Merge branch 'master' into copilot/fix-10840

- Add github.event_name == 'workflow_dispatch' to allow manual testing
- Add null safety check for github.event.pull_request to prevent runtime errors
- Maintains all existing Dependabot detection while fixing manual trigger capability

Co-authored-by: ntindle <8845353+ntindle@users.noreply.github.com>

.github/workflows/claude-dependabot.yml

@@ -14,11 +14,15 @@ name: Claude Dependabot PR Review
 on:
   pull_request:
     types: [opened, synchronize]
+  workflow_dispatch:  # Allow manual testing
 
 jobs:
   dependabot-review:
-    # Only run on Dependabot PRs
-    if: github.actor == 'dependabot[bot]'
+    # Only run on Dependabot PRs or manual dispatch
+    if: |
+      github.event_name == 'workflow_dispatch' ||
+      github.actor == 'dependabot[bot]' || 
+      (github.event.pull_request && github.event.pull_request.user.login == 'dependabot[bot]')
     runs-on: ubuntu-latest
     timeout-minutes: 30
 

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

@@ -32,9 +32,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        # Use Python 3.13 to match Docker image (see backend/Dockerfile)
-        # ClamAV tests moved to platform-backend-security-ci.yml (runs on merge to master)
-        python-version: ["3.13"]
+        python-version: ["3.11", "3.12", "3.13"]
     runs-on: ubuntu-latest
 
     services:
@@ -50,6 +48,23 @@ jobs:
         env:
           RABBITMQ_DEFAULT_USER: ${{ env.RABBITMQ_DEFAULT_USER }}
           RABBITMQ_DEFAULT_PASS: ${{ env.RABBITMQ_DEFAULT_PASS }}
+      clamav:
+        image: clamav/clamav-debian:latest
+        ports:
+          - 3310:3310
+        env:
+          CLAMAV_NO_FRESHCLAMD: false
+          CLAMD_CONF_StreamMaxLength: 50M
+          CLAMD_CONF_MaxFileSize: 100M
+          CLAMD_CONF_MaxScanSize: 100M
+          CLAMD_CONF_MaxThreads: 4
+          CLAMD_CONF_ReadTimeout: 300
+        options: >-
+          --health-cmd "clamdscan --version || exit 1"
+          --health-interval 30s
+          --health-timeout 10s
+          --health-retries 5
+          --health-start-period 180s
 
     steps:
       - name: Checkout repository
@@ -131,6 +146,35 @@ jobs:
         # outputs:
         # DB_URL, API_URL, GRAPHQL_URL, ANON_KEY, SERVICE_ROLE_KEY, JWT_SECRET
 
+      - name: Wait for ClamAV to be ready
+        run: |
+          echo "Waiting for ClamAV daemon to start..."
+          max_attempts=60
+          attempt=0
+          
+          until nc -z localhost 3310 || [ $attempt -eq $max_attempts ]; do
+            echo "ClamAV is unavailable - sleeping (attempt $((attempt+1))/$max_attempts)"
+            sleep 5
+            attempt=$((attempt+1))
+          done
+          
+          if [ $attempt -eq $max_attempts ]; then
+            echo "ClamAV failed to start after $((max_attempts*5)) seconds"
+            echo "Checking ClamAV service logs..."
+            docker logs $(docker ps -q --filter "ancestor=clamav/clamav-debian:latest") 2>&1 | tail -50 || echo "No ClamAV container found"
+            exit 1
+          fi
+          
+          echo "ClamAV is ready!"
+          
+          # Verify ClamAV is responsive
+          echo "Testing ClamAV connection..."
+          timeout 10 bash -c 'echo "PING" | nc localhost 3310' || {
+            echo "ClamAV is not responding to PING"
+            docker logs $(docker ps -q --filter "ancestor=clamav/clamav-debian:latest") 2>&1 | tail -50 || echo "No ClamAV container found"
+            exit 1
+          }
+
       - name: Run Database Migrations
         run: poetry run prisma migrate dev --name updates
         env:

