0
+    command_registry = CommandRegistry.with_command_modules(COMMAND_CATEGORIES, config)
     config.memory_backend = "no_memory"
     ai_profile = AIProfile(
         ai_name="AutoGPT",
         ai_role="a multi-purpose AI assistant.",
         ai_goals=[task],
     )
-    # FIXME this won't work - ai_profile and triggering_prompt is not a valid argument,
-    # lacks file_storage, settings and llm_provider
     return Agent(
+        command_registry=command_registry,
         ai_profile=ai_profile,
-        legacy_config=config,
+        config=config,
         triggering_prompt=DEFAULT_TRIGGERING_PROMPT,
     )

autogpts/autogpt/autogpt/core/runner/client_lib/logging/helpers.py

@@ -3,25 +3,21 @@ from typing import TYPE_CHECKING
 
 if TYPE_CHECKING:
     from autogpt.core.prompting import ChatPrompt
-    from autogpt.core.resource.model_providers import ChatMessage
 
 SEPARATOR_LENGTH = 42
 
 
-def dump_prompt(prompt: "ChatPrompt | list[ChatMessage]") -> str:
+def dump_prompt(prompt: "ChatPrompt") -> str:
     def separator(text: str):
         half_sep_len = (SEPARATOR_LENGTH - 2 - len(text)) / 2
         return f"{floor(half_sep_len)*'-'} {text.upper()} {ceil(half_sep_len)*'-'}"
 
-    if not isinstance(prompt, list):
-        prompt = prompt.messages
-
     formatted_messages = "\n".join(
-        [f"{separator(m.role)}\n{m.content}" for m in prompt]
+        [f"{separator(m.role)}\n{m.content}" for m in prompt.messages]
     )
     return f"""
 ============== {prompt.__class__.__name__} ==============
-Length: {len(prompt)} messages
+Length: {len(prompt.messages)} messages
 {formatted_messages}
 ==========================================
 """

autogpts/autogpt/autogpt/core/utils/json_schema.py

@@ -1,8 +1,9 @@
 import enum
+from logging import Logger
 from textwrap import indent
-from typing import Optional
+from typing import Literal, Optional
 
-from jsonschema import Draft7Validator, ValidationError
+from jsonschema import Draft7Validator
 from pydantic import BaseModel
 
 
@@ -57,35 +58,10 @@ class JSONSchema(BaseModel):
 
     @staticmethod
     def from_dict(schema: dict) -> "JSONSchema":
-        def resolve_references(schema: dict, definitions: dict) -> dict:
-            """
-            Recursively resolve type $refs in the JSON schema with their definitions.
-            """
-            if isinstance(schema, dict):
-                if "$ref" in schema:
-                    ref_path = schema["$ref"].split("/")[
-                        2:
-                    ]  # Split and remove '#/definitions'
-                    ref_value = definitions
-                    for key in ref_path:
-                        ref_value = ref_value[key]
-                    return resolve_references(ref_value, definitions)
-                else:
-                    return {
-                        k: resolve_references(v, definitions) for k, v in schema.items()
-                    }
-            elif isinstance(schema, list):
-                return [resolve_references(item, definitions) for item in schema]
-            else:
-                return schema
-
-        definitions = schema.get("definitions", {})
-        schema = resolve_references(schema, definitions)
-
         return JSONSchema(
             description=schema.get("description"),
             type=schema["type"],
-            enum=schema.get("enum"),
+            enum=schema["enum"] if "enum" in schema else None,
             items=JSONSchema.from_dict(schema["items"]) if "items" in schema else None,
             properties=JSONSchema.parse_properties(schema)
             if schema["type"] == "object"
@@ -108,24 +84,27 @@ class JSONSchema(BaseModel):
                 v.required = k in schema_node["required"]
         return properties
 
-    def validate_object(self, object: object) -> tuple[bool, list[ValidationError]]:
+    def validate_object(
+        self, object: object, logger: Logger
+    ) -> tuple[Literal[True], None] | tuple[Literal[False], list]:
         """
-        Validates an object or a value against the JSONSchema.
+        Validates a dictionary object against the JSONSchema.
 
         Params:
-            object: The value/object to validate.
+            object: The dictionary object to validate.
             schema (JSONSchema): The JSONSchema to validate against.
 
         Returns:
-            bool: Indicates whether the given value or object is valid for the schema.
-            list[ValidationError]: The issues with the value or object (if any).
+            tuple: A tuple where the first element is a boolean indicating whether the
+                object is valid or not, and the second element is a list of errors found
+                in the object, or None if the object is valid.
         """
         validator = Draft7Validator(self.to_dict())
 
         if errors := sorted(validator.iter_errors(object), key=lambda e: e.path):
             return False, errors
 
-        return True, []
+        return True, None
 
     def to_typescript_object_interface(self, interface_name: str = "") -> str:
         if self.type != JSONSchema.Type.OBJECT:

autogpts/autogpt/autogpt/core/utils/json_utils.py

@@ -1,93 +0,0 @@
-import logging
-import re
-from typing import Any
-
-import demjson3
-
-logger = logging.getLogger(__name__)
-
-
-def json_loads(json_str: str) -> Any:
-    """Parse a JSON string, tolerating minor syntax issues:
-    - Missing, extra and trailing commas
-    - Extraneous newlines and whitespace outside of string literals
-    - Inconsistent spacing after colons and commas
-    - Missing closing brackets or braces
-    - Numbers: binary, hex, octal, trailing and prefixed decimal points
-    - Different encodings
-    - Surrounding markdown code block
-    - Comments
-
-    Args:
-        json_str: The JSON string to parse.
-
-    Returns:
-        The parsed JSON object, same as built-in json.loads.
-    """
-    # Remove possible code block
-    pattern = r"```(?:json|JSON)*([\s\S]*?)```"
-    match = re.search(pattern, json_str)
-
-    if match:
-        json_str = match.group(1).strip()
-
-    json_result = demjson3.decode(json_str, return_errors=True)
-    assert json_result is not None  # by virtue of return_errors=True
-
-    if json_result.errors:
-        logger.debug(
-            "JSON parse errors:\n" + "\n".join(str(e) for e in json_result.errors)
-        )
-
-    if json_result.object in (demjson3.syntax_error, demjson3.undefined):
-        raise ValueError(
-            f"Failed to parse JSON string: {json_str}", *json_result.errors
-        )
-
-    return json_result.object
-
-
-def extract_dict_from_json(json_str: str) -> dict[str, Any]:
-    # Sometimes the response includes the JSON in a code block with ```
-    pattern = r"```(?:json|JSON)*([\s\S]*?)```"
-    match = re.search(pattern, json_str)
-
-    if match:
-        json_str = match.group(1).strip()
-    else:
-        # The string may contain JSON.
-        json_pattern = r"{[\s\S]*}"
-        match = re.search(json_pattern, json_str)
-
-        if match:
-            json_str = match.group()
-
-    result = json_loads(json_str)
-    if not isinstance(result, dict):
-        raise ValueError(
-            f"Response '''{json_str}''' evaluated to non-dict value {repr(result)}"
-        )
-    return result
-
-
-def extract_list_from_json(json_str: str) -> list[Any]:
-    # Sometimes the response includes the JSON in a code block with ```
-    pattern = r"```(?:json|JSON)*([\s\S]*?)```"
-    match = re.search(pattern, json_str)
-
-    if match:
-        json_str = match.group(1).strip()
-    else:
-        # The string may contain JSON.
-        json_pattern = r"\[[\s\S]*\]"
-        match = re.search(json_pattern, json_str)
-
-        if match:
-            json_str = match.group()
-
-    result = json_loads(json_str)
-    if not isinstance(result, list):
-        raise ValueError(
-            f"Response '''{json_str}''' evaluated to non-list value {repr(result)}"
-        )
-    return result

autogpts/autogpt/autogpt/file_storage/__init__.py

@@ -1,44 +0,0 @@
-import enum
-from pathlib import Path
-
-from .base import FileStorage
-
-
-class FileStorageBackendName(str, enum.Enum):
-    LOCAL = "local"
-    GCS = "gcs"
-    S3 = "s3"
-
-
-def get_storage(
-    backend: FileStorageBackendName,
-    root_path: Path = ".",
-    restrict_to_root: bool = True,
-) -> FileStorage:
-    match backend:
-        case FileStorageBackendName.LOCAL:
-            from .local import FileStorageConfiguration, LocalFileStorage
-
-            config = FileStorageConfiguration.from_env()
-            config.root = root_path
-            config.restrict_to_root = restrict_to_root
-            return LocalFileStorage(config)
-        case FileStorageBackendName.S3:
-            from .s3 import S3FileStorage, S3FileStorageConfiguration
-
-            config = S3FileStorageConfiguration.from_env()
-            config.root = root_path
-            return S3FileStorage(config)
-        case FileStorageBackendName.GCS:
-            from .gcs import GCSFileStorage, GCSFileStorageConfiguration
-
-            config = GCSFileStorageConfiguration.from_env()
-            config.root = root_path
-            return GCSFileStorage(config)
-
-
-__all__ = [
-    "FileStorage",
-    "FileStorageBackendName",
-    "get_storage",
-]

