refactor: address Pwuts review feedback

- Remove unused error_response() and format_inputs_as_markdown() from helpers.py - Remove _get_inputs_list() wrapper from run_agent.py, use get_inputs_from_schema directly - Fix type annotations: get_user_credentials, find_matching_credential, create_credential_meta_from_match - Remove check_scopes parameter - always check scopes (original missing check was broken behavior) - Reorder _credential_has_required_scopes to be defined before find_matching_credential
fix: enable scope checking for block credentials (consistency with graphs)
2026-02-12 07:45:14 -05:00 · 2026-02-08 16:43:21 +00:00 · 2026-02-08 16:42:58 +00:00 · 2026-02-08 16:42:58 +00:00 · 2026-02-08 16:42:53 +00:00 · 2026-02-08 16:42:50 +00:00
392 changed files with 11322 additions and 15853 deletions
--- a/.github/workflows/classic-frontend-ci.yml
+++ b/.github/workflows/classic-frontend-ci.yml
@@ -49,7 +49,7 @@ jobs:
      - name: Create PR ${{ env.BUILD_BRANCH }} -> ${{ github.ref_name }}
        if: github.event_name == 'push'
-        uses: peter-evans/create-pull-request@v8
+        uses: peter-evans/create-pull-request@v7
        with:
          add-paths: classic/frontend/build/web
          base: ${{ github.ref_name }}
--- a/.github/workflows/claude-ci-failure-auto-fix.yml
+++ b/.github/workflows/claude-ci-failure-auto-fix.yml
@@ -22,7 +22,7 @@ jobs:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout code
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
        with:
          ref: ${{ github.event.workflow_run.head_branch }}
          fetch-depth: 0
@@ -42,7 +42,7 @@ jobs:
      - name: Get CI failure details
        id: failure_details
-        uses: actions/github-script@v8
+        uses: actions/github-script@v7
        with:
          script: |
            const run = await github.rest.actions.getWorkflowRun({
--- a/.github/workflows/claude-dependabot.yml
+++ b/.github/workflows/claude-dependabot.yml
@@ -30,7 +30,7 @@ jobs:
      actions: read # Required for CI access
    steps:
      - name: Checkout code
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
        with:
          fetch-depth: 1
@@ -41,7 +41,7 @@ jobs:
          python-version: "3.11"  # Use standard version matching CI
      - name: Set up Python dependency cache
-        uses: actions/cache@v5
+        uses: actions/cache@v4
        with:
          path: ~/.cache/pypoetry
          key: poetry-${{ runner.os }}-${{ hashFiles('autogpt_platform/backend/poetry.lock') }}
@@ -78,7 +78,7 @@ jobs:
      # Frontend Node.js/pnpm setup (mirrors platform-frontend-ci.yml)
      - name: Set up Node.js
-        uses: actions/setup-node@v6
+        uses: actions/setup-node@v4
        with:
          node-version: "22"
@@ -91,7 +91,7 @@ jobs:
          echo "PNPM_HOME=$HOME/.pnpm-store" >> $GITHUB_ENV
      - name: Cache frontend dependencies
-        uses: actions/cache@v5
+        uses: actions/cache@v4
        with:
          path: ~/.pnpm-store
          key: ${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml', 'autogpt_platform/frontend/package.json') }}
@@ -124,7 +124,7 @@ jobs:
      # Phase 1: Cache and load Docker images for faster setup
      - name: Set up Docker image cache
        id: docker-cache
-        uses: actions/cache@v5
+        uses: actions/cache@v4
        with:
          path: ~/docker-cache
          # Use a versioned key for cache invalidation when image list changes
@@ -309,7 +309,6 @@ jobs:
        uses: anthropics/claude-code-action@v1
        with:
          claude_code_oauth_token: ${{ secrets.CLAUDE_CODE_OAUTH_TOKEN }}
          allowed_bots: "dependabot[bot]"
          claude_args: |
            --allowedTools "Bash(npm:*),Bash(pnpm:*),Bash(poetry:*),Bash(git:*),Edit,Replace,NotebookEditCell,mcp__github_inline_comment__create_inline_comment,Bash(gh pr comment:*), Bash(gh pr diff:*), Bash(gh pr view:*)"
          prompt: |
--- a/.github/workflows/claude.yml
+++ b/.github/workflows/claude.yml
@@ -40,7 +40,7 @@ jobs:
      actions: read # Required for CI access
    steps:
      - name: Checkout code
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
        with:
          fetch-depth: 1
@@ -57,7 +57,7 @@ jobs:
          python-version: "3.11"  # Use standard version matching CI
      - name: Set up Python dependency cache
-        uses: actions/cache@v5
+        uses: actions/cache@v4
        with:
          path: ~/.cache/pypoetry
          key: poetry-${{ runner.os }}-${{ hashFiles('autogpt_platform/backend/poetry.lock') }}
@@ -94,7 +94,7 @@ jobs:
      # Frontend Node.js/pnpm setup (mirrors platform-frontend-ci.yml)
      - name: Set up Node.js
-        uses: actions/setup-node@v6
+        uses: actions/setup-node@v4
        with:
          node-version: "22"
@@ -107,7 +107,7 @@ jobs:
          echo "PNPM_HOME=$HOME/.pnpm-store" >> $GITHUB_ENV
      - name: Cache frontend dependencies
-        uses: actions/cache@v5
+        uses: actions/cache@v4
        with:
          path: ~/.pnpm-store
          key: ${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml', 'autogpt_platform/frontend/package.json') }}
@@ -140,7 +140,7 @@ jobs:
      # Phase 1: Cache and load Docker images for faster setup
      - name: Set up Docker image cache
        id: docker-cache
-        uses: actions/cache@v5
+        uses: actions/cache@v4
        with:
          path: ~/docker-cache
          # Use a versioned key for cache invalidation when image list changes
--- a/.github/workflows/codeql.yml
+++ b/.github/workflows/codeql.yml
@@ -58,7 +58,7 @@ jobs:
        # your codebase is analyzed, see https://docs.github.com/en/code-security/code-scanning/creating-an-advanced-setup-for-code-scanning/codeql-code-scanning-for-compiled-languages
    steps:
    - name: Checkout repository
-      uses: actions/checkout@v6
+      uses: actions/checkout@v4
    # Initializes the CodeQL tools for scanning.
    - name: Initialize CodeQL
--- a/.github/workflows/copilot-setup-steps.yml
+++ b/.github/workflows/copilot-setup-steps.yml
@@ -27,7 +27,7 @@ jobs:
    # If you do not check out your code, Copilot will do this for you.
    steps:
      - name: Checkout code
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
        with:
          fetch-depth: 0
          submodules: true
@@ -39,7 +39,7 @@ jobs:
          python-version: "3.11"  # Use standard version matching CI
      - name: Set up Python dependency cache
-        uses: actions/cache@v5
+        uses: actions/cache@v4
        with:
          path: ~/.cache/pypoetry
          key: poetry-${{ runner.os }}-${{ hashFiles('autogpt_platform/backend/poetry.lock') }}
@@ -76,7 +76,7 @@ jobs:
      # Frontend Node.js/pnpm setup (mirrors platform-frontend-ci.yml)
      - name: Set up Node.js
-        uses: actions/setup-node@v6
+        uses: actions/setup-node@v4
        with:
          node-version: "22"
@@ -89,7 +89,7 @@ jobs:
          echo "PNPM_HOME=$HOME/.pnpm-store" >> $GITHUB_ENV
      - name: Cache frontend dependencies
-        uses: actions/cache@v5
+        uses: actions/cache@v4
        with:
          path: ~/.pnpm-store
          key: ${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml', 'autogpt_platform/frontend/package.json') }}
@@ -132,7 +132,7 @@ jobs:
      # Phase 1: Cache and load Docker images for faster setup
      - name: Set up Docker image cache
        id: docker-cache
-        uses: actions/cache@v5
+        uses: actions/cache@v4
        with:
          path: ~/docker-cache
          # Use a versioned key for cache invalidation when image list changes
--- a/.github/workflows/docs-block-sync.yml
+++ b/.github/workflows/docs-block-sync.yml
@@ -23,7 +23,7 @@ jobs:
    steps:
      - name: Checkout code
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
        with:
          fetch-depth: 1
@@ -33,7 +33,7 @@ jobs:
          python-version: "3.11"
      - name: Set up Python dependency cache
-        uses: actions/cache@v5
+        uses: actions/cache@v4
        with:
          path: ~/.cache/pypoetry
          key: poetry-${{ runner.os }}-${{ hashFiles('autogpt_platform/backend/poetry.lock') }}
--- a/.github/workflows/docs-claude-review.yml
+++ b/.github/workflows/docs-claude-review.yml
@@ -23,7 +23,7 @@ jobs:
    steps:
      - name: Checkout code
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
        with:
          fetch-depth: 0
@@ -33,7 +33,7 @@ jobs:
          python-version: "3.11"
      - name: Set up Python dependency cache
-        uses: actions/cache@v5
+        uses: actions/cache@v4
        with:
          path: ~/.cache/pypoetry
          key: poetry-${{ runner.os }}-${{ hashFiles('autogpt_platform/backend/poetry.lock') }}
--- a/.github/workflows/docs-enhance.yml
+++ b/.github/workflows/docs-enhance.yml
@@ -28,7 +28,7 @@ jobs:
    steps:
      - name: Checkout code
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
        with:
          fetch-depth: 1
@@ -38,7 +38,7 @@ jobs:
          python-version: "3.11"
      - name: Set up Python dependency cache
-        uses: actions/cache@v5
+        uses: actions/cache@v4
        with:
          path: ~/.cache/pypoetry
          key: poetry-${{ runner.os }}-${{ hashFiles('autogpt_platform/backend/poetry.lock') }}
--- a/.github/workflows/platform-autogpt-deploy-dev.yaml
+++ b/.github/workflows/platform-autogpt-deploy-dev.yaml
@@ -25,7 +25,7 @@ jobs:
    steps:
      - name: Checkout code
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
        with:
          ref: ${{ github.event.inputs.git_ref || github.ref_name }}
@@ -52,7 +52,7 @@ jobs:
    runs-on: ubuntu-latest
    steps:
      - name: Trigger deploy workflow
-        uses: peter-evans/repository-dispatch@v4
+        uses: peter-evans/repository-dispatch@v3
        with:
          token: ${{ secrets.DEPLOY_TOKEN }}
          repository: Significant-Gravitas/AutoGPT_cloud_infrastructure
--- a/.github/workflows/platform-autogpt-deploy-prod.yml
+++ b/.github/workflows/platform-autogpt-deploy-prod.yml
@@ -17,7 +17,7 @@ jobs:
    steps:
      - name: Checkout code
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
        with:
          ref: ${{ github.ref_name || 'master' }}
@@ -45,7 +45,7 @@ jobs:
    runs-on: ubuntu-latest
    steps:
      - name: Trigger deploy workflow
-        uses: peter-evans/repository-dispatch@v4
+        uses: peter-evans/repository-dispatch@v3
        with:
          token: ${{ secrets.DEPLOY_TOKEN }}
          repository: Significant-Gravitas/AutoGPT_cloud_infrastructure
--- a/.github/workflows/platform-backend-ci.yml
+++ b/.github/workflows/platform-backend-ci.yml
@@ -68,7 +68,7 @@ jobs:
    steps:
      - name: Checkout repository
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
        with:
          fetch-depth: 0
          submodules: true
@@ -88,7 +88,7 @@ jobs:
        run: echo "date=$(date +'%Y-%m-%d')" >> $GITHUB_OUTPUT
      - name: Set up Python dependency cache
-        uses: actions/cache@v5
+        uses: actions/cache@v4
        with:
          path: ~/.cache/pypoetry
          key: poetry-${{ runner.os }}-${{ hashFiles('autogpt_platform/backend/poetry.lock') }}
--- a/.github/workflows/platform-dev-deploy-event-dispatcher.yml
+++ b/.github/workflows/platform-dev-deploy-event-dispatcher.yml
@@ -17,7 +17,7 @@ jobs:
      - name: Check comment permissions and deployment status
        id: check_status
        if: github.event_name == 'issue_comment' && github.event.issue.pull_request
-        uses: actions/github-script@v8
+        uses: actions/github-script@v7
        with:
          script: |
            const commentBody = context.payload.comment.body.trim();
@@ -55,7 +55,7 @@ jobs:
      - name: Post permission denied comment
        if: steps.check_status.outputs.permission_denied == 'true'
-        uses: actions/github-script@v8
+        uses: actions/github-script@v7
        with:
          script: |
            await github.rest.issues.createComment({
@@ -68,7 +68,7 @@ jobs:
      - name: Get PR details for deployment
        id: pr_details
        if: steps.check_status.outputs.should_deploy == 'true' || steps.check_status.outputs.should_undeploy == 'true'
-        uses: actions/github-script@v8
+        uses: actions/github-script@v7
        with:
          script: |
            const pr = await github.rest.pulls.get({
@@ -82,7 +82,7 @@ jobs:
      - name: Dispatch Deploy Event
        if: steps.check_status.outputs.should_deploy == 'true'
-        uses: peter-evans/repository-dispatch@v4
+        uses: peter-evans/repository-dispatch@v3
        with:
          token: ${{ secrets.DISPATCH_TOKEN }}
          repository: Significant-Gravitas/AutoGPT_cloud_infrastructure
@@ -98,7 +98,7 @@ jobs:
      - name: Post deploy success comment
        if: steps.check_status.outputs.should_deploy == 'true'
-        uses: actions/github-script@v8
+        uses: actions/github-script@v7
        with:
          script: |
            await github.rest.issues.createComment({
@@ -110,7 +110,7 @@ jobs:
      - name: Dispatch Undeploy Event (from comment)
        if: steps.check_status.outputs.should_undeploy == 'true'
-        uses: peter-evans/repository-dispatch@v4
+        uses: peter-evans/repository-dispatch@v3
        with:
          token: ${{ secrets.DISPATCH_TOKEN }}
          repository: Significant-Gravitas/AutoGPT_cloud_infrastructure
@@ -126,7 +126,7 @@ jobs:
      - name: Post undeploy success comment
        if: steps.check_status.outputs.should_undeploy == 'true'
-        uses: actions/github-script@v8
+        uses: actions/github-script@v7
        with:
          script: |
            await github.rest.issues.createComment({
@@ -139,7 +139,7 @@ jobs:
      - name: Check deployment status on PR close
        id: check_pr_close
        if: github.event_name == 'pull_request' && github.event.action == 'closed'
-        uses: actions/github-script@v8
+        uses: actions/github-script@v7
        with:
          script: |
            const comments = await github.rest.issues.listComments({
@@ -168,7 +168,7 @@ jobs:
          github.event_name == 'pull_request' &&
          github.event.action == 'closed' &&
          steps.check_pr_close.outputs.should_undeploy == 'true'
-        uses: peter-evans/repository-dispatch@v4
+        uses: peter-evans/repository-dispatch@v3
        with:
          token: ${{ secrets.DISPATCH_TOKEN }}
          repository: Significant-Gravitas/AutoGPT_cloud_infrastructure
@@ -187,7 +187,7 @@ jobs:
          github.event_name == 'pull_request' &&
          github.event.action == 'closed' &&
          steps.check_pr_close.outputs.should_undeploy == 'true'
-        uses: actions/github-script@v8
+        uses: actions/github-script@v7
        with:
          script: |
            await github.rest.issues.createComment({
--- a/.github/workflows/platform-frontend-ci.yml
+++ b/.github/workflows/platform-frontend-ci.yml
@@ -31,7 +31,7 @@ jobs:
    steps:
      - name: Checkout repository
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
      - name: Check for component changes
        uses: dorny/paths-filter@v3
@@ -42,7 +42,7 @@ jobs:
              - 'autogpt_platform/frontend/src/components/**'
      - name: Set up Node.js
-        uses: actions/setup-node@v6
+        uses: actions/setup-node@v4
        with:
          node-version: "22.18.0"
@@ -54,7 +54,7 @@ jobs:
        run: echo "key=${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml', 'autogpt_platform/frontend/package.json') }}" >> $GITHUB_OUTPUT
      - name: Cache dependencies
-        uses: actions/cache@v5
+        uses: actions/cache@v4
        with:
          path: ~/.pnpm-store
          key: ${{ steps.cache-key.outputs.key }}
@@ -71,10 +71,10 @@ jobs:
    steps:
      - name: Checkout repository
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
      - name: Set up Node.js
-        uses: actions/setup-node@v6
+        uses: actions/setup-node@v4
        with:
          node-version: "22.18.0"
@@ -82,7 +82,7 @@ jobs:
        run: corepack enable
      - name: Restore dependencies cache
-        uses: actions/cache@v5
+        uses: actions/cache@v4
        with:
          path: ~/.pnpm-store
          key: ${{ needs.setup.outputs.cache-key }}
@@ -107,12 +107,12 @@ jobs:
    steps:
      - name: Checkout repository
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
        with:
          fetch-depth: 0
      - name: Set up Node.js
-        uses: actions/setup-node@v6
+        uses: actions/setup-node@v4
        with:
          node-version: "22.18.0"
@@ -120,7 +120,7 @@ jobs:
        run: corepack enable
      - name: Restore dependencies cache
-        uses: actions/cache@v5
+        uses: actions/cache@v4
        with:
          path: ~/.pnpm-store
          key: ${{ needs.setup.outputs.cache-key }}
@@ -148,12 +148,12 @@ jobs:
    steps:
      - name: Checkout repository
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
        with:
          submodules: recursive
      - name: Set up Node.js
-        uses: actions/setup-node@v6
+        uses: actions/setup-node@v4
        with:
          node-version: "22.18.0"
@@ -176,7 +176,7 @@ jobs:
        uses: docker/setup-buildx-action@v3
      - name: Cache Docker layers
-        uses: actions/cache@v5
+        uses: actions/cache@v4
        with:
          path: /tmp/.buildx-cache
          key: ${{ runner.os }}-buildx-frontend-test-${{ hashFiles('autogpt_platform/docker-compose.yml', 'autogpt_platform/backend/Dockerfile', 'autogpt_platform/backend/pyproject.toml', 'autogpt_platform/backend/poetry.lock') }}
@@ -231,7 +231,7 @@ jobs:
          fi
      - name: Restore dependencies cache
-        uses: actions/cache@v5
+        uses: actions/cache@v4
        with:
          path: ~/.pnpm-store
          key: ${{ needs.setup.outputs.cache-key }}
@@ -277,12 +277,12 @@ jobs:
    steps:
      - name: Checkout repository
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
        with:
          submodules: recursive
      - name: Set up Node.js
-        uses: actions/setup-node@v6
+        uses: actions/setup-node@v4
        with:
          node-version: "22.18.0"
@@ -290,7 +290,7 @@ jobs:
        run: corepack enable
      - name: Restore dependencies cache
-        uses: actions/cache@v5
+        uses: actions/cache@v4
        with:
          path: ~/.pnpm-store
          key: ${{ needs.setup.outputs.cache-key }}
--- a/.github/workflows/platform-fullstack-ci.yml
+++ b/.github/workflows/platform-fullstack-ci.yml
@@ -29,10 +29,10 @@ jobs:
    steps:
      - name: Checkout repository
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
      - name: Set up Node.js
-        uses: actions/setup-node@v6
+        uses: actions/setup-node@v4
        with:
          node-version: "22.18.0"
@@ -44,7 +44,7 @@ jobs:
        run: echo "key=${{ runner.os }}-pnpm-${{ hashFiles('autogpt_platform/frontend/pnpm-lock.yaml', 'autogpt_platform/frontend/package.json') }}" >> $GITHUB_OUTPUT
      - name: Cache dependencies
-        uses: actions/cache@v5
+        uses: actions/cache@v4
        with:
          path: ~/.pnpm-store
          key: ${{ steps.cache-key.outputs.key }}
@@ -56,19 +56,19 @@ jobs:
        run: pnpm install --frozen-lockfile
  types:
-    runs-on: big-boi
+    runs-on: ubuntu-latest
    needs: setup
    strategy:
      fail-fast: false
    steps:
      - name: Checkout repository
-        uses: actions/checkout@v6
+        uses: actions/checkout@v4
        with:
          submodules: recursive
      - name: Set up Node.js
-        uses: actions/setup-node@v6
+        uses: actions/setup-node@v4
        with:
          node-version: "22.18.0"
@@ -85,10 +85,10 @@ jobs:
      - name: Run docker compose
        run: |
-          docker compose -f ../docker-compose.yml --profile local up -d deps_backend
+          docker compose -f ../docker-compose.yml --profile local --profile deps_backend up -d
      - name: Restore dependencies cache
-        uses: actions/cache@v5
+        uses: actions/cache@v4
        with:
          path: ~/.pnpm-store
          key: ${{ needs.setup.outputs.cache-key }}
--- a/.github/workflows/repo-workflow-checker.yml
+++ b/.github/workflows/repo-workflow-checker.yml
@@ -11,7 +11,7 @@ jobs:
    steps:
      # - name: Wait some time for all actions to start
      #   run: sleep 30
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v4
        # with:
          # fetch-depth: 0
      - name: Set up Python
--- a/autogpt_platform/autogpt_libs/poetry.lock
+++ b/autogpt_platform/autogpt_libs/poetry.lock
@@ -99,101 +99,84 @@ files = [
 [[package]]
 name = "cffi"
-version = "2.0.0"
+version = "1.17.1"
 description = "Foreign Function Interface for Python calling C code."
 optional = false
-python-versions = ">=3.9"
+python-versions = ">=3.8"
 groups = ["main"]
 markers = "platform_python_implementation != \"PyPy\""
 files = [
-    {file = "cffi-2.0.0-cp310-cp310-macosx_10_13_x86_64.whl", hash = "sha256:0cf2d91ecc3fcc0625c2c530fe004f82c110405f101548512cce44322fa8ac44"},
+    {file = "cffi-1.17.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:df8b1c11f177bc2313ec4b2d46baec87a5f3e71fc8b45dab2ee7cae86d9aba14"},
-    {file = "cffi-2.0.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:f73b96c41e3b2adedc34a7356e64c8eb96e03a3782b535e043a986276ce12a49"},
+    {file = "cffi-1.17.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:8f2cdc858323644ab277e9bb925ad72ae0e67f69e804f4898c070998d50b1a67"},
-    {file = "cffi-2.0.0-cp310-cp310-manylinux1_i686.manylinux2014_i686.manylinux_2_17_i686.manylinux_2_5_i686.whl", hash = "sha256:53f77cbe57044e88bbd5ed26ac1d0514d2acf0591dd6bb02a3ae37f76811b80c"},
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 [[package]]
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 optional = false
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 python-versions = ">=3.8"
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 optional = false
-python-versions = ">=3.9"
+python-versions = ">=3.8"
 groups = ["dev"]
 files = [
-    {file = "pytest_mock-3.15.1-py3-none-any.whl", hash = "sha256:0a25e2eb88fe5168d535041d09a4529a188176ae608a6d249ee65abc0949630d"},
+    {file = "pytest_mock-3.14.1-py3-none-any.whl", hash = "sha256:178aefcd11307d874b4cd3100344e7e2d888d9791a6a1d9bfe90fbc1b74fd1d0"},
-    {file = "pytest_mock-3.15.1.tar.gz", hash = "sha256:1849a238f6f396da19762269de72cb1814ab44416fa73a8686deac10b0d87a0f"},
+    {file = "pytest_mock-3.14.1.tar.gz", hash = "sha256:159e9edac4c451ce77a5cdb9fc5d1100708d2dd4ba3c3df572f14097351af80e"},
 ]
 [package.dependencies]
@@ -2341,30 +2292,31 @@ pyasn1 = ">=0.1.3"
 [[package]]
 name = "ruff"
-version = "0.15.0"
+version = "0.12.11"
 description = "An extremely fast Python linter and code formatter, written in Rust."
 optional = false
 python-versions = ">=3.7"
 groups = ["dev"]
 files = [
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-    {file = "ruff-0.15.0.tar.gz", hash = "sha256:6bdea47cdbea30d40f8f8d7d69c0854ba7c15420ec75a26f463290949d7f7e9a"},
+    {file = "ruff-0.12.11-py3-none-win_arm64.whl", hash = "sha256:bae4d6e6a2676f8fb0f98b74594a048bae1b944aab17e9f5d504062303c6dbea"},
    {file = "ruff-0.12.11.tar.gz", hash = "sha256:c6b09ae8426a65bbee5425b9d0b82796dbb07cb1af045743c79bfb163001165d"},
 ]
 [[package]]
@@ -2911,4 +2863,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.10,<4.0"
-content-hash = "40eae94995dc0a388fa832ed4af9b6137f28d5b5ced3aaea70d5f91d4d9a179d"
+content-hash = "5f15a9c9381c9a374f3d18e087c23b1f1ba8cce192d6f67463a3e3a7a18fee44"
--- a/autogpt_platform/autogpt_libs/pyproject.toml
+++ b/autogpt_platform/autogpt_libs/pyproject.toml
@@ -9,7 +9,7 @@ packages = [{ include = "autogpt_libs" }]
 [tool.poetry.dependencies]
 python = ">=3.10,<4.0"
 colorama = "^0.4.6"
-cryptography = "^46.0"
+cryptography = "^45.0"
 expiringdict = "^1.2.2"
 fastapi = "^0.128.0"
 google-cloud-logging = "^3.13.0"
@@ -22,12 +22,12 @@ supabase = "^2.27.2"
 uvicorn = "^0.40.0"
 [tool.poetry.group.dev.dependencies]
-pyright = "^1.1.408"
+pyright = "^1.1.404"
 pytest = "^8.4.1"
-pytest-asyncio = "^1.3.0"
+pytest-asyncio = "^1.1.0"
-pytest-mock = "^3.15.1"
+pytest-mock = "^3.14.1"
-pytest-cov = "^7.0.0"
+pytest-cov = "^6.2.1"
-ruff = "^0.15.0"
+ruff = "^0.12.11"
 [build-system]
 requires = ["poetry-core"]
--- a/autogpt_platform/backend/backend/api/external/v1/routes.py
+++ b/autogpt_platform/backend/backend/api/external/v1/routes.py
@@ -10,7 +10,7 @@ from typing_extensions import TypedDict
 import backend.api.features.store.cache as store_cache
 import backend.api.features.store.model as store_model
-import backend.blocks
+import backend.data.block
 from backend.api.external.middleware import require_permission
 from backend.data import execution as execution_db
 from backend.data import graph as graph_db
@@ -67,7 +67,7 @@ async def get_user_info(
    dependencies=[Security(require_permission(APIKeyPermission.READ_BLOCK))],
 )
 async def get_graph_blocks() -> Sequence[dict[Any, Any]]:
-    blocks = [block() for block in backend.blocks.get_blocks().values()]
+    blocks = [block() for block in backend.data.block.get_blocks().values()]
    return [b.to_dict() for b in blocks if not b.disabled]
@@ -83,7 +83,7 @@ async def execute_graph_block(
        require_permission(APIKeyPermission.EXECUTE_BLOCK)
    ),
 ) -> CompletedBlockOutput:
-    obj = backend.blocks.get_block(block_id)
+    obj = backend.data.block.get_block(block_id)
    if not obj:
        raise HTTPException(status_code=404, detail=f"Block #{block_id} not found.")