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

@@ -1,145 +0,0 @@
-name: AutoGPT Platform - Backend Security CI
-
-# This workflow runs ClamAV-dependent security tests.
-# It only runs on merge to master to avoid the 3-5 minute ClamAV startup time on every PR.
-
-on:
-  push:
-    branches: [master]
-    paths:
-      - "autogpt_platform/backend/**/file*.py"
-      - "autogpt_platform/backend/**/scan*.py"
-      - "autogpt_platform/backend/**/virus*.py"
-      - "autogpt_platform/backend/**/media*.py"
-      - ".github/workflows/platform-backend-security-ci.yml"
-
-concurrency:
-  group: ${{ format('backend-security-ci-{0}', github.sha) }}
-  cancel-in-progress: false
-
-defaults:
-  run:
-    shell: bash
-    working-directory: autogpt_platform/backend
-
-jobs:
-  security-tests:
-    runs-on: ubuntu-latest
-    timeout-minutes: 15
-
-    services:
-      redis:
-        image: redis:latest
-        ports:
-          - 6379:6379
-      clamav:
-        image: clamav/clamav-debian:latest
-        ports:
-          - 3310:3310
-        env:
-          CLAMAV_NO_FRESHCLAMD: false
-          CLAMD_CONF_StreamMaxLength: 50M
-          CLAMD_CONF_MaxFileSize: 100M
-          CLAMD_CONF_MaxScanSize: 100M
-          CLAMD_CONF_MaxThreads: 4
-          CLAMD_CONF_ReadTimeout: 300
-        options: >-
-          --health-cmd "clamdscan --version || exit 1"
-          --health-interval 30s
-          --health-timeout 10s
-          --health-retries 5
-          --health-start-period 180s
-
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-          submodules: true
-
-      - name: Set up Python 3.13
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.13"
-
-      - name: Setup Supabase
-        uses: supabase/setup-cli@v1
-        with:
-          version: 1.178.1
-
-      - name: Set up Python dependency cache
-        uses: actions/cache@v4
-        with:
-          path: ~/.cache/pypoetry
-          key: poetry-${{ runner.os }}-${{ hashFiles('autogpt_platform/backend/poetry.lock') }}
-
-      - name: Install Poetry
-        run: |
-          HEAD_POETRY_VERSION=$(python ../../.github/workflows/scripts/get_package_version_from_lockfile.py poetry)
-          echo "Using Poetry version ${HEAD_POETRY_VERSION}"
-          curl -sSL https://install.python-poetry.org | POETRY_VERSION=$HEAD_POETRY_VERSION python3 -
-
-      - name: Install Python dependencies
-        run: poetry install
-
-      - name: Generate Prisma Client
-        run: poetry run prisma generate
-
-      - id: supabase
-        name: Start Supabase
-        working-directory: .
-        run: |
-          supabase init
-          supabase start --exclude postgres-meta,realtime,storage-api,imgproxy,inbucket,studio,edge-runtime,logflare,vector,supavisor
-          supabase status -o env | sed 's/="/=/; s/"$//' >> $GITHUB_OUTPUT
-
-      - name: Wait for ClamAV to be ready
-        run: |
-          echo "Waiting for ClamAV daemon to start..."
-          max_attempts=60
-          attempt=0
-
-          until nc -z localhost 3310 || [ $attempt -eq $max_attempts ]; do
-            echo "ClamAV is unavailable - sleeping (attempt $((attempt+1))/$max_attempts)"
-            sleep 5
-            attempt=$((attempt+1))
-          done
-
-          if [ $attempt -eq $max_attempts ]; then
-            echo "ClamAV failed to start after $((max_attempts*5)) seconds"
-            exit 1
-          fi
-
-          echo "ClamAV is ready!"
-
-      - name: Run Database Migrations
-        run: poetry run prisma migrate dev --name updates
-        env:
-          DATABASE_URL: ${{ steps.supabase.outputs.DB_URL }}
-          DIRECT_URL: ${{ steps.supabase.outputs.DB_URL }}
-
-      - name: Run security-related tests
-        run: |
-          poetry run pytest -v \
-            backend/util/virus_scanner_test.py \
-            backend/util/file_test.py \
-            backend/server/v2/store/media_test.py \
-            -x
-        env:
-          DATABASE_URL: ${{ steps.supabase.outputs.DB_URL }}
-          DIRECT_URL: ${{ steps.supabase.outputs.DB_URL }}
-          SUPABASE_URL: ${{ steps.supabase.outputs.API_URL }}
-          SUPABASE_SERVICE_ROLE_KEY: ${{ steps.supabase.outputs.SERVICE_ROLE_KEY }}
-          JWT_VERIFY_KEY: ${{ steps.supabase.outputs.JWT_SECRET }}
-          REDIS_HOST: "localhost"
-          REDIS_PORT: "6379"
-          ENCRYPTION_KEY: "dvziYgz0KSK8FENhju0ZYi8-fRTfAdlz6YLhdB_jhNw="
-          CLAMAV_SERVICE_HOST: "localhost"
-          CLAMAV_SERVICE_PORT: "3310"
-          CLAMAV_SERVICE_ENABLED: "true"
-
-    env:
-      CI: true
-      PLAIN_OUTPUT: True
-      RUN_ENV: local
-      PORT: 8080

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

@@ -154,78 +154,35 @@ jobs:
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v3
 