autogpts/autogpt/autogpt/file_storage/base.py

@@ -1,204 +0,0 @@
-"""
-The FileStorage class provides an interface for interacting with a file storage.
-"""
-
-from __future__ import annotations
-
-import logging
-import os
-from abc import ABC, abstractmethod
-from io import IOBase, TextIOBase
-from pathlib import Path
-from typing import IO, Any, BinaryIO, Callable, Literal, TextIO, overload
-
-from autogpt.core.configuration.schema import SystemConfiguration
-
-logger = logging.getLogger(__name__)
-
-
-class FileStorageConfiguration(SystemConfiguration):
-    restrict_to_root: bool = True
-    root: Path = Path("/")
-
-
-class FileStorage(ABC):
-    """A class that represents a file storage."""
-
-    on_write_file: Callable[[Path], Any] | None = None
-    """
-    Event hook, executed after writing a file.
-
-    Params:
-        Path: The path of the file that was written, relative to the storage root.
-    """
-
-    @property
-    @abstractmethod
-    def root(self) -> Path:
-        """The root path of the file storage."""
-
-    @property
-    @abstractmethod
-    def restrict_to_root(self) -> bool:
-        """Whether to restrict file access to within the storage's root path."""
-
-    @property
-    @abstractmethod
-    def is_local(self) -> bool:
-        """Whether the storage is local (i.e. on the same machine, not cloud-based)."""
-
-    @abstractmethod
-    def initialize(self) -> None:
-        """
-        Calling `initialize()` should bring the storage to a ready-to-use state.
-        For example, it can create the resource in which files will be stored, if it
-        doesn't exist yet. E.g. a folder on disk, or an S3 Bucket.
-        """
-
-    @overload
-    @abstractmethod
-    def open_file(
-        self,
-        path: str | Path,
-        mode: Literal["w", "r"] = "r",
-        binary: Literal[False] = False,
-    ) -> TextIO | TextIOBase:
-        """Returns a readable text file-like object representing the file."""
-
-    @overload
-    @abstractmethod
-    def open_file(
-        self,
-        path: str | Path,
-        mode: Literal["w", "r"] = "r",
-        binary: Literal[True] = True,
-    ) -> BinaryIO | IOBase:
-        """Returns a readable binary file-like object representing the file."""
-
-    @abstractmethod
-    def open_file(
-        self, path: str | Path, mode: Literal["w", "r"] = "r", binary: bool = False
-    ) -> IO | IOBase:
-        """Returns a readable file-like object representing the file."""
-
-    @overload
-    @abstractmethod
-    def read_file(self, path: str | Path, binary: Literal[False] = False) -> str:
-        """Read a file in the storage as text."""
-        ...
-
-    @overload
-    @abstractmethod
-    def read_file(self, path: str | Path, binary: Literal[True] = True) -> bytes:
-        """Read a file in the storage as binary."""
-        ...
-
-    @abstractmethod
-    def read_file(self, path: str | Path, binary: bool = False) -> str | bytes:
-        """Read a file in the storage."""
-
-    @abstractmethod
-    async def write_file(self, path: str | Path, content: str | bytes) -> None:
-        """Write to a file in the storage."""
-
-    @abstractmethod
-    def list_files(self, path: str | Path = ".") -> list[Path]:
-        """List all files (recursively) in a directory in the storage."""
-
-    @abstractmethod
-    def list_folders(
-        self, path: str | Path = ".", recursive: bool = False
-    ) -> list[Path]:
-        """List all folders in a directory in the storage."""
-
-    @abstractmethod
-    def delete_file(self, path: str | Path) -> None:
-        """Delete a file in the storage."""
-
-    @abstractmethod
-    def delete_dir(self, path: str | Path) -> None:
-        """Delete an empty folder in the storage."""
-
-    @abstractmethod
-    def exists(self, path: str | Path) -> bool:
-        """Check if a file or folder exists in the storage."""
-
-    @abstractmethod
-    def rename(self, old_path: str | Path, new_path: str | Path) -> None:
-        """Rename a file or folder in the storage."""
-
-    @abstractmethod
-    def copy(self, source: str | Path, destination: str | Path) -> None:
-        """Copy a file or folder with all contents in the storage."""
-
-    @abstractmethod
-    def make_dir(self, path: str | Path) -> None:
-        """Create a directory in the storage if doesn't exist."""
-
-    @abstractmethod
-    def clone_with_subroot(self, subroot: str | Path) -> FileStorage:
-        """Create a new FileStorage with a subroot of the current storage."""
-
-    def get_path(self, relative_path: str | Path) -> Path:
-        """Get the full path for an item in the storage.
-
-        Parameters:
-            relative_path: The relative path to resolve in the storage.
-
-        Returns:
-            Path: The resolved path relative to the storage.
-        """
-        return self._sanitize_path(relative_path)
-
-    def _sanitize_path(
-        self,
-        path: str | Path,
-    ) -> Path:
-        """Resolve the relative path within the given root if possible.
-
-        Parameters:
-            relative_path: The relative path to resolve.
-
-        Returns:
-            Path: The resolved path.
-
-        Raises:
-            ValueError: If the path is absolute and a root is provided.
-            ValueError: If the path is outside the root and the root is restricted.
-        """
-
-        # Posix systems disallow null bytes in paths. Windows is agnostic about it.
-        # Do an explicit check here for all sorts of null byte representations.
-        if "\0" in str(path):
-            raise ValueError("Embedded null byte")
-
-        logger.debug(f"Resolving path '{path}' in storage '{self.root}'")
-
-        relative_path = Path(path)
-
-        # Allow absolute paths if they are contained in the storage.
-        if (
-            relative_path.is_absolute()
-            and self.restrict_to_root
-            and not relative_path.is_relative_to(self.root)
-        ):
-            raise ValueError(
-                f"Attempted to access absolute path '{relative_path}' "
-                f"in storage '{self.root}'"
-            )
-
-        full_path = self.root / relative_path
-        if self.is_local:
-            full_path = full_path.resolve()
-        else:
-            full_path = Path(os.path.normpath(full_path))
-
-        logger.debug(f"Joined paths as '{full_path}'")
-
-        if self.restrict_to_root and not full_path.is_relative_to(self.root):
-            raise ValueError(
-                f"Attempted to access path '{full_path}' "
-                f"outside of storage '{self.root}'."
-            )
-
-        return full_path