    if obj.disabled:
--- a/autogpt_platform/backend/backend/api/features/builder/db.py
+++ b/autogpt_platform/backend/backend/api/features/builder/db.py
@@ -10,15 +10,10 @@ import backend.api.features.library.db as library_db
 import backend.api.features.library.model as library_model
 import backend.api.features.store.db as store_db
 import backend.api.features.store.model as store_model
 import backend.data.block
 from backend.blocks import load_all_blocks
 from backend.blocks._base import (
    AnyBlockSchema,
    BlockCategory,
    BlockInfo,
    BlockSchema,
    BlockType,
 )
 from backend.blocks.llm import LlmModel
 from backend.data.block import AnyBlockSchema, BlockCategory, BlockInfo, BlockSchema
 from backend.data.db import query_raw_with_schema
 from backend.integrations.providers import ProviderName
 from backend.util.cache import cached
@@ -27,7 +22,7 @@ from backend.util.models import Pagination
 from .model import (
    BlockCategoryResponse,
    BlockResponse,
-    BlockTypeFilter,
+    BlockType,
    CountResponse,
    FilterType,
    Provider,
@@ -93,7 +88,7 @@ def get_block_categories(category_blocks: int = 3) -> list[BlockCategoryResponse
 def get_blocks(
    *,
    category: str | None = None,
-    type: BlockTypeFilter | None = None,
+    type: BlockType | None = None,
    provider: ProviderName | None = None,
    page: int = 1,
    page_size: int = 50,
@@ -674,9 +669,9 @@ async def get_suggested_blocks(count: int = 5) -> list[BlockInfo]:
    for block_type in load_all_blocks().values():
        block: AnyBlockSchema = block_type()
        if block.disabled or block.block_type in (
-            BlockType.INPUT,
+            backend.data.block.BlockType.INPUT,
-            BlockType.OUTPUT,
+            backend.data.block.BlockType.OUTPUT,
-            BlockType.AGENT,
+            backend.data.block.BlockType.AGENT,
        ):
            continue
        # Find the execution count for this block
--- a/autogpt_platform/backend/backend/api/features/builder/model.py
+++ b/autogpt_platform/backend/backend/api/features/builder/model.py
@@ -4,7 +4,7 @@ from pydantic import BaseModel
 import backend.api.features.library.model as library_model
 import backend.api.features.store.model as store_model
-from backend.blocks._base import BlockInfo
+from backend.data.block import BlockInfo
 from backend.integrations.providers import ProviderName
 from backend.util.models import Pagination
@@ -15,7 +15,7 @@ FilterType = Literal[
    "my_agents",
 ]
-BlockTypeFilter = Literal["all", "input", "action", "output"]
+BlockType = Literal["all", "input", "action", "output"]
 class SearchEntry(BaseModel):
--- a/autogpt_platform/backend/backend/api/features/builder/routes.py
+++ b/autogpt_platform/backend/backend/api/features/builder/routes.py
@@ -88,7 +88,7 @@ async def get_block_categories(
 )
 async def get_blocks(
    category: Annotated[str | None, fastapi.Query()] = None,
-    type: Annotated[builder_model.BlockTypeFilter | None, fastapi.Query()] = None,
+    type: Annotated[builder_model.BlockType | None, fastapi.Query()] = None,
    provider: Annotated[ProviderName | None, fastapi.Query()] = None,
    page: Annotated[int, fastapi.Query()] = 1,
    page_size: Annotated[int, fastapi.Query()] = 50,
--- a/autogpt_platform/backend/backend/api/features/chat/config.py
+++ b/autogpt_platform/backend/backend/api/features/chat/config.py
@@ -93,12 +93,6 @@ class ChatConfig(BaseSettings):
        description="Name of the prompt in Langfuse to fetch",
    )
    # Extended thinking configuration for Claude models
    thinking_enabled: bool = Field(
        default=True,
        description="Enable adaptive thinking for Claude models via OpenRouter",
    )
    @field_validator("api_key", mode="before")
    @classmethod
    def get_api_key(cls, v):
--- a/autogpt_platform/backend/backend/api/features/chat/db.py
+++ b/autogpt_platform/backend/backend/api/features/chat/db.py
@@ -45,7 +45,10 @@ async def create_chat_session(
        successfulAgentRuns=SafeJson({}),
        successfulAgentSchedules=SafeJson({}),
    )
-    return await PrismaChatSession.prisma().create(data=data)
+    return await PrismaChatSession.prisma().create(
        data=data,
        include={"Messages": True},
    )
 async def update_chat_session(
--- a/autogpt_platform/backend/backend/api/features/chat/model.py
+++ b/autogpt_platform/backend/backend/api/features/chat/model.py
@@ -2,7 +2,7 @@ import asyncio
 import logging
 import uuid
 from datetime import UTC, datetime
-from typing import Any, cast
+from typing import Any
 from weakref import WeakValueDictionary
 from openai.types.chat import (
@@ -104,26 +104,6 @@ class ChatSession(BaseModel):
    successful_agent_runs: dict[str, int] = {}
    successful_agent_schedules: dict[str, int] = {}
    def add_tool_call_to_current_turn(self, tool_call: dict) -> None:
        """Attach a tool_call to the current turn's assistant message.
        Searches backwards for the most recent assistant message (stopping at
        any user message boundary). If found, appends the tool_call to it.
        Otherwise creates a new assistant message with the tool_call.
        """
        for msg in reversed(self.messages):
            if msg.role == "user":
                break
            if msg.role == "assistant":
                if not msg.tool_calls:
                    msg.tool_calls = []
                msg.tool_calls.append(tool_call)
                return
        self.messages.append(
            ChatMessage(role="assistant", content="", tool_calls=[tool_call])
        )
    @staticmethod
    def new(user_id: str) -> "ChatSession":
        return ChatSession(
@@ -192,47 +172,6 @@ class ChatSession(BaseModel):
            successful_agent_schedules=successful_agent_schedules,
        )
    @staticmethod
    def _merge_consecutive_assistant_messages(
        messages: list[ChatCompletionMessageParam],
    ) -> list[ChatCompletionMessageParam]:
        """Merge consecutive assistant messages into single messages.
        Long-running tool flows can create split assistant messages: one with
        text content and another with tool_calls. Anthropic's API requires
        tool_result blocks to reference a tool_use in the immediately preceding
        assistant message, so these splits cause 400 errors via OpenRouter.
        """
        if len(messages) < 2:
            return messages
        result: list[ChatCompletionMessageParam] = [messages[0]]
        for msg in messages[1:]:
            prev = result[-1]
            if prev.get("role") != "assistant" or msg.get("role") != "assistant":
                result.append(msg)
                continue
            prev = cast(ChatCompletionAssistantMessageParam, prev)
            curr = cast(ChatCompletionAssistantMessageParam, msg)
            curr_content = curr.get("content") or ""
            if curr_content:
                prev_content = prev.get("content") or ""
                prev["content"] = (
                    f"{prev_content}\n{curr_content}" if prev_content else curr_content
                )
            curr_tool_calls = curr.get("tool_calls")
            if curr_tool_calls:
                prev_tool_calls = prev.get("tool_calls")
                prev["tool_calls"] = (
                    list(prev_tool_calls) + list(curr_tool_calls)
                    if prev_tool_calls
                    else list(curr_tool_calls)
                )
        return result
    def to_openai_messages(self) -> list[ChatCompletionMessageParam]:
        messages = []
        for message in self.messages:
@@ -319,7 +258,7 @@ class ChatSession(BaseModel):
                        name=message.name or "",
                    )
                )
-        return self._merge_consecutive_assistant_messages(messages)
+        return messages
 async def _get_session_from_cache(session_id: str) -> ChatSession | None:
--- a/autogpt_platform/backend/backend/api/features/chat/model_test.py
+++ b/autogpt_platform/backend/backend/api/features/chat/model_test.py
@@ -1,16 +1,4 @@
 from typing import cast
 import pytest
 from openai.types.chat import (
    ChatCompletionAssistantMessageParam,
    ChatCompletionMessageParam,
    ChatCompletionToolMessageParam,
    ChatCompletionUserMessageParam,
 )
 from openai.types.chat.chat_completion_message_tool_call_param import (
    ChatCompletionMessageToolCallParam,
    Function,
 )
 from .model import (
    ChatMessage,
@@ -129,205 +117,3 @@ async def test_chatsession_db_storage(setup_test_user, test_user_id):
                loaded.tool_calls is not None
            ), f"Tool calls missing for {orig.role} message"
            assert len(orig.tool_calls) == len(loaded.tool_calls)
 # --------------------------------------------------------------------------- #
 #  _merge_consecutive_assistant_messages                                       #
 # --------------------------------------------------------------------------- #
 _tc = ChatCompletionMessageToolCallParam(
    id="tc1", type="function", function=Function(name="do_stuff", arguments="{}")
 )
 _tc2 = ChatCompletionMessageToolCallParam(
    id="tc2", type="function", function=Function(name="other", arguments="{}")
 )
 def test_merge_noop_when_no_consecutive_assistants():
    """Messages without consecutive assistants are returned unchanged."""
    msgs = [
        ChatCompletionUserMessageParam(role="user", content="hi"),
        ChatCompletionAssistantMessageParam(role="assistant", content="hello"),
        ChatCompletionUserMessageParam(role="user", content="bye"),
    ]
    merged = ChatSession._merge_consecutive_assistant_messages(msgs)
    assert len(merged) == 3
    assert [m["role"] for m in merged] == ["user", "assistant", "user"]
 def test_merge_splits_text_and_tool_calls():
    """The exact bug scenario: text-only assistant followed by tool_calls-only assistant."""
    msgs = [
        ChatCompletionUserMessageParam(role="user", content="build agent"),
        ChatCompletionAssistantMessageParam(
            role="assistant", content="Let me build that"
        ),
        ChatCompletionAssistantMessageParam(
            role="assistant", content="", tool_calls=[_tc]
        ),
        ChatCompletionToolMessageParam(role="tool", content="ok", tool_call_id="tc1"),
    ]
    merged = ChatSession._merge_consecutive_assistant_messages(msgs)
    assert len(merged) == 3
    assert merged[0]["role"] == "user"
    assert merged[2]["role"] == "tool"
    a = cast(ChatCompletionAssistantMessageParam, merged[1])
    assert a["role"] == "assistant"
    assert a.get("content") == "Let me build that"
    assert a.get("tool_calls") == [_tc]
 def test_merge_combines_tool_calls_from_both():
    """Both consecutive assistants have tool_calls — they get merged."""
    msgs: list[ChatCompletionAssistantMessageParam] = [
        ChatCompletionAssistantMessageParam(
            role="assistant", content="text", tool_calls=[_tc]
        ),
        ChatCompletionAssistantMessageParam(
            role="assistant", content="", tool_calls=[_tc2]
        ),
    ]
    merged = ChatSession._merge_consecutive_assistant_messages(msgs)  # type: ignore[arg-type]
    assert len(merged) == 1
    a = cast(ChatCompletionAssistantMessageParam, merged[0])
    assert a.get("tool_calls") == [_tc, _tc2]
    assert a.get("content") == "text"
 def test_merge_three_consecutive_assistants():
    """Three consecutive assistants collapse into one."""
    msgs: list[ChatCompletionAssistantMessageParam] = [
        ChatCompletionAssistantMessageParam(role="assistant", content="a"),
        ChatCompletionAssistantMessageParam(role="assistant", content="b"),
        ChatCompletionAssistantMessageParam(
            role="assistant", content="", tool_calls=[_tc]
        ),
    ]
    merged = ChatSession._merge_consecutive_assistant_messages(msgs)  # type: ignore[arg-type]
    assert len(merged) == 1
    a = cast(ChatCompletionAssistantMessageParam, merged[0])
    assert a.get("content") == "a\nb"
    assert a.get("tool_calls") == [_tc]
 def test_merge_empty_and_single_message():
    """Edge cases: empty list and single message."""
    assert ChatSession._merge_consecutive_assistant_messages([]) == []
    single: list[ChatCompletionMessageParam] = [
        ChatCompletionUserMessageParam(role="user", content="hi")
    ]
    assert ChatSession._merge_consecutive_assistant_messages(single) == single
 # --------------------------------------------------------------------------- #
 #  add_tool_call_to_current_turn                                               #
 # --------------------------------------------------------------------------- #
 _raw_tc = {
    "id": "tc1",
    "type": "function",
    "function": {"name": "f", "arguments": "{}"},
 }
 _raw_tc2 = {
    "id": "tc2",
    "type": "function",
    "function": {"name": "g", "arguments": "{}"},
 }
 def test_add_tool_call_appends_to_existing_assistant():
    """When the last assistant is from the current turn, tool_call is added to it."""
    session = ChatSession.new(user_id="u")
    session.messages = [
        ChatMessage(role="user", content="hi"),
        ChatMessage(role="assistant", content="working on it"),
    ]
    session.add_tool_call_to_current_turn(_raw_tc)
    assert len(session.messages) == 2  # no new message created
    assert session.messages[1].tool_calls == [_raw_tc]
 def test_add_tool_call_creates_assistant_when_none_exists():
    """When there's no current-turn assistant, a new one is created."""
    session = ChatSession.new(user_id="u")
    session.messages = [
        ChatMessage(role="user", content="hi"),
    ]
    session.add_tool_call_to_current_turn(_raw_tc)
    assert len(session.messages) == 2
    assert session.messages[1].role == "assistant"
    assert session.messages[1].tool_calls == [_raw_tc]
 def test_add_tool_call_does_not_cross_user_boundary():
    """A user message acts as a boundary — previous assistant is not modified."""
    session = ChatSession.new(user_id="u")
    session.messages = [
        ChatMessage(role="assistant", content="old turn"),
        ChatMessage(role="user", content="new message"),
    ]
    session.add_tool_call_to_current_turn(_raw_tc)
    assert len(session.messages) == 3  # new assistant was created
    assert session.messages[0].tool_calls is None  # old assistant untouched
    assert session.messages[2].role == "assistant"
    assert session.messages[2].tool_calls == [_raw_tc]
 def test_add_tool_call_multiple_times():
    """Multiple long-running tool calls accumulate on the same assistant."""
    session = ChatSession.new(user_id="u")
    session.messages = [
        ChatMessage(role="user", content="hi"),
        ChatMessage(role="assistant", content="doing stuff"),
    ]
    session.add_tool_call_to_current_turn(_raw_tc)
    # Simulate a pending tool result in between (like _yield_tool_call does)
    session.messages.append(
        ChatMessage(role="tool", content="pending", tool_call_id="tc1")
    )
    session.add_tool_call_to_current_turn(_raw_tc2)
    assert len(session.messages) == 3  # user, assistant, tool — no extra assistant
    assert session.messages[1].tool_calls == [_raw_tc, _raw_tc2]
 def test_to_openai_messages_merges_split_assistants():
    """End-to-end: session with split assistants produces valid OpenAI messages."""
    session = ChatSession.new(user_id="u")
    session.messages = [
        ChatMessage(role="user", content="build agent"),
        ChatMessage(role="assistant", content="Let me build that"),
        ChatMessage(
            role="assistant",
            content="",
            tool_calls=[
                {
                    "id": "tc1",
                    "type": "function",
                    "function": {"name": "create_agent", "arguments": "{}"},
                }
            ],
        ),
        ChatMessage(role="tool", content="done", tool_call_id="tc1"),
        ChatMessage(role="assistant", content="Saved!"),
        ChatMessage(role="user", content="show me an example run"),
    ]
    openai_msgs = session.to_openai_messages()
    # The two consecutive assistants at index 1,2 should be merged
    roles = [m["role"] for m in openai_msgs]
    assert roles == ["user", "assistant", "tool", "assistant", "user"]
    # The merged assistant should have both content and tool_calls
    merged = cast(ChatCompletionAssistantMessageParam, openai_msgs[1])
    assert merged.get("content") == "Let me build that"
    tc_list = merged.get("tool_calls")
    assert tc_list is not None and len(list(tc_list)) == 1
    assert list(tc_list)[0]["id"] == "tc1"
--- a/autogpt_platform/backend/backend/api/features/chat/response_model.py
+++ b/autogpt_platform/backend/backend/api/features/chat/response_model.py
@@ -10,8 +10,6 @@ from typing import Any
 from pydantic import BaseModel, Field
 from backend.util.json import dumps as json_dumps
 class ResponseType(str, Enum):
    """Types of streaming responses following AI SDK protocol."""
@@ -20,10 +18,6 @@ class ResponseType(str, Enum):
    START = "start"
    FINISH = "finish"
    # Step lifecycle (one LLM API call within a message)
    START_STEP = "start-step"
    FINISH_STEP = "finish-step"
    # Text streaming
    TEXT_START = "text-start"
    TEXT_DELTA = "text-delta"
@@ -63,16 +57,6 @@ class StreamStart(StreamBaseResponse):
        description="Task ID for SSE reconnection. Clients can reconnect using GET /tasks/{taskId}/stream",
    )
    def to_sse(self) -> str:
        """Convert to SSE format, excluding non-protocol fields like taskId."""
        import json
        data: dict[str, Any] = {
            "type": self.type.value,
            "messageId": self.messageId,
        }
        return f"data: {json.dumps(data)}\n\n"
 class StreamFinish(StreamBaseResponse):
    """End of message/stream."""
@@ -80,26 +64,6 @@ class StreamFinish(StreamBaseResponse):
    type: ResponseType = ResponseType.FINISH
 class StreamStartStep(StreamBaseResponse):
    """Start of a step (one LLM API call within a message).
    The AI SDK uses this to add a step-start boundary to message.parts,
    enabling visual separation between multiple LLM calls in a single message.
    """
    type: ResponseType = ResponseType.START_STEP
 class StreamFinishStep(StreamBaseResponse):
    """End of a step (one LLM API call within a message).
    The AI SDK uses this to reset activeTextParts and activeReasoningParts,
    so the next LLM call in a tool-call continuation starts with clean state.
    """
    type: ResponseType = ResponseType.FINISH_STEP
 # ========== Text Streaming ==========
@@ -153,7 +117,7 @@ class StreamToolOutputAvailable(StreamBaseResponse):
    type: ResponseType = ResponseType.TOOL_OUTPUT_AVAILABLE
    toolCallId: str = Field(..., description="Tool call ID this responds to")
    output: str | dict[str, Any] = Field(..., description="Tool execution output")
-    # Keep these for internal backend use
+    # Additional fields for internal use (not part of AI SDK spec but useful)
    toolName: str | None = Field(
        default=None, description="Name of the tool that was executed"
    )
@@ -161,17 +125,6 @@ class StreamToolOutputAvailable(StreamBaseResponse):
        default=True, description="Whether the tool execution succeeded"
    )
    def to_sse(self) -> str:
        """Convert to SSE format, excluding non-spec fields."""
        import json
        data = {
            "type": self.type.value,
            "toolCallId": self.toolCallId,
            "output": self.output,
        }
        return f"data: {json.dumps(data)}\n\n"
 # ========== Other ==========
@@ -195,18 +148,6 @@ class StreamError(StreamBaseResponse):
        default=None, description="Additional error details"
    )
    def to_sse(self) -> str:
        """Convert to SSE format, only emitting fields required by AI SDK protocol.
        The AI SDK uses z.strictObject({type, errorText}) which rejects
        any extra fields like `code` or `details`.
        """
        data = {
            "type": self.type.value,
            "errorText": self.errorText,
        }
        return f"data: {json_dumps(data)}\n\n"
 class StreamHeartbeat(StreamBaseResponse):
    """Heartbeat to keep SSE connection alive during long-running operations.
--- a/autogpt_platform/backend/backend/api/features/chat/routes.py
+++ b/autogpt_platform/backend/backend/api/features/chat/routes.py
@@ -1,48 +1,29 @@
 """Chat API routes for chat session management and streaming via SSE."""
 import logging
 import uuid as uuid_module
 from collections.abc import AsyncGenerator
 from typing import Annotated
 from autogpt_libs import auth
-from fastapi import APIRouter, Depends, Header, HTTPException, Query, Response, Security
+from fastapi import APIRouter, Depends, Query, Security
 from fastapi.responses import StreamingResponse
 from pydantic import BaseModel
 from backend.util.exceptions import NotFoundError
 from . import service as chat_service
 from . import stream_registry
 from .completion_handler import process_operation_failure, process_operation_success
 from .config import ChatConfig
 from .model import ChatSession, create_chat_session, get_chat_session, get_user_sessions
 from .response_model import StreamFinish, StreamHeartbeat
 from .tools.models import (
    AgentDetailsResponse,
    AgentOutputResponse,
    AgentPreviewResponse,
    AgentSavedResponse,
    AgentsFoundResponse,
    BlockListResponse,
    BlockOutputResponse,
    ClarificationNeededResponse,
    DocPageResponse,
    DocSearchResultsResponse,
    ErrorResponse,
    ExecutionStartedResponse,
    InputValidationErrorResponse,
    NeedLoginResponse,
    NoResultsResponse,
    OperationInProgressResponse,
    OperationPendingResponse,
    OperationStartedResponse,
    SetupRequirementsResponse,
    UnderstandingUpdatedResponse,
 )
 config = ChatConfig()
 SSE_RESPONSE_HEADERS = {
    "Cache-Control": "no-cache",
    "Connection": "keep-alive",
    "X-Accel-Buffering": "no",
    "x-vercel-ai-ui-message-stream": "v1",
 }
 logger = logging.getLogger(__name__)
@@ -58,6 +39,48 @@ async def _validate_and_get_session(
    return session
 async def _create_stream_generator(
    session_id: str,
    message: str,
    user_id: str | None,
    session: ChatSession,
    is_user_message: bool = True,
    context: dict[str, str] | None = None,
 ) -> AsyncGenerator[str, None]:
    """Create SSE event generator for chat streaming."""
    chunk_count = 0
    first_chunk_type: str | None = None
    async for chunk in chat_service.stream_chat_completion(
        session_id,
        message,
        is_user_message=is_user_message,
        user_id=user_id,
        session=session,
        context=context,
    ):
        if chunk_count < 3:
            logger.info(
                "Chat stream chunk",
                extra={
                    "session_id": session_id,
                    "chunk_type": str(chunk.type),
                },
            )
        if not first_chunk_type:
            first_chunk_type = str(chunk.type)
        chunk_count += 1
        yield chunk.to_sse()
    logger.info(
        "Chat stream completed",
        extra={
            "session_id": session_id,
            "chunk_count": chunk_count,
            "first_chunk_type": first_chunk_type,
        },
    )
    yield "data: [DONE]\n\n"
 router = APIRouter(
    tags=["chat"],
 )
@@ -81,15 +104,6 @@ class CreateSessionResponse(BaseModel):
    user_id: str | None
 class ActiveStreamInfo(BaseModel):
    """Information about an active stream for reconnection."""
    task_id: str
    last_message_id: str  # Redis Stream message ID for resumption
    operation_id: str  # Operation ID for completion tracking
    tool_name: str  # Name of the tool being executed
 class SessionDetailResponse(BaseModel):
    """Response model providing complete details for a chat session, including messages."""
@@ -98,7 +112,6 @@ class SessionDetailResponse(BaseModel):
    updated_at: str
    user_id: str | None
    messages: list[dict]
    active_stream: ActiveStreamInfo | None = None  # Present if stream is still active
 class SessionSummaryResponse(BaseModel):
@@ -117,14 +130,6 @@ class ListSessionsResponse(BaseModel):
    total: int
 class OperationCompleteRequest(BaseModel):
    """Request model for external completion webhook."""
    success: bool
    result: dict | str | None = None
    error: str | None = None
 # ========== Routes ==========
@@ -210,14 +215,13 @@ async def get_session(
    Retrieve the details of a specific chat session.
    Looks up a chat session by ID for the given user (if authenticated) and returns all session data including messages.
    If there's an active stream for this session, returns the task_id for reconnection.
    Args:
        session_id: The unique identifier for the desired chat session.
        user_id: The optional authenticated user ID, or None for anonymous access.
    Returns:
-        SessionDetailResponse: Details for the requested session, including active_stream info if applicable.
+        SessionDetailResponse: Details for the requested session, or None if not found.