-      # Docker image tar caching - loads images from cache in parallel for faster startup
-      - name: Set up Docker image cache
-        id: docker-cache
+      - name: Cache Docker layers
         uses: actions/cache@v4
         with:
-          path: ~/docker-cache
-          key: docker-images-frontend-${{ runner.os }}-${{ hashFiles('autogpt_platform/docker-compose.yml') }}
+          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') }}
           restore-keys: |
-            docker-images-frontend-${{ runner.os }}-
-
-      - name: Load or pull Docker images
-        working-directory: autogpt_platform
-        run: |
-          mkdir -p ~/docker-cache
-
-          # Define image list for easy maintenance
-          IMAGES=(
-            "redis:latest"
-            "rabbitmq:management"
-            "kong:2.8.1"
-            "supabase/gotrue:v2.170.0"
-            "supabase/postgres:15.8.1.049"
-          )
-
-          # Check if any cached tar files exist
-          if ls ~/docker-cache/*.tar 1> /dev/null 2>&1; then
-            echo "Docker cache found, loading images in parallel..."
-            for image in "${IMAGES[@]}"; do
-              filename=$(echo "$image" | tr ':/' '--')
-              if [ -f ~/docker-cache/${filename}.tar ]; then
-                echo "Loading $image..."
-                docker load -i ~/docker-cache/${filename}.tar || echo "Warning: Failed to load $image from cache" &
-              fi
-            done
-            wait
-            echo "All cached images loaded"
-          else
-            echo "No Docker cache found, pulling images in parallel..."
-            for image in "${IMAGES[@]}"; do
-              docker pull "$image" &
-            done
-            wait
-
-            # Only save cache on main branches (not PRs) to avoid cache pollution
-            if [[ "${{ github.ref }}" == "refs/heads/master" ]] || [[ "${{ github.ref }}" == "refs/heads/dev" ]]; then
-              echo "Saving Docker images to cache in parallel..."
-              for image in "${IMAGES[@]}"; do
-                filename=$(echo "$image" | tr ':/' '--')
-                echo "Saving $image..."
-                docker save -o ~/docker-cache/${filename}.tar "$image" || echo "Warning: Failed to save $image" &
-              done
-              wait
-              echo "Docker image cache saved"
-            else
-              echo "Skipping cache save for PR/feature branch"
-            fi
-          fi
-
-          echo "Docker images ready for use"
+            ${{ runner.os }}-buildx-frontend-test-
 
       - name: Run docker compose
         run: |
           NEXT_PUBLIC_PW_TEST=true docker compose -f ../docker-compose.yml up -d
         env:
           DOCKER_BUILDKIT: 1
+          BUILDX_CACHE_FROM: type=local,src=/tmp/.buildx-cache
+          BUILDX_CACHE_TO: type=local,dest=/tmp/.buildx-cache-new,mode=max
+
+      - name: Move cache
+        run: |
+          rm -rf /tmp/.buildx-cache
+          if [ -d "/tmp/.buildx-cache-new" ]; then
+            mv /tmp/.buildx-cache-new /tmp/.buildx-cache
+          fi
 
       - name: Wait for services to be ready
         run: |
           echo "Waiting for rest_server to be ready..."
-          timeout 30 sh -c 'until curl -f http://localhost:8006/health 2>/dev/null; do sleep 2; done' || echo "Rest server health check timeout, continuing..."
+          timeout 60 sh -c 'until curl -f http://localhost:8006/health 2>/dev/null; do sleep 2; done' || echo "Rest server health check timeout, continuing..."
           echo "Waiting for database to be ready..."
-          timeout 30 sh -c 'until docker compose -f ../docker-compose.yml exec -T db pg_isready -U postgres 2>/dev/null; do sleep 2; done' || echo "Database ready check timeout, continuing..."
+          timeout 60 sh -c 'until docker compose -f ../docker-compose.yml exec -T db pg_isready -U postgres 2>/dev/null; do sleep 2; done' || echo "Database ready check timeout, continuing..."
 
       - name: Create E2E test data
         run: |
@@ -264,27 +221,9 @@ jobs:
       - name: Install dependencies
         run: pnpm install --frozen-lockfile
 
-      # Playwright browser caching - saves 30-60s when cache hits
-      - name: Get Playwright version
-        id: playwright-version
-        run: |
-          echo "version=$(pnpm list @playwright/test --json | jq -r '.[0].dependencies["@playwright/test"].version')" >> $GITHUB_OUTPUT
-
-      - name: Cache Playwright browsers
-        uses: actions/cache@v4
-        id: playwright-cache
-        with:
-          path: ~/.cache/ms-playwright
-          key: playwright-${{ runner.os }}-${{ steps.playwright-version.outputs.version }}
-
-      - name: Install Playwright browsers
-        if: steps.playwright-cache.outputs.cache-hit != 'true'
+      - name: Install Browser 'chromium'
         run: pnpm playwright install --with-deps chromium
 
-      - name: Install Playwright deps only (when cache hit)
-        if: steps.playwright-cache.outputs.cache-hit == 'true'
-        run: pnpm playwright install-deps chromium
-
       - name: Run Playwright tests
         run: pnpm test:no-build
 

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

@@ -83,66 +83,6 @@ jobs:
         run: |
           cp ../backend/.env.default ../backend/.env
 