autogpts/autogpt/autogpt/file_storage/gcs.py

@@ -1,213 +0,0 @@
-"""
-The GCSWorkspace class provides an interface for interacting with a file workspace, and
-stores the files in a Google Cloud Storage bucket.
-"""
-
-from __future__ import annotations
-
-import inspect
-import logging
-from io import IOBase
-from pathlib import Path
-from typing import Literal
-
-from google.cloud import storage
-from google.cloud.exceptions import NotFound
-
-from autogpt.core.configuration.schema import UserConfigurable
-
-from .base import FileStorage, FileStorageConfiguration
-
-logger = logging.getLogger(__name__)
-
-
-class GCSFileStorageConfiguration(FileStorageConfiguration):
-    bucket: str = UserConfigurable("autogpt", from_env="STORAGE_BUCKET")
-
-
-class GCSFileStorage(FileStorage):
-    """A class that represents a Google Cloud Storage."""
-
-    _bucket: storage.Bucket
-
-    def __init__(self, config: GCSFileStorageConfiguration):
-        self._bucket_name = config.bucket
-        self._root = config.root
-        # Add / at the beginning of the root path
-        if not self._root.is_absolute():
-            self._root = Path("/").joinpath(self._root)
-
-        self._gcs = storage.Client()
-        super().__init__()
-
-    @property
-    def root(self) -> Path:
-        """The root directory of the file storage."""
-        return self._root
-
-    @property
-    def restrict_to_root(self) -> bool:
-        """Whether to restrict generated paths to the root."""
-        return True
-
-    @property
-    def is_local(self) -> bool:
-        """Whether the storage is local (i.e. on the same machine, not cloud-based)."""
-        return False
-
-    def initialize(self) -> None:
-        logger.debug(f"Initializing {repr(self)}...")
-        try:
-            self._bucket = self._gcs.get_bucket(self._bucket_name)
-        except NotFound:
-            logger.info(f"Bucket '{self._bucket_name}' does not exist; creating it...")
-            self._bucket = self._gcs.create_bucket(self._bucket_name)
-
-    def get_path(self, relative_path: str | Path) -> Path:
-        # We set GCS root with "/" at the beginning
-        # but relative_to("/") will remove it
-        # because we don't actually want it in the storage filenames
-        return super().get_path(relative_path).relative_to("/")
-
-    def _get_blob(self, path: str | Path) -> storage.Blob:
-        path = self.get_path(path)
-        return self._bucket.blob(str(path))
-
-    def open_file(
-        self, path: str | Path, mode: Literal["w", "r"] = "r", binary: bool = False
-    ) -> IOBase:
-        """Open a file in the storage."""
-        blob = self._get_blob(path)
-        blob.reload()  # pin revision number to prevent version mixing while reading
-        return blob.open(f"{mode}b" if binary else mode)
-
-    def read_file(self, path: str | Path, binary: bool = False) -> str | bytes:
-        """Read a file in the storage."""
-        return self.open_file(path, "r", binary).read()
-
-    async def write_file(self, path: str | Path, content: str | bytes) -> None:
-        """Write to a file in the storage."""
-        blob = self._get_blob(path)
-
-        blob.upload_from_string(
-            data=content,
-            content_type=(
-                "text/plain"
-                if type(content) is str
-                # TODO: get MIME type from file extension or binary content
-                else "application/octet-stream"
-            ),
-        )
-
-        if self.on_write_file:
-            path = Path(path)
-            if path.is_absolute():
-                path = path.relative_to(self.root)
-            res = self.on_write_file(path)
-            if inspect.isawaitable(res):
-                await res
-
-    def list_files(self, path: str | Path = ".") -> list[Path]:
-        """List all files (recursively) in a directory in the storage."""
-        path = self.get_path(path)
-        return [
-            Path(blob.name).relative_to(path)
-            for blob in self._bucket.list_blobs(
-                prefix=f"{path}/" if path != Path(".") else None
-            )
-        ]
-
-    def list_folders(
-        self, path: str | Path = ".", recursive: bool = False
-    ) -> list[Path]:
-        """List 'directories' directly in a given path or recursively in the storage."""
-        path = self.get_path(path)
-        folder_names = set()
-
-        # List objects with the specified prefix and delimiter
-        for blob in self._bucket.list_blobs(prefix=path):
-            # Remove path prefix and the object name (last part)
-            folder = Path(blob.name).relative_to(path).parent
-            if not folder or folder == Path("."):
-                continue
-            # For non-recursive, only add the first level of folders
-            if not recursive:
-                folder_names.add(folder.parts[0])
-            else:
-                # For recursive, need to add all nested folders
-                for i in range(len(folder.parts)):
-                    folder_names.add("/".join(folder.parts[: i + 1]))
-
-        return [Path(f) for f in folder_names]
-
-    def delete_file(self, path: str | Path) -> None:
-        """Delete a file in the storage."""
-        path = self.get_path(path)
-        blob = self._bucket.blob(str(path))
-        blob.delete()
-
-    def delete_dir(self, path: str | Path) -> None:
-        """Delete an empty folder in the storage."""
-        # Since GCS does not have directories, we don't need to do anything
-        pass
-
-    def exists(self, path: str | Path) -> bool:
-        """Check if a file or folder exists in GCS storage."""
-        path = self.get_path(path)
-        # Check for exact blob match (file)
-        blob = self._bucket.blob(str(path))
-        if blob.exists():
-            return True
-        # Check for any blobs with prefix (folder)
-        prefix = f"{str(path).rstrip('/')}/"
-        blobs = self._bucket.list_blobs(prefix=prefix, max_results=1)
-        return next(blobs, None) is not None
-
-    def make_dir(self, path: str | Path) -> None:
-        """Create a directory in the storage if doesn't exist."""
-        # GCS does not have directories, so we don't need to do anything
-        pass
-
-    def rename(self, old_path: str | Path, new_path: str | Path) -> None:
-        """Rename a file or folder in the storage."""
-        old_path = self.get_path(old_path)
-        new_path = self.get_path(new_path)
-        blob = self._bucket.blob(str(old_path))
-        # If the blob with exact name exists, rename it
-        if blob.exists():
-            self._bucket.rename_blob(blob, new_name=str(new_path))
-            return
-        # Otherwise, rename all blobs with the prefix (folder)
-        for blob in self._bucket.list_blobs(prefix=f"{old_path}/"):
-            new_name = str(blob.name).replace(str(old_path), str(new_path), 1)
-            self._bucket.rename_blob(blob, new_name=new_name)
-
-    def copy(self, source: str | Path, destination: str | Path) -> None:
-        """Copy a file or folder with all contents in the storage."""
-        source = self.get_path(source)
-        destination = self.get_path(destination)
-        # If the source is a file, copy it
-        if self._bucket.blob(str(source)).exists():
-            self._bucket.copy_blob(
-                self._bucket.blob(str(source)), self._bucket, str(destination)
-            )
-            return
-        # Otherwise, copy all blobs with the prefix (folder)
-        for blob in self._bucket.list_blobs(prefix=f"{source}/"):
-            new_name = str(blob.name).replace(str(source), str(destination), 1)
-            self._bucket.copy_blob(blob, self._bucket, new_name)
-
-    def clone_with_subroot(self, subroot: str | Path) -> GCSFileStorage:
-        """Create a new GCSFileStorage with a subroot of the current storage."""
-        file_storage = GCSFileStorage(
-            GCSFileStorageConfiguration(
-                root=Path("/").joinpath(self.get_path(subroot)),
-                bucket=self._bucket_name,
-            )
-        )
-        file_storage._gcs = self._gcs
-        file_storage._bucket = self._bucket
-        return file_storage
-
-    def __repr__(self) -> str:
-        return f"{__class__.__name__}(bucket='{self._bucket_name}', root={self._root})"

autogpts/autogpt/autogpt/file_storage/local.py

@@ -1,139 +0,0 @@
-"""
-The LocalFileStorage class implements a FileStorage that works with local files.
-"""
-
-from __future__ import annotations
-
-import inspect
-import logging
-from pathlib import Path
-from typing import IO, Literal
-
-from .base import FileStorage, FileStorageConfiguration
-
-logger = logging.getLogger(__name__)
-
-
-class LocalFileStorage(FileStorage):
-    """A class that represents a file storage."""
-
-    def __init__(self, config: FileStorageConfiguration):
-        self._root = config.root.resolve()
-        self._restrict_to_root = config.restrict_to_root
-        self.make_dir(self.root)
-        super().__init__()
-
-    @property
-    def root(self) -> Path:
-        """The root directory of the file storage."""
-        return self._root
-
-    @property
-    def restrict_to_root(self) -> bool:
-        """Whether to restrict generated paths to the root."""
-        return self._restrict_to_root
-
-    @property
-    def is_local(self) -> bool:
-        """Whether the storage is local (i.e. on the same machine, not cloud-based)."""
-        return True
-
-    def initialize(self) -> None:
-        self.root.mkdir(exist_ok=True, parents=True)
-
-    def open_file(
-        self, path: str | Path, mode: Literal["w", "r"] = "r", binary: bool = False
-    ) -> IO:
-        """Open a file in the storage."""
-        return self._open_file(path, f"{mode}b" if binary else mode)
-
-    def _open_file(self, path: str | Path, mode: str) -> IO:
-        full_path = self.get_path(path)
-        return open(full_path, mode)  # type: ignore
-
-    def read_file(self, path: str | Path, binary: bool = False) -> str | bytes:
-        """Read a file in the storage."""
-        with self._open_file(path, "rb" if binary else "r") as file:
-            return file.read()
-
-    async def write_file(self, path: str | Path, content: str | bytes) -> None:
-        """Write to a file in the storage."""
-        with self._open_file(path, "wb" if type(content) is bytes else "w") as file:
-            file.write(content)
-
-        if self.on_write_file:
-            path = Path(path)
-            if path.is_absolute():
-                path = path.relative_to(self.root)
-            res = self.on_write_file(path)
-            if inspect.isawaitable(res):
-                await res
-
-    def list_files(self, path: str | Path = ".") -> list[Path]:
-        """List all files (recursively) in a directory in the storage."""
-        path = self.get_path(path)
-        return [file.relative_to(path) for file in path.rglob("*") if file.is_file()]
-
-    def list_folders(
-        self, path: str | Path = ".", recursive: bool = False
-    ) -> list[Path]:
-        """List directories directly in a given path or recursively."""
-        path = self.get_path(path)
-        if recursive:
-            return [
-                folder.relative_to(path)
-                for folder in path.rglob("*")
-                if folder.is_dir()
-            ]
-        else:
-            return [
-                folder.relative_to(path) for folder in path.iterdir() if folder.is_dir()
-            ]
-
-    def delete_file(self, path: str | Path) -> None:
-        """Delete a file in the storage."""
-        full_path = self.get_path(path)
-        full_path.unlink()
-
-    def delete_dir(self, path: str | Path) -> None:
-        """Delete an empty folder in the storage."""
-        full_path = self.get_path(path)
-        full_path.rmdir()
-
-    def exists(self, path: str | Path) -> bool:
-        """Check if a file or folder exists in the storage."""
-        return self.get_path(path).exists()
-
-    def make_dir(self, path: str | Path) -> None:
-        """Create a directory in the storage if doesn't exist."""
-        full_path = self.get_path(path)
-        full_path.mkdir(exist_ok=True, parents=True)
-
-    def rename(self, old_path: str | Path, new_path: str | Path) -> None:
-        """Rename a file or folder in the storage."""
-        old_path = self.get_path(old_path)
-        new_path = self.get_path(new_path)
-        old_path.rename(new_path)
-
-    def copy(self, source: str | Path, destination: str | Path) -> None:
-        """Copy a file or folder with all contents in the storage."""
-        source = self.get_path(source)
-        destination = self.get_path(destination)
-        if source.is_file():
-            destination.write_bytes(source.read_bytes())
-        else:
-            destination.mkdir(exist_ok=True, parents=True)
-            for file in source.rglob("*"):
-                if file.is_file():
-                    target = destination / file.relative_to(source)
-                    target.parent.mkdir(exist_ok=True, parents=True)
-                    target.write_bytes(file.read_bytes())
-
-    def clone_with_subroot(self, subroot: str | Path) -> FileStorage:
-        """Create a new LocalFileStorage with a subroot of the current storage."""
-        return LocalFileStorage(
-            FileStorageConfiguration(
-                root=self.get_path(subroot),
-                restrict_to_root=self.restrict_to_root,
-            )
-        )