    """
    session = await get_chat_session(session_id, user_id)
@@ -225,28 +229,11 @@ async def get_session(
        raise NotFoundError(f"Session {session_id} not found.")
    messages = [message.model_dump() for message in session.messages]
-
+    logger.info(
-    # Check if there's an active stream for this session
+        f"Returning session {session_id}: "
-    active_stream_info = None
+        f"message_count={len(messages)}, "
-    active_task, last_message_id = await stream_registry.get_active_task_for_session(
+        f"roles={[m.get('role') for m in messages]}"
        session_id, user_id
    )
    if active_task:
        # Filter out the in-progress assistant message from the session response.
        # The client will receive the complete assistant response through the SSE
        # stream replay instead, preventing duplicate content.
        if messages and messages[-1].get("role") == "assistant":
            messages = messages[:-1]
        # Use "0-0" as last_message_id to replay the stream from the beginning.
        # Since we filtered out the cached assistant message, the client needs
        # the full stream to reconstruct the response.
        active_stream_info = ActiveStreamInfo(
            task_id=active_task.task_id,
            last_message_id="0-0",
            operation_id=active_task.operation_id,
            tool_name=active_task.tool_name,
        )
    return SessionDetailResponse(
        id=session.session_id,
@@ -254,7 +241,6 @@ async def get_session(
        updated_at=session.updated_at.isoformat(),
        user_id=session.user_id or None,
        messages=messages,
        active_stream=active_stream_info,
    )
@@ -274,374 +260,68 @@ async def stream_chat_post(
      - Tool call UI elements (if invoked)
      - Tool execution results
    The AI generation runs in a background task that continues even if the client disconnects.
    All chunks are written to Redis for reconnection support. If the client disconnects,
    they can reconnect using GET /tasks/{task_id}/stream to resume from where they left off.
    Args:
        session_id: The chat session identifier to associate with the streamed messages.
        request: Request body containing message, is_user_message, and optional context.
        user_id: Optional authenticated user ID.
    Returns:
-        StreamingResponse: SSE-formatted response chunks. First chunk is a "start" event
+        StreamingResponse: SSE-formatted response chunks.
        containing the task_id for reconnection.
    """
    import asyncio
    import time
    stream_start_time = time.perf_counter()
    log_meta = {"component": "ChatStream", "session_id": session_id}
    if user_id:
        log_meta["user_id"] = user_id
    logger.info(
        f"[TIMING] stream_chat_post STARTED, session={session_id}, "
        f"user={user_id}, message_len={len(request.message)}",
        extra={"json_fields": log_meta},
    )
    session = await _validate_and_get_session(session_id, user_id)
    logger.info(
        f"[TIMING] session validated in {(time.perf_counter() - stream_start_time)*1000:.1f}ms",
        extra={
            "json_fields": {
                **log_meta,
                "duration_ms": (time.perf_counter() - stream_start_time) * 1000,
            }
        },
    )
    # Create a task in the stream registry for reconnection support
    task_id = str(uuid_module.uuid4())
    operation_id = str(uuid_module.uuid4())
    log_meta["task_id"] = task_id
    task_create_start = time.perf_counter()
    await stream_registry.create_task(
        task_id=task_id,
        session_id=session_id,
        user_id=user_id,
        tool_call_id="chat_stream",  # Not a tool call, but needed for the model
        tool_name="chat",
        operation_id=operation_id,
    )
    logger.info(
        f"[TIMING] create_task completed in {(time.perf_counter() - task_create_start)*1000:.1f}ms",
        extra={
            "json_fields": {
                **log_meta,
                "duration_ms": (time.perf_counter() - task_create_start) * 1000,
            }
        },
    )
    # Background task that runs the AI generation independently of SSE connection
    async def run_ai_generation():
        import time as time_module
        gen_start_time = time_module.perf_counter()
        logger.info(
            f"[TIMING] run_ai_generation STARTED, task={task_id}, session={session_id}, user={user_id}",
            extra={"json_fields": log_meta},
        )
        first_chunk_time, ttfc = None, None
        chunk_count = 0
        try:
            async for chunk in chat_service.stream_chat_completion(
                session_id,
                request.message,
                is_user_message=request.is_user_message,
                user_id=user_id,
                session=session,  # Pass pre-fetched session to avoid double-fetch
                context=request.context,
                _task_id=task_id,  # Pass task_id so service emits start with taskId for reconnection
            ):
                chunk_count += 1
                if first_chunk_time is None:
                    first_chunk_time = time_module.perf_counter()
                    ttfc = first_chunk_time - gen_start_time
                    logger.info(
                        f"[TIMING] FIRST AI CHUNK at {ttfc:.2f}s, type={type(chunk).__name__}",
                        extra={
                            "json_fields": {
                                **log_meta,
                                "chunk_type": type(chunk).__name__,
                                "time_to_first_chunk_ms": ttfc * 1000,
                            }
                        },
                    )
                # Write to Redis (subscribers will receive via XREAD)
                await stream_registry.publish_chunk(task_id, chunk)
            gen_end_time = time_module.perf_counter()
            total_time = (gen_end_time - gen_start_time) * 1000
            logger.info(
                f"[TIMING] run_ai_generation FINISHED in {total_time/1000:.1f}s; "
                f"task={task_id}, session={session_id}, "
                f"ttfc={ttfc or -1:.2f}s, n_chunks={chunk_count}",
                extra={
                    "json_fields": {
                        **log_meta,
                        "total_time_ms": total_time,
                        "time_to_first_chunk_ms": (
                            ttfc * 1000 if ttfc is not None else None
                        ),
                        "n_chunks": chunk_count,
                    }
                },
            )
            await stream_registry.mark_task_completed(task_id, "completed")
        except Exception as e:
            elapsed = time_module.perf_counter() - gen_start_time
            logger.error(
                f"[TIMING] run_ai_generation ERROR after {elapsed:.2f}s: {e}",
                extra={
                    "json_fields": {
                        **log_meta,
                        "elapsed_ms": elapsed * 1000,
                        "error": str(e),
                    }
                },
            )
            await stream_registry.mark_task_completed(task_id, "failed")
    # Start the AI generation in a background task
    bg_task = asyncio.create_task(run_ai_generation())
    await stream_registry.set_task_asyncio_task(task_id, bg_task)
    setup_time = (time.perf_counter() - stream_start_time) * 1000
    logger.info(
        f"[TIMING] Background task started, setup={setup_time:.1f}ms",
        extra={"json_fields": {**log_meta, "setup_time_ms": setup_time}},
    )
    # SSE endpoint that subscribes to the task's stream
    async def event_generator() -> AsyncGenerator[str, None]:
        import time as time_module
        event_gen_start = time_module.perf_counter()
        logger.info(
            f"[TIMING] event_generator STARTED, task={task_id}, session={session_id}, "
            f"user={user_id}",
            extra={"json_fields": log_meta},
        )
        subscriber_queue = None
        first_chunk_yielded = False
        chunks_yielded = 0
        try:
            # Subscribe to the task stream (this replays existing messages + live updates)
            subscriber_queue = await stream_registry.subscribe_to_task(
                task_id=task_id,
                user_id=user_id,
                last_message_id="0-0",  # Get all messages from the beginning
            )
            if subscriber_queue is None:
                yield StreamFinish().to_sse()
                yield "data: [DONE]\n\n"
                return
            # Read from the subscriber queue and yield to SSE
            logger.info(
                "[TIMING] Starting to read from subscriber_queue",
                extra={"json_fields": log_meta},
            )
            while True:
                try:
                    chunk = await asyncio.wait_for(subscriber_queue.get(), timeout=30.0)
                    chunks_yielded += 1
                    if not first_chunk_yielded:
                        first_chunk_yielded = True
                        elapsed = time_module.perf_counter() - event_gen_start
                        logger.info(
                            f"[TIMING] FIRST CHUNK from queue at {elapsed:.2f}s, "
                            f"type={type(chunk).__name__}",
                            extra={
                                "json_fields": {
                                    **log_meta,
                                    "chunk_type": type(chunk).__name__,
                                    "elapsed_ms": elapsed * 1000,
                                }
                            },
                        )
                    yield chunk.to_sse()
                    # Check for finish signal
                    if isinstance(chunk, StreamFinish):
                        total_time = time_module.perf_counter() - event_gen_start
                        logger.info(
                            f"[TIMING] StreamFinish received in {total_time:.2f}s; "
                            f"n_chunks={chunks_yielded}",
                            extra={
                                "json_fields": {
                                    **log_meta,
                                    "chunks_yielded": chunks_yielded,
                                    "total_time_ms": total_time * 1000,
                                }
                            },
                        )
                        break
                except asyncio.TimeoutError:
                    yield StreamHeartbeat().to_sse()
        except GeneratorExit:
            logger.info(
                f"[TIMING] GeneratorExit (client disconnected), chunks={chunks_yielded}",
                extra={
                    "json_fields": {
                        **log_meta,
                        "chunks_yielded": chunks_yielded,
                        "reason": "client_disconnect",
                    }
                },
            )
            pass  # Client disconnected - background task continues
        except Exception as e:
            elapsed = (time_module.perf_counter() - event_gen_start) * 1000
            logger.error(
                f"[TIMING] event_generator ERROR after {elapsed:.1f}ms: {e}",
                extra={
                    "json_fields": {**log_meta, "elapsed_ms": elapsed, "error": str(e)}
                },
            )
        finally:
            # Unsubscribe when client disconnects or stream ends to prevent resource leak
            if subscriber_queue is not None:
                try:
                    await stream_registry.unsubscribe_from_task(
                        task_id, subscriber_queue
                    )
                except Exception as unsub_err:
                    logger.error(
                        f"Error unsubscribing from task {task_id}: {unsub_err}",
                        exc_info=True,
                    )
            # AI SDK protocol termination - always yield even if unsubscribe fails
            total_time = time_module.perf_counter() - event_gen_start
            logger.info(
                f"[TIMING] event_generator FINISHED in {total_time:.2f}s; "
                f"task={task_id}, session={session_id}, n_chunks={chunks_yielded}",
                extra={
                    "json_fields": {
                        **log_meta,
                        "total_time_ms": total_time * 1000,
                        "chunks_yielded": chunks_yielded,
                    }
                },
            )
            yield "data: [DONE]\n\n"
    return StreamingResponse(
-        event_generator(),
+        _create_stream_generator(
            session_id=session_id,
            message=request.message,
            user_id=user_id,
            session=session,
            is_user_message=request.is_user_message,
            context=request.context,
        ),
        media_type="text/event-stream",
-        headers={
+        headers=SSE_RESPONSE_HEADERS,
            "Cache-Control": "no-cache",
            "Connection": "keep-alive",
            "X-Accel-Buffering": "no",  # Disable nginx buffering
            "x-vercel-ai-ui-message-stream": "v1",  # AI SDK protocol header
        },
    )
@router.get(
    "/sessions/{session_id}/stream",
 )
-async def resume_session_stream(
+async def stream_chat_get(
    session_id: str,
    message: Annotated[str, Query(min_length=1, max_length=10000)],
    user_id: str | None = Depends(auth.get_user_id),
    is_user_message: bool = Query(default=True),
 ):
    """
-    Resume an active stream for a session.
+    Stream chat responses for a session (GET - legacy endpoint).
-    Called by the AI SDK's ``useChat(resume: true)`` on page load.
+    Streams the AI/completion responses in real time over Server-Sent Events (SSE), including:
-    Checks for an active (in-progress) task on the session and either replays
+      - Text fragments as they are generated
-    the full SSE stream or returns 204 No Content if nothing is running.
+      - Tool call UI elements (if invoked)
      - Tool execution results
    Args:
-        session_id: The chat session identifier.
+        session_id: The chat session identifier to associate with the streamed messages.
        message: The user's new message to process.
        user_id: Optional authenticated user ID.
-
+        is_user_message: Whether the message is a user message.
    Returns:
-        StreamingResponse (SSE) when an active stream exists,
+        StreamingResponse: SSE-formatted response chunks.
-        or 204 No Content when there is nothing to resume.
+
    """
-    import asyncio
+    session = await _validate_and_get_session(session_id, user_id)
    active_task, _last_id = await stream_registry.get_active_task_for_session(
        session_id, user_id
    )
    if not active_task:
        return Response(status_code=204)
    subscriber_queue = await stream_registry.subscribe_to_task(
        task_id=active_task.task_id,
        user_id=user_id,
        last_message_id="0-0",  # Full replay so useChat rebuilds the message
    )
    if subscriber_queue is None:
        return Response(status_code=204)
    async def event_generator() -> AsyncGenerator[str, None]:
        chunk_count = 0
        first_chunk_type: str | None = None
        try:
            while True:
                try:
                    chunk = await asyncio.wait_for(subscriber_queue.get(), timeout=30.0)
                    if chunk_count < 3:
                        logger.info(
                            "Resume stream chunk",
                            extra={
                                "session_id": session_id,
                                "chunk_type": str(chunk.type),
                            },
                        )
                    if not first_chunk_type:
                        first_chunk_type = str(chunk.type)
                    chunk_count += 1
                    yield chunk.to_sse()
                    if isinstance(chunk, StreamFinish):
                        break
                except asyncio.TimeoutError:
                    yield StreamHeartbeat().to_sse()
        except GeneratorExit:
            pass
        except Exception as e:
            logger.error(f"Error in resume stream for session {session_id}: {e}")
        finally:
            try:
                await stream_registry.unsubscribe_from_task(
                    active_task.task_id, subscriber_queue
                )
            except Exception as unsub_err:
                logger.error(
                    f"Error unsubscribing from task {active_task.task_id}: {unsub_err}",
                    exc_info=True,
                )
            logger.info(
                "Resume stream completed",
                extra={
                    "session_id": session_id,
                    "n_chunks": chunk_count,
                    "first_chunk_type": first_chunk_type,
                },
            )
            yield "data: [DONE]\n\n"
    return StreamingResponse(
-        event_generator(),
+        _create_stream_generator(
            session_id=session_id,
            message=message,
            user_id=user_id,
            session=session,
            is_user_message=is_user_message,
        ),
        media_type="text/event-stream",
-        headers={
+        headers=SSE_RESPONSE_HEADERS,
            "Cache-Control": "no-cache",
            "Connection": "keep-alive",
            "X-Accel-Buffering": "no",
            "x-vercel-ai-ui-message-stream": "v1",
        },
    )
@@ -671,251 +351,6 @@ async def session_assign_user(
    return {"status": "ok"}
 # ========== Task Streaming (SSE Reconnection) ==========
@router.get(
    "/tasks/{task_id}/stream",
 )
 async def stream_task(
    task_id: str,
    user_id: str | None = Depends(auth.get_user_id),
    last_message_id: str = Query(
        default="0-0",
        description="Last Redis Stream message ID received (e.g., '1706540123456-0'). Use '0-0' for full replay.",
    ),
 ):
    """
    Reconnect to a long-running task's SSE stream.
    When a long-running operation (like agent generation) starts, the client
    receives a task_id. If the connection drops, the client can reconnect
    using this endpoint to resume receiving updates.
    Args:
        task_id: The task ID from the operation_started response.
        user_id: Authenticated user ID for ownership validation.
        last_message_id: Last Redis Stream message ID received ("0-0" for full replay).
    Returns:
        StreamingResponse: SSE-formatted response chunks starting after last_message_id.
    Raises:
        HTTPException: 404 if task not found, 410 if task expired, 403 if access denied.
    """
    # Check task existence and expiry before subscribing
    task, error_code = await stream_registry.get_task_with_expiry_info(task_id)
    if error_code == "TASK_EXPIRED":
        raise HTTPException(
            status_code=410,
            detail={
                "code": "TASK_EXPIRED",
                "message": "This operation has expired. Please try again.",
            },
        )
    if error_code == "TASK_NOT_FOUND":
        raise HTTPException(
            status_code=404,
            detail={
                "code": "TASK_NOT_FOUND",
                "message": f"Task {task_id} not found.",
            },
        )
    # Validate ownership if task has an owner
    if task and task.user_id and user_id != task.user_id:
        raise HTTPException(
            status_code=403,
            detail={
                "code": "ACCESS_DENIED",
                "message": "You do not have access to this task.",
            },
        )
    # Get subscriber queue from stream registry
    subscriber_queue = await stream_registry.subscribe_to_task(
        task_id=task_id,
        user_id=user_id,
        last_message_id=last_message_id,
    )
    if subscriber_queue is None:
        raise HTTPException(
            status_code=404,
            detail={
                "code": "TASK_NOT_FOUND",
                "message": f"Task {task_id} not found or access denied.",
            },
        )
    async def event_generator() -> AsyncGenerator[str, None]:
        import asyncio
        heartbeat_interval = 15.0  # Send heartbeat every 15 seconds
        try:
            while True:
                try:
                    # Wait for next chunk with timeout for heartbeats
                    chunk = await asyncio.wait_for(
                        subscriber_queue.get(), timeout=heartbeat_interval
                    )
                    yield chunk.to_sse()
                    # Check for finish signal
                    if isinstance(chunk, StreamFinish):
                        break
                except asyncio.TimeoutError:
                    # Send heartbeat to keep connection alive
                    yield StreamHeartbeat().to_sse()
        except Exception as e:
            logger.error(f"Error in task stream {task_id}: {e}", exc_info=True)
        finally:
            # Unsubscribe when client disconnects or stream ends
            try:
                await stream_registry.unsubscribe_from_task(task_id, subscriber_queue)
            except Exception as unsub_err:
                logger.error(
                    f"Error unsubscribing from task {task_id}: {unsub_err}",
                    exc_info=True,
                )
            # AI SDK protocol termination - always yield even if unsubscribe fails
            yield "data: [DONE]\n\n"
    return StreamingResponse(
        event_generator(),
        media_type="text/event-stream",
        headers={
            "Cache-Control": "no-cache",
            "Connection": "keep-alive",
            "X-Accel-Buffering": "no",
            "x-vercel-ai-ui-message-stream": "v1",
        },
    )
@router.get(
    "/tasks/{task_id}",
 )
 async def get_task_status(
    task_id: str,
    user_id: str | None = Depends(auth.get_user_id),
 ) -> dict:
    """
    Get the status of a long-running task.
    Args:
        task_id: The task ID to check.
        user_id: Authenticated user ID for ownership validation.
    Returns:
        dict: Task status including task_id, status, tool_name, and operation_id.
    Raises:
        NotFoundError: If task_id is not found or user doesn't have access.
    """
    task = await stream_registry.get_task(task_id)
    if task is None:
        raise NotFoundError(f"Task {task_id} not found.")
    # Validate ownership - if task has an owner, requester must match
    if task.user_id and user_id != task.user_id:
        raise NotFoundError(f"Task {task_id} not found.")
    return {
        "task_id": task.task_id,
        "session_id": task.session_id,
        "status": task.status,
        "tool_name": task.tool_name,
        "operation_id": task.operation_id,
        "created_at": task.created_at.isoformat(),
    }
 # ========== External Completion Webhook ==========
@router.post(
    "/operations/{operation_id}/complete",
    status_code=200,
 )
 async def complete_operation(
    operation_id: str,
    request: OperationCompleteRequest,
    x_api_key: str | None = Header(default=None),
 ) -> dict:
    """
    External completion webhook for long-running operations.
    Called by Agent Generator (or other services) when an operation completes.
    This triggers the stream registry to publish completion and continue LLM generation.
    Args:
        operation_id: The operation ID to complete.
        request: Completion payload with success status and result/error.
        x_api_key: Internal API key for authentication.
    Returns:
        dict: Status of the completion.
    Raises:
        HTTPException: If API key is invalid or operation not found.
    """
    # Validate internal API key - reject if not configured or invalid
    if not config.internal_api_key:
        logger.error(
            "Operation complete webhook rejected: CHAT_INTERNAL_API_KEY not configured"
        )
        raise HTTPException(
            status_code=503,
            detail="Webhook not available: internal API key not configured",
        )
    if x_api_key != config.internal_api_key:
        raise HTTPException(status_code=401, detail="Invalid API key")
    # Find task by operation_id
    task = await stream_registry.find_task_by_operation_id(operation_id)
    if task is None:
        raise HTTPException(
            status_code=404,
            detail=f"Operation {operation_id} not found",
        )
    logger.info(
        f"Received completion webhook for operation {operation_id} "
        f"(task_id={task.task_id}, success={request.success})"
    )
    if request.success:
        await process_operation_success(task, request.result)
    else:
        await process_operation_failure(task, request.error)
    return {"status": "ok", "task_id": task.task_id}
 # ========== Configuration ==========
@router.get("/config/ttl", status_code=200)
 async def get_ttl_config() -> dict:
    """
    Get the stream TTL configuration.
    Returns the Time-To-Live settings for chat streams, which determines
    how long clients can reconnect to an active stream.
    Returns:
        dict: TTL configuration with seconds and milliseconds values.
    """
    return {
        "stream_ttl_seconds": config.stream_ttl,
        "stream_ttl_ms": config.stream_ttl * 1000,
    }
 # ========== Health Check ==========
@@ -952,42 +387,3 @@ async def health_check() -> dict:
        "service": "chat",
        "version": "0.1.0",
    }
 # ========== Schema Export (for OpenAPI / Orval codegen) ==========
 ToolResponseUnion = (
    AgentsFoundResponse
    | NoResultsResponse
    | AgentDetailsResponse
    | SetupRequirementsResponse
    | ExecutionStartedResponse
    | NeedLoginResponse
    | ErrorResponse
    | InputValidationErrorResponse
    | AgentOutputResponse
    | UnderstandingUpdatedResponse
    | AgentPreviewResponse
    | AgentSavedResponse
    | ClarificationNeededResponse
    | BlockListResponse
    | BlockOutputResponse
    | DocSearchResultsResponse
    | DocPageResponse
    | OperationStartedResponse
    | OperationPendingResponse
    | OperationInProgressResponse
 )
@router.get(
    "/schema/tool-responses",
    response_model=ToolResponseUnion,
    include_in_schema=True,
    summary="[Dummy] Tool response type export for codegen",
    description="This endpoint is not meant to be called. It exists solely to "
    "expose tool response models in the OpenAPI schema for frontend codegen.",
 )
 async def _tool_response_schema() -> ToolResponseUnion:  # type: ignore[return]
    """Never called at runtime. Exists only so Orval generates TS types."""
    raise HTTPException(status_code=501, detail="Schema-only endpoint")
--- a/autogpt_platform/backend/backend/api/features/chat/service.py
+++ b/autogpt_platform/backend/backend/api/features/chat/service.py
@@ -52,10 +52,8 @@ from .response_model import (
    StreamBaseResponse,
    StreamError,
    StreamFinish,
    StreamFinishStep,
    StreamHeartbeat,
    StreamStart,
    StreamStartStep,
    StreamTextDelta,
    StreamTextEnd,
    StreamTextStart,
@@ -353,10 +351,6 @@ async def stream_chat_completion(
    retry_count: int = 0,
    session: ChatSession | None = None,
    context: dict[str, str] | None = None,  # {url: str, content: str}
    _continuation_message_id: (
        str | None
    ) = None,  # Internal: reuse message ID for tool call continuations
    _task_id: str | None = None,  # Internal: task ID for SSE reconnection support
 ) -> AsyncGenerator[StreamBaseResponse, None]:
    """Main entry point for streaming chat completions with database handling.
@@ -377,45 +371,21 @@ async def stream_chat_completion(
        ValueError: If max_context_messages is exceeded
    """
    completion_start = time.monotonic()
    # Build log metadata for structured logging
    log_meta = {"component": "ChatService", "session_id": session_id}
    if user_id:
        log_meta["user_id"] = user_id
    logger.info(
-        f"[TIMING] stream_chat_completion STARTED, session={session_id}, user={user_id}, "
+        f"Streaming chat completion for session {session_id} for message {message} and user id {user_id}. Message is user message: {is_user_message}"
        f"message_len={len(message) if message else 0}, is_user={is_user_message}",
        extra={
            "json_fields": {
                **log_meta,
                "message_len": len(message) if message else 0,
                "is_user_message": is_user_message,
            }
        },
    )
    # Only fetch from Redis if session not provided (initial call)
    if session is None:
        fetch_start = time.monotonic()
        session = await get_chat_session(session_id, user_id)
        fetch_time = (time.monotonic() - fetch_start) * 1000
        logger.info(
-            f"[TIMING] get_chat_session took {fetch_time:.1f}ms, "
+            f"Fetched session from Redis: {session.session_id if session else 'None'}, "
-            f"n_messages={len(session.messages) if session else 0}",
+            f"message_count={len(session.messages) if session else 0}"
            extra={
                "json_fields": {
                    **log_meta,
                    "duration_ms": fetch_time,
                    "n_messages": len(session.messages) if session else 0,
                }
            },
        )
    else:
        logger.info(
-            f"[TIMING] Using provided session, messages={len(session.messages)}",
+            f"Using provided session object: {session.session_id}, "
-            extra={"json_fields": {**log_meta, "n_messages": len(session.messages)}},
+            f"message_count={len(session.messages)}"
        )
    if not session:
@@ -436,25 +406,17 @@ async def stream_chat_completion(
        # Track user message in PostHog
        if is_user_message:
            posthog_start = time.monotonic()
            track_user_message(
                user_id=user_id,
                session_id=session_id,
                message_length=len(message),
            )
            posthog_time = (time.monotonic() - posthog_start) * 1000
            logger.info(
                f"[TIMING] track_user_message took {posthog_time:.1f}ms",
                extra={"json_fields": {**log_meta, "duration_ms": posthog_time}},
            )
    upsert_start = time.monotonic()
    session = await upsert_chat_session(session)
    upsert_time = (time.monotonic() - upsert_start) * 1000
    logger.info(
-        f"[TIMING] upsert_chat_session took {upsert_time:.1f}ms",
+        f"Upserting session: {session.session_id} with user id {session.user_id}, "
-        extra={"json_fields": {**log_meta, "duration_ms": upsert_time}},
+        f"message_count={len(session.messages)}"
    )
    session = await upsert_chat_session(session)
    assert session, "Session not found"
    # Generate title for new sessions on first user message (non-blocking)
@@ -492,13 +454,7 @@ async def stream_chat_completion(
            asyncio.create_task(_update_title())
    # Build system prompt with business understanding
    prompt_start = time.monotonic()
    system_prompt, understanding = await _build_system_prompt(user_id)
    prompt_time = (time.monotonic() - prompt_start) * 1000
    logger.info(
        f"[TIMING] _build_system_prompt took {prompt_time:.1f}ms",
        extra={"json_fields": {**log_meta, "duration_ms": prompt_time}},
    )
    # Initialize variables for streaming
    assistant_response = ChatMessage(
@@ -523,27 +479,13 @@ async def stream_chat_completion(
    # Generate unique IDs for AI SDK protocol
    import uuid as uuid_module
-    is_continuation = _continuation_message_id is not None
+    message_id = str(uuid_module.uuid4())
    message_id = _continuation_message_id or str(uuid_module.uuid4())
    text_block_id = str(uuid_module.uuid4())
-    # Only yield message start for the initial call, not for continuations.