-      # Docker image tar caching - loads images from cache in parallel for faster startup
-      - name: Set up Docker image cache
-        id: docker-cache
-        uses: actions/cache@v4
-        with:
-          path: ~/docker-cache
-          key: docker-images-fullstack-${{ runner.os }}-${{ hashFiles('autogpt_platform/docker-compose.yml') }}
-          restore-keys: |
-            docker-images-fullstack-${{ runner.os }}-
-
-      - name: Load or pull Docker images
-        working-directory: autogpt_platform
-        run: |
-          mkdir -p ~/docker-cache
-
-          # Define image list for easy maintenance
-          IMAGES=(
-            "redis:latest"
-            "rabbitmq:management"
-            "kong:2.8.1"
-            "supabase/gotrue:v2.170.0"
-            "supabase/postgres:15.8.1.049"
-          )
-
-          # Check if any cached tar files exist
-          if ls ~/docker-cache/*.tar 1> /dev/null 2>&1; then
-            echo "Docker cache found, loading images in parallel..."
-            for image in "${IMAGES[@]}"; do
-              filename=$(echo "$image" | tr ':/' '--')
-              if [ -f ~/docker-cache/${filename}.tar ]; then
-                echo "Loading $image..."
-                docker load -i ~/docker-cache/${filename}.tar || echo "Warning: Failed to load $image from cache" &
-              fi
-            done
-            wait
-            echo "All cached images loaded"
-          else
-            echo "No Docker cache found, pulling images in parallel..."
-            for image in "${IMAGES[@]}"; do
-              docker pull "$image" &
-            done
-            wait
-
-            # Only save cache on main branches (not PRs) to avoid cache pollution
-            if [[ "${{ github.ref }}" == "refs/heads/master" ]] || [[ "${{ github.ref }}" == "refs/heads/dev" ]]; then
-              echo "Saving Docker images to cache in parallel..."
-              for image in "${IMAGES[@]}"; do
-                filename=$(echo "$image" | tr ':/' '--')
-                echo "Saving $image..."
-                docker save -o ~/docker-cache/${filename}.tar "$image" || echo "Warning: Failed to save $image" &
-              done
-              wait
-              echo "Docker image cache saved"
-            else
-              echo "Skipping cache save for PR/feature branch"
-            fi
-          fi
-
-          echo "Docker images ready for use"
-
       - name: Run docker compose
         run: |
           docker compose -f ../docker-compose.yml --profile local --profile deps_backend up -d
@@ -164,9 +104,9 @@ jobs:
       - name: Wait for services to be ready
         run: |
           echo "Waiting for rest_server to be ready..."
-          timeout 30 sh -c 'until curl -f http://localhost:8006/health 2>/dev/null; do sleep 2; done' || echo "Rest server health check timeout, continuing..."
+          timeout 60 sh -c 'until curl -f http://localhost:8006/health 2>/dev/null; do sleep 2; done' || echo "Rest server health check timeout, continuing..."
           echo "Waiting for database to be ready..."
-          timeout 30 sh -c 'until docker compose -f ../docker-compose.yml exec -T db pg_isready -U postgres 2>/dev/null; do sleep 2; done' || echo "Database ready check timeout, continuing..."
+          timeout 60 sh -c 'until docker compose -f ../docker-compose.yml exec -T db pg_isready -U postgres 2>/dev/null; do sleep 2; done' || echo "Database ready check timeout, continuing..."
 
       - name: Generate API queries
         run: pnpm generate:api:force

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

@@ -11,7 +11,7 @@ jobs:
   stale:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/stale@v10
+      - uses: actions/stale@v9
         with:
           # operations-per-run: 5000
           stale-issue-message: >

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

@@ -61,6 +61,6 @@ jobs:
       pull-requests: write
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/labeler@v6
+      - uses: actions/labeler@v5
         with:
           sync-labels: true

autogpt_platform/backend/backend/blocks/human_in_the_loop.py

@@ -1,5 +1,5 @@
 import logging
-from typing import Any
+from typing import Any, Literal
 
 from prisma.enums import ReviewStatus
 
@@ -45,11 +45,11 @@ class HumanInTheLoopBlock(Block):
         )
 
     class Output(BlockSchemaOutput):
-        approved_data: Any = SchemaField(
-            description="The data when approved (may be modified by reviewer)"
+        reviewed_data: Any = SchemaField(
+            description="The data after human review (may be modified)"
         )
-        rejected_data: Any = SchemaField(
-            description="The data when rejected (may be modified by reviewer)"
+        status: Literal["approved", "rejected"] = SchemaField(
+            description="Status of the review: 'approved' or 'rejected'"
         )
         review_message: str = SchemaField(
             description="Any message provided by the reviewer", default=""
@@ -69,7 +69,8 @@ class HumanInTheLoopBlock(Block):
                 "editable": True,
             },
             test_output=[
-                ("approved_data", {"name": "John Doe", "age": 30}),
+                ("status", "approved"),
+                ("reviewed_data", {"name": "John Doe", "age": 30}),
             ],
             test_mock={
                 "get_or_create_human_review": lambda *_args, **_kwargs: ReviewResult(
@@ -115,7 +116,8 @@ class HumanInTheLoopBlock(Block):
             logger.info(
                 f"HITL block skipping review for node {node_exec_id} - safe mode disabled"
             )
-            yield "approved_data", input_data.data
+            yield "status", "approved"
+            yield "reviewed_data", input_data.data
             yield "review_message", "Auto-approved (safe mode disabled)"
             return
 
@@ -156,11 +158,12 @@ class HumanInTheLoopBlock(Block):
             )
 
             if result.status == ReviewStatus.APPROVED:
-                yield "approved_data", result.data
+                yield "status", "approved"
+                yield "reviewed_data", result.data
                 if result.message:
                     yield "review_message", result.message
 
             elif result.status == ReviewStatus.REJECTED:
-                yield "rejected_data", result.data
+                yield "status", "rejected"
                 if result.message:
                     yield "review_message", result.message

autogpt_platform/backend/backend/blocks/smart_decision_maker.py

@@ -1,11 +1,8 @@
 import logging
 import re
 from collections import Counter
-from concurrent.futures import Future
 from typing import TYPE_CHECKING, Any
 