autogpts/autogpt/autogpt/file_storage/s3.py

@@ -1,265 +0,0 @@
-"""
-The S3Workspace class provides an interface for interacting with a file workspace, and
-stores the files in an S3 bucket.
-"""
-
-from __future__ import annotations
-
-import contextlib
-import inspect
-import logging
-from io import IOBase, TextIOWrapper
-from pathlib import Path
-from typing import TYPE_CHECKING, Literal, Optional
-
-import boto3
-import botocore.exceptions
-from pydantic import SecretStr
-
-from autogpt.core.configuration.schema import UserConfigurable
-
-from .base import FileStorage, FileStorageConfiguration
-
-if TYPE_CHECKING:
-    import mypy_boto3_s3
-
-logger = logging.getLogger(__name__)
-
-
-class S3FileStorageConfiguration(FileStorageConfiguration):
-    bucket: str = UserConfigurable("autogpt", from_env="STORAGE_BUCKET")
-    s3_endpoint_url: Optional[SecretStr] = UserConfigurable(from_env="S3_ENDPOINT_URL")
-
-
-class S3FileStorage(FileStorage):
-    """A class that represents an S3 storage."""
-
-    _bucket: mypy_boto3_s3.service_resource.Bucket
-
-    def __init__(self, config: S3FileStorageConfiguration):
-        self._bucket_name = config.bucket
-        self._root = config.root
-        # Add / at the beginning of the root path
-        if not self._root.is_absolute():
-            self._root = Path("/").joinpath(self._root)
-
-        # https://boto3.amazonaws.com/v1/documentation/api/latest/guide/configuration.html
-        self._s3 = boto3.resource(
-            "s3",
-            endpoint_url=(
-                config.s3_endpoint_url.get_secret_value()
-                if config.s3_endpoint_url
-                else None
-            ),
-        )
-
-        super().__init__()
-
-    @property
-    def root(self) -> Path:
-        """The root directory of the file storage."""
-        return self._root
-
-    @property
-    def restrict_to_root(self):
-        """Whether to restrict generated paths to the root."""
-        return True
-
-    @property
-    def is_local(self) -> bool:
-        """Whether the storage is local (i.e. on the same machine, not cloud-based)."""
-        return False
-
-    def initialize(self) -> None:
-        logger.debug(f"Initializing {repr(self)}...")
-        try:
-            self._s3.meta.client.head_bucket(Bucket=self._bucket_name)
-            self._bucket = self._s3.Bucket(self._bucket_name)
-        except botocore.exceptions.ClientError as e:
-            if "(404)" not in str(e):
-                raise
-            logger.info(f"Bucket '{self._bucket_name}' does not exist; creating it...")
-            self._bucket = self._s3.create_bucket(Bucket=self._bucket_name)
-
-    def get_path(self, relative_path: str | Path) -> Path:
-        # We set S3 root with "/" at the beginning
-        # but relative_to("/") will remove it
-        # because we don't actually want it in the storage filenames
-        return super().get_path(relative_path).relative_to("/")
-
-    def _get_obj(self, path: str | Path) -> mypy_boto3_s3.service_resource.Object:
-        """Get an S3 object."""
-        path = self.get_path(path)
-        obj = self._bucket.Object(str(path))
-        with contextlib.suppress(botocore.exceptions.ClientError):
-            obj.load()
-        return obj
-
-    def open_file(
-        self, path: str | Path, mode: Literal["w", "r"] = "r", binary: bool = False
-    ) -> IOBase:
-        """Open a file in the storage."""
-        obj = self._get_obj(path)
-        return obj.get()["Body"] if binary else TextIOWrapper(obj.get()["Body"])
-
-    def read_file(self, path: str | Path, binary: bool = False) -> str | bytes:
-        """Read a file in the storage."""
-        return self.open_file(path, binary=binary).read()
-
-    async def write_file(self, path: str | Path, content: str | bytes) -> None:
-        """Write to a file in the storage."""
-        obj = self._get_obj(path)
-        obj.put(Body=content)
-
-        if self.on_write_file:
-            path = Path(path)
-            if path.is_absolute():
-                path = path.relative_to(self.root)
-            res = self.on_write_file(path)
-            if inspect.isawaitable(res):
-                await res
-
-    def list_files(self, path: str | Path = ".") -> list[Path]:
-        """List all files (recursively) in a directory in the storage."""
-        path = self.get_path(path)
-        if path == Path("."):  # root level of bucket
-            return [Path(obj.key) for obj in self._bucket.objects.all()]
-        else:
-            return [
-                Path(obj.key).relative_to(path)
-                for obj in self._bucket.objects.filter(Prefix=f"{path}/")
-            ]
-
-    def list_folders(
-        self, path: str | Path = ".", recursive: bool = False
-    ) -> list[Path]:
-        """List 'directories' directly in a given path or recursively in the storage."""
-        path = self.get_path(path)
-        folder_names = set()
-
-        # List objects with the specified prefix and delimiter
-        for obj_summary in self._bucket.objects.filter(Prefix=str(path)):
-            # Remove path prefix and the object name (last part)
-            folder = Path(obj_summary.key).relative_to(path).parent
-            if not folder or folder == Path("."):
-                continue
-            # For non-recursive, only add the first level of folders
-            if not recursive:
-                folder_names.add(folder.parts[0])
-            else:
-                # For recursive, need to add all nested folders
-                for i in range(len(folder.parts)):
-                    folder_names.add("/".join(folder.parts[: i + 1]))
-
-        return [Path(f) for f in folder_names]
-
-    def delete_file(self, path: str | Path) -> None:
-        """Delete a file in the storage."""
-        path = self.get_path(path)
-        obj = self._s3.Object(self._bucket_name, str(path))
-        obj.delete()
-
-    def delete_dir(self, path: str | Path) -> None:
-        """Delete an empty folder in the storage."""
-        # S3 does not have directories, so we don't need to do anything
-        pass
-
-    def exists(self, path: str | Path) -> bool:
-        """Check if a file or folder exists in S3 storage."""
-        path = self.get_path(path)
-        try:
-            # Check for exact object match (file)
-            self._s3.meta.client.head_object(Bucket=self._bucket_name, Key=str(path))
-            return True
-        except botocore.exceptions.ClientError as e:
-            if int(e.response["ResponseMetadata"]["HTTPStatusCode"]) == 404:
-                # If the object does not exist,
-                # check for objects with the prefix (folder)
-                prefix = f"{str(path).rstrip('/')}/"
-                objs = list(self._bucket.objects.filter(Prefix=prefix, MaxKeys=1))
-                return len(objs) > 0  # True if any objects exist with the prefix
-            else:
-                raise  # Re-raise for any other client errors
-
-    def make_dir(self, path: str | Path) -> None:
-        """Create a directory in the storage if doesn't exist."""
-        # S3 does not have directories, so we don't need to do anything
-        pass
-
-    def rename(self, old_path: str | Path, new_path: str | Path) -> None:
-        """Rename a file or folder in the storage."""
-        old_path = str(self.get_path(old_path))
-        new_path = str(self.get_path(new_path))
-
-        try:
-            # If file exists, rename it
-            self._s3.meta.client.head_object(Bucket=self._bucket_name, Key=old_path)
-            self._s3.meta.client.copy_object(
-                CopySource={"Bucket": self._bucket_name, "Key": old_path},
-                Bucket=self._bucket_name,
-                Key=new_path,
-            )
-            self._s3.meta.client.delete_object(Bucket=self._bucket_name, Key=old_path)
-        except botocore.exceptions.ClientError as e:
-            if int(e.response["ResponseMetadata"]["HTTPStatusCode"]) == 404:
-                # If the object does not exist,
-                # it may be a folder
-                prefix = f"{old_path.rstrip('/')}/"
-                objs = list(self._bucket.objects.filter(Prefix=prefix))
-                for obj in objs:
-                    new_key = new_path + obj.key[len(old_path) :]
-                    self._s3.meta.client.copy_object(
-                        CopySource={"Bucket": self._bucket_name, "Key": obj.key},
-                        Bucket=self._bucket_name,
-                        Key=new_key,
-                    )
-                    self._s3.meta.client.delete_object(
-                        Bucket=self._bucket_name, Key=obj.key
-                    )
-            else:
-                raise  # Re-raise for any other client errors
-
-    def copy(self, source: str | Path, destination: str | Path) -> None:
-        """Copy a file or folder with all contents in the storage."""
-        source = str(self.get_path(source))
-        destination = str(self.get_path(destination))
-
-        try:
-            # If source is a file, copy it
-            self._s3.meta.client.head_object(Bucket=self._bucket_name, Key=source)
-            self._s3.meta.client.copy_object(
-                CopySource={"Bucket": self._bucket_name, "Key": source},
-                Bucket=self._bucket_name,
-                Key=destination,
-            )
-        except botocore.exceptions.ClientError as e:
-            if int(e.response["ResponseMetadata"]["HTTPStatusCode"]) == 404:
-                # If the object does not exist,
-                # it may be a folder
-                prefix = f"{source.rstrip('/')}/"
-                objs = list(self._bucket.objects.filter(Prefix=prefix))
-                for obj in objs:
-                    new_key = destination + obj.key[len(source) :]
-                    self._s3.meta.client.copy_object(
-                        CopySource={"Bucket": self._bucket_name, "Key": obj.key},
-                        Bucket=self._bucket_name,
-                        Key=new_key,
-                    )
-            else:
-                raise
-
-    def clone_with_subroot(self, subroot: str | Path) -> S3FileStorage:
-        """Create a new S3FileStorage with a subroot of the current storage."""
-        file_storage = S3FileStorage(
-            S3FileStorageConfiguration(
-                bucket=self._bucket_name,
-                root=Path("/").joinpath(self.get_path(subroot)),
-                s3_endpoint_url=self._s3.meta.client.meta.endpoint_url,
-            )
-        )
-        file_storage._s3 = self._s3
-        file_storage._bucket = self._bucket
-        return file_storage
-
-    def __repr__(self) -> str:
-        return f"{__class__.__name__}(bucket='{self._bucket_name}', root={self._root})"