+    # Yield message start
-    setup_time = (time.monotonic() - completion_start) * 1000
+    yield StreamStart(messageId=message_id)
    logger.info(
        f"[TIMING] Setup complete, yielding StreamStart at {setup_time:.1f}ms",
        extra={"json_fields": {**log_meta, "setup_time_ms": setup_time}},
    )
    if not is_continuation:
        yield StreamStart(messageId=message_id, taskId=_task_id)
    # Emit start-step before each LLM call (AI SDK uses this to add step boundaries)
    yield StreamStartStep()
    try:
        logger.info(
            "[TIMING] Calling _stream_chat_chunks",
            extra={"json_fields": log_meta},
        )
        async for chunk in _stream_chat_chunks(
            session=session,
            tools=tools,
@@ -643,10 +585,6 @@ async def stream_chat_completion(
                    )
                yield chunk
            elif isinstance(chunk, StreamFinish):
                if has_done_tool_call:
                    # Tool calls happened — close the step but don't send message-level finish.
                    # The continuation will open a new step, and finish will come at the end.
                    yield StreamFinishStep()
                if not has_done_tool_call:
                    # Emit text-end before finish if we received text but haven't closed it
                    if has_received_text and not text_streaming_ended:
@@ -678,8 +616,6 @@ async def stream_chat_completion(
                            has_saved_assistant_message = True
                    has_yielded_end = True
                    # Emit finish-step before finish (resets AI SDK text/reasoning state)
                    yield StreamFinishStep()
                    yield chunk
            elif isinstance(chunk, StreamError):
                has_yielded_error = True
@@ -729,10 +665,6 @@ async def stream_chat_completion(
            logger.info(
                f"Retryable error encountered. Attempt {retry_count + 1}/{config.max_retries}"
            )
            # Close the current step before retrying so the recursive call's
            # StreamStartStep doesn't produce unbalanced step events.
            if not has_yielded_end:
                yield StreamFinishStep()
            should_retry = True
        else:
            # Non-retryable error or max retries exceeded
@@ -768,7 +700,6 @@ async def stream_chat_completion(
                error_response = StreamError(errorText=error_message)
                yield error_response
            if not has_yielded_end:
                yield StreamFinishStep()
                yield StreamFinish()
            return
@@ -783,8 +714,6 @@ async def stream_chat_completion(
            retry_count=retry_count + 1,
            session=session,
            context=context,
            _continuation_message_id=message_id,  # Reuse message ID since start was already sent
            _task_id=_task_id,
        ):
            yield chunk
        return  # Exit after retry to avoid double-saving in finally block
@@ -800,13 +729,9 @@ async def stream_chat_completion(
        # Build the messages list in the correct order
        messages_to_save: list[ChatMessage] = []
-        # Add assistant message with tool_calls if any.
+        # Add assistant message with tool_calls if any
        # Use extend (not assign) to preserve tool_calls already added by
        # _yield_tool_call for long-running tools.
        if accumulated_tool_calls:
-            if not assistant_response.tool_calls:
+            assistant_response.tool_calls = accumulated_tool_calls
                assistant_response.tool_calls = []
            assistant_response.tool_calls.extend(accumulated_tool_calls)
            logger.info(
                f"Added {len(accumulated_tool_calls)} tool calls to assistant message"
            )
@@ -858,8 +783,6 @@ async def stream_chat_completion(
            session=session,  # Pass session object to avoid Redis refetch
            context=context,
            tool_call_response=str(tool_response_messages),
            _continuation_message_id=message_id,  # Reuse message ID to avoid duplicates
            _task_id=_task_id,
        ):
            yield chunk
@@ -970,21 +893,9 @@ async def _stream_chat_chunks(
        SSE formatted JSON response objects
    """
    import time as time_module
    stream_chunks_start = time_module.perf_counter()
    model = config.model
-    # Build log metadata for structured logging
+    logger.info("Starting pure chat stream")
    log_meta = {"component": "ChatService", "session_id": session.session_id}
    if session.user_id:
        log_meta["user_id"] = session.user_id
    logger.info(
        f"[TIMING] _stream_chat_chunks STARTED, session={session.session_id}, "
        f"user={session.user_id}, n_messages={len(session.messages)}",
        extra={"json_fields": {**log_meta, "n_messages": len(session.messages)}},
    )
    messages = session.to_openai_messages()
    if system_prompt:
@@ -995,18 +906,12 @@ async def _stream_chat_chunks(
        messages = [system_message] + messages
    # Apply context window management
    context_start = time_module.perf_counter()
    context_result = await _manage_context_window(
        messages=messages,
        model=model,
        api_key=config.api_key,
        base_url=config.base_url,
    )
    context_time = (time_module.perf_counter() - context_start) * 1000
    logger.info(
        f"[TIMING] _manage_context_window took {context_time:.1f}ms",
        extra={"json_fields": {**log_meta, "duration_ms": context_time}},
    )
    if context_result.error:
        if "System prompt dropped" in context_result.error:
@@ -1041,19 +946,9 @@ async def _stream_chat_chunks(
        while retry_count <= MAX_RETRIES:
            try:
                elapsed = (time_module.perf_counter() - stream_chunks_start) * 1000
                retry_info = (
                    f" (retry {retry_count}/{MAX_RETRIES})" if retry_count > 0 else ""
                )
                logger.info(
-                    f"[TIMING] Creating OpenAI stream at {elapsed:.1f}ms{retry_info}",
+                    f"Creating OpenAI chat completion stream..."
-                    extra={
+                    f"{f' (retry {retry_count}/{MAX_RETRIES})' if retry_count > 0 else ''}"
                        "json_fields": {
                            **log_meta,
                            "elapsed_ms": elapsed,
                            "retry_count": retry_count,
                        }
                    },
                )
                # Build extra_body for OpenRouter tracing and PostHog analytics
@@ -1070,11 +965,6 @@ async def _stream_chat_chunks(
                        :128
                    ]  # OpenRouter limit
                # Enable adaptive thinking for Anthropic models via OpenRouter
                if config.thinking_enabled and "anthropic" in model.lower():
                    extra_body["reasoning"] = {"enabled": True}
                api_call_start = time_module.perf_counter()
                stream = await client.chat.completions.create(
                    model=model,
                    messages=cast(list[ChatCompletionMessageParam], messages),
@@ -1084,11 +974,6 @@ async def _stream_chat_chunks(
                    stream_options=ChatCompletionStreamOptionsParam(include_usage=True),
                    extra_body=extra_body,
                )
                api_init_time = (time_module.perf_counter() - api_call_start) * 1000
                logger.info(
                    f"[TIMING] OpenAI stream object returned in {api_init_time:.1f}ms",
                    extra={"json_fields": {**log_meta, "duration_ms": api_init_time}},
                )
                # Variables to accumulate tool calls
                tool_calls: list[dict[str, Any]] = []
@@ -1099,13 +984,10 @@ async def _stream_chat_chunks(
                # Track if we've started the text block
                text_started = False
                first_content_chunk = True
                chunk_count = 0
                # Process the stream
                chunk: ChatCompletionChunk
                async for chunk in stream:
                    chunk_count += 1
                    if chunk.usage:
                        yield StreamUsage(
                            promptTokens=chunk.usage.prompt_tokens,
@@ -1128,23 +1010,6 @@ async def _stream_chat_chunks(
                            if not text_started and text_block_id:
                                yield StreamTextStart(id=text_block_id)
                                text_started = True
                            # Log timing for first content chunk
                            if first_content_chunk:
                                first_content_chunk = False
                                ttfc = (
                                    time_module.perf_counter() - api_call_start
                                ) * 1000
                                logger.info(
                                    f"[TIMING] FIRST CONTENT CHUNK at {ttfc:.1f}ms "
                                    f"(since API call), n_chunks={chunk_count}",
                                    extra={
                                        "json_fields": {
                                            **log_meta,
                                            "time_to_first_chunk_ms": ttfc,
                                            "n_chunks": chunk_count,
                                        }
                                    },
                                )
                            # Stream the text delta
                            text_response = StreamTextDelta(
                                id=text_block_id or "",
@@ -1201,21 +1066,7 @@ async def _stream_chat_chunks(
                                        toolName=tool_calls[idx]["function"]["name"],
                                    )
                                    emitted_start_for_idx.add(idx)
-                stream_duration = time_module.perf_counter() - api_call_start
+                logger.info(f"Stream complete. Finish reason: {finish_reason}")
                logger.info(
                    f"[TIMING] OpenAI stream COMPLETE, finish_reason={finish_reason}, "
                    f"duration={stream_duration:.2f}s, "
                    f"n_chunks={chunk_count}, n_tool_calls={len(tool_calls)}",
                    extra={
                        "json_fields": {
                            **log_meta,
                            "stream_duration_ms": stream_duration * 1000,
                            "finish_reason": finish_reason,
                            "n_chunks": chunk_count,
                            "n_tool_calls": len(tool_calls),
                        }
                    },
                )
                # Yield all accumulated tool calls after the stream is complete
                # This ensures all tool call arguments have been fully received
@@ -1235,12 +1086,6 @@ async def _stream_chat_chunks(
                        # Re-raise to trigger retry logic in the parent function
                        raise
                total_time = (time_module.perf_counter() - stream_chunks_start) * 1000
                logger.info(
                    f"[TIMING] _stream_chat_chunks COMPLETED in {total_time/1000:.1f}s; "
                    f"session={session.session_id}, user={session.user_id}",
                    extra={"json_fields": {**log_meta, "total_time_ms": total_time}},
                )
                yield StreamFinish()
                return
            except Exception as e:
@@ -1408,9 +1253,13 @@ async def _yield_tool_call(
                operation_id=operation_id,
            )
-            # Attach the tool_call to the current turn's assistant message
+            # Save assistant message with tool_call FIRST (required by LLM)
-            # (or create one if this is a tool-only response with no text).
+            assistant_message = ChatMessage(
-            session.add_tool_call_to_current_turn(tool_calls[yield_idx])
+                role="assistant",
                content="",
                tool_calls=[tool_calls[yield_idx]],
            )
            session.messages.append(assistant_message)
            # Then save pending tool result
            pending_message = ChatMessage(
@@ -1716,7 +1565,6 @@ async def _execute_long_running_tool_with_streaming(
            task_id,
            StreamError(errorText=str(e)),
        )
        await stream_registry.publish_chunk(task_id, StreamFinishStep())
        await stream_registry.publish_chunk(task_id, StreamFinish())
        await _update_pending_operation(
@@ -1833,10 +1681,6 @@ async def _generate_llm_continuation(
        if session_id:
            extra_body["session_id"] = session_id[:128]
        # Enable adaptive thinking for Anthropic models via OpenRouter
        if config.thinking_enabled and "anthropic" in config.model.lower():
            extra_body["reasoning"] = {"enabled": True}
        retry_count = 0
        last_error: Exception | None = None
        response = None
@@ -1967,10 +1811,6 @@ async def _generate_llm_continuation_with_streaming(
        if session_id:
            extra_body["session_id"] = session_id[:128]
        # Enable adaptive thinking for Anthropic models via OpenRouter
        if config.thinking_enabled and "anthropic" in config.model.lower():
            extra_body["reasoning"] = {"enabled": True}
        # Make streaming LLM call (no tools - just text response)
        from typing import cast
@@ -1982,7 +1822,6 @@ async def _generate_llm_continuation_with_streaming(
        # Publish start event
        await stream_registry.publish_chunk(task_id, StreamStart(messageId=message_id))
        await stream_registry.publish_chunk(task_id, StreamStartStep())
        await stream_registry.publish_chunk(task_id, StreamTextStart(id=text_block_id))
        # Stream the response
@@ -2006,7 +1845,6 @@ async def _generate_llm_continuation_with_streaming(
        # Publish end events
        await stream_registry.publish_chunk(task_id, StreamTextEnd(id=text_block_id))
        await stream_registry.publish_chunk(task_id, StreamFinishStep())
        if assistant_content:
            # Reload session from DB to avoid race condition with user messages
@@ -2048,5 +1886,4 @@ async def _generate_llm_continuation_with_streaming(
            task_id,
            StreamError(errorText=f"Failed to generate response: {e}"),
        )
        await stream_registry.publish_chunk(task_id, StreamFinishStep())
        await stream_registry.publish_chunk(task_id, StreamFinish())
--- a/autogpt_platform/backend/backend/api/features/chat/stream_registry.py
+++ b/autogpt_platform/backend/backend/api/features/chat/stream_registry.py
@@ -104,24 +104,6 @@ async def create_task(
    Returns:
        The created ActiveTask instance (metadata only)
    """
    import time
    start_time = time.perf_counter()
    # Build log metadata for structured logging
    log_meta = {
        "component": "StreamRegistry",
        "task_id": task_id,
        "session_id": session_id,
    }
    if user_id:
        log_meta["user_id"] = user_id
    logger.info(
        f"[TIMING] create_task STARTED, task={task_id}, session={session_id}, user={user_id}",
        extra={"json_fields": log_meta},
    )
    task = ActiveTask(
        task_id=task_id,
        session_id=session_id,
@@ -132,18 +114,10 @@ async def create_task(
    )
    # Store metadata in Redis
    redis_start = time.perf_counter()
    redis = await get_redis_async()
    redis_time = (time.perf_counter() - redis_start) * 1000
    logger.info(
        f"[TIMING] get_redis_async took {redis_time:.1f}ms",
        extra={"json_fields": {**log_meta, "duration_ms": redis_time}},
    )
    meta_key = _get_task_meta_key(task_id)
    op_key = _get_operation_mapping_key(operation_id)
    hset_start = time.perf_counter()
    await redis.hset(  # type: ignore[misc]
        meta_key,
        mapping={
@@ -157,22 +131,12 @@ async def create_task(
            "created_at": task.created_at.isoformat(),
        },
    )
    hset_time = (time.perf_counter() - hset_start) * 1000
    logger.info(
        f"[TIMING] redis.hset took {hset_time:.1f}ms",
        extra={"json_fields": {**log_meta, "duration_ms": hset_time}},
    )
    await redis.expire(meta_key, config.stream_ttl)
    # Create operation_id -> task_id mapping for webhook lookups
    await redis.set(op_key, task_id, ex=config.stream_ttl)
-    total_time = (time.perf_counter() - start_time) * 1000
+    logger.debug(f"Created task {task_id} for session {session_id}")
    logger.info(
        f"[TIMING] create_task COMPLETED in {total_time:.1f}ms; task={task_id}, session={session_id}",
        extra={"json_fields": {**log_meta, "total_time_ms": total_time}},
    )
    return task
@@ -192,60 +156,26 @@ async def publish_chunk(
    Returns:
        The Redis Stream message ID
    """
    import time
    start_time = time.perf_counter()
    chunk_type = type(chunk).__name__
    chunk_json = chunk.model_dump_json()
    message_id = "0-0"
    # Build log metadata
    log_meta = {
        "component": "StreamRegistry",
        "task_id": task_id,
        "chunk_type": chunk_type,
    }
    try:
        redis = await get_redis_async()
        stream_key = _get_task_stream_key(task_id)
        # Write to Redis Stream for persistence and real-time delivery
        xadd_start = time.perf_counter()
        raw_id = await redis.xadd(
            stream_key,
            {"data": chunk_json},
            maxlen=config.stream_max_length,
        )
        xadd_time = (time.perf_counter() - xadd_start) * 1000
        message_id = raw_id if isinstance(raw_id, str) else raw_id.decode()
        # Set TTL on stream to match task metadata TTL
        await redis.expire(stream_key, config.stream_ttl)
        total_time = (time.perf_counter() - start_time) * 1000
        # Only log timing for significant chunks or slow operations
        if (
            chunk_type
            in ("StreamStart", "StreamFinish", "StreamTextStart", "StreamTextEnd")
            or total_time > 50
        ):
            logger.info(
                f"[TIMING] publish_chunk {chunk_type} in {total_time:.1f}ms (xadd={xadd_time:.1f}ms)",
                extra={
                    "json_fields": {
                        **log_meta,
                        "total_time_ms": total_time,
                        "xadd_time_ms": xadd_time,
                        "message_id": message_id,
                    }
                },
            )
    except Exception as e:
        elapsed = (time.perf_counter() - start_time) * 1000
        logger.error(
-            f"[TIMING] Failed to publish chunk {chunk_type} after {elapsed:.1f}ms: {e}",
+            f"Failed to publish chunk for task {task_id}: {e}",
            extra={"json_fields": {**log_meta, "elapsed_ms": elapsed, "error": str(e)}},
            exc_info=True,
        )
@@ -270,61 +200,24 @@ async def subscribe_to_task(
        An asyncio Queue that will receive stream chunks, or None if task not found
        or user doesn't have access
    """
    import time
    start_time = time.perf_counter()
    # Build log metadata
    log_meta = {"component": "StreamRegistry", "task_id": task_id}
    if user_id:
        log_meta["user_id"] = user_id
    logger.info(
        f"[TIMING] subscribe_to_task STARTED, task={task_id}, user={user_id}, last_msg={last_message_id}",
        extra={"json_fields": {**log_meta, "last_message_id": last_message_id}},
    )
    redis_start = time.perf_counter()
    redis = await get_redis_async()
    meta_key = _get_task_meta_key(task_id)
    meta: dict[Any, Any] = await redis.hgetall(meta_key)  # type: ignore[misc]
    hgetall_time = (time.perf_counter() - redis_start) * 1000
    logger.info(
        f"[TIMING] Redis hgetall took {hgetall_time:.1f}ms",
        extra={"json_fields": {**log_meta, "duration_ms": hgetall_time}},
    )
    if not meta:
-        elapsed = (time.perf_counter() - start_time) * 1000
+        logger.debug(f"Task {task_id} not found in Redis")
        logger.info(
            f"[TIMING] Task not found in Redis after {elapsed:.1f}ms",
            extra={
                "json_fields": {
                    **log_meta,
                    "elapsed_ms": elapsed,
                    "reason": "task_not_found",
                }
            },
        )
        return None
    # Note: Redis client uses decode_responses=True, so keys are strings
    task_status = meta.get("status", "")
    task_user_id = meta.get("user_id", "") or None
    log_meta["session_id"] = meta.get("session_id", "")
    # Validate ownership - if task has an owner, requester must match
    if task_user_id:
        if user_id != task_user_id:
            logger.warning(
-                f"[TIMING] Access denied: user {user_id} tried to access task owned by {task_user_id}",
+                f"User {user_id} denied access to task {task_id} "
-                extra={
+                f"owned by {task_user_id}"
                    "json_fields": {
                        **log_meta,
                        "task_owner": task_user_id,
                        "reason": "access_denied",
                    }
                },
            )
            return None
@@ -332,19 +225,7 @@ async def subscribe_to_task(
    stream_key = _get_task_stream_key(task_id)
    # Step 1: Replay messages from Redis Stream
    xread_start = time.perf_counter()
    messages = await redis.xread({stream_key: last_message_id}, block=0, count=1000)
    xread_time = (time.perf_counter() - xread_start) * 1000
    logger.info(
        f"[TIMING] Redis xread (replay) took {xread_time:.1f}ms, status={task_status}",
        extra={
            "json_fields": {
                **log_meta,
                "duration_ms": xread_time,
                "task_status": task_status,
            }
        },
    )
    replayed_count = 0
    replay_last_id = last_message_id
@@ -363,48 +244,19 @@ async def subscribe_to_task(
                    except Exception as e:
                        logger.warning(f"Failed to replay message: {e}")
-    logger.info(
+    logger.debug(f"Task {task_id}: replayed {replayed_count} messages")
        f"[TIMING] Replayed {replayed_count} messages, last_id={replay_last_id}",
        extra={
            "json_fields": {
                **log_meta,
                "n_messages_replayed": replayed_count,
                "replay_last_id": replay_last_id,
            }
        },
    )
    # Step 2: If task is still running, start stream listener for live updates
    if task_status == "running":
        logger.info(
            "[TIMING] Task still running, starting _stream_listener",
            extra={"json_fields": {**log_meta, "task_status": task_status}},
        )
        listener_task = asyncio.create_task(
-            _stream_listener(task_id, subscriber_queue, replay_last_id, log_meta)
+            _stream_listener(task_id, subscriber_queue, replay_last_id)
        )
        # Track listener task for cleanup on unsubscribe
        _listener_tasks[id(subscriber_queue)] = (task_id, listener_task)
    else:
        # Task is completed/failed - add finish marker
        logger.info(
            f"[TIMING] Task already {task_status}, adding StreamFinish",
            extra={"json_fields": {**log_meta, "task_status": task_status}},
        )
        await subscriber_queue.put(StreamFinish())
    total_time = (time.perf_counter() - start_time) * 1000
    logger.info(
        f"[TIMING] subscribe_to_task COMPLETED in {total_time:.1f}ms; task={task_id}, "
        f"n_messages_replayed={replayed_count}",
        extra={
            "json_fields": {
                **log_meta,
                "total_time_ms": total_time,
                "n_messages_replayed": replayed_count,
            }
        },
    )
    return subscriber_queue
@@ -412,7 +264,6 @@ async def _stream_listener(
    task_id: str,
    subscriber_queue: asyncio.Queue[StreamBaseResponse],
    last_replayed_id: str,
    log_meta: dict | None = None,
 ) -> None:
    """Listen to Redis Stream for new messages using blocking XREAD.
@@ -423,27 +274,10 @@ async def _stream_listener(
        task_id: Task ID to listen for
        subscriber_queue: Queue to deliver messages to
        last_replayed_id: Last message ID from replay (continue from here)
        log_meta: Structured logging metadata
    """
    import time
    start_time = time.perf_counter()
    # Use provided log_meta or build minimal one
    if log_meta is None:
        log_meta = {"component": "StreamRegistry", "task_id": task_id}
    logger.info(
        f"[TIMING] _stream_listener STARTED, task={task_id}, last_id={last_replayed_id}",
        extra={"json_fields": {**log_meta, "last_replayed_id": last_replayed_id}},
    )
    queue_id = id(subscriber_queue)
    # Track the last successfully delivered message ID for recovery hints
    last_delivered_id = last_replayed_id
    messages_delivered = 0
    first_message_time = None
    xread_count = 0
    try:
        redis = await get_redis_async()
@@ -453,39 +287,9 @@ async def _stream_listener(
        while True:
            # Block for up to 30 seconds waiting for new messages
            # This allows periodic checking if task is still running
            xread_start = time.perf_counter()
            xread_count += 1
            messages = await redis.xread(
                {stream_key: current_id}, block=30000, count=100
            )
            xread_time = (time.perf_counter() - xread_start) * 1000
            if messages:
                msg_count = sum(len(msgs) for _, msgs in messages)
                logger.info(
                    f"[TIMING] xread #{xread_count} returned {msg_count} messages in {xread_time:.1f}ms",
                    extra={
                        "json_fields": {
                            **log_meta,
                            "xread_count": xread_count,
                            "n_messages": msg_count,
                            "duration_ms": xread_time,
                        }
                    },
                )
            elif xread_time > 1000:
                # Only log timeouts (30s blocking)
                logger.info(
                    f"[TIMING] xread #{xread_count} timeout after {xread_time:.1f}ms",
                    extra={
                        "json_fields": {
                            **log_meta,
                            "xread_count": xread_count,
                            "duration_ms": xread_time,
                            "reason": "timeout",
                        }
                    },
                )
            if not messages:
                # Timeout - check if task is still running
@@ -522,30 +326,10 @@ async def _stream_listener(
                                )
                                # Update last delivered ID on successful delivery
                                last_delivered_id = current_id
                                messages_delivered += 1
                                if first_message_time is None:
                                    first_message_time = time.perf_counter()
                                    elapsed = (first_message_time - start_time) * 1000
                                    logger.info(
                                        f"[TIMING] FIRST live message at {elapsed:.1f}ms, type={type(chunk).__name__}",
                                        extra={
                                            "json_fields": {
                                                **log_meta,
                                                "elapsed_ms": elapsed,
                                                "chunk_type": type(chunk).__name__,
                                            }
                                        },
                                    )
                            except asyncio.TimeoutError:
                                logger.warning(
-                                    f"[TIMING] Subscriber queue full, delivery timed out after {QUEUE_PUT_TIMEOUT}s",
+                                    f"Subscriber queue full for task {task_id}, "
-                                    extra={
+                                    f"message delivery timed out after {QUEUE_PUT_TIMEOUT}s"
                                        "json_fields": {
                                            **log_meta,
                                            "timeout_s": QUEUE_PUT_TIMEOUT,
                                            "reason": "queue_full",
                                        }
                                    },
                                )
                                # Send overflow error with recovery info
                                try:
@@ -567,44 +351,15 @@ async def _stream_listener(
                            # Stop listening on finish
                            if isinstance(chunk, StreamFinish):
                                total_time = (time.perf_counter() - start_time) * 1000
                                logger.info(
                                    f"[TIMING] StreamFinish received in {total_time/1000:.1f}s; delivered={messages_delivered}",
                                    extra={
                                        "json_fields": {
                                            **log_meta,
                                            "total_time_ms": total_time,
                                            "messages_delivered": messages_delivered,
                                        }
                                    },
                                )
                                return
                    except Exception as e:
-                        logger.warning(
+                        logger.warning(f"Error processing stream message: {e}")
                            f"Error processing stream message: {e}",
                            extra={"json_fields": {**log_meta, "error": str(e)}},
                        )
    except asyncio.CancelledError:
-        elapsed = (time.perf_counter() - start_time) * 1000
+        logger.debug(f"Stream listener cancelled for task {task_id}")
        logger.info(
            f"[TIMING] _stream_listener CANCELLED after {elapsed:.1f}ms, delivered={messages_delivered}",
            extra={
                "json_fields": {
                    **log_meta,
                    "elapsed_ms": elapsed,
                    "messages_delivered": messages_delivered,
                    "reason": "cancelled",
                }
            },
        )
        raise  # Re-raise to propagate cancellation
    except Exception as e:
-        elapsed = (time.perf_counter() - start_time) * 1000
+        logger.error(f"Stream listener error for task {task_id}: {e}")
        logger.error(
            f"[TIMING] _stream_listener ERROR after {elapsed:.1f}ms: {e}",
            extra={"json_fields": {**log_meta, "elapsed_ms": elapsed, "error": str(e)}},
        )
        # On error, send finish to unblock subscriber
        try:
            await asyncio.wait_for(
@@ -613,24 +368,10 @@ async def _stream_listener(
            )
        except (asyncio.TimeoutError, asyncio.QueueFull):
            logger.warning(
-                "Could not deliver finish event after error",
+                f"Could not deliver finish event for task {task_id} after error"
                extra={"json_fields": log_meta},
            )
    finally:
        # Clean up listener task mapping on exit
        total_time = (time.perf_counter() - start_time) * 1000
        logger.info(
            f"[TIMING] _stream_listener FINISHED in {total_time/1000:.1f}s; task={task_id}, "
            f"delivered={messages_delivered}, xread_count={xread_count}",
            extra={
                "json_fields": {
                    **log_meta,
                    "total_time_ms": total_time,
                    "messages_delivered": messages_delivered,
                    "xread_count": xread_count,
                }
            },
        )
        _listener_tasks.pop(queue_id, None)
@@ -857,10 +598,8 @@ def _reconstruct_chunk(chunk_data: dict) -> StreamBaseResponse | None:
        ResponseType,
        StreamError,
        StreamFinish,
        StreamFinishStep,
        StreamHeartbeat,
        StreamStart,
        StreamStartStep,
        StreamTextDelta,
        StreamTextEnd,
        StreamTextStart,
@@ -874,8 +613,6 @@ def _reconstruct_chunk(chunk_data: dict) -> StreamBaseResponse | None:
    type_to_class: dict[str, type[StreamBaseResponse]] = {
        ResponseType.START.value: StreamStart,
        ResponseType.FINISH.value: StreamFinish,
        ResponseType.START_STEP.value: StreamStartStep,
        ResponseType.FINISH_STEP.value: StreamFinishStep,
        ResponseType.TEXT_START.value: StreamTextStart,
        ResponseType.TEXT_DELTA.value: StreamTextDelta,
        ResponseType.TEXT_END.value: StreamTextEnd,
--- a/autogpt_platform/backend/backend/api/features/chat/tools/find_block.py
+++ b/autogpt_platform/backend/backend/api/features/chat/tools/find_block.py
@@ -13,33 +13,10 @@ from backend.api.features.chat.tools.models import (
    NoResultsResponse,
 )
 from backend.api.features.store.hybrid_search import unified_hybrid_search
-from backend.blocks import get_block
+from backend.data.block import get_block
 from backend.blocks._base import BlockType
 logger = logging.getLogger(__name__)