-from pydantic import BaseModel
-
 import backend.blocks.llm as llm
 from backend.blocks.agent import AgentExecutorBlock
 from backend.data.block import (
@@ -23,41 +20,16 @@ from backend.data.dynamic_fields import (
     is_dynamic_field,
     is_tool_pin,
 )
-from backend.data.execution import ExecutionContext
 from backend.data.model import NodeExecutionStats, SchemaField
 from backend.util import json
 from backend.util.clients import get_database_manager_async_client
-from backend.util.prompt import MAIN_OBJECTIVE_PREFIX
 
 if TYPE_CHECKING:
     from backend.data.graph import Link, Node
-    from backend.executor.manager import ExecutionProcessor
 
 logger = logging.getLogger(__name__)
 
 
-class ToolInfo(BaseModel):
-    """Processed tool call information."""
-
-    tool_call: Any  # The original tool call object from LLM response
-    tool_name: str  # The function name
-    tool_def: dict[str, Any]  # The tool definition from tool_functions
-    input_data: dict[str, Any]  # Processed input data ready for tool execution
-    field_mapping: dict[str, str]  # Field name mapping for the tool
-
-
-class ExecutionParams(BaseModel):
-    """Tool execution parameters."""
-
-    user_id: str
-    graph_id: str
-    node_id: str
-    graph_version: int
-    graph_exec_id: str
-    node_exec_id: str
-    execution_context: "ExecutionContext"
-
-
 def _get_tool_requests(entry: dict[str, Any]) -> list[str]:
     """
     Return a list of tool_call_ids if the entry is a tool request.
@@ -133,50 +105,6 @@ def _create_tool_response(call_id: str, output: Any) -> dict[str, Any]:
     return {"role": "tool", "tool_call_id": call_id, "content": content}
 
 
-def _combine_tool_responses(tool_outputs: list[dict[str, Any]]) -> list[dict[str, Any]]:
-    """
-    Combine multiple Anthropic tool responses into a single user message.
-    For non-Anthropic formats, returns the original list unchanged.
-    """
-    if len(tool_outputs) <= 1:
-        return tool_outputs
-
-    # Anthropic responses have role="user", type="message", and content is a list with tool_result items
-    anthropic_responses = [
-        output
-        for output in tool_outputs
-        if (
-            output.get("role") == "user"
-            and output.get("type") == "message"
-            and isinstance(output.get("content"), list)
-            and any(
-                item.get("type") == "tool_result"
-                for item in output.get("content", [])
-                if isinstance(item, dict)
-            )
-        )
-    ]
-
-    if len(anthropic_responses) > 1:
-        combined_content = [
-            item for response in anthropic_responses for item in response["content"]
-        ]
-
-        combined_response = {
-            "role": "user",
-            "type": "message",
-            "content": combined_content,
-        }
-
-        non_anthropic_responses = [
-            output for output in tool_outputs if output not in anthropic_responses
-        ]
-
-        return [combined_response] + non_anthropic_responses
-
-    return tool_outputs
-
-
 def _convert_raw_response_to_dict(raw_response: Any) -> dict[str, Any]:
     """
     Safely convert raw_response to dictionary format for conversation history.
@@ -276,17 +204,6 @@ class SmartDecisionMakerBlock(Block):
             default="localhost:11434",
             description="Ollama host for local  models",
         )
-        agent_mode_max_iterations: int = SchemaField(
-            title="Agent Mode Max Iterations",
-            description="Maximum iterations for agent mode. 0 = traditional mode (single LLM call, yield tool calls for external execution), -1 = infinite agent mode (loop until finished), 1+ = agent mode with max iterations limit.",
-            advanced=True,
-            default=0,
-        )
-        conversation_compaction: bool = SchemaField(
-            default=True,
-            title="Context window auto-compaction",
-            description="Automatically compact the context window once it hits the limit",
-        )
 
         @classmethod
         def get_missing_links(cls, data: BlockInput, links: list["Link"]) -> set[str]:
@@ -589,7 +506,6 @@ class SmartDecisionMakerBlock(Block):
         Returns the response if successful, raises ValueError if validation fails.
         """
         resp = await llm.llm_call(
-            compress_prompt_to_fit=input_data.conversation_compaction,
             credentials=credentials,
             llm_model=input_data.model,
             prompt=current_prompt,
@@ -677,291 +593,6 @@ class SmartDecisionMakerBlock(Block):
 