autogpts/autogpt/autogpt/file_workspace/__init__.py

@@ -0,0 +1,46 @@
+import enum
+from pathlib import Path
+from typing import Optional
+
+from .base import FileWorkspace
+
+
+class FileWorkspaceBackendName(str, enum.Enum):
+    LOCAL = "local"
+    GCS = "gcs"
+    S3 = "s3"
+
+
+def get_workspace(
+    backend: FileWorkspaceBackendName, *, id: str = "", root_path: Optional[Path] = None
+) -> FileWorkspace:
+    assert bool(root_path) != bool(id), "Specify root_path or id to get workspace"
+    if root_path is None:
+        root_path = Path(f"/workspaces/{id}")
+
+    match backend:
+        case FileWorkspaceBackendName.LOCAL:
+            from .local import FileWorkspaceConfiguration, LocalFileWorkspace
+
+            config = FileWorkspaceConfiguration.from_env()
+            config.root = root_path
+            return LocalFileWorkspace(config)
+        case FileWorkspaceBackendName.S3:
+            from .s3 import S3FileWorkspace, S3FileWorkspaceConfiguration
+
+            config = S3FileWorkspaceConfiguration.from_env()
+            config.root = root_path
+            return S3FileWorkspace(config)
+        case FileWorkspaceBackendName.GCS:
+            from .gcs import GCSFileWorkspace, GCSFileWorkspaceConfiguration
+
+            config = GCSFileWorkspaceConfiguration.from_env()
+            config.root = root_path
+            return GCSFileWorkspace(config)
+
+
+__all__ = [
+    "FileWorkspace",
+    "FileWorkspaceBackendName",
+    "get_workspace",
+]

autogpts/autogpt/autogpt/file_workspace/base.py

@@ -0,0 +1,164 @@
+"""
+The FileWorkspace class provides an interface for interacting with a file workspace.
+"""
+from __future__ import annotations
+
+import logging
+from abc import ABC, abstractmethod
+from io import IOBase, TextIOBase
+from pathlib import Path
+from typing import IO, Any, BinaryIO, Callable, Literal, Optional, TextIO, overload
+
+from autogpt.core.configuration.schema import SystemConfiguration
+
+logger = logging.getLogger(__name__)
+
+
+class FileWorkspaceConfiguration(SystemConfiguration):
+    restrict_to_root: bool = True
+    root: Path = Path("/")
+
+
+class FileWorkspace(ABC):
+    """A class that represents a file workspace."""
+
+    on_write_file: Callable[[Path], Any] | None = None
+    """
+    Event hook, executed after writing a file.
+
+    Params:
+        Path: The path of the file that was written, relative to the workspace root.
+    """
+
+    @property
+    @abstractmethod
+    def root(self) -> Path:
+        """The root path of the file workspace."""
+
+    @property
+    @abstractmethod
+    def restrict_to_root(self) -> bool:
+        """Whether to restrict file access to within the workspace's root path."""
+
+    @abstractmethod
+    def initialize(self) -> None:
+        """
+        Calling `initialize()` should bring the workspace to a ready-to-use state.
+        For example, it can create the resource in which files will be stored, if it
+        doesn't exist yet. E.g. a folder on disk, or an S3 Bucket.
+        """
+
+    @overload
+    @abstractmethod
+    def open_file(
+        self, path: str | Path, binary: Literal[False] = False
+    ) -> TextIO | TextIOBase:
+        """Returns a readable text file-like object representing the file."""
+
+    @overload
+    @abstractmethod
+    def open_file(
+        self, path: str | Path, binary: Literal[True] = True
+    ) -> BinaryIO | IOBase:
+        """Returns a readable binary file-like object representing the file."""
+
+    @abstractmethod
+    def open_file(self, path: str | Path, binary: bool = False) -> IO | IOBase:
+        """Returns a readable file-like object representing the file."""
+
+    @overload
+    @abstractmethod
+    def read_file(self, path: str | Path, binary: Literal[False] = False) -> str:
+        """Read a file in the workspace as text."""
+        ...
+
+    @overload
+    @abstractmethod
+    def read_file(self, path: str | Path, binary: Literal[True] = True) -> bytes:
+        """Read a file in the workspace as binary."""
+        ...
+
+    @abstractmethod
+    def read_file(self, path: str | Path, binary: bool = False) -> str | bytes:
+        """Read a file in the workspace."""
+
+    @abstractmethod
+    async def write_file(self, path: str | Path, content: str | bytes) -> None:
+        """Write to a file in the workspace."""
+
+    @abstractmethod
+    def list(self, path: str | Path = ".") -> list[Path]:
+        """List all files (recursively) in a directory in the workspace."""
+
+    @abstractmethod
+    def delete_file(self, path: str | Path) -> None:
+        """Delete a file in the workspace."""
+
+    def get_path(self, relative_path: str | Path) -> Path:
+        """Get the full path for an item in the workspace.
+
+        Parameters:
+            relative_path: The relative path to resolve in the workspace.
+
+        Returns:
+            Path: The resolved path relative to the workspace.
+        """
+        return self._sanitize_path(relative_path, self.root)
+
+    @staticmethod
+    def _sanitize_path(
+        relative_path: str | Path,
+        root: Optional[str | Path] = None,
+        restrict_to_root: bool = True,
+    ) -> Path:
+        """Resolve the relative path within the given root if possible.
+
+        Parameters:
+            relative_path: The relative path to resolve.
+            root: The root path to resolve the relative path within.
+            restrict_to_root: Whether to restrict the path to the root.
+
+        Returns:
+            Path: The resolved path.
+
+        Raises:
+            ValueError: If the path is absolute and a root is provided.
+            ValueError: If the path is outside the root and the root is restricted.
+        """
+
+        # Posix systems disallow null bytes in paths. Windows is agnostic about it.
+        # Do an explicit check here for all sorts of null byte representations.
+
+        if "\0" in str(relative_path) or "\0" in str(root):
+            raise ValueError("embedded null byte")
+
+        if root is None:
+            return Path(relative_path).resolve()
+
+        logger.debug(f"Resolving path '{relative_path}' in workspace '{root}'")
+
+        root, relative_path = Path(root).resolve(), Path(relative_path)
+
+        logger.debug(f"Resolved root as '{root}'")
+
+        # Allow absolute paths if they are contained in the workspace.
+        if (
+            relative_path.is_absolute()
+            and restrict_to_root
+            and not relative_path.is_relative_to(root)
+        ):
+            raise ValueError(
+                f"Attempted to access absolute path '{relative_path}' "
+                f"in workspace '{root}'."
+            )
+
+        full_path = root.joinpath(relative_path).resolve()
+
+        logger.debug(f"Joined paths as '{full_path}'")
+
+        if restrict_to_root and not full_path.is_relative_to(root):
+            raise ValueError(
+                f"Attempted to access path '{full_path}' outside of workspace '{root}'."
+            )
+
+        return full_path