 _TARGET_RESULTS = 10
 # Over-fetch to compensate for post-hoc filtering of graph-only blocks.
 # 40 is 2x current removed; speed of query 10 vs 40 is minimial
 _OVERFETCH_PAGE_SIZE = 40
 # Block types that only work within graphs and cannot run standalone in CoPilot.
 COPILOT_EXCLUDED_BLOCK_TYPES = {
    BlockType.INPUT,  # Graph interface definition - data enters via chat, not graph inputs
    BlockType.OUTPUT,  # Graph interface definition - data exits via chat, not graph outputs
    BlockType.WEBHOOK,  # Wait for external events - would hang forever in CoPilot
    BlockType.WEBHOOK_MANUAL,  # Same as WEBHOOK
    BlockType.NOTE,  # Visual annotation only - no runtime behavior
    BlockType.HUMAN_IN_THE_LOOP,  # Pauses for human approval - CoPilot IS human-in-the-loop
    BlockType.AGENT,  # AgentExecutorBlock requires execution_context - use run_agent tool
 }
 # Specific block IDs excluded from CoPilot (STANDARD type but still require graph context)
 COPILOT_EXCLUDED_BLOCK_IDS = {
    # SmartDecisionMakerBlock - dynamically discovers downstream blocks via graph topology
    "3b191d9f-356f-482d-8238-ba04b6d18381",
 }
 class FindBlockTool(BaseTool):
    """Tool for searching available blocks."""
@@ -111,7 +88,7 @@ class FindBlockTool(BaseTool):
                query=query,
                content_types=[ContentType.BLOCK],
                page=1,
-                page_size=_OVERFETCH_PAGE_SIZE,
+                page_size=10,
            )
            if not results:
@@ -131,90 +108,60 @@ class FindBlockTool(BaseTool):
                block = get_block(block_id)
                # Skip disabled blocks
-                if not block or block.disabled:
+                if block and not block.disabled:
-                    continue
+                    # Get input/output schemas
                    input_schema = {}
                    output_schema = {}
                    try:
                        input_schema = block.input_schema.jsonschema()
                    except Exception:
                        pass
                    try:
                        output_schema = block.output_schema.jsonschema()
                    except Exception:
                        pass
-                # Skip blocks excluded from CoPilot (graph-only blocks)
+                    # Get categories from block instance
-                if (
+                    categories = []
-                    block.block_type in COPILOT_EXCLUDED_BLOCK_TYPES
+                    if hasattr(block, "categories") and block.categories:
-                    or block.id in COPILOT_EXCLUDED_BLOCK_IDS
+                        categories = [cat.value for cat in block.categories]
                ):
                    continue
-                # Get input/output schemas
+                    # Extract required inputs for easier use
-                input_schema = {}
+                    required_inputs: list[BlockInputFieldInfo] = []
-                output_schema = {}
+                    if input_schema:
-                try:
+                        properties = input_schema.get("properties", {})
-                    input_schema = block.input_schema.jsonschema()
+                        required_fields = set(input_schema.get("required", []))
-                except Exception as e:
+                        # Get credential field names to exclude from required inputs
-                    logger.debug(
+                        credentials_fields = set(
-                        "Failed to generate input schema for block %s: %s",
+                            block.input_schema.get_credentials_fields().keys()
                        block_id,
                        e,
                    )
                try:
                    output_schema = block.output_schema.jsonschema()
                except Exception as e:
                    logger.debug(
                        "Failed to generate output schema for block %s: %s",
                        block_id,
                        e,
                    )
                # Get categories from block instance
                categories = []
                if hasattr(block, "categories") and block.categories:
                    categories = [cat.value for cat in block.categories]
                # Extract required inputs for easier use
                required_inputs: list[BlockInputFieldInfo] = []
                if input_schema:
                    properties = input_schema.get("properties", {})
                    required_fields = set(input_schema.get("required", []))
                    # Get credential field names to exclude from required inputs
                    credentials_fields = set(
                        block.input_schema.get_credentials_fields().keys()
                    )
                    for field_name, field_schema in properties.items():
                        # Skip credential fields - they're handled separately
                        if field_name in credentials_fields:
                            continue
                        required_inputs.append(
                            BlockInputFieldInfo(
                                name=field_name,
                                type=field_schema.get("type", "string"),
                                description=field_schema.get("description", ""),
                                required=field_name in required_fields,
                                default=field_schema.get("default"),
                            )
                        )
-                blocks.append(
+                        for field_name, field_schema in properties.items():
-                    BlockInfoSummary(
+                            # Skip credential fields - they're handled separately
-                        id=block_id,
+                            if field_name in credentials_fields:
-                        name=block.name,
+                                continue
-                        description=block.description or "",
+
-                        categories=categories,
+                            required_inputs.append(
-                        input_schema=input_schema,
+                                BlockInputFieldInfo(
-                        output_schema=output_schema,
+                                    name=field_name,
-                        required_inputs=required_inputs,
+                                    type=field_schema.get("type", "string"),
                                    description=field_schema.get("description", ""),
                                    required=field_name in required_fields,
                                    default=field_schema.get("default"),
                                )
                            )
                    blocks.append(
                        BlockInfoSummary(
                            id=block_id,
                            name=block.name,
                            description=block.description or "",
                            categories=categories,
                            input_schema=input_schema,
                            output_schema=output_schema,
                            required_inputs=required_inputs,
                        )
                    )
                )
                if len(blocks) >= _TARGET_RESULTS:
                    break
            if blocks and len(blocks) < _TARGET_RESULTS:
                logger.debug(
                    "find_block returned %d/%d results for query '%s' "
                    "(filtered %d excluded/disabled blocks)",
                    len(blocks),
                    _TARGET_RESULTS,
                    query,
                    len(results) - len(blocks),
                )
            if not blocks:
                return NoResultsResponse(
--- a/autogpt_platform/backend/backend/api/features/chat/tools/find_block_test.py
+++ b/autogpt_platform/backend/backend/api/features/chat/tools/find_block_test.py
@@ -1,139 +0,0 @@
 """Tests for block filtering in FindBlockTool."""
 from unittest.mock import AsyncMock, MagicMock, patch
 import pytest
 from backend.api.features.chat.tools.find_block import (
    COPILOT_EXCLUDED_BLOCK_IDS,
    COPILOT_EXCLUDED_BLOCK_TYPES,
    FindBlockTool,
 )
 from backend.api.features.chat.tools.models import BlockListResponse
 from backend.blocks._base import BlockType
 from ._test_data import make_session
 _TEST_USER_ID = "test-user-find-block"
 def make_mock_block(
    block_id: str, name: str, block_type: BlockType, disabled: bool = False
 ):
    """Create a mock block for testing."""
    mock = MagicMock()
    mock.id = block_id
    mock.name = name
    mock.description = f"{name} description"
    mock.block_type = block_type
    mock.disabled = disabled
    mock.input_schema = MagicMock()
    mock.input_schema.jsonschema.return_value = {"properties": {}, "required": []}
    mock.input_schema.get_credentials_fields.return_value = {}
    mock.output_schema = MagicMock()
    mock.output_schema.jsonschema.return_value = {}
    mock.categories = []
    return mock
 class TestFindBlockFiltering:
    """Tests for block filtering in FindBlockTool."""
    def test_excluded_block_types_contains_expected_types(self):
        """Verify COPILOT_EXCLUDED_BLOCK_TYPES contains all graph-only types."""
        assert BlockType.INPUT in COPILOT_EXCLUDED_BLOCK_TYPES
        assert BlockType.OUTPUT in COPILOT_EXCLUDED_BLOCK_TYPES
        assert BlockType.WEBHOOK in COPILOT_EXCLUDED_BLOCK_TYPES
        assert BlockType.WEBHOOK_MANUAL in COPILOT_EXCLUDED_BLOCK_TYPES
        assert BlockType.NOTE in COPILOT_EXCLUDED_BLOCK_TYPES
        assert BlockType.HUMAN_IN_THE_LOOP in COPILOT_EXCLUDED_BLOCK_TYPES
        assert BlockType.AGENT in COPILOT_EXCLUDED_BLOCK_TYPES
    def test_excluded_block_ids_contains_smart_decision_maker(self):
        """Verify SmartDecisionMakerBlock is in COPILOT_EXCLUDED_BLOCK_IDS."""
        assert "3b191d9f-356f-482d-8238-ba04b6d18381" in COPILOT_EXCLUDED_BLOCK_IDS
    @pytest.mark.asyncio(loop_scope="session")
    async def test_excluded_block_type_filtered_from_results(self):
        """Verify blocks with excluded BlockTypes are filtered from search results."""
        session = make_session(user_id=_TEST_USER_ID)
        # Mock search returns an INPUT block (excluded) and a STANDARD block (included)
        search_results = [
            {"content_id": "input-block-id", "score": 0.9},
            {"content_id": "standard-block-id", "score": 0.8},
        ]
        input_block = make_mock_block("input-block-id", "Input Block", BlockType.INPUT)
        standard_block = make_mock_block(
            "standard-block-id", "HTTP Request", BlockType.STANDARD
        )
        def mock_get_block(block_id):
            return {
                "input-block-id": input_block,
                "standard-block-id": standard_block,
            }.get(block_id)
        with patch(
            "backend.api.features.chat.tools.find_block.unified_hybrid_search",
            new_callable=AsyncMock,
            return_value=(search_results, 2),
        ):
            with patch(
                "backend.api.features.chat.tools.find_block.get_block",
                side_effect=mock_get_block,
            ):
                tool = FindBlockTool()
                response = await tool._execute(
                    user_id=_TEST_USER_ID, session=session, query="test"
                )
        # Should only return the standard block, not the INPUT block
        assert isinstance(response, BlockListResponse)
        assert len(response.blocks) == 1
        assert response.blocks[0].id == "standard-block-id"
    @pytest.mark.asyncio(loop_scope="session")
    async def test_excluded_block_id_filtered_from_results(self):
        """Verify SmartDecisionMakerBlock is filtered from search results."""
        session = make_session(user_id=_TEST_USER_ID)
        smart_decision_id = "3b191d9f-356f-482d-8238-ba04b6d18381"
        search_results = [
            {"content_id": smart_decision_id, "score": 0.9},
            {"content_id": "normal-block-id", "score": 0.8},
        ]
        # SmartDecisionMakerBlock has STANDARD type but is excluded by ID
        smart_block = make_mock_block(
            smart_decision_id, "Smart Decision Maker", BlockType.STANDARD
        )
        normal_block = make_mock_block(
            "normal-block-id", "Normal Block", BlockType.STANDARD
        )
        def mock_get_block(block_id):
            return {
                smart_decision_id: smart_block,
                "normal-block-id": normal_block,
            }.get(block_id)
        with patch(
            "backend.api.features.chat.tools.find_block.unified_hybrid_search",
            new_callable=AsyncMock,
            return_value=(search_results, 2),
        ):
            with patch(
                "backend.api.features.chat.tools.find_block.get_block",
                side_effect=mock_get_block,
            ):
                tool = FindBlockTool()
                response = await tool._execute(
                    user_id=_TEST_USER_ID, session=session, query="decision"
                )
        # Should only return normal block, not SmartDecisionMakerBlock
        assert isinstance(response, BlockListResponse)
        assert len(response.blocks) == 1
        assert response.blocks[0].id == "normal-block-id"
--- a/autogpt_platform/backend/backend/api/features/chat/tools/run_block.py
+++ b/autogpt_platform/backend/backend/api/features/chat/tools/run_block.py
@@ -8,12 +8,7 @@ from typing import Any
 from pydantic_core import PydanticUndefined
 from backend.api.features.chat.model import ChatSession
-from backend.api.features.chat.tools.find_block import (
+from backend.data.block import get_block
    COPILOT_EXCLUDED_BLOCK_IDS,
    COPILOT_EXCLUDED_BLOCK_TYPES,
 )
 from backend.blocks import get_block
 from backend.blocks._base import AnyBlockSchema
 from backend.data.execution import ExecutionContext
 from backend.data.model import CredentialsFieldInfo, CredentialsMetaInput
 from backend.data.workspace import get_or_create_workspace
@@ -82,6 +77,69 @@ class RunBlockTool(BaseTool):
    def requires_auth(self) -> bool:
        return True
    def _resolve_discriminated_credentials(
        self,
        block: Any,
        input_data: dict[str, Any],
    ) -> dict[str, CredentialsFieldInfo]:
        """Resolve credential requirements, applying discriminator logic where needed."""
        credentials_fields_info = block.input_schema.get_credentials_fields_info()
        if not credentials_fields_info:
            return {}
        resolved: dict[str, CredentialsFieldInfo] = {}
        for field_name, field_info in credentials_fields_info.items():
            effective_field_info = field_info
            if field_info.discriminator and field_info.discriminator_mapping:
                discriminator_value = input_data.get(field_info.discriminator)
                if discriminator_value is None:
                    field = block.input_schema.model_fields.get(
                        field_info.discriminator
                    )
                    if field and field.default is not PydanticUndefined:
                        discriminator_value = field.default
                if (
                    discriminator_value
                    and discriminator_value in field_info.discriminator_mapping
                ):
                    effective_field_info = field_info.discriminate(discriminator_value)
                    logger.debug(
                        f"Discriminated provider for {field_name}: "
                        f"{discriminator_value} -> {effective_field_info.provider}"
                    )
            resolved[field_name] = effective_field_info
        return resolved
    async def _check_block_credentials(
        self,
        user_id: str,
        block: Any,
        input_data: dict[str, Any] | None = None,
    ) -> tuple[dict[str, CredentialsMetaInput], list[CredentialsMetaInput]]:
        """
        Check if user has required credentials for a block.
        Args:
            user_id: User ID
            block: Block to check credentials for
            input_data: Input data for the block (used to determine provider via discriminator)
        Returns:
            tuple[matched_credentials, missing_credentials]
        """
        input_data = input_data or {}
        requirements = self._resolve_discriminated_credentials(block, input_data)
        if not requirements:
            return {}, []
        return await match_credentials_to_requirements(user_id, requirements)
    async def _execute(
        self,
        user_id: str | None,
@@ -136,24 +194,11 @@ class RunBlockTool(BaseTool):
                session_id=session_id,
            )
        # Check if block is excluded from CoPilot (graph-only blocks)
        if (
            block.block_type in COPILOT_EXCLUDED_BLOCK_TYPES
            or block.id in COPILOT_EXCLUDED_BLOCK_IDS
        ):
            return ErrorResponse(
                message=(
                    f"Block '{block.name}' cannot be run directly in CoPilot. "
                    "This block is designed for use within graphs only."
                ),
                session_id=session_id,
            )
        logger.info(f"Executing block {block.name} ({block_id}) for user {user_id}")
        creds_manager = IntegrationCredentialsManager()
-        matched_credentials, missing_credentials = (
+        matched_credentials, missing_credentials = await self._check_block_credentials(
-            await self._resolve_block_credentials(user_id, block, input_data)
+            user_id, block, input_data
        )
        if missing_credentials:
@@ -282,75 +327,8 @@ class RunBlockTool(BaseTool):
                session_id=session_id,
            )
-    async def _resolve_block_credentials(
+    def _get_inputs_list(self, block: Any) -> list[dict[str, Any]]:
        self,
        user_id: str,
        block: AnyBlockSchema,
        input_data: dict[str, Any] | None = None,
    ) -> tuple[dict[str, CredentialsMetaInput], list[CredentialsMetaInput]]:
        """
        Resolve credentials for a block by matching user's available credentials.
        Args:
            user_id: User ID
            block: Block to resolve credentials for
            input_data: Input data for the block (used to determine provider via discriminator)
        Returns:
            tuple of (matched_credentials, missing_credentials) - matched credentials
            are used for block execution, missing ones indicate setup requirements.
        """
        input_data = input_data or {}
        requirements = self._resolve_discriminated_credentials(block, input_data)
        if not requirements:
            return {}, []
        return await match_credentials_to_requirements(user_id, requirements)
    def _get_inputs_list(self, block: AnyBlockSchema) -> list[dict[str, Any]]:
        """Extract non-credential inputs from block schema."""
        schema = block.input_schema.jsonschema()
        credentials_fields = set(block.input_schema.get_credentials_fields().keys())
        return get_inputs_from_schema(schema, exclude_fields=credentials_fields)
    def _resolve_discriminated_credentials(
        self,
        block: AnyBlockSchema,
        input_data: dict[str, Any],
    ) -> dict[str, CredentialsFieldInfo]:
        """Resolve credential requirements, applying discriminator logic where needed."""
        credentials_fields_info = block.input_schema.get_credentials_fields_info()
        if not credentials_fields_info:
            return {}
        resolved: dict[str, CredentialsFieldInfo] = {}
        for field_name, field_info in credentials_fields_info.items():
            effective_field_info = field_info
            if field_info.discriminator and field_info.discriminator_mapping:
                discriminator_value = input_data.get(field_info.discriminator)
                if discriminator_value is None:
                    field = block.input_schema.model_fields.get(
                        field_info.discriminator
                    )
                    if field and field.default is not PydanticUndefined:
                        discriminator_value = field.default
                if (
                    discriminator_value
                    and discriminator_value in field_info.discriminator_mapping
                ):
                    effective_field_info = field_info.discriminate(discriminator_value)
                    # For host-scoped credentials, add the discriminator value
                    # (e.g., URL) so _credential_is_for_host can match it
                    effective_field_info.discriminator_values.add(discriminator_value)
                    logger.debug(
                        f"Discriminated provider for {field_name}: "
                        f"{discriminator_value} -> {effective_field_info.provider}"
                    )
            resolved[field_name] = effective_field_info
        return resolved
--- a/autogpt_platform/backend/backend/api/features/chat/tools/run_block_test.py
+++ b/autogpt_platform/backend/backend/api/features/chat/tools/run_block_test.py
@@ -1,106 +0,0 @@
 """Tests for block execution guards in RunBlockTool."""
 from unittest.mock import MagicMock, patch
 import pytest
 from backend.api.features.chat.tools.models import ErrorResponse
 from backend.api.features.chat.tools.run_block import RunBlockTool
 from backend.blocks._base import BlockType
 from ._test_data import make_session
 _TEST_USER_ID = "test-user-run-block"
 def make_mock_block(
    block_id: str, name: str, block_type: BlockType, disabled: bool = False
 ):
    """Create a mock block for testing."""
    mock = MagicMock()
    mock.id = block_id
    mock.name = name
    mock.block_type = block_type
    mock.disabled = disabled
    mock.input_schema = MagicMock()
    mock.input_schema.jsonschema.return_value = {"properties": {}, "required": []}
    mock.input_schema.get_credentials_fields_info.return_value = []
    return mock
 class TestRunBlockFiltering:
    """Tests for block execution guards in RunBlockTool."""
    @pytest.mark.asyncio(loop_scope="session")
    async def test_excluded_block_type_returns_error(self):
        """Attempting to execute a block with excluded BlockType returns error."""
        session = make_session(user_id=_TEST_USER_ID)
        input_block = make_mock_block("input-block-id", "Input Block", BlockType.INPUT)
        with patch(
            "backend.api.features.chat.tools.run_block.get_block",
            return_value=input_block,
        ):
            tool = RunBlockTool()
            response = await tool._execute(
                user_id=_TEST_USER_ID,
                session=session,
                block_id="input-block-id",
                input_data={},
            )
        assert isinstance(response, ErrorResponse)
        assert "cannot be run directly in CoPilot" in response.message
        assert "designed for use within graphs only" in response.message
    @pytest.mark.asyncio(loop_scope="session")
    async def test_excluded_block_id_returns_error(self):
        """Attempting to execute SmartDecisionMakerBlock returns error."""
        session = make_session(user_id=_TEST_USER_ID)
        smart_decision_id = "3b191d9f-356f-482d-8238-ba04b6d18381"
        smart_block = make_mock_block(
            smart_decision_id, "Smart Decision Maker", BlockType.STANDARD
        )
        with patch(
            "backend.api.features.chat.tools.run_block.get_block",
            return_value=smart_block,
        ):
            tool = RunBlockTool()
            response = await tool._execute(
                user_id=_TEST_USER_ID,
                session=session,
                block_id=smart_decision_id,
                input_data={},
            )
        assert isinstance(response, ErrorResponse)
        assert "cannot be run directly in CoPilot" in response.message
    @pytest.mark.asyncio(loop_scope="session")
    async def test_non_excluded_block_passes_guard(self):
        """Non-excluded blocks pass the filtering guard (may fail later for other reasons)."""
        session = make_session(user_id=_TEST_USER_ID)
        standard_block = make_mock_block(
            "standard-id", "HTTP Request", BlockType.STANDARD
        )
        with patch(
            "backend.api.features.chat.tools.run_block.get_block",
            return_value=standard_block,
        ):
            tool = RunBlockTool()
            response = await tool._execute(
                user_id=_TEST_USER_ID,
                session=session,
                block_id="standard-id",
                input_data={},
            )
        # Should NOT be an ErrorResponse about CoPilot exclusion
        # (may be other errors like missing credentials, but not the exclusion guard)
        if isinstance(response, ErrorResponse):
            assert "cannot be run directly in CoPilot" not in response.message
--- a/autogpt_platform/backend/backend/api/features/chat/tools/utils.py
+++ b/autogpt_platform/backend/backend/api/features/chat/tools/utils.py
@@ -8,7 +8,6 @@ from backend.api.features.library import model as library_model
 from backend.api.features.store import db as store_db
 from backend.data.graph import GraphModel
 from backend.data.model import (
    Credentials,
    CredentialsFieldInfo,
    CredentialsMetaInput,
    HostScopedCredentials,
@@ -224,14 +223,54 @@ async def get_or_create_library_agent(
    return library_agents[0]
 async def get_user_credentials(user_id: str) -> list:
    """Get all available credentials for a user."""
    creds_manager = IntegrationCredentialsManager()
    return await creds_manager.store.get_all_creds(user_id)
 def find_matching_credential(
    available_creds: list,
    field_info: CredentialsFieldInfo,
    check_scopes: bool = True,
 ):
    """Find a credential that matches the required provider, type, and optionally scopes."""
    for cred in available_creds:
        if cred.provider not in field_info.provider:
            continue
        if cred.type not in field_info.supported_types:
            continue
        if check_scopes and not _credential_has_required_scopes(cred, field_info):
            continue
        return cred
    return None
 def create_credential_meta_from_match(matching_cred) -> CredentialsMetaInput:
    """Create a CredentialsMetaInput from a matched credential."""
    return CredentialsMetaInput(
        id=matching_cred.id,
        provider=matching_cred.provider,  # type: ignore
        type=matching_cred.type,
        title=matching_cred.title,
    )
 async def match_credentials_to_requirements(
    user_id: str,
    requirements: dict[str, CredentialsFieldInfo],
    check_scopes: bool = True,
 ) -> tuple[dict[str, CredentialsMetaInput], list[CredentialsMetaInput]]:
    """
    Match user's credentials against a dictionary of credential requirements.
    This is the core matching logic shared by both graph and block credential matching.
    Args:
        user_id: User ID to fetch credentials for
        requirements: Dict mapping field names to CredentialsFieldInfo
        check_scopes: Whether to verify OAuth2 scopes match requirements (default True).
            Set to False to preserve original run_block behavior which didn't check scopes.