         return resp
 
-    def _process_tool_calls(
-        self, response, tool_functions: list[dict[str, Any]]
-    ) -> list[ToolInfo]:
-        """Process tool calls and extract tool definitions, arguments, and input data.
-
-        Returns a list of tool info dicts with:
-        - tool_call: The original tool call object
-        - tool_name: The function name
-        - tool_def: The tool definition from tool_functions
-        - input_data: Processed input data dict (includes None values)
-        - field_mapping: Field name mapping for the tool
-        """
-        if not response.tool_calls:
-            return []
-
-        processed_tools = []
-        for tool_call in response.tool_calls:
-            tool_name = tool_call.function.name
-            tool_args = json.loads(tool_call.function.arguments)
-
-            tool_def = next(
-                (
-                    tool
-                    for tool in tool_functions
-                    if tool["function"]["name"] == tool_name
-                ),
-                None,
-            )
-            if not tool_def:
-                if len(tool_functions) == 1:
-                    tool_def = tool_functions[0]
-                else:
-                    continue
-
-            # Build input data for the tool
-            input_data = {}
-            field_mapping = tool_def["function"].get("_field_mapping", {})
-            if "function" in tool_def and "parameters" in tool_def["function"]:
-                expected_args = tool_def["function"]["parameters"].get("properties", {})
-                for clean_arg_name in expected_args:
-                    original_field_name = field_mapping.get(
-                        clean_arg_name, clean_arg_name
-                    )
-                    arg_value = tool_args.get(clean_arg_name)
-                    # Include all expected parameters, even if None (for backward compatibility with tests)
-                    input_data[original_field_name] = arg_value
-
-            processed_tools.append(
-                ToolInfo(
-                    tool_call=tool_call,
-                    tool_name=tool_name,
-                    tool_def=tool_def,
-                    input_data=input_data,
-                    field_mapping=field_mapping,
-                )
-            )
-
-        return processed_tools
-
-    def _update_conversation(
-        self, prompt: list[dict], response, tool_outputs: list | None = None
-    ):
-        """Update conversation history with response and tool outputs."""
-        # Don't add separate reasoning message with tool calls (breaks Anthropic's tool_use->tool_result pairing)
-        assistant_message = _convert_raw_response_to_dict(response.raw_response)
-        has_tool_calls = isinstance(assistant_message.get("content"), list) and any(
-            item.get("type") == "tool_use"
-            for item in assistant_message.get("content", [])
-        )
-
-        if response.reasoning and not has_tool_calls:
-            prompt.append(
-                {"role": "assistant", "content": f"[Reasoning]: {response.reasoning}"}
-            )
-
-        prompt.append(assistant_message)
-
-        if tool_outputs:
-            prompt.extend(tool_outputs)
-
-    async def _execute_single_tool_with_manager(
-        self,
-        tool_info: ToolInfo,
-        execution_params: ExecutionParams,
-        execution_processor: "ExecutionProcessor",
-    ) -> dict:
-        """Execute a single tool using the execution manager for proper integration."""
-        # Lazy imports to avoid circular dependencies
-        from backend.data.execution import NodeExecutionEntry
-
-        tool_call = tool_info.tool_call
-        tool_def = tool_info.tool_def
-        raw_input_data = tool_info.input_data
-
-        # Get sink node and field mapping
-        sink_node_id = tool_def["function"]["_sink_node_id"]
-
-        # Use proper database operations for tool execution
-        db_client = get_database_manager_async_client()
-
-        # Get target node
-        target_node = await db_client.get_node(sink_node_id)
-        if not target_node:
-            raise ValueError(f"Target node {sink_node_id} not found")
-
-        # Create proper node execution using upsert_execution_input
-        node_exec_result = None
-        final_input_data = None
-
-        # Add all inputs to the execution
-        if not raw_input_data:
-            raise ValueError(f"Tool call has no input data: {tool_call}")
-
-        for input_name, input_value in raw_input_data.items():
-            node_exec_result, final_input_data = await db_client.upsert_execution_input(
-                node_id=sink_node_id,
-                graph_exec_id=execution_params.graph_exec_id,
-                input_name=input_name,
-                input_data=input_value,
-            )
-
-        assert node_exec_result is not None, "node_exec_result should not be None"
-
-        # Create NodeExecutionEntry for execution manager
-        node_exec_entry = NodeExecutionEntry(
-            user_id=execution_params.user_id,
-            graph_exec_id=execution_params.graph_exec_id,
-            graph_id=execution_params.graph_id,
-            graph_version=execution_params.graph_version,
-            node_exec_id=node_exec_result.node_exec_id,
-            node_id=sink_node_id,
-            block_id=target_node.block_id,
-            inputs=final_input_data or {},
-            execution_context=execution_params.execution_context,
-        )
-
-        # Use the execution manager to execute the tool node
-        try:
-            # Get NodeExecutionProgress from the execution manager's running nodes
-            node_exec_progress = execution_processor.running_node_execution[
-                sink_node_id
-            ]
-
-            # Use the execution manager's own graph stats
-            graph_stats_pair = (
-                execution_processor.execution_stats,
-                execution_processor.execution_stats_lock,
-            )
-
-            # Create a completed future for the task tracking system