autogpts/autogpt/autogpt/file_workspace/gcs.py

@@ -0,0 +1,113 @@
+"""
+The GCSWorkspace class provides an interface for interacting with a file workspace, and
+stores the files in a Google Cloud Storage bucket.
+"""
+from __future__ import annotations
+
+import inspect
+import logging
+from io import IOBase
+from pathlib import Path
+
+from google.cloud import storage
+from google.cloud.exceptions import NotFound
+
+from autogpt.core.configuration.schema import UserConfigurable
+
+from .base import FileWorkspace, FileWorkspaceConfiguration
+
+logger = logging.getLogger(__name__)
+
+
+class GCSFileWorkspaceConfiguration(FileWorkspaceConfiguration):
+    bucket: str = UserConfigurable("autogpt", from_env="WORKSPACE_STORAGE_BUCKET")
+
+
+class GCSFileWorkspace(FileWorkspace):
+    """A class that represents a Google Cloud Storage workspace."""
+
+    _bucket: storage.Bucket
+
+    def __init__(self, config: GCSFileWorkspaceConfiguration):
+        self._bucket_name = config.bucket
+        self._root = config.root
+        assert self._root.is_absolute()
+
+        self._gcs = storage.Client()
+        super().__init__()
+
+    @property
+    def root(self) -> Path:
+        """The root directory of the file workspace."""
+        return self._root
+
+    @property
+    def restrict_to_root(self) -> bool:
+        """Whether to restrict generated paths to the root."""
+        return True
+
+    def initialize(self) -> None:
+        logger.debug(f"Initializing {repr(self)}...")
+        try:
+            self._bucket = self._gcs.get_bucket(self._bucket_name)
+        except NotFound:
+            logger.info(f"Bucket '{self._bucket_name}' does not exist; creating it...")
+            self._bucket = self._gcs.create_bucket(self._bucket_name)
+
+    def get_path(self, relative_path: str | Path) -> Path:
+        return super().get_path(relative_path).relative_to("/")
+
+    def _get_blob(self, path: str | Path) -> storage.Blob:
+        path = self.get_path(path)
+        return self._bucket.blob(str(path))
+
+    def open_file(self, path: str | Path, binary: bool = False) -> IOBase:
+        """Open a file in the workspace."""
+        blob = self._get_blob(path)
+        blob.reload()  # pin revision number to prevent version mixing while reading
+        return blob.open("rb" if binary else "r")
+
+    def read_file(self, path: str | Path, binary: bool = False) -> str | bytes:
+        """Read a file in the workspace."""
+        return self.open_file(path, binary).read()
+
+    async def write_file(self, path: str | Path, content: str | bytes) -> None:
+        """Write to a file in the workspace."""
+        blob = self._get_blob(path)
+
+        blob.upload_from_string(
+            data=content,
+            content_type=(
+                "text/plain"
+                if type(content) is str
+                # TODO: get MIME type from file extension or binary content
+                else "application/octet-stream"
+            ),
+        )
+
+        if self.on_write_file:
+            path = Path(path)
+            if path.is_absolute():
+                path = path.relative_to(self.root)
+            res = self.on_write_file(path)
+            if inspect.isawaitable(res):
+                await res
+
+    def list(self, path: str | Path = ".") -> list[Path]:
+        """List all files (recursively) in a directory in the workspace."""
+        path = self.get_path(path)
+        return [
+            Path(blob.name).relative_to(path)
+            for blob in self._bucket.list_blobs(
+                prefix=f"{path}/" if path != Path(".") else None
+            )
+        ]
+
+    def delete_file(self, path: str | Path) -> None:
+        """Delete a file in the workspace."""
+        path = self.get_path(path)
+        blob = self._bucket.blob(str(path))
+        blob.delete()
+
+    def __repr__(self) -> str:
+        return f"{__class__.__name__}(bucket='{self._bucket_name}', root={self._root})"

autogpts/autogpt/autogpt/file_workspace/local.py

@@ -0,0 +1,71 @@
+"""
+The LocalFileWorkspace class implements a FileWorkspace that works with local files.
+"""
+from __future__ import annotations
+
+import inspect
+import logging
+from pathlib import Path
+from typing import IO
+
+from .base import FileWorkspace, FileWorkspaceConfiguration
+
+logger = logging.getLogger(__name__)
+
+
+class LocalFileWorkspace(FileWorkspace):
+    """A class that represents a file workspace."""
+
+    def __init__(self, config: FileWorkspaceConfiguration):
+        self._root = self._sanitize_path(config.root)
+        self._restrict_to_root = config.restrict_to_root
+        super().__init__()
+
+    @property
+    def root(self) -> Path:
+        """The root directory of the file workspace."""
+        return self._root
+
+    @property
+    def restrict_to_root(self) -> bool:
+        """Whether to restrict generated paths to the root."""
+        return self._restrict_to_root
+
+    def initialize(self) -> None:
+        self.root.mkdir(exist_ok=True, parents=True)
+
+    def open_file(self, path: str | Path, binary: bool = False) -> IO:
+        """Open a file in the workspace."""
+        return self._open_file(path, "rb" if binary else "r")
+
+    def _open_file(self, path: str | Path, mode: str = "r") -> IO:
+        full_path = self.get_path(path)
+        return open(full_path, mode)  # type: ignore
+
+    def read_file(self, path: str | Path, binary: bool = False) -> str | bytes:
+        """Read a file in the workspace."""
+        with self._open_file(path, "rb" if binary else "r") as file:
+            return file.read()
+
+    async def write_file(self, path: str | Path, content: str | bytes) -> None:
+        """Write to a file in the workspace."""
+        with self._open_file(path, "wb" if type(content) is bytes else "w") as file:
+            file.write(content)
+
+        if self.on_write_file:
+            path = Path(path)
+            if path.is_absolute():
+                path = path.relative_to(self.root)
+            res = self.on_write_file(path)
+            if inspect.isawaitable(res):
+                await res
+
+    def list(self, path: str | Path = ".") -> list[Path]:
+        """List all files (recursively) in a directory in the workspace."""
+        path = self.get_path(path)
+        return [file.relative_to(path) for file in path.rglob("*") if file.is_file()]
+
+    def delete_file(self, path: str | Path) -> None:
+        """Delete a file in the workspace."""
+        full_path = self.get_path(path)
+        full_path.unlink()

autogpts/autogpt/autogpt/file_workspace/s3.py

@@ -0,0 +1,128 @@
+"""
+The S3Workspace class provides an interface for interacting with a file workspace, and
+stores the files in an S3 bucket.
+"""
+from __future__ import annotations
+
+import contextlib
+import inspect
+import logging
+import os
+from io import IOBase, TextIOWrapper
+from pathlib import Path
+from typing import TYPE_CHECKING, Optional
+
+import boto3
+import botocore.exceptions
+from pydantic import SecretStr
+
+from autogpt.core.configuration.schema import UserConfigurable
+
+from .base import FileWorkspace, FileWorkspaceConfiguration
+
+if TYPE_CHECKING:
+    import mypy_boto3_s3
+
+logger = logging.getLogger(__name__)
+
+
+class S3FileWorkspaceConfiguration(FileWorkspaceConfiguration):
+    bucket: str = UserConfigurable("autogpt", from_env="WORKSPACE_STORAGE_BUCKET")
+    s3_endpoint_url: Optional[SecretStr] = UserConfigurable(
+        from_env=lambda: SecretStr(v) if (v := os.getenv("S3_ENDPOINT_URL")) else None
+    )
+
+
+class S3FileWorkspace(FileWorkspace):
+    """A class that represents an S3 workspace."""
+
+    _bucket: mypy_boto3_s3.service_resource.Bucket
+
+    def __init__(self, config: S3FileWorkspaceConfiguration):
+        self._bucket_name = config.bucket
+        self._root = config.root
+        assert self._root.is_absolute()
+
+        # https://boto3.amazonaws.com/v1/documentation/api/latest/guide/configuration.html
+        self._s3 = boto3.resource(
+            "s3",
+            endpoint_url=config.s3_endpoint_url.get_secret_value()
+            if config.s3_endpoint_url
+            else None,
+        )
+
+        super().__init__()
+
+    @property
+    def root(self) -> Path:
+        """The root directory of the file workspace."""
+        return self._root
+
+    @property
+    def restrict_to_root(self):
+        """Whether to restrict generated paths to the root."""
+        return True
+
+    def initialize(self) -> None:
+        logger.debug(f"Initializing {repr(self)}...")
+        try:
+            self._s3.meta.client.head_bucket(Bucket=self._bucket_name)
+            self._bucket = self._s3.Bucket(self._bucket_name)
+        except botocore.exceptions.ClientError as e:
+            if "(404)" not in str(e):
+                raise
+            logger.info(f"Bucket '{self._bucket_name}' does not exist; creating it...")
+            self._bucket = self._s3.create_bucket(Bucket=self._bucket_name)
+
+    def get_path(self, relative_path: str | Path) -> Path:
+        return super().get_path(relative_path).relative_to("/")
+
+    def _get_obj(self, path: str | Path) -> mypy_boto3_s3.service_resource.Object:
+        """Get an S3 object."""
+        path = self.get_path(path)
+        obj = self._bucket.Object(str(path))
+        with contextlib.suppress(botocore.exceptions.ClientError):
+            obj.load()
+        return obj
+
+    def open_file(self, path: str | Path, binary: bool = False) -> IOBase:
+        """Open a file in the workspace."""
+        obj = self._get_obj(path)
+        return obj.get()["Body"] if binary else TextIOWrapper(obj.get()["Body"])
+
+    def read_file(self, path: str | Path, binary: bool = False) -> str | bytes:
+        """Read a file in the workspace."""
+        return self.open_file(path, binary).read()
+
+    async def write_file(self, path: str | Path, content: str | bytes) -> None:
+        """Write to a file in the workspace."""
+        obj = self._get_obj(path)
+        obj.put(Body=content)
+
+        if self.on_write_file:
+            path = Path(path)
+            if path.is_absolute():
+                path = path.relative_to(self.root)
+            res = self.on_write_file(path)
+            if inspect.isawaitable(res):
+                await res
+
+    def list(self, path: str | Path = ".") -> list[Path]:
+        """List all files (recursively) in a directory in the workspace."""
+        path = self.get_path(path)
+        if path == Path("."):  # root level of bucket
+            return [Path(obj.key) for obj in self._bucket.objects.all()]
+        else:
+            return [
+                Path(obj.key).relative_to(path)
+                for obj in self._bucket.objects.filter(Prefix=f"{path}/")
+            ]
+
+    def delete_file(self, path: str | Path) -> None:
+        """Delete a file in the workspace."""
+        path = self.get_path(path)
+        obj = self._s3.Object(self._bucket_name, str(path))
+        obj.delete()
+
+    def __repr__(self) -> str:
+        return f"{__class__.__name__}(bucket='{self._bucket_name}', root={self._root})"