    """
    matched: dict[str, CredentialsMetaInput] = {}
    missing: list[CredentialsMetaInput] = []
@@ -242,7 +281,9 @@ async def match_credentials_to_requirements(
    available_creds = await get_user_credentials(user_id)
    for field_name, field_info in requirements.items():
-        matching_cred = find_matching_credential(available_creds, field_info)
+        matching_cred = find_matching_credential(
            available_creds, field_info, check_scopes=check_scopes
        )
        if matching_cred:
            try:
@@ -279,44 +320,6 @@ async def match_credentials_to_requirements(
    return matched, missing
 async def get_user_credentials(user_id: str) -> list[Credentials]:
    """Get all available credentials for a user."""
    creds_manager = IntegrationCredentialsManager()
    return await creds_manager.store.get_all_creds(user_id)
 def find_matching_credential(
    available_creds: list[Credentials],
    field_info: CredentialsFieldInfo,
 ) -> Credentials | None:
    """Find a credential that matches the required provider, type, scopes, and host."""
    for cred in available_creds:
        if cred.provider not in field_info.provider:
            continue
        if cred.type not in field_info.supported_types:
            continue
        if cred.type == "oauth2" and not _credential_has_required_scopes(
            cred, field_info
        ):
            continue
        if cred.type == "host_scoped" and not _credential_is_for_host(cred, field_info):
            continue
        return cred
    return None
 def create_credential_meta_from_match(
    matching_cred: Credentials,
 ) -> CredentialsMetaInput:
    """Create a CredentialsMetaInput from a matched credential."""
    return CredentialsMetaInput(
        id=matching_cred.id,
        provider=matching_cred.provider,  # type: ignore
        type=matching_cred.type,
        title=matching_cred.title,
    )
 async def match_user_credentials_to_graph(
    user_id: str,
    graph: GraphModel,
@@ -425,6 +428,8 @@ def _credential_has_required_scopes(
    # If no scopes are required, any credential matches
    if not requirements.required_scopes:
        return True
    # Check that credential scopes are a superset of required scopes
    return set(credential.scopes).issuperset(requirements.required_scopes)
--- a/autogpt_platform/backend/backend/api/features/library/db.py
+++ b/autogpt_platform/backend/backend/api/features/library/db.py
@@ -12,11 +12,12 @@ import backend.api.features.store.image_gen as store_image_gen
 import backend.api.features.store.media as store_media
 import backend.data.graph as graph_db
 import backend.data.integrations as integrations_db
 from backend.data.block import BlockInput
 from backend.data.db import transaction
 from backend.data.execution import get_graph_execution
 from backend.data.graph import GraphSettings
 from backend.data.includes import AGENT_PRESET_INCLUDE, library_agent_include
-from backend.data.model import CredentialsMetaInput, GraphInput
+from backend.data.model import CredentialsMetaInput
 from backend.integrations.creds_manager import IntegrationCredentialsManager
 from backend.integrations.webhooks.graph_lifecycle_hooks import (
    on_graph_activate,
@@ -1129,7 +1130,7 @@ async def create_preset_from_graph_execution(
 async def update_preset(
    user_id: str,
    preset_id: str,
-    inputs: Optional[GraphInput] = None,
+    inputs: Optional[BlockInput] = None,
    credentials: Optional[dict[str, CredentialsMetaInput]] = None,
    name: Optional[str] = None,
    description: Optional[str] = None,
--- a/autogpt_platform/backend/backend/api/features/library/model.py
+++ b/autogpt_platform/backend/backend/api/features/library/model.py
@@ -6,12 +6,9 @@ import prisma.enums
 import prisma.models
 import pydantic
 from backend.data.block import BlockInput
 from backend.data.graph import GraphModel, GraphSettings, GraphTriggerInfo
-from backend.data.model import (
+from backend.data.model import CredentialsMetaInput, is_credentials_field_name
    CredentialsMetaInput,
    GraphInput,
    is_credentials_field_name,
 )
 from backend.util.json import loads as json_loads
 from backend.util.models import Pagination
@@ -326,7 +323,7 @@ class LibraryAgentPresetCreatable(pydantic.BaseModel):
    graph_id: str
    graph_version: int
-    inputs: GraphInput
+    inputs: BlockInput
    credentials: dict[str, CredentialsMetaInput]
    name: str
@@ -355,7 +352,7 @@ class LibraryAgentPresetUpdatable(pydantic.BaseModel):
    Request model used when updating a preset for a library agent.
    """
-    inputs: Optional[GraphInput] = None
+    inputs: Optional[BlockInput] = None
    credentials: Optional[dict[str, CredentialsMetaInput]] = None
    name: Optional[str] = None
@@ -398,7 +395,7 @@ class LibraryAgentPreset(LibraryAgentPresetCreatable):
                "Webhook must be included in AgentPreset query when webhookId is set"
            )
-        input_data: GraphInput = {}
+        input_data: BlockInput = {}
        input_credentials: dict[str, CredentialsMetaInput] = {}
        for preset_input in preset.InputPresets:
--- a/autogpt_platform/backend/backend/api/features/otto/service.py
+++ b/autogpt_platform/backend/backend/api/features/otto/service.py
@@ -5,8 +5,8 @@ from typing import Optional
 import aiohttp
 from fastapi import HTTPException
 from backend.blocks import get_block
 from backend.data import graph as graph_db
 from backend.data.block import get_block
 from backend.util.settings import Settings
 from .models import ApiResponse, ChatRequest, GraphData
--- a/autogpt_platform/backend/backend/api/features/store/content_handlers.py
+++ b/autogpt_platform/backend/backend/api/features/store/content_handlers.py
@@ -152,7 +152,7 @@ class BlockHandler(ContentHandler):
    async def get_missing_items(self, batch_size: int) -> list[ContentItem]:
        """Fetch blocks without embeddings."""
-        from backend.blocks import get_blocks
+        from backend.data.block import get_blocks
        # Get all available blocks
        all_blocks = get_blocks()
@@ -249,7 +249,7 @@ class BlockHandler(ContentHandler):
    async def get_stats(self) -> dict[str, int]:
        """Get statistics about block embedding coverage."""
-        from backend.blocks import get_blocks
+        from backend.data.block import get_blocks
        all_blocks = get_blocks()
--- a/autogpt_platform/backend/backend/api/features/store/content_handlers_test.py
+++ b/autogpt_platform/backend/backend/api/features/store/content_handlers_test.py
@@ -93,7 +93,7 @@ async def test_block_handler_get_missing_items(mocker):
    mock_existing = []
    with patch(
-        "backend.blocks.get_blocks",
+        "backend.data.block.get_blocks",
        return_value=mock_blocks,
    ):
        with patch(
@@ -135,7 +135,7 @@ async def test_block_handler_get_stats(mocker):
    mock_embedded = [{"count": 2}]
    with patch(
-        "backend.blocks.get_blocks",
+        "backend.data.block.get_blocks",
        return_value=mock_blocks,
    ):
        with patch(
@@ -327,7 +327,7 @@ async def test_block_handler_handles_missing_attributes():
    mock_blocks = {"block-minimal": mock_block_class}
    with patch(
-        "backend.blocks.get_blocks",
+        "backend.data.block.get_blocks",
        return_value=mock_blocks,
    ):
        with patch(
@@ -360,7 +360,7 @@ async def test_block_handler_skips_failed_blocks():
    mock_blocks = {"good-block": good_block, "bad-block": bad_block}
    with patch(
-        "backend.blocks.get_blocks",
+        "backend.data.block.get_blocks",
        return_value=mock_blocks,
    ):
        with patch(
--- a/autogpt_platform/backend/backend/api/features/store/embeddings.py
+++ b/autogpt_platform/backend/backend/api/features/store/embeddings.py
@@ -662,7 +662,7 @@ async def cleanup_orphaned_embeddings() -> dict[str, Any]:
                )
                current_ids = {row["id"] for row in valid_agents}
            elif content_type == ContentType.BLOCK:
-                from backend.blocks import get_blocks
+                from backend.data.block import get_blocks
                current_ids = set(get_blocks().keys())
            elif content_type == ContentType.DOCUMENTATION:
--- a/autogpt_platform/backend/backend/api/features/store/hybrid_search.py
+++ b/autogpt_platform/backend/backend/api/features/store/hybrid_search.py
@@ -8,7 +8,6 @@ Includes BM25 reranking for improved lexical relevance.
 import logging
 import re
 import time
 from dataclasses import dataclass
 from typing import Any, Literal
@@ -363,11 +362,7 @@ async def unified_hybrid_search(
        LIMIT {limit_param} OFFSET {offset_param}
    """
-    try:
+    results = await query_raw_with_schema(sql_query, *params)
        results = await query_raw_with_schema(sql_query, *params)
    except Exception as e:
        await _log_vector_error_diagnostics(e)
        raise
    total = results[0]["total_count"] if results else 0
    # Apply BM25 reranking
@@ -691,11 +686,7 @@ async def hybrid_search(
        LIMIT {limit_param} OFFSET {offset_param}
    """
-    try:
+    results = await query_raw_with_schema(sql_query, *params)
        results = await query_raw_with_schema(sql_query, *params)
    except Exception as e:
        await _log_vector_error_diagnostics(e)
        raise
    total = results[0]["total_count"] if results else 0
@@ -727,87 +718,6 @@ async def hybrid_search_simple(
    return await hybrid_search(query=query, page=page, page_size=page_size)
 # ============================================================================
 # Diagnostics
 # ============================================================================
 # Rate limit: only log vector error diagnostics once per this interval
 _VECTOR_DIAG_INTERVAL_SECONDS = 60
 _last_vector_diag_time: float = 0
 async def _log_vector_error_diagnostics(error: Exception) -> None:
    """Log diagnostic info when 'type vector does not exist' error occurs.
    Note: Diagnostic queries use query_raw_with_schema which may run on a different
    pooled connection than the one that failed. Session-level search_path can differ,
    so these diagnostics show cluster-wide state, not necessarily the failed session.
    Includes rate limiting to avoid log spam - only logs once per minute.
    Caller should re-raise the error after calling this function.
    """
    global _last_vector_diag_time
    # Check if this is the vector type error
    error_str = str(error).lower()
    if not (
        "type" in error_str and "vector" in error_str and "does not exist" in error_str
    ):
        return
    # Rate limit: only log once per interval
    now = time.time()
    if now - _last_vector_diag_time < _VECTOR_DIAG_INTERVAL_SECONDS:
        return
    _last_vector_diag_time = now
    try:
        diagnostics: dict[str, object] = {}
        try:
            search_path_result = await query_raw_with_schema("SHOW search_path")
            diagnostics["search_path"] = search_path_result
        except Exception as e:
            diagnostics["search_path"] = f"Error: {e}"
        try:
            schema_result = await query_raw_with_schema("SELECT current_schema()")
            diagnostics["current_schema"] = schema_result
        except Exception as e:
            diagnostics["current_schema"] = f"Error: {e}"
        try:
            user_result = await query_raw_with_schema(
                "SELECT current_user, session_user, current_database()"
            )
            diagnostics["user_info"] = user_result
        except Exception as e:
            diagnostics["user_info"] = f"Error: {e}"
        try:
            # Check pgvector extension installation (cluster-wide, stable info)
            ext_result = await query_raw_with_schema(
                "SELECT extname, extversion, nspname as schema "
                "FROM pg_extension e "
                "JOIN pg_namespace n ON e.extnamespace = n.oid "
                "WHERE extname = 'vector'"
            )
            diagnostics["pgvector_extension"] = ext_result
        except Exception as e:
            diagnostics["pgvector_extension"] = f"Error: {e}"
        logger.error(
            f"Vector type error diagnostics:\n"
            f"  Error: {error}\n"
            f"  search_path: {diagnostics.get('search_path')}\n"
            f"  current_schema: {diagnostics.get('current_schema')}\n"
            f"  user_info: {diagnostics.get('user_info')}\n"
            f"  pgvector_extension: {diagnostics.get('pgvector_extension')}"
        )
    except Exception as diag_error:
        logger.error(f"Failed to collect vector error diagnostics: {diag_error}")
 # Backward compatibility alias - HybridSearchWeights maps to StoreAgentSearchWeights
 # for existing code that expects the popularity parameter
 HybridSearchWeights = StoreAgentSearchWeights
--- a/autogpt_platform/backend/backend/api/features/store/image_gen.py
+++ b/autogpt_platform/backend/backend/api/features/store/image_gen.py
@@ -7,6 +7,15 @@ from replicate.client import Client as ReplicateClient
 from replicate.exceptions import ReplicateError
 from replicate.helpers import FileOutput
 from backend.blocks.ideogram import (
    AspectRatio,
    ColorPalettePreset,
    IdeogramModelBlock,
    IdeogramModelName,
    MagicPromptOption,
    StyleType,
    UpscaleOption,
 )
 from backend.data.graph import GraphBaseMeta
 from backend.data.model import CredentialsMetaInput, ProviderName
 from backend.integrations.credentials_store import ideogram_credentials
@@ -41,16 +50,6 @@ async def generate_agent_image_v2(graph: GraphBaseMeta | AgentGraph) -> io.Bytes
    if not ideogram_credentials.api_key:
        raise ValueError("Missing Ideogram API key")
    from backend.blocks.ideogram import (
        AspectRatio,
        ColorPalettePreset,
        IdeogramModelBlock,
        IdeogramModelName,
        MagicPromptOption,
        StyleType,
        UpscaleOption,
    )
    name = graph.name
    description = f"{name} ({graph.description})" if graph.description else name
--- a/autogpt_platform/backend/backend/api/features/v1.py
+++ b/autogpt_platform/backend/backend/api/features/v1.py
@@ -40,11 +40,10 @@ from backend.api.model import (
    UpdateTimezoneRequest,
    UploadFileResponse,
 )
 from backend.blocks import get_block, get_blocks
 from backend.data import execution as execution_db
 from backend.data import graph as graph_db
 from backend.data.auth import api_key as api_key_db
-from backend.data.block import BlockInput, CompletedBlockOutput
+from backend.data.block import BlockInput, CompletedBlockOutput, get_block, get_blocks
 from backend.data.credit import (
    AutoTopUpConfig,
    RefundRequest,
--- a/autogpt_platform/backend/backend/blocks/init.py
+++ b/autogpt_platform/backend/backend/blocks/init.py
@@ -3,19 +3,22 @@ import logging
 import os
 import re
 from pathlib import Path
-from typing import Sequence, Type, TypeVar
+from typing import TYPE_CHECKING, TypeVar
 from backend.blocks._base import AnyBlockSchema, BlockType
 from backend.util.cache import cached
 logger = logging.getLogger(__name__)
 if TYPE_CHECKING:
    from backend.data.block import Block
 T = TypeVar("T")
@cached(ttl_seconds=3600)
-def load_all_blocks() -> dict[str, type["AnyBlockSchema"]]:
+def load_all_blocks() -> dict[str, type["Block"]]:
-    from backend.blocks._base import Block
+    from backend.data.block import Block
    from backend.util.settings import Config
    # Check if example blocks should be loaded from settings
@@ -47,8 +50,8 @@ def load_all_blocks() -> dict[str, type["AnyBlockSchema"]]:
        importlib.import_module(f".{module}", package=__name__)
    # Load all Block instances from the available modules
-    available_blocks: dict[str, type["AnyBlockSchema"]] = {}
+    available_blocks: dict[str, type["Block"]] = {}
-    for block_cls in _all_subclasses(Block):
+    for block_cls in all_subclasses(Block):
        class_name = block_cls.__name__
        if class_name.endswith("Base"):
@@ -61,7 +64,7 @@ def load_all_blocks() -> dict[str, type["AnyBlockSchema"]]:
                "please name the class with 'Base' at the end"
            )
-        block = block_cls()  # pyright: ignore[reportAbstractUsage]
+        block = block_cls.create()
        if not isinstance(block.id, str) or len(block.id) != 36:
            raise ValueError(
@@ -102,7 +105,7 @@ def load_all_blocks() -> dict[str, type["AnyBlockSchema"]]:
        available_blocks[block.id] = block_cls
    # Filter out blocks with incomplete auth configs, e.g. missing OAuth server secrets
-    from ._utils import is_block_auth_configured
+    from backend.data.block import is_block_auth_configured
    filtered_blocks = {}
    for block_id, block_cls in available_blocks.items():
@@ -112,48 +115,11 @@ def load_all_blocks() -> dict[str, type["AnyBlockSchema"]]:
    return filtered_blocks
-def _all_subclasses(cls: type[T]) -> list[type[T]]:
+__all__ = ["load_all_blocks"]
 def all_subclasses(cls: type[T]) -> list[type[T]]:
    subclasses = cls.__subclasses__()
    for subclass in subclasses:
-        subclasses += _all_subclasses(subclass)
+        subclasses += all_subclasses(subclass)
    return subclasses
 # ============== Block access helper functions ============== #
 def get_blocks() -> dict[str, Type["AnyBlockSchema"]]:
    return load_all_blocks()
 # Note on the return type annotation: https://github.com/microsoft/pyright/issues/10281
 def get_block(block_id: str) -> "AnyBlockSchema | None":
    cls = get_blocks().get(block_id)
    return cls() if cls else None
@cached(ttl_seconds=3600)
 def get_webhook_block_ids() -> Sequence[str]:
    return [
        id
        for id, B in get_blocks().items()
        if B().block_type in (BlockType.WEBHOOK, BlockType.WEBHOOK_MANUAL)
    ]
@cached(ttl_seconds=3600)
 def get_io_block_ids() -> Sequence[str]:
    return [
        id
        for id, B in get_blocks().items()
        if B().block_type in (BlockType.INPUT, BlockType.OUTPUT)
    ]
@cached(ttl_seconds=3600)
 def get_human_in_the_loop_block_ids() -> Sequence[str]:
    return [
        id
        for id, B in get_blocks().items()
        if B().block_type == BlockType.HUMAN_IN_THE_LOOP
    ]
--- a/autogpt_platform/backend/backend/blocks/_base.py
+++ b/autogpt_platform/backend/backend/blocks/_base.py
@@ -1,739 +0,0 @@
 import inspect
 import logging
 from abc import ABC, abstractmethod
 from enum import Enum
 from typing import (
    TYPE_CHECKING,
    Any,
    Callable,
    ClassVar,
    Generic,
    Optional,
    Type,
    TypeAlias,
    TypeVar,
    cast,
    get_origin,
 )
 import jsonref
 import jsonschema
 from pydantic import BaseModel
 from backend.data.block import BlockInput, BlockOutput, BlockOutputEntry
 from backend.data.model import (
    Credentials,
    CredentialsFieldInfo,
    CredentialsMetaInput,
    SchemaField,
    is_credentials_field_name,
 )
 from backend.integrations.providers import ProviderName
 from backend.util import json
 from backend.util.exceptions import (
    BlockError,
    BlockExecutionError,
    BlockInputError,
    BlockOutputError,
    BlockUnknownError,
 )
 from backend.util.settings import Config
 logger = logging.getLogger(__name__)
 if TYPE_CHECKING:
    from backend.data.execution import ExecutionContext
    from backend.data.model import ContributorDetails, NodeExecutionStats
    from ..data.graph import Link
 app_config = Config()
 BlockTestOutput = BlockOutputEntry | tuple[str, Callable[[Any], bool]]
 class BlockType(Enum):
    STANDARD = "Standard"
    INPUT = "Input"
    OUTPUT = "Output"
    NOTE = "Note"
    WEBHOOK = "Webhook"
    WEBHOOK_MANUAL = "Webhook (manual)"
    AGENT = "Agent"
    AI = "AI"
    AYRSHARE = "Ayrshare"
    HUMAN_IN_THE_LOOP = "Human In The Loop"
 class BlockCategory(Enum):
    AI = "Block that leverages AI to perform a task."
    SOCIAL = "Block that interacts with social media platforms."
    TEXT = "Block that processes text data."
    SEARCH = "Block that searches or extracts information from the internet."
    BASIC = "Block that performs basic operations."
    INPUT = "Block that interacts with input of the graph."
    OUTPUT = "Block that interacts with output of the graph."
    LOGIC = "Programming logic to control the flow of your agent"
    COMMUNICATION = "Block that interacts with communication platforms."
    DEVELOPER_TOOLS = "Developer tools such as GitHub blocks."
    DATA = "Block that interacts with structured data."
    HARDWARE = "Block that interacts with hardware."
    AGENT = "Block that interacts with other agents."
    CRM = "Block that interacts with CRM services."
    SAFETY = (
        "Block that provides AI safety mechanisms such as detecting harmful content"
    )
    PRODUCTIVITY = "Block that helps with productivity"
    ISSUE_TRACKING = "Block that helps with issue tracking"
    MULTIMEDIA = "Block that interacts with multimedia content"
    MARKETING = "Block that helps with marketing"
    def dict(self) -> dict[str, str]:
        return {"category": self.name, "description": self.value}
 class BlockCostType(str, Enum):
    RUN = "run"  # cost X credits per run
    BYTE = "byte"  # cost X credits per byte
    SECOND = "second"  # cost X credits per second
 class BlockCost(BaseModel):
    cost_amount: int
    cost_filter: BlockInput
    cost_type: BlockCostType
    def __init__(
        self,
        cost_amount: int,
        cost_type: BlockCostType = BlockCostType.RUN,
        cost_filter: Optional[BlockInput] = None,
        **data: Any,
    ) -> None:
        super().__init__(
            cost_amount=cost_amount,
            cost_filter=cost_filter or {},
            cost_type=cost_type,
            **data,
        )
 class BlockInfo(BaseModel):
    id: str
    name: str
    inputSchema: dict[str, Any]
    outputSchema: dict[str, Any]
    costs: list[BlockCost]
    description: str
    categories: list[dict[str, str]]
    contributors: list[dict[str, Any]]
    staticOutput: bool
    uiType: str
 class BlockSchema(BaseModel):
    cached_jsonschema: ClassVar[dict[str, Any]]
    @classmethod
    def jsonschema(cls) -> dict[str, Any]:
        if cls.cached_jsonschema:
            return cls.cached_jsonschema
        model = jsonref.replace_refs(cls.model_json_schema(), merge_props=True)
        def ref_to_dict(obj):
            if isinstance(obj, dict):
                # OpenAPI <3.1 does not support sibling fields that has a $ref key
                # So sometimes, the schema has an "allOf"/"anyOf"/"oneOf" with 1 item.
                keys = {"allOf", "anyOf", "oneOf"}
                one_key = next((k for k in keys if k in obj and len(obj[k]) == 1), None)
                if one_key:
                    obj.update(obj[one_key][0])
                return {
                    key: ref_to_dict(value)
                    for key, value in obj.items()
                    if not key.startswith("$") and key != one_key
                }
            elif isinstance(obj, list):
                return [ref_to_dict(item) for item in obj]
            return obj
        cls.cached_jsonschema = cast(dict[str, Any], ref_to_dict(model))
        return cls.cached_jsonschema
    @classmethod
    def validate_data(cls, data: BlockInput) -> str | None:
        return json.validate_with_jsonschema(
            schema=cls.jsonschema(),
            data={k: v for k, v in data.items() if v is not None},
        )
    @classmethod
    def get_mismatch_error(cls, data: BlockInput) -> str | None:
        return cls.validate_data(data)
    @classmethod
    def get_field_schema(cls, field_name: str) -> dict[str, Any]:
        model_schema = cls.jsonschema().get("properties", {})
        if not model_schema:
            raise ValueError(f"Invalid model schema {cls}")
        property_schema = model_schema.get(field_name)
        if not property_schema:
            raise ValueError(f"Invalid property name {field_name}")
        return property_schema
    @classmethod
    def validate_field(cls, field_name: str, data: BlockInput) -> str | None:
        """
        Validate the data against a specific property (one of the input/output name).
        Returns the validation error message if the data does not match the schema.
        """
        try:
            property_schema = cls.get_field_schema(field_name)
            jsonschema.validate(json.to_dict(data), property_schema)
            return None
        except jsonschema.ValidationError as e:
            return str(e)
    @classmethod
    def get_fields(cls) -> set[str]:
        return set(cls.model_fields.keys())
    @classmethod
    def get_required_fields(cls) -> set[str]:
        return {
            field
            for field, field_info in cls.model_fields.items()
            if field_info.is_required()
        }
    @classmethod
    def __pydantic_init_subclass__(cls, **kwargs):
        """Validates the schema definition. Rules:
        - Fields with annotation `CredentialsMetaInput` MUST be
          named `credentials` or `*_credentials`
        - Fields named `credentials` or `*_credentials` MUST be
          of type `CredentialsMetaInput`
        """
        super().__pydantic_init_subclass__(**kwargs)
        # Reset cached JSON schema to prevent inheriting it from parent class
        cls.cached_jsonschema = {}
        credentials_fields = cls.get_credentials_fields()
        for field_name in cls.get_fields():
            if is_credentials_field_name(field_name):
                if field_name not in credentials_fields:
                    raise TypeError(
                        f"Credentials field '{field_name}' on {cls.__qualname__} "
                        f"is not of type {CredentialsMetaInput.__name__}"
                    )
                CredentialsMetaInput.validate_credentials_field_schema(
                    cls.get_field_schema(field_name), field_name
                )
            elif field_name in credentials_fields:
                raise KeyError(
                    f"Credentials field '{field_name}' on {cls.__qualname__} "
                    "has invalid name: must be 'credentials' or *_credentials"
                )
    @classmethod
    def get_credentials_fields(cls) -> dict[str, type[CredentialsMetaInput]]:
        return {
            field_name: info.annotation
            for field_name, info in cls.model_fields.items()
            if (
                inspect.isclass(info.annotation)
                and issubclass(
                    get_origin(info.annotation) or info.annotation,
                    CredentialsMetaInput,
                )
            )
        }
    @classmethod
    def get_auto_credentials_fields(cls) -> dict[str, dict[str, Any]]:
        """
        Get fields that have auto_credentials metadata (e.g., GoogleDriveFileInput).
        Returns a dict mapping kwarg_name -> {field_name, auto_credentials_config}
        Raises:
            ValueError: If multiple fields have the same kwarg_name, as this would
                cause silent overwriting and only the last field would be processed.