-            node_exec_future = Future()
-            node_exec_progress.add_task(
-                node_exec_id=node_exec_result.node_exec_id,
-                task=node_exec_future,
-            )
-
-            # Execute the node directly since we're in the SmartDecisionMaker context
-            node_exec_future.set_result(
-                await execution_processor.on_node_execution(
-                    node_exec=node_exec_entry,
-                    node_exec_progress=node_exec_progress,
-                    nodes_input_masks=None,
-                    graph_stats_pair=graph_stats_pair,
-                )
-            )
-
-            # Get outputs from database after execution completes using database manager client
-            node_outputs = await db_client.get_execution_outputs_by_node_exec_id(
-                node_exec_result.node_exec_id
-            )
-
-            # Create tool response
-            tool_response_content = (
-                json.dumps(node_outputs)
-                if node_outputs
-                else "Tool executed successfully"
-            )
-            return _create_tool_response(tool_call.id, tool_response_content)
-
-        except Exception as e:
-            logger.error(f"Tool execution with manager failed: {e}")
-            # Return error response
-            return _create_tool_response(
-                tool_call.id, f"Tool execution failed: {str(e)}"
-            )
-
-    async def _execute_tools_agent_mode(
-        self,
-        input_data,
-        credentials,
-        tool_functions: list[dict[str, Any]],
-        prompt: list[dict],
-        graph_exec_id: str,
-        node_id: str,
-        node_exec_id: str,
-        user_id: str,
-        graph_id: str,
-        graph_version: int,
-        execution_context: ExecutionContext,
-        execution_processor: "ExecutionProcessor",
-    ):
-        """Execute tools in agent mode with a loop until finished."""
-        max_iterations = input_data.agent_mode_max_iterations
-        iteration = 0
-
-        # Execution parameters for tool execution
-        execution_params = ExecutionParams(
-            user_id=user_id,
-            graph_id=graph_id,
-            node_id=node_id,
-            graph_version=graph_version,
-            graph_exec_id=graph_exec_id,
-            node_exec_id=node_exec_id,
-            execution_context=execution_context,
-        )
-
-        current_prompt = list(prompt)
-
-        while max_iterations < 0 or iteration < max_iterations:
-            iteration += 1
-            logger.debug(f"Agent mode iteration {iteration}")
-
-            # Prepare prompt for this iteration
-            iteration_prompt = list(current_prompt)
-
-            # On the last iteration, add a special system message to encourage completion
-            if max_iterations > 0 and iteration == max_iterations:
-                last_iteration_message = {
-                    "role": "system",
-                    "content": f"{MAIN_OBJECTIVE_PREFIX}This is your last iteration ({iteration}/{max_iterations}). "
-                    "Try to complete the task with the information you have. If you cannot fully complete it, "
-                    "provide a summary of what you've accomplished and what remains to be done. "
-                    "Prefer finishing with a clear response rather than making additional tool calls.",
-                }
-                iteration_prompt.append(last_iteration_message)
-
-            # Get LLM response
-            try:
-                response = await self._attempt_llm_call_with_validation(
-                    credentials, input_data, iteration_prompt, tool_functions
-                )
-            except Exception as e:
-                yield "error", f"LLM call failed in agent mode iteration {iteration}: {str(e)}"
-                return
-
-            # Process tool calls
-            processed_tools = self._process_tool_calls(response, tool_functions)
-
-            # If no tool calls, we're done
-            if not processed_tools:
-                yield "finished", response.response
-                self._update_conversation(current_prompt, response)
-                yield "conversations", current_prompt
-                return
-
-            # Execute tools and collect responses
-            tool_outputs = []
-            for tool_info in processed_tools:
-                try:
-                    tool_response = await self._execute_single_tool_with_manager(
-                        tool_info, execution_params, execution_processor
-                    )
-                    tool_outputs.append(tool_response)
-                except Exception as e:
-                    logger.error(f"Tool execution failed: {e}")
-                    # Create error response for the tool
-                    error_response = _create_tool_response(
-                        tool_info.tool_call.id, f"Error: {str(e)}"
-                    )
-                    tool_outputs.append(error_response)
-
-            tool_outputs = _combine_tool_responses(tool_outputs)
-
-            self._update_conversation(current_prompt, response, tool_outputs)
-
-            # Yield intermediate conversation state
-            yield "conversations", current_prompt
-
-        # If we reach max iterations, yield the current state
-        if max_iterations < 0:
-            yield "finished", f"Agent mode completed after {iteration} iterations"
-        else:
-            yield "finished", f"Agent mode completed after {max_iterations} iterations (limit reached)"
-        yield "conversations", current_prompt
-
     async def run(
         self,
         input_data: Input,
@@ -972,12 +603,8 @@ class SmartDecisionMakerBlock(Block):
         graph_exec_id: str,
         node_exec_id: str,
         user_id: str,
-        graph_version: int,
-        execution_context: ExecutionContext,
-        execution_processor: "ExecutionProcessor",
         **kwargs,
     ) -> BlockOutput:
-
         tool_functions = await self._create_tool_node_signatures(node_id)
         yield "tool_functions", json.dumps(tool_functions)
 