autogpts/autogpt/autogpt/json_utils/utilities.py

@@ -0,0 +1,35 @@
+"""Utilities for the json_fixes package."""
+import ast
+import logging
+import re
+from typing import Any
+
+logger = logging.getLogger(__name__)
+
+
+def extract_dict_from_response(response_content: str) -> dict[str, Any]:
+    # Sometimes the response includes the JSON in a code block with ```
+    pattern = r"```([\s\S]*?)```"
+    match = re.search(pattern, response_content)
+
+    if match:
+        response_content = match.group(1).strip()
+        # Remove language names in code blocks
+        response_content = response_content.lstrip("json")
+    else:
+        # The string may contain JSON.
+        json_pattern = r"{[\s\S]*}"
+        match = re.search(json_pattern, response_content)
+
+        if match:
+            response_content = match.group()
+
+    # Response content comes from OpenAI as a Python `str(content_dict)`.
+    # `literal_eval` does the reverse of `str(dict)`.
+    result = ast.literal_eval(response_content)
+    if not isinstance(result, dict):
+        raise ValueError(
+            f"Response '''{response_content}''' evaluated to "
+            f"non-dict value {repr(result)}"
+        )
+    return result

autogpts/autogpt/autogpt/llm/api_manager.py

@@ -0,0 +1,115 @@
+from __future__ import annotations
+
+import logging
+from typing import List, Optional
+
+from openai import OpenAI
+from openai.types import Model
+
+from autogpt.core.resource.model_providers.openai import (
+    OPEN_AI_MODELS,
+    OpenAICredentials,
+)
+from autogpt.core.resource.model_providers.schema import ChatModelInfo
+from autogpt.singleton import Singleton
+
+logger = logging.getLogger(__name__)
+
+
+class ApiManager(metaclass=Singleton):
+    def __init__(self):
+        self.total_prompt_tokens = 0
+        self.total_completion_tokens = 0
+        self.total_cost = 0
+        self.total_budget = 0
+        self.models: Optional[list[Model]] = None
+
+    def reset(self):
+        self.total_prompt_tokens = 0
+        self.total_completion_tokens = 0
+        self.total_cost = 0
+        self.total_budget = 0.0
+        self.models = None
+
+    def update_cost(self, prompt_tokens, completion_tokens, model):
+        """
+        Update the total cost, prompt tokens, and completion tokens.
+
+        Args:
+        prompt_tokens (int): The number of tokens used in the prompt.
+        completion_tokens (int): The number of tokens used in the completion.
+        model (str): The model used for the API call.
+        """
+        # the .model property in API responses can contain version suffixes like -v2
+        model = model[:-3] if model.endswith("-v2") else model
+        model_info = OPEN_AI_MODELS[model]
+
+        self.total_prompt_tokens += prompt_tokens
+        self.total_completion_tokens += completion_tokens
+        self.total_cost += prompt_tokens * model_info.prompt_token_cost / 1000
+        if isinstance(model_info, ChatModelInfo):
+            self.total_cost += (
+                completion_tokens * model_info.completion_token_cost / 1000
+            )
+
+        logger.debug(f"Total running cost: ${self.total_cost:.3f}")
+
+    def set_total_budget(self, total_budget):
+        """
+        Sets the total user-defined budget for API calls.
+
+        Args:
+        total_budget (float): The total budget for API calls.
+        """
+        self.total_budget = total_budget
+
+    def get_total_prompt_tokens(self):
+        """
+        Get the total number of prompt tokens.
+
+        Returns:
+        int: The total number of prompt tokens.
+        """
+        return self.total_prompt_tokens
+
+    def get_total_completion_tokens(self):
+        """
+        Get the total number of completion tokens.
+
+        Returns:
+        int: The total number of completion tokens.
+        """
+        return self.total_completion_tokens
+
+    def get_total_cost(self):
+        """
+        Get the total cost of API calls.
+
+        Returns:
+        float: The total cost of API calls.
+        """
+        return self.total_cost
+
+    def get_total_budget(self):
+        """
+        Get the total user-defined budget for API calls.
+
+        Returns:
+        float: The total budget for API calls.
+        """
+        return self.total_budget
+
+    def get_models(self, openai_credentials: OpenAICredentials) -> List[Model]:
+        """
+        Get list of available GPT models.
+
+        Returns:
+            list[Model]: List of available GPT models.
+        """
+        if self.models is None:
+            all_models = (
+                OpenAI(**openai_credentials.get_api_access_kwargs()).models.list().data
+            )
+            self.models = [model for model in all_models if "gpt" in model.id]
+
+        return self.models

autogpts/autogpt/autogpt/llm/providers/openai.py

@@ -1,12 +1,10 @@
 from __future__ import annotations
 
 import logging
-from typing import TYPE_CHECKING, Callable, Iterable, TypeVar
-
-if TYPE_CHECKING:
-    from autogpt.models.command import Command
+from typing import Callable, Iterable, TypeVar
 
 from autogpt.core.resource.model_providers import CompletionModelFunction
+from autogpt.models.command import Command
 
 logger = logging.getLogger(__name__)
 
@@ -14,7 +12,7 @@ logger = logging.getLogger(__name__)
 T = TypeVar("T", bound=Callable)
 
 
-def function_specs_from_commands(
+def get_openai_command_specs(
     commands: Iterable[Command],
 ) -> list[CompletionModelFunction]:
     """Get OpenAI-consumable function specs for the agent's available commands.
@@ -22,7 +20,7 @@ def function_specs_from_commands(
     """
     return [
         CompletionModelFunction(
-            name=command.names[0],
+            name=command.name,
             description=command.description,
             parameters={param.name: param.spec for param in command.parameters},
         )

autogpts/autogpt/autogpt/logs/__init__.py

@@ -1,4 +1,4 @@
-from .config import configure_logging
+from .config import configure_chat_plugins, configure_logging
 from .helpers import user_friendly_output
 from .log_cycle import (
     CURRENT_CONTEXT_FILE_NAME,
@@ -13,6 +13,7 @@ from .log_cycle import (
 
 __all__ = [
     "configure_logging",
+    "configure_chat_plugins",
     "user_friendly_output",
     "CURRENT_CONTEXT_FILE_NAME",
     "NEXT_ACTION_FILE_NAME",

autogpts/autogpt/autogpt/logs/config.py

@@ -8,9 +8,11 @@ import sys
 from pathlib import Path
 from typing import TYPE_CHECKING, Optional
 
+from auto_gpt_plugin_template import AutoGPTPluginTemplate
 from openai._base_client import log as openai_logger
 
 if TYPE_CHECKING:
+    from autogpt.config import Config
     from autogpt.speech import TTSConfig
 
 from autogpt.core.configuration import SystemConfiguration, UserConfigurable
@@ -32,6 +34,8 @@ DEBUG_LOG_FORMAT = (
 SPEECH_OUTPUT_LOGGER = "VOICE"
 USER_FRIENDLY_OUTPUT_LOGGER = "USER_FRIENDLY_OUTPUT"
 