        """
        result: dict[str, dict[str, Any]] = {}
        schema = cls.jsonschema()
        properties = schema.get("properties", {})
        for field_name, field_schema in properties.items():
            auto_creds = field_schema.get("auto_credentials")
            if auto_creds:
                kwarg_name = auto_creds.get("kwarg_name", "credentials")
                if kwarg_name in result:
                    raise ValueError(
                        f"Duplicate auto_credentials kwarg_name '{kwarg_name}' "
                        f"in fields '{result[kwarg_name]['field_name']}' and "
                        f"'{field_name}' on {cls.__qualname__}"
                    )
                result[kwarg_name] = {
                    "field_name": field_name,
                    "config": auto_creds,
                }
        return result
    @classmethod
    def get_credentials_fields_info(cls) -> dict[str, CredentialsFieldInfo]:
        result = {}
        # Regular credentials fields
        for field_name in cls.get_credentials_fields().keys():
            result[field_name] = CredentialsFieldInfo.model_validate(
                cls.get_field_schema(field_name), by_alias=True
            )
        # Auto-generated credentials fields (from GoogleDriveFileInput etc.)
        for kwarg_name, info in cls.get_auto_credentials_fields().items():
            config = info["config"]
            # Build a schema-like dict that CredentialsFieldInfo can parse
            auto_schema = {
                "credentials_provider": [config.get("provider", "google")],
                "credentials_types": [config.get("type", "oauth2")],
                "credentials_scopes": config.get("scopes"),
            }
            result[kwarg_name] = CredentialsFieldInfo.model_validate(
                auto_schema, by_alias=True
            )
        return result
    @classmethod
    def get_input_defaults(cls, data: BlockInput) -> BlockInput:
        return data  # Return as is, by default.
    @classmethod
    def get_missing_links(cls, data: BlockInput, links: list["Link"]) -> set[str]:
        input_fields_from_nodes = {link.sink_name for link in links}
        return input_fields_from_nodes - set(data)
    @classmethod
    def get_missing_input(cls, data: BlockInput) -> set[str]:
        return cls.get_required_fields() - set(data)
 class BlockSchemaInput(BlockSchema):
    """
    Base schema class for block inputs.
    All block input schemas should extend this class for consistency.
    """
    pass
 class BlockSchemaOutput(BlockSchema):
    """
    Base schema class for block outputs that includes a standard error field.
    All block output schemas should extend this class to ensure consistent error handling.
    """
    error: str = SchemaField(
        description="Error message if the operation failed", default=""
    )
 BlockSchemaInputType = TypeVar("BlockSchemaInputType", bound=BlockSchemaInput)
 BlockSchemaOutputType = TypeVar("BlockSchemaOutputType", bound=BlockSchemaOutput)
 class EmptyInputSchema(BlockSchemaInput):
    pass
 class EmptyOutputSchema(BlockSchemaOutput):
    pass
 # For backward compatibility - will be deprecated
 EmptySchema = EmptyOutputSchema
 # --8<-- [start:BlockWebhookConfig]
 class BlockManualWebhookConfig(BaseModel):
    """
    Configuration model for webhook-triggered blocks on which
    the user has to manually set up the webhook at the provider.
    """
    provider: ProviderName
    """The service provider that the webhook connects to"""
    webhook_type: str
    """
    Identifier for the webhook type. E.g. GitHub has repo and organization level hooks.
    Only for use in the corresponding `WebhooksManager`.
    """
    event_filter_input: str = ""
    """
    Name of the block's event filter input.
    Leave empty if the corresponding webhook doesn't have distinct event/payload types.
    """
    event_format: str = "{event}"
    """
    Template string for the event(s) that a block instance subscribes to.
    Applied individually to each event selected in the event filter input.
    Example: `"pull_request.{event}"` -> `"pull_request.opened"`
    """
 class BlockWebhookConfig(BlockManualWebhookConfig):
    """
    Configuration model for webhook-triggered blocks for which
    the webhook can be automatically set up through the provider's API.
    """
    resource_format: str
    """
    Template string for the resource that a block instance subscribes to.
    Fields will be filled from the block's inputs (except `payload`).
    Example: `f"{repo}/pull_requests"` (note: not how it's actually implemented)
    Only for use in the corresponding `WebhooksManager`.
    """
    # --8<-- [end:BlockWebhookConfig]
 class Block(ABC, Generic[BlockSchemaInputType, BlockSchemaOutputType]):
    def __init__(
        self,
        id: str = "",
        description: str = "",
        contributors: list["ContributorDetails"] = [],
        categories: set[BlockCategory] | None = None,
        input_schema: Type[BlockSchemaInputType] = EmptyInputSchema,
        output_schema: Type[BlockSchemaOutputType] = EmptyOutputSchema,
        test_input: BlockInput | list[BlockInput] | None = None,
        test_output: BlockTestOutput | list[BlockTestOutput] | None = None,
        test_mock: dict[str, Any] | None = None,
        test_credentials: Optional[Credentials | dict[str, Credentials]] = None,
        disabled: bool = False,
        static_output: bool = False,
        block_type: BlockType = BlockType.STANDARD,
        webhook_config: Optional[BlockWebhookConfig | BlockManualWebhookConfig] = None,
        is_sensitive_action: bool = False,
    ):
        """
        Initialize the block with the given schema.
        Args:
            id: The unique identifier for the block, this value will be persisted in the
                DB. So it should be a unique and constant across the application run.
                Use the UUID format for the ID.
            description: The description of the block, explaining what the block does.
            contributors: The list of contributors who contributed to the block.
            input_schema: The schema, defined as a Pydantic model, for the input data.
            output_schema: The schema, defined as a Pydantic model, for the output data.
            test_input: The list or single sample input data for the block, for testing.
            test_output: The list or single expected output if the test_input is run.
            test_mock: function names on the block implementation to mock on test run.
            disabled: If the block is disabled, it will not be available for execution.
            static_output: Whether the output links of the block are static by default.
        """
        from backend.data.model import NodeExecutionStats
        self.id = id
        self.input_schema = input_schema
        self.output_schema = output_schema
        self.test_input = test_input
        self.test_output = test_output
        self.test_mock = test_mock
        self.test_credentials = test_credentials
        self.description = description
        self.categories = categories or set()
        self.contributors = contributors or set()
        self.disabled = disabled
        self.static_output = static_output
        self.block_type = block_type
        self.webhook_config = webhook_config
        self.is_sensitive_action = is_sensitive_action
        self.execution_stats: "NodeExecutionStats" = NodeExecutionStats()
        if self.webhook_config:
            if isinstance(self.webhook_config, BlockWebhookConfig):
                # Enforce presence of credentials field on auto-setup webhook blocks
                if not (cred_fields := self.input_schema.get_credentials_fields()):
                    raise TypeError(
                        "credentials field is required on auto-setup webhook blocks"
                    )
                # Disallow multiple credentials inputs on webhook blocks
                elif len(cred_fields) > 1:
                    raise ValueError(
                        "Multiple credentials inputs not supported on webhook blocks"
                    )
                self.block_type = BlockType.WEBHOOK
            else:
                self.block_type = BlockType.WEBHOOK_MANUAL
            # Enforce shape of webhook event filter, if present
            if self.webhook_config.event_filter_input:
                event_filter_field = self.input_schema.model_fields[
                    self.webhook_config.event_filter_input
                ]
                if not (
                    isinstance(event_filter_field.annotation, type)
                    and issubclass(event_filter_field.annotation, BaseModel)
                    and all(
                        field.annotation is bool
                        for field in event_filter_field.annotation.model_fields.values()
                    )
                ):
                    raise NotImplementedError(
                        f"{self.name} has an invalid webhook event selector: "
                        "field must be a BaseModel and all its fields must be boolean"
                    )
            # Enforce presence of 'payload' input
            if "payload" not in self.input_schema.model_fields:
                raise TypeError(
                    f"{self.name} is webhook-triggered but has no 'payload' input"
                )
            # Disable webhook-triggered block if webhook functionality not available
            if not app_config.platform_base_url:
                self.disabled = True
    @abstractmethod
    async def run(self, input_data: BlockSchemaInputType, **kwargs) -> BlockOutput:
        """
        Run the block with the given input data.
        Args:
            input_data: The input data with the structure of input_schema.
        Kwargs: Currently 14/02/2025 these include
            graph_id: The ID of the graph.
            node_id: The ID of the node.
            graph_exec_id: The ID of the graph execution.
            node_exec_id: The ID of the node execution.
            user_id: The ID of the user.
        Returns:
            A Generator that yields (output_name, output_data).
            output_name: One of the output name defined in Block's output_schema.
            output_data: The data for the output_name, matching the defined schema.
        """
        # --- satisfy the type checker, never executed -------------
        if False:  # noqa: SIM115
            yield "name", "value"  # pyright: ignore[reportMissingYield]
        raise NotImplementedError(f"{self.name} does not implement the run method.")
    async def run_once(
        self, input_data: BlockSchemaInputType, output: str, **kwargs
    ) -> Any:
        async for item in self.run(input_data, **kwargs):
            name, data = item
            if name == output:
                return data
        raise ValueError(f"{self.name} did not produce any output for {output}")
    def merge_stats(self, stats: "NodeExecutionStats") -> "NodeExecutionStats":
        self.execution_stats += stats
        return self.execution_stats
    @property
    def name(self):
        return self.__class__.__name__
    def to_dict(self):
        return {
            "id": self.id,
            "name": self.name,
            "inputSchema": self.input_schema.jsonschema(),
            "outputSchema": self.output_schema.jsonschema(),
            "description": self.description,
            "categories": [category.dict() for category in self.categories],
            "contributors": [
                contributor.model_dump() for contributor in self.contributors
            ],
            "staticOutput": self.static_output,
            "uiType": self.block_type.value,
        }
    def get_info(self) -> BlockInfo:
        from backend.data.credit import get_block_cost
        return BlockInfo(
            id=self.id,
            name=self.name,
            inputSchema=self.input_schema.jsonschema(),
            outputSchema=self.output_schema.jsonschema(),
            costs=get_block_cost(self),
            description=self.description,
            categories=[category.dict() for category in self.categories],
            contributors=[
                contributor.model_dump() for contributor in self.contributors
            ],
            staticOutput=self.static_output,
            uiType=self.block_type.value,
        )
    async def execute(self, input_data: BlockInput, **kwargs) -> BlockOutput:
        try:
            async for output_name, output_data in self._execute(input_data, **kwargs):
                yield output_name, output_data
        except Exception as ex:
            if isinstance(ex, BlockError):
                raise ex
            else:
                raise (
                    BlockExecutionError
                    if isinstance(ex, ValueError)
                    else BlockUnknownError
                )(
                    message=str(ex),
                    block_name=self.name,
                    block_id=self.id,
                ) from ex
    async def is_block_exec_need_review(
        self,
        input_data: BlockInput,
        *,
        user_id: str,
        node_id: str,
        node_exec_id: str,
        graph_exec_id: str,
        graph_id: str,
        graph_version: int,
        execution_context: "ExecutionContext",
        **kwargs,
    ) -> tuple[bool, BlockInput]:
        """
        Check if this block execution needs human review and handle the review process.
        Returns:
            Tuple of (should_pause, input_data_to_use)
            - should_pause: True if execution should be paused for review
            - input_data_to_use: The input data to use (may be modified by reviewer)
        """
        if not (
            self.is_sensitive_action and execution_context.sensitive_action_safe_mode
        ):
            return False, input_data
        from backend.blocks.helpers.review import HITLReviewHelper
        # Handle the review request and get decision
        decision = await HITLReviewHelper.handle_review_decision(
            input_data=input_data,
            user_id=user_id,
            node_id=node_id,
            node_exec_id=node_exec_id,
            graph_exec_id=graph_exec_id,
            graph_id=graph_id,
            graph_version=graph_version,
            block_name=self.name,
            editable=True,
        )
        if decision is None:
            # We're awaiting review - pause execution
            return True, input_data
        if not decision.should_proceed:
            # Review was rejected, raise an error to stop execution
            raise BlockExecutionError(
                message=f"Block execution rejected by reviewer: {decision.message}",
                block_name=self.name,
                block_id=self.id,
            )
        # Review was approved - use the potentially modified data
        # ReviewResult.data must be a dict for block inputs
        reviewed_data = decision.review_result.data
        if not isinstance(reviewed_data, dict):
            raise BlockExecutionError(
                message=f"Review data must be a dict for block input, got {type(reviewed_data).__name__}",
                block_name=self.name,
                block_id=self.id,
            )
        return False, reviewed_data
    async def _execute(self, input_data: BlockInput, **kwargs) -> BlockOutput:
        # Check for review requirement only if running within a graph execution context
        # Direct block execution (e.g., from chat) skips the review process
        has_graph_context = all(
            key in kwargs
            for key in (
                "node_exec_id",
                "graph_exec_id",
                "graph_id",
                "execution_context",
            )
        )
        if has_graph_context:
            should_pause, input_data = await self.is_block_exec_need_review(
                input_data, **kwargs
            )
            if should_pause:
                return
        # Validate the input data (original or reviewer-modified) once
        if error := self.input_schema.validate_data(input_data):
            raise BlockInputError(
                message=f"Unable to execute block with invalid input data: {error}",
                block_name=self.name,
                block_id=self.id,
            )
        # Use the validated input data
        async for output_name, output_data in self.run(
            self.input_schema(**{k: v for k, v in input_data.items() if v is not None}),
            **kwargs,
        ):
            if output_name == "error":
                raise BlockExecutionError(
                    message=output_data, block_name=self.name, block_id=self.id
                )
            if self.block_type == BlockType.STANDARD and (
                error := self.output_schema.validate_field(output_name, output_data)
            ):
                raise BlockOutputError(
                    message=f"Block produced an invalid output data: {error}",
                    block_name=self.name,
                    block_id=self.id,
                )
            yield output_name, output_data
    def is_triggered_by_event_type(
        self, trigger_config: dict[str, Any], event_type: str
    ) -> bool:
        if not self.webhook_config:
            raise TypeError("This method can't be used on non-trigger blocks")
        if not self.webhook_config.event_filter_input:
            return True
        event_filter = trigger_config.get(self.webhook_config.event_filter_input)
        if not event_filter:
            raise ValueError("Event filter is not configured on trigger")
        return event_type in [
            self.webhook_config.event_format.format(event=k)
            for k in event_filter
            if event_filter[k] is True
        ]
 # Type alias for any block with standard input/output schemas
 AnyBlockSchema: TypeAlias = Block[BlockSchemaInput, BlockSchemaOutput]
--- a/autogpt_platform/backend/backend/blocks/_utils.py
+++ b/autogpt_platform/backend/backend/blocks/_utils.py
@@ -1,122 +0,0 @@
 import logging
 import os
 from backend.integrations.providers import ProviderName
 from ._base import AnyBlockSchema
 logger = logging.getLogger(__name__)
 def is_block_auth_configured(
    block_cls: type[AnyBlockSchema],
 ) -> bool:
    """
    Check if a block has a valid authentication method configured at runtime.
    For example if a block is an OAuth-only block and there env vars are not set,
    do not show it in the UI.
    """
    from backend.sdk.registry import AutoRegistry
    # Create an instance to access input_schema
    try:
        block = block_cls()
    except Exception as e:
        # If we can't create a block instance, assume it's not OAuth-only
        logger.error(f"Error creating block instance for {block_cls.__name__}: {e}")
        return True
    logger.debug(
        f"Checking if block {block_cls.__name__} has a valid provider configured"
    )
    # Get all credential inputs from input schema
    credential_inputs = block.input_schema.get_credentials_fields_info()
    required_inputs = block.input_schema.get_required_fields()
    if not credential_inputs:
        logger.debug(
            f"Block {block_cls.__name__} has no credential inputs - Treating as valid"
        )
        return True
    # Check credential inputs
    if len(required_inputs.intersection(credential_inputs.keys())) == 0:
        logger.debug(
            f"Block {block_cls.__name__} has only optional credential inputs"
            " - will work without credentials configured"
        )
    # Check if the credential inputs for this block are correctly configured
    for field_name, field_info in credential_inputs.items():
        provider_names = field_info.provider
        if not provider_names:
            logger.warning(
                f"Block {block_cls.__name__} "
                f"has credential input '{field_name}' with no provider options"
                " - Disabling"
            )
            return False
        # If a field has multiple possible providers, each one needs to be usable to
        # prevent breaking the UX
        for _provider_name in provider_names:
            provider_name = _provider_name.value
            if provider_name in ProviderName.__members__.values():
                logger.debug(
                    f"Block {block_cls.__name__} credential input '{field_name}' "
                    f"provider '{provider_name}' is part of the legacy provider system"
                    " - Treating as valid"
                )
                break
            provider = AutoRegistry.get_provider(provider_name)
            if not provider:
                logger.warning(
                    f"Block {block_cls.__name__} credential input '{field_name}' "
                    f"refers to unknown provider '{provider_name}' - Disabling"
                )
                return False
            # Check the provider's supported auth types
            if field_info.supported_types != provider.supported_auth_types:
                logger.warning(
                    f"Block {block_cls.__name__} credential input '{field_name}' "
                    f"has mismatched supported auth types (field <> Provider): "
                    f"{field_info.supported_types} != {provider.supported_auth_types}"
                )
            if not (supported_auth_types := provider.supported_auth_types):
                # No auth methods are been configured for this provider
                logger.warning(
                    f"Block {block_cls.__name__} credential input '{field_name}' "
                    f"provider '{provider_name}' "
                    "has no authentication methods configured - Disabling"
                )
                return False
            # Check if provider supports OAuth
            if "oauth2" in supported_auth_types:
                # Check if OAuth environment variables are set
                if (oauth_config := provider.oauth_config) and bool(
                    os.getenv(oauth_config.client_id_env_var)
                    and os.getenv(oauth_config.client_secret_env_var)
                ):
                    logger.debug(
                        f"Block {block_cls.__name__} credential input '{field_name}' "
                        f"provider '{provider_name}' is configured for OAuth"
                    )
                else:
                    logger.error(
                        f"Block {block_cls.__name__} credential input '{field_name}' "
                        f"provider '{provider_name}' "
                        "is missing OAuth client ID or secret - Disabling"
                    )
                    return False
        logger.debug(
            f"Block {block_cls.__name__} credential input '{field_name}' is valid; "
            f"supported credential types: {', '.join(field_info.supported_types)}"
        )
    return True
--- a/autogpt_platform/backend/backend/blocks/agent.py
+++ b/autogpt_platform/backend/backend/blocks/agent.py
@@ -1,7 +1,7 @@
 import logging
-from typing import TYPE_CHECKING, Any, Optional
+from typing import Any, Optional
-from backend.blocks._base import (
+from backend.data.block import (
    Block,
    BlockCategory,
    BlockInput,
@@ -9,15 +9,13 @@ from backend.blocks._base import (
    BlockSchema,
    BlockSchemaInput,
    BlockType,
    get_block,
 )
 from backend.data.execution import ExecutionContext, ExecutionStatus, NodesInputMasks
 from backend.data.model import NodeExecutionStats, SchemaField
 from backend.util.json import validate_with_jsonschema
 from backend.util.retry import func_retry
 if TYPE_CHECKING:
    from backend.executor.utils import LogMetadata
 _logger = logging.getLogger(__name__)
@@ -126,10 +124,9 @@ class AgentExecutorBlock(Block):
        graph_version: int,
        graph_exec_id: str,
        user_id: str,
-        logger: "LogMetadata",
+        logger,
    ) -> BlockOutput:
        from backend.blocks import get_block
        from backend.data.execution import ExecutionEventType
        from backend.executor import utils as execution_utils
@@ -201,7 +198,7 @@ class AgentExecutorBlock(Block):
        self,
        graph_exec_id: str,
        user_id: str,
-        logger: "LogMetadata",
+        logger,
    ) -> None:
        from backend.executor import utils as execution_utils
--- a/autogpt_platform/backend/backend/blocks/ai_condition.py
+++ b/autogpt_platform/backend/backend/blocks/ai_condition.py
@@ -1,11 +1,5 @@
 from typing import Any
 from backend.blocks._base import (
    BlockCategory,
    BlockOutput,
    BlockSchemaInput,
    BlockSchemaOutput,
 )
 from backend.blocks.llm import (
    DEFAULT_LLM_MODEL,
    TEST_CREDENTIALS,
@@ -17,6 +11,12 @@ from backend.blocks.llm import (
    LLMResponse,
    llm_call,
 )
 from backend.data.block import (
    BlockCategory,
    BlockOutput,
    BlockSchemaInput,
    BlockSchemaOutput,
 )
 from backend.data.model import APIKeyCredentials, NodeExecutionStats, SchemaField
--- a/autogpt_platform/backend/backend/blocks/ai_image_customizer.py
+++ b/autogpt_platform/backend/backend/blocks/ai_image_customizer.py
@@ -6,7 +6,7 @@ from pydantic import SecretStr
 from replicate.client import Client as ReplicateClient
 from replicate.helpers import FileOutput
-from backend.blocks._base import (
+from backend.data.block import (
    Block,
    BlockCategory,
    BlockOutput,
--- a/autogpt_platform/backend/backend/blocks/ai_image_generator_block.py
+++ b/autogpt_platform/backend/backend/blocks/ai_image_generator_block.py
@@ -5,12 +5,7 @@ from pydantic import SecretStr
 from replicate.client import Client as ReplicateClient
 from replicate.helpers import FileOutput
-from backend.blocks._base import (
+from backend.data.block import Block, BlockCategory, BlockSchemaInput, BlockSchemaOutput
    Block,
    BlockCategory,
    BlockSchemaInput,
    BlockSchemaOutput,
 )
 from backend.data.execution import ExecutionContext
 from backend.data.model import (
    APIKeyCredentials,
--- a/autogpt_platform/backend/backend/blocks/ai_music_generator.py
+++ b/autogpt_platform/backend/backend/blocks/ai_music_generator.py
@@ -6,7 +6,7 @@ from typing import Literal
 from pydantic import SecretStr
 from replicate.client import Client as ReplicateClient
-from backend.blocks._base import (
+from backend.data.block import (
    Block,
    BlockCategory,
    BlockOutput,
--- a/autogpt_platform/backend/backend/blocks/ai_shortform_video_block.py
+++ b/autogpt_platform/backend/backend/blocks/ai_shortform_video_block.py
@@ -6,7 +6,7 @@ from typing import Literal
 from pydantic import SecretStr
-from backend.blocks._base import (
+from backend.data.block import (
    Block,
    BlockCategory,
    BlockOutput,
--- a/autogpt_platform/backend/backend/blocks/apollo/organization.py
+++ b/autogpt_platform/backend/backend/blocks/apollo/organization.py
@@ -1,10 +1,3 @@
 from backend.blocks._base import (
    Block,
    BlockCategory,
    BlockOutput,
    BlockSchemaInput,
    BlockSchemaOutput,
 )
 from backend.blocks.apollo._api import ApolloClient
 from backend.blocks.apollo._auth import (
    TEST_CREDENTIALS,
@@ -17,6 +10,13 @@ from backend.blocks.apollo.models import (
    PrimaryPhone,
    SearchOrganizationsRequest,
 )
 from backend.data.block import (
    Block,
    BlockCategory,
    BlockOutput,
    BlockSchemaInput,
    BlockSchemaOutput,
 )
 from backend.data.model import CredentialsField, SchemaField
--- a/autogpt_platform/backend/backend/blocks/apollo/people.py
+++ b/autogpt_platform/backend/backend/blocks/apollo/people.py
@@ -1,12 +1,5 @@
 import asyncio
 from backend.blocks._base import (
    Block,
    BlockCategory,
    BlockOutput,
    BlockSchemaInput,
    BlockSchemaOutput,
 )
 from backend.blocks.apollo._api import ApolloClient
 from backend.blocks.apollo._auth import (
    TEST_CREDENTIALS,
@@ -21,6 +14,13 @@ from backend.blocks.apollo.models import (
    SearchPeopleRequest,
    SenorityLevels,
 )
 from backend.data.block import (
    Block,
    BlockCategory,
    BlockOutput,
    BlockSchemaInput,
    BlockSchemaOutput,
 )
 from backend.data.model import CredentialsField, SchemaField
--- a/autogpt_platform/backend/backend/blocks/apollo/person.py
+++ b/autogpt_platform/backend/backend/blocks/apollo/person.py
@@ -1,10 +1,3 @@
 from backend.blocks._base import (
    Block,
    BlockCategory,
    BlockOutput,
    BlockSchemaInput,
    BlockSchemaOutput,
 )
 from backend.blocks.apollo._api import ApolloClient
 from backend.blocks.apollo._auth import (
    TEST_CREDENTIALS,
@@ -13,6 +6,13 @@ from backend.blocks.apollo._auth import (
    ApolloCredentialsInput,
 )
 from backend.blocks.apollo.models import Contact, EnrichPersonRequest
 from backend.data.block import (
    Block,
    BlockCategory,
    BlockOutput,
    BlockSchemaInput,
    BlockSchemaOutput,
 )
 from backend.data.model import CredentialsField, SchemaField
--- a/autogpt_platform/backend/backend/blocks/ayrshare/_util.py
+++ b/autogpt_platform/backend/backend/blocks/ayrshare/_util.py
@@ -3,7 +3,7 @@ from typing import Optional
 from pydantic import BaseModel, Field
-from backend.blocks._base import BlockSchemaInput
+from backend.data.block import BlockSchemaInput
 from backend.data.model import SchemaField, UserIntegrations
 from backend.integrations.ayrshare import AyrshareClient
 from backend.util.clients import get_database_manager_async_client
--- a/autogpt_platform/backend/backend/blocks/basic.py
+++ b/autogpt_platform/backend/backend/blocks/basic.py
@@ -1,7 +1,7 @@
 import enum
 from typing import Any
-from backend.blocks._base import (
+from backend.data.block import (
    Block,
    BlockCategory,
    BlockOutput,
--- a/autogpt_platform/backend/backend/blocks/block.py
+++ b/autogpt_platform/backend/backend/blocks/block.py
@@ -2,7 +2,7 @@ import os
 import re
 from typing import Type
-from backend.blocks._base import (
+from backend.data.block import (
    Block,
    BlockCategory,
    BlockOutput,
--- a/autogpt_platform/backend/backend/blocks/branching.py
+++ b/autogpt_platform/backend/backend/blocks/branching.py
@@ -1,7 +1,7 @@
 from enum import Enum
 from typing import Any
-from backend.blocks._base import (
+from backend.data.block import (
    Block,
    BlockCategory,
    BlockOutput,
--- a/autogpt_platform/backend/backend/blocks/claude_code.py
+++ b/autogpt_platform/backend/backend/blocks/claude_code.py
@@ -6,7 +6,7 @@ from typing import Literal, Optional
 from e2b import AsyncSandbox as BaseAsyncSandbox
 from pydantic import BaseModel, SecretStr
-from backend.blocks._base import (
+from backend.data.block import (
    Block,
    BlockCategory,
    BlockOutput,
--- a/autogpt_platform/backend/backend/blocks/code_executor.py
+++ b/autogpt_platform/backend/backend/blocks/code_executor.py
@@ -6,7 +6,7 @@ from e2b_code_interpreter import Result as E2BExecutionResult
 from e2b_code_interpreter.charts import Chart as E2BExecutionResultChart
 from pydantic import BaseModel, Field, JsonValue, SecretStr
-from backend.blocks._base import (
+from backend.data.block import (
    Block,
    BlockCategory,
    BlockOutput,
--- a/autogpt_platform/backend/backend/blocks/code_extraction_block.py
+++ b/autogpt_platform/backend/backend/blocks/code_extraction_block.py
@@ -1,6 +1,6 @@
 import re
-from backend.blocks._base import (
+from backend.data.block import (
    Block,
    BlockCategory,
    BlockOutput,
--- a/autogpt_platform/backend/backend/blocks/codex.py
+++ b/autogpt_platform/backend/backend/blocks/codex.py
@@ -6,7 +6,7 @@ from openai import AsyncOpenAI
 from openai.types.responses import Response as OpenAIResponse
 from pydantic import SecretStr
-from backend.blocks._base import (
+from backend.data.block import (
    Block,
    BlockCategory,
    BlockOutput,
--- a/autogpt_platform/backend/backend/blocks/compass/triggers.py
+++ b/autogpt_platform/backend/backend/blocks/compass/triggers.py
@@ -1,6 +1,6 @@
 from pydantic import BaseModel
-from backend.blocks._base import (
+from backend.data.block import (
    Block,
    BlockCategory,
    BlockManualWebhookConfig,
--- a/autogpt_platform/backend/backend/blocks/count_words_and_char_block.py
+++ b/autogpt_platform/backend/backend/blocks/count_words_and_char_block.py
@@ -1,4 +1,4 @@
-from backend.blocks._base import (
+from backend.data.block import (
    Block,
    BlockCategory,
    BlockOutput,
--- a/autogpt_platform/backend/backend/blocks/data_manipulation.py
+++ b/autogpt_platform/backend/backend/blocks/data_manipulation.py
@@ -1,6 +1,6 @@
 from typing import Any, List
-from backend.blocks._base import (
+from backend.data.block import (
    Block,
    BlockCategory,
    BlockOutput,
--- a/autogpt_platform/backend/backend/blocks/decoder_block.py
+++ b/autogpt_platform/backend/backend/blocks/decoder_block.py
@@ -1,6 +1,6 @@
 import codecs
-from backend.blocks._base import (
+from backend.data.block import (
    Block,
    BlockCategory,
    BlockOutput,
--- a/autogpt_platform/backend/backend/blocks/discord/bot_blocks.py
+++ b/autogpt_platform/backend/backend/blocks/discord/bot_blocks.py
@@ -8,7 +8,7 @@ from typing import Any, Literal, cast
 import discord
 from pydantic import SecretStr
-from backend.blocks._base import (
+from backend.data.block import (
    Block,
    BlockCategory,
    BlockOutput,
--- a/autogpt_platform/backend/backend/blocks/discord/oauth_blocks.py
+++ b/autogpt_platform/backend/backend/blocks/discord/oauth_blocks.py
@@ -2,7 +2,7 @@
 Discord OAuth-based blocks.