@@ -1021,52 +648,24 @@ class SmartDecisionMakerBlock(Block):
             input_data.prompt = llm.fmt.format_string(input_data.prompt, values)
             input_data.sys_prompt = llm.fmt.format_string(input_data.sys_prompt, values)
 
+        prefix = "[Main Objective Prompt]: "
+
         if input_data.sys_prompt and not any(
-            p["role"] == "system" and p["content"].startswith(MAIN_OBJECTIVE_PREFIX)
-            for p in prompt
+            p["role"] == "system" and p["content"].startswith(prefix) for p in prompt
         ):
-            prompt.append(
-                {
-                    "role": "system",
-                    "content": MAIN_OBJECTIVE_PREFIX + input_data.sys_prompt,
-                }
-            )
+            prompt.append({"role": "system", "content": prefix + input_data.sys_prompt})
 
         if input_data.prompt and not any(
-            p["role"] == "user" and p["content"].startswith(MAIN_OBJECTIVE_PREFIX)
-            for p in prompt
+            p["role"] == "user" and p["content"].startswith(prefix) for p in prompt
         ):
-            prompt.append(
-                {"role": "user", "content": MAIN_OBJECTIVE_PREFIX + input_data.prompt}
-            )
+            prompt.append({"role": "user", "content": prefix + input_data.prompt})
 
-        # Execute tools based on the selected mode
-        if input_data.agent_mode_max_iterations != 0:
-            # In agent mode, execute tools directly in a loop until finished
-            async for result in self._execute_tools_agent_mode(
-                input_data=input_data,
-                credentials=credentials,
-                tool_functions=tool_functions,
-                prompt=prompt,
-                graph_exec_id=graph_exec_id,
-                node_id=node_id,
-                node_exec_id=node_exec_id,
-                user_id=user_id,
-                graph_id=graph_id,
-                graph_version=graph_version,
-                execution_context=execution_context,
-                execution_processor=execution_processor,
-            ):
-                yield result
-            return
-
-        # One-off mode: single LLM call and yield tool calls for external execution
         current_prompt = list(prompt)
         max_attempts = max(1, int(input_data.retry))
         response = None
 
         last_error = None
-        for _ in range(max_attempts):
+        for attempt in range(max_attempts):
             try:
                 response = await self._attempt_llm_call_with_validation(
                     credentials, input_data, current_prompt, tool_functions

autogpt_platform/backend/backend/blocks/test/test_smart_decision_maker.py

@@ -1,11 +1,7 @@
 import logging
-import threading
-from collections import defaultdict
-from unittest.mock import AsyncMock, MagicMock, patch
 
 import pytest
 
-from backend.data.execution import ExecutionContext
 from backend.data.model import ProviderName, User
 from backend.server.model import CreateGraph
 from backend.server.rest_api import AgentServer
@@ -21,10 +17,10 @@ async def create_graph(s: SpinTestServer, g, u: User):
 
 
 async def create_credentials(s: SpinTestServer, u: User):
-    import backend.blocks.llm as llm_module
+    import backend.blocks.llm as llm
 
     provider = ProviderName.OPENAI
-    credentials = llm_module.TEST_CREDENTIALS
+    credentials = llm.TEST_CREDENTIALS
     return await s.agent_server.test_create_credentials(u.id, provider, credentials)
 
 
@@ -200,6 +196,8 @@ async def test_smart_decision_maker_function_signature(server: SpinTestServer):
 @pytest.mark.asyncio
 async def test_smart_decision_maker_tracks_llm_stats():
     """Test that SmartDecisionMakerBlock correctly tracks LLM usage stats."""
+    from unittest.mock import MagicMock, patch
+
     import backend.blocks.llm as llm_module
     from backend.blocks.smart_decision_maker import SmartDecisionMakerBlock
 
@@ -218,6 +216,7 @@ async def test_smart_decision_maker_tracks_llm_stats():
     }
 
     # Mock the _create_tool_node_signatures method to avoid database calls
+    from unittest.mock import AsyncMock
 
     with patch(
         "backend.blocks.llm.llm_call",
@@ -235,19 +234,10 @@ async def test_smart_decision_maker_tracks_llm_stats():
             prompt="Should I continue with this task?",
             model=llm_module.LlmModel.GPT4O,
             credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
-            agent_mode_max_iterations=0,
         )
 
         # Execute the block
         outputs = {}
-        # Create execution context
-
-        mock_execution_context = ExecutionContext(safe_mode=False)
-
-        # Create a mock execution processor for tests
-
-        mock_execution_processor = MagicMock()
-
         async for output_name, output_data in block.run(
             input_data,
             credentials=llm_module.TEST_CREDENTIALS,
@@ -256,9 +246,6 @@ async def test_smart_decision_maker_tracks_llm_stats():
             graph_exec_id="test-exec-id",
             node_exec_id="test-node-exec-id",
             user_id="test-user-id",
-            graph_version=1,
-            execution_context=mock_execution_context,
-            execution_processor=mock_execution_processor,
         ):
             outputs[output_name] = output_data
 
@@ -276,6 +263,8 @@ async def test_smart_decision_maker_tracks_llm_stats():
 @pytest.mark.asyncio
 async def test_smart_decision_maker_parameter_validation():
     """Test that SmartDecisionMakerBlock correctly validates tool call parameters."""
+    from unittest.mock import MagicMock, patch
+
     import backend.blocks.llm as llm_module
     from backend.blocks.smart_decision_maker import SmartDecisionMakerBlock
 
@@ -322,6 +311,8 @@ async def test_smart_decision_maker_parameter_validation():
     mock_response_with_typo.reasoning = None
     mock_response_with_typo.raw_response = {"role": "assistant", "content": None}
 
+    from unittest.mock import AsyncMock
+
     with patch(
         "backend.blocks.llm.llm_call",
         new_callable=