+_chat_plugins: list[AutoGPTPluginTemplate] = []
+
 
 class LogFormatName(str, enum.Enum):
     SIMPLE = "simple"
@@ -53,7 +57,8 @@ class LoggingConfig(SystemConfiguration):
 
     # Console output
     log_format: LogFormatName = UserConfigurable(
-        default=LogFormatName.SIMPLE, from_env="LOG_FORMAT"
+        default=LogFormatName.SIMPLE,
+        from_env=lambda: LogFormatName(os.getenv("LOG_FORMAT", "simple")),
     )
     plain_console_output: bool = UserConfigurable(
         default=False,
@@ -64,80 +69,46 @@ class LoggingConfig(SystemConfiguration):
     log_dir: Path = LOG_DIR
     log_file_format: Optional[LogFormatName] = UserConfigurable(
         default=LogFormatName.SIMPLE,
-        from_env=lambda: os.getenv(
-            "LOG_FILE_FORMAT", os.getenv("LOG_FORMAT", "simple")
+        from_env=lambda: LogFormatName(
+            os.getenv("LOG_FILE_FORMAT", os.getenv("LOG_FORMAT", "simple"))
         ),
     )
 
 
 def configure_logging(
-    debug: bool = False,
-    level: Optional[int | str] = None,
-    log_dir: Optional[Path] = None,
-    log_format: Optional[LogFormatName | str] = None,
-    log_file_format: Optional[LogFormatName | str] = None,
-    plain_console_output: Optional[bool] = None,
-    config: Optional[LoggingConfig] = None,
+    level: int = logging.INFO,
+    log_dir: Path = LOG_DIR,
+    log_format: Optional[LogFormatName] = None,
+    log_file_format: Optional[LogFormatName] = None,
+    plain_console_output: bool = False,
     tts_config: Optional[TTSConfig] = None,
 ) -> None:
-    """Configure the native logging module, based on the environment config and any
-    specified overrides.
-
-    Arguments override values specified in the environment.
-    Overrides are also applied to `config`, if passed.
+    """Configure the native logging module.
 
     Should be usable as `configure_logging(**config.logging.dict())`, where
     `config.logging` is a `LoggingConfig` object.
     """
-    if debug and level:
-        raise ValueError("Only one of either 'debug' and 'level' arguments may be set")
 
-    # Parse arguments
-    if isinstance(level, str):
-        if type(_level := logging.getLevelName(level.upper())) is int:
-            level = _level
-        else:
-            raise ValueError(f"Unknown log level '{level}'")
-    if isinstance(log_format, str):
-        if log_format in LogFormatName._value2member_map_:
-            log_format = LogFormatName(log_format)
-        elif not isinstance(log_format, LogFormatName):
-            raise ValueError(f"Unknown log format '{log_format}'")
-    if isinstance(log_file_format, str):
-        if log_file_format in LogFormatName._value2member_map_:
-            log_file_format = LogFormatName(log_file_format)
-        elif not isinstance(log_file_format, LogFormatName):
-            raise ValueError(f"Unknown log format '{log_format}'")
-
-    config = config or LoggingConfig.from_env()
-
-    # Aggregate env config + arguments
-    config.level = logging.DEBUG if debug else level or config.level
-    config.log_dir = log_dir or config.log_dir
-    config.log_format = log_format or (
-        LogFormatName.DEBUG if debug else config.log_format
-    )
-    config.log_file_format = log_file_format or log_format or config.log_file_format
-    config.plain_console_output = (
-        plain_console_output
-        if plain_console_output is not None
-        else config.plain_console_output
+    # Auto-adjust default log format based on log level
+    log_format = log_format or (
+        LogFormatName.SIMPLE if level != logging.DEBUG else LogFormatName.DEBUG
     )
+    log_file_format = log_file_format or log_format
 
-    # Structured logging is used for cloud environments,
-    # where logging to a file makes no sense.
-    if config.log_format == LogFormatName.STRUCTURED:
-        config.plain_console_output = True
-        config.log_file_format = None
+    structured_logging = log_format == LogFormatName.STRUCTURED
+
+    if structured_logging:
+        plain_console_output = True
+        log_file_format = None
 
     # create log directory if it doesn't exist
-    if not config.log_dir.exists():
-        config.log_dir.mkdir()
+    if not log_dir.exists():
+        log_dir.mkdir()
 
     log_handlers: list[logging.Handler] = []
 
-    if config.log_format in (LogFormatName.DEBUG, LogFormatName.SIMPLE):
-        console_format_template = TEXT_LOG_FORMAT_MAP[config.log_format]
+    if log_format in (LogFormatName.DEBUG, LogFormatName.SIMPLE):
+        console_format_template = TEXT_LOG_FORMAT_MAP[log_format]
         console_formatter = AutoGptFormatter(console_format_template)
     else:
         console_formatter = StructuredLoggingFormatter()
@@ -145,7 +116,7 @@ def configure_logging(
 
     # Console output handlers
     stdout = logging.StreamHandler(stream=sys.stdout)
-    stdout.setLevel(config.level)
+    stdout.setLevel(level)
     stdout.addFilter(BelowLevelFilter(logging.WARNING))
     stdout.setFormatter(console_formatter)
     stderr = logging.StreamHandler()
@@ -162,7 +133,7 @@ def configure_logging(
     user_friendly_output_logger = logging.getLogger(USER_FRIENDLY_OUTPUT_LOGGER)
     user_friendly_output_logger.setLevel(logging.INFO)
     user_friendly_output_logger.addHandler(
-        typing_console_handler if not config.plain_console_output else stdout
+        typing_console_handler if not plain_console_output else stdout
     )
     if tts_config:
         user_friendly_output_logger.addHandler(TTSHandler(tts_config))
@@ -170,26 +141,22 @@ def configure_logging(
     user_friendly_output_logger.propagate = False
 
     # File output handlers
-    if config.log_file_format is not None:
-        if config.level < logging.ERROR:
-            file_output_format_template = TEXT_LOG_FORMAT_MAP[config.log_file_format]
+    if log_file_format is not None:
+        if level < logging.ERROR:
+            file_output_format_template = TEXT_LOG_FORMAT_MAP[log_file_format]
             file_output_formatter = AutoGptFormatter(
                 file_output_format_template, no_color=True
             )
 
             # INFO log file handler
-            activity_log_handler = logging.FileHandler(
-                config.log_dir / LOG_FILE, "a", "utf-8"
-            )
-            activity_log_handler.setLevel(config.level)
+            activity_log_handler = logging.FileHandler(log_dir / LOG_FILE, "a", "utf-8")
+            activity_log_handler.setLevel(level)
             activity_log_handler.setFormatter(file_output_formatter)
             log_handlers += [activity_log_handler]
             user_friendly_output_logger.addHandler(activity_log_handler)
 
         # ERROR log file handler
-        error_log_handler = logging.FileHandler(
-            config.log_dir / ERROR_LOG_FILE, "a", "utf-8"
-        )
+        error_log_handler = logging.FileHandler(log_dir / ERROR_LOG_FILE, "a", "utf-8")
         error_log_handler.setLevel(logging.ERROR)
         error_log_handler.setFormatter(
             AutoGptFormatter(DEBUG_LOG_FORMAT, no_color=True)
@@ -200,7 +167,7 @@ def configure_logging(
     # Configure the root logger
     logging.basicConfig(
         format=console_format_template,
-        level=config.level,
+        level=level,
         handlers=log_handlers,
     )
 
@@ -218,3 +185,19 @@ def configure_logging(
 
     # Disable debug logging from OpenAI library
     openai_logger.setLevel(logging.WARNING)
+
+
+def configure_chat_plugins(config: Config) -> None:
+    """Configure chat plugins for use by the logging module"""
+
+    logger = logging.getLogger(__name__)
+
+    # Add chat plugins capable of report to logger
+    if config.chat_messages_enabled:
+        if _chat_plugins:
+            _chat_plugins.clear()
+
+        for plugin in config.plugins:
+            if hasattr(plugin, "can_handle_report") and plugin.can_handle_report():
+                logger.debug(f"Loaded plugin into logger: {plugin.__class__.__name__}")
+                _chat_plugins.append(plugin)

autogpts/autogpt/autogpt/logs/helpers.py

@@ -3,7 +3,7 @@ from typing import Any, Optional
 
 from colorama import Fore
 
-from .config import SPEECH_OUTPUT_LOGGER, USER_FRIENDLY_OUTPUT_LOGGER
+from .config import SPEECH_OUTPUT_LOGGER, USER_FRIENDLY_OUTPUT_LOGGER, _chat_plugins
 
 
 def user_friendly_output(
@@ -21,6 +21,10 @@ def user_friendly_output(
     """
     logger = logging.getLogger(USER_FRIENDLY_OUTPUT_LOGGER)
 
+    if _chat_plugins:
+        for plugin in _chat_plugins:
+            plugin.report(f"{title}: {message}")
+
     logger.log(
         level,
         message,