 """
-from backend.blocks._base import (
+from backend.data.block import (
    Block,
    BlockCategory,
    BlockOutput,
--- a/autogpt_platform/backend/backend/blocks/email_block.py
+++ b/autogpt_platform/backend/backend/blocks/email_block.py
@@ -7,7 +7,7 @@ from typing import Literal
 from pydantic import BaseModel, ConfigDict, SecretStr
-from backend.blocks._base import (
+from backend.data.block import (
    Block,
    BlockCategory,
    BlockOutput,
--- a/autogpt_platform/backend/backend/blocks/encoder_block.py
+++ b/autogpt_platform/backend/backend/blocks/encoder_block.py
@@ -2,7 +2,7 @@
 import codecs
-from backend.blocks._base import (
+from backend.data.block import (
    Block,
    BlockCategory,
    BlockOutput,
--- a/autogpt_platform/backend/backend/blocks/enrichlayer/linkedin.py
+++ b/autogpt_platform/backend/backend/blocks/enrichlayer/linkedin.py
@@ -8,7 +8,7 @@ which provides access to LinkedIn profile data and related information.
 import logging
 from typing import Optional
-from backend.blocks._base import (
+from backend.data.block import (
    Block,
    BlockCategory,
    BlockOutput,
--- a/autogpt_platform/backend/backend/blocks/fal/ai_video_generator.py
+++ b/autogpt_platform/backend/backend/blocks/fal/ai_video_generator.py
@@ -3,13 +3,6 @@ import logging
 from enum import Enum
 from typing import Any
 from backend.blocks._base import (
    Block,
    BlockCategory,
    BlockOutput,
    BlockSchemaInput,
    BlockSchemaOutput,
 )
 from backend.blocks.fal._auth import (
    TEST_CREDENTIALS,
    TEST_CREDENTIALS_INPUT,
@@ -17,6 +10,13 @@ from backend.blocks.fal._auth import (
    FalCredentialsField,
    FalCredentialsInput,
 )
 from backend.data.block import (
    Block,
    BlockCategory,
    BlockOutput,
    BlockSchemaInput,
    BlockSchemaOutput,
 )
 from backend.data.execution import ExecutionContext
 from backend.data.model import SchemaField
 from backend.util.file import store_media_file
--- a/autogpt_platform/backend/backend/blocks/flux_kontext.py
+++ b/autogpt_platform/backend/backend/blocks/flux_kontext.py
@@ -5,7 +5,7 @@ from pydantic import SecretStr
 from replicate.client import Client as ReplicateClient
 from replicate.helpers import FileOutput
-from backend.blocks._base import (
+from backend.data.block import (
    Block,
    BlockCategory,
    BlockOutput,
--- a/autogpt_platform/backend/backend/blocks/github/checks.py
+++ b/autogpt_platform/backend/backend/blocks/github/checks.py
@@ -3,7 +3,7 @@ from typing import Optional
 from pydantic import BaseModel
-from backend.blocks._base import (
+from backend.data.block import (
    Block,
    BlockCategory,
    BlockOutput,
--- a/autogpt_platform/backend/backend/blocks/github/ci.py
+++ b/autogpt_platform/backend/backend/blocks/github/ci.py
@@ -5,7 +5,7 @@ from typing import Optional
 from typing_extensions import TypedDict
-from backend.blocks._base import (
+from backend.data.block import (
    Block,
    BlockCategory,
    BlockOutput,
--- a/autogpt_platform/backend/backend/blocks/github/issues.py
+++ b/autogpt_platform/backend/backend/blocks/github/issues.py
@@ -3,7 +3,7 @@ from urllib.parse import urlparse
 from typing_extensions import TypedDict
-from backend.blocks._base import (
+from backend.data.block import (
    Block,
    BlockCategory,
    BlockOutput,
--- a/autogpt_platform/backend/backend/blocks/github/pull_requests.py
+++ b/autogpt_platform/backend/backend/blocks/github/pull_requests.py
@@ -2,7 +2,7 @@ import re
 from typing_extensions import TypedDict
-from backend.blocks._base import (
+from backend.data.block import (
    Block,
    BlockCategory,
    BlockOutput,
--- a/autogpt_platform/backend/backend/blocks/github/repo.py
+++ b/autogpt_platform/backend/backend/blocks/github/repo.py
@@ -2,7 +2,7 @@ import base64
 from typing_extensions import TypedDict
-from backend.blocks._base import (
+from backend.data.block import (
    Block,
    BlockCategory,
    BlockOutput,
--- a/autogpt_platform/backend/backend/blocks/github/reviews.py
+++ b/autogpt_platform/backend/backend/blocks/github/reviews.py
@@ -4,7 +4,7 @@ from typing import Any, List, Optional
 from typing_extensions import TypedDict
-from backend.blocks._base import (
+from backend.data.block import (
    Block,
    BlockCategory,
    BlockOutput,
--- a/autogpt_platform/backend/backend/blocks/github/statuses.py
+++ b/autogpt_platform/backend/backend/blocks/github/statuses.py
@@ -3,7 +3,7 @@ from typing import Optional
 from pydantic import BaseModel
-from backend.blocks._base import (
+from backend.data.block import (
    Block,
    BlockCategory,
    BlockOutput,
--- a/autogpt_platform/backend/backend/blocks/github/triggers.py
+++ b/autogpt_platform/backend/backend/blocks/github/triggers.py
@@ -4,7 +4,7 @@ from pathlib import Path
 from pydantic import BaseModel
-from backend.blocks._base import (
+from backend.data.block import (
    Block,
    BlockCategory,
    BlockOutput,
--- a/autogpt_platform/backend/backend/blocks/google/calendar.py
+++ b/autogpt_platform/backend/backend/blocks/google/calendar.py
@@ -8,7 +8,7 @@ from google.oauth2.credentials import Credentials
 from googleapiclient.discovery import build
 from pydantic import BaseModel
-from backend.blocks._base import (
+from backend.data.block import (
    Block,
    BlockCategory,
    BlockOutput,
--- a/autogpt_platform/backend/backend/blocks/google/docs.py
+++ b/autogpt_platform/backend/backend/blocks/google/docs.py
@@ -7,14 +7,14 @@ from google.oauth2.credentials import Credentials
 from googleapiclient.discovery import build
 from gravitas_md2gdocs import to_requests
-from backend.blocks._base import (
+from backend.blocks.google._drive import GoogleDriveFile, GoogleDriveFileField
 from backend.data.block import (
    Block,
    BlockCategory,
    BlockOutput,
    BlockSchemaInput,
    BlockSchemaOutput,
 )
 from backend.blocks.google._drive import GoogleDriveFile, GoogleDriveFileField
 from backend.data.model import SchemaField
 from backend.util.settings import Settings
--- a/autogpt_platform/backend/backend/blocks/google/gmail.py
+++ b/autogpt_platform/backend/backend/blocks/google/gmail.py
@@ -14,7 +14,7 @@ from google.oauth2.credentials import Credentials
 from googleapiclient.discovery import build
 from pydantic import BaseModel, Field
-from backend.blocks._base import (
+from backend.data.block import (
    Block,
    BlockCategory,
    BlockOutput,
--- a/autogpt_platform/backend/backend/blocks/google/sheets.py
+++ b/autogpt_platform/backend/backend/blocks/google/sheets.py
@@ -7,14 +7,14 @@ from enum import Enum
 from google.oauth2.credentials import Credentials
 from googleapiclient.discovery import build
-from backend.blocks._base import (
+from backend.blocks.google._drive import GoogleDriveFile, GoogleDriveFileField
 from backend.data.block import (
    Block,
    BlockCategory,
    BlockOutput,
    BlockSchemaInput,
    BlockSchemaOutput,
 )
 from backend.blocks.google._drive import GoogleDriveFile, GoogleDriveFileField
 from backend.data.model import SchemaField
 from backend.util.settings import Settings
--- a/autogpt_platform/backend/backend/blocks/google_maps.py
+++ b/autogpt_platform/backend/backend/blocks/google_maps.py
@@ -3,7 +3,7 @@ from typing import Literal
 import googlemaps
 from pydantic import BaseModel, SecretStr
-from backend.blocks._base import (
+from backend.data.block import (
    Block,
    BlockCategory,
    BlockOutput,
--- a/autogpt_platform/backend/backend/blocks/helpers/review.py
+++ b/autogpt_platform/backend/backend/blocks/helpers/review.py
@@ -9,7 +9,9 @@ from typing import Any, Optional
 from prisma.enums import ReviewStatus
 from pydantic import BaseModel
 from backend.data.execution import ExecutionStatus
 from backend.data.human_review import ReviewResult
 from backend.executor.manager import async_update_node_execution_status
 from backend.util.clients import get_database_manager_async_client
 logger = logging.getLogger(__name__)
@@ -41,8 +43,6 @@ class HITLReviewHelper:
    @staticmethod
    async def update_node_execution_status(**kwargs) -> None:
        """Update the execution status of a node."""
        from backend.executor.manager import async_update_node_execution_status
        await async_update_node_execution_status(
            db_client=get_database_manager_async_client(), **kwargs
        )
@@ -88,13 +88,12 @@ class HITLReviewHelper:
        Raises:
            Exception: If review creation or status update fails
        """
        from backend.data.execution import ExecutionStatus
        # Note: Safe mode checks (human_in_the_loop_safe_mode, sensitive_action_safe_mode)
        # are handled by the caller:
        # - HITL blocks check human_in_the_loop_safe_mode in their run() method
        # - Sensitive action blocks check sensitive_action_safe_mode in is_block_exec_need_review()
        # This function only handles checking for existing approvals.
        # Check if this node has already been approved (normal or auto-approval)
        if approval_result := await HITLReviewHelper.check_approval(
            node_exec_id=node_exec_id,
--- a/autogpt_platform/backend/backend/blocks/http.py
+++ b/autogpt_platform/backend/backend/blocks/http.py
@@ -8,7 +8,7 @@ from typing import Literal
 import aiofiles
 from pydantic import SecretStr
-from backend.blocks._base import (
+from backend.data.block import (
    Block,
    BlockCategory,
    BlockOutput,
--- a/autogpt_platform/backend/backend/blocks/hubspot/company.py
+++ b/autogpt_platform/backend/backend/blocks/hubspot/company.py
@@ -1,15 +1,15 @@
-from backend.blocks._base import (
+from backend.blocks.hubspot._auth import (
    HubSpotCredentials,
    HubSpotCredentialsField,
    HubSpotCredentialsInput,
 )
 from backend.data.block import (
    Block,
    BlockCategory,
    BlockOutput,
    BlockSchemaInput,
    BlockSchemaOutput,
 )
 from backend.blocks.hubspot._auth import (
    HubSpotCredentials,
    HubSpotCredentialsField,
    HubSpotCredentialsInput,
 )
 from backend.data.model import SchemaField
 from backend.util.request import Requests
--- a/autogpt_platform/backend/backend/blocks/hubspot/contact.py
+++ b/autogpt_platform/backend/backend/blocks/hubspot/contact.py
@@ -1,15 +1,15 @@
-from backend.blocks._base import (
+from backend.blocks.hubspot._auth import (
    HubSpotCredentials,
    HubSpotCredentialsField,
    HubSpotCredentialsInput,
 )
 from backend.data.block import (
    Block,
    BlockCategory,
    BlockOutput,
    BlockSchemaInput,
    BlockSchemaOutput,
 )
 from backend.blocks.hubspot._auth import (
    HubSpotCredentials,
    HubSpotCredentialsField,
    HubSpotCredentialsInput,
 )
 from backend.data.model import SchemaField
 from backend.util.request import Requests
--- a/autogpt_platform/backend/backend/blocks/hubspot/engagement.py
+++ b/autogpt_platform/backend/backend/blocks/hubspot/engagement.py
@@ -1,17 +1,17 @@
 from datetime import datetime, timedelta
-from backend.blocks._base import (
+from backend.blocks.hubspot._auth import (
    HubSpotCredentials,
    HubSpotCredentialsField,
    HubSpotCredentialsInput,
 )
 from backend.data.block import (
    Block,
    BlockCategory,
    BlockOutput,
    BlockSchemaInput,
    BlockSchemaOutput,
 )
 from backend.blocks.hubspot._auth import (
    HubSpotCredentials,
    HubSpotCredentialsField,
    HubSpotCredentialsInput,
 )
 from backend.data.model import SchemaField
 from backend.util.request import Requests
--- a/autogpt_platform/backend/backend/blocks/human_in_the_loop.py
+++ b/autogpt_platform/backend/backend/blocks/human_in_the_loop.py
@@ -3,7 +3,8 @@ from typing import Any
 from prisma.enums import ReviewStatus
-from backend.blocks._base import (
+from backend.blocks.helpers.review import HITLReviewHelper
 from backend.data.block import (
    Block,
    BlockCategory,
    BlockOutput,
@@ -11,7 +12,6 @@ from backend.blocks._base import (
    BlockSchemaOutput,
    BlockType,
 )
 from backend.blocks.helpers.review import HITLReviewHelper
 from backend.data.execution import ExecutionContext
 from backend.data.human_review import ReviewResult
 from backend.data.model import SchemaField
@@ -21,71 +21,43 @@ logger = logging.getLogger(__name__)
 class HumanInTheLoopBlock(Block):
    """
-    Pauses execution and waits for human approval or rejection of the data.
+    This block pauses execution and waits for human approval or modification of the data.
-    When executed, this block creates a pending review entry and sets the node execution
+    When executed, it creates a pending review entry and sets the node execution status
-    status to REVIEW. The execution remains paused until a human user either approves
+    to REVIEW. The execution will remain paused until a human user either:
-    or rejects the data.
+    - Approves the data (with or without modifications)
    - Rejects the data
-    **How it works:**
+    This is useful for workflows that require human validation or intervention before
-    - The input data is presented to a human reviewer
+    proceeding to the next steps.
    - The reviewer can approve or reject (and optionally modify the data if editable)
    - On approval: the data flows out through the `approved_data` output pin
    - On rejection: the data flows out through the `rejected_data` output pin
    **Important:** The output pins yield the actual data itself, NOT status strings.
    The approval/rejection decision determines WHICH output pin fires, not the value.
    You do NOT need to compare the output to "APPROVED" or "REJECTED" - simply connect
    downstream blocks to the appropriate output pin for each case.
    **Example usage:**
    - Connect `approved_data` → next step in your workflow (data was approved)
    - Connect `rejected_data` → error handling or notification (data was rejected)
    """
    class Input(BlockSchemaInput):
-        data: Any = SchemaField(
+        data: Any = SchemaField(description="The data to be reviewed by a human user")
            description="The data to be reviewed by a human user. "
            "This exact data will be passed through to either approved_data or "
            "rejected_data output based on the reviewer's decision."
        )
        name: str = SchemaField(
-            description="A descriptive name for what this data represents. "
+            description="A descriptive name for what this data represents",
            "This helps the reviewer understand what they are reviewing.",
        )
        editable: bool = SchemaField(
-            description="Whether the human reviewer can edit the data before "
+            description="Whether the human reviewer can edit the data",
            "approving or rejecting it",
            default=True,
            advanced=True,
        )
    class Output(BlockSchemaOutput):
        approved_data: Any = SchemaField(
-            description="Outputs the input data when the reviewer APPROVES it. "
+            description="The data when approved (may be modified by reviewer)"
            "The value is the actual data itself (not a status string like 'APPROVED'). "
            "If the reviewer edited the data, this contains the modified version. "
            "Connect downstream blocks here for the 'approved' workflow path."
        )
        rejected_data: Any = SchemaField(
-            description="Outputs the input data when the reviewer REJECTS it. "
+            description="The data when rejected (may be modified by reviewer)"
            "The value is the actual data itself (not a status string like 'REJECTED'). "
            "If the reviewer edited the data, this contains the modified version. "
            "Connect downstream blocks here for the 'rejected' workflow path."
        )
        review_message: str = SchemaField(
-            description="Optional message provided by the reviewer explaining their "
+            description="Any message provided by the reviewer", default=""
            "decision. Only outputs when the reviewer provides a message; "
            "this pin does not fire if no message was given.",
            default="",
        )
    def __init__(self):
        super().__init__(
            id="8b2a7b3c-6e9d-4a5f-8c1b-2e3f4a5b6c7d",
-            description="Pause execution for human review. Data flows through "
+            description="Pause execution and wait for human approval or modification of data",
            "approved_data or rejected_data output based on the reviewer's decision. "
            "Outputs contain the actual data, not status strings.",
            categories={BlockCategory.BASIC},
            input_schema=HumanInTheLoopBlock.Input,
            output_schema=HumanInTheLoopBlock.Output,
--- a/autogpt_platform/backend/backend/blocks/ideogram.py
+++ b/autogpt_platform/backend/backend/blocks/ideogram.py
@@ -3,7 +3,7 @@ from typing import Any, Dict, Literal, Optional
 from pydantic import SecretStr
-from backend.blocks._base import (
+from backend.data.block import (
    Block,
    BlockCategory,
    BlockOutput,
--- a/autogpt_platform/backend/backend/blocks/io.py
+++ b/autogpt_platform/backend/backend/blocks/io.py
@@ -2,7 +2,9 @@ import copy
 from datetime import date, time
 from typing import Any, Optional
-from backend.blocks._base import (
+# Import for Google Drive file input block
 from backend.blocks.google._drive import AttachmentView, GoogleDriveFile
 from backend.data.block import (
    Block,
    BlockCategory,
    BlockOutput,
@@ -10,9 +12,6 @@ from backend.blocks._base import (
    BlockSchemaInput,
    BlockType,
 )
 # Import for Google Drive file input block
 from backend.blocks.google._drive import AttachmentView, GoogleDriveFile
 from backend.data.execution import ExecutionContext
 from backend.data.model import SchemaField
 from backend.util.file import store_media_file
--- a/autogpt_platform/backend/backend/blocks/iteration.py
+++ b/autogpt_platform/backend/backend/blocks/iteration.py
@@ -1,6 +1,6 @@
 from typing import Any
-from backend.blocks._base import (
+from backend.data.block import (
    Block,
    BlockCategory,
    BlockOutput,
--- a/autogpt_platform/backend/backend/blocks/jina/chunking.py
+++ b/autogpt_platform/backend/backend/blocks/jina/chunking.py
@@ -1,15 +1,15 @@
-from backend.blocks._base import (
+from backend.blocks.jina._auth import (
    JinaCredentials,
    JinaCredentialsField,
    JinaCredentialsInput,
 )
 from backend.data.block import (
    Block,
    BlockCategory,
    BlockOutput,
    BlockSchemaInput,
    BlockSchemaOutput,
 )
 from backend.blocks.jina._auth import (
    JinaCredentials,
    JinaCredentialsField,
    JinaCredentialsInput,
 )
 from backend.data.model import SchemaField
 from backend.util.request import Requests
--- a/autogpt_platform/backend/backend/blocks/jina/embeddings.py
+++ b/autogpt_platform/backend/backend/blocks/jina/embeddings.py
@@ -1,15 +1,15 @@
-from backend.blocks._base import (
+from backend.blocks.jina._auth import (
    JinaCredentials,
    JinaCredentialsField,
    JinaCredentialsInput,
 )
 from backend.data.block import (
    Block,
    BlockCategory,
    BlockOutput,
    BlockSchemaInput,
    BlockSchemaOutput,
 )
 from backend.blocks.jina._auth import (
    JinaCredentials,
    JinaCredentialsField,
    JinaCredentialsInput,
 )
 from backend.data.model import SchemaField
 from backend.util.request import Requests
--- a/autogpt_platform/backend/backend/blocks/jina/fact_checker.py
+++ b/autogpt_platform/backend/backend/blocks/jina/fact_checker.py
@@ -3,18 +3,18 @@ from urllib.parse import quote
 from typing_extensions import TypedDict
-from backend.blocks._base import (
+from backend.blocks.jina._auth import (
    JinaCredentials,
    JinaCredentialsField,
    JinaCredentialsInput,
 )
 from backend.data.block import (
    Block,
    BlockCategory,
    BlockOutput,
    BlockSchemaInput,
    BlockSchemaOutput,
 )
 from backend.blocks.jina._auth import (
    JinaCredentials,
    JinaCredentialsField,
    JinaCredentialsInput,
 )
 from backend.data.model import SchemaField
 from backend.util.request import Requests
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Otto	435738f2f9	refactor: address Pwuts review feedback - Remove unused error_response() and format_inputs_as_markdown() from helpers.py - Remove _get_inputs_list() wrapper from run_agent.py, use get_inputs_from_schema directly - Fix type annotations: get_user_credentials, find_matching_credential, create_credential_meta_from_match - Remove check_scopes parameter - always check scopes (original missing check was broken behavior) - Reorder _credential_has_required_scopes to be defined before find_matching_credential	2026-02-08 16:43:21 +00:00
Otto	10ebaf5104	fix: enable scope checking for block credentials (consistency with graphs) Previously run_block didn't check OAuth2 scopes while run_agent did. Now both use the same scope-checking logic for credential matching.	2026-02-08 16:42:58 +00:00
Otto	59e58d5e9b	fix: preserve original credential matching behavior - Add check_scopes parameter to find_matching_credential and match_credentials_to_requirements (default True) - run_block uses check_scopes=False to preserve original behavior (original run_block did not verify OAuth2 scopes) - Add isinstance check to get_inputs_from_schema for safety (original returned [] if input_schema wasn't a dict)	2026-02-08 16:42:58 +00:00
Otto	f458adf2a5	chore: remove docstrings and use sorted() for deterministic UUID ordering	2026-02-08 16:42:53 +00:00
Otto	1613e41cab	refactor(copilot): code cleanup - extract shared helpers and reduce duplication - Create tools/helpers.py with shared utilities (get_inputs_from_schema) - Add shared credential matching utilities to utils.py - Refactor run_block to use shared matching with discriminator support - Update run_agent.py to use shared helpers - Add validation.py with UUID helpers Note: routes.py streaming refactor dropped - dev has new stream registry implementation	2026-02-08 16:42:50 +00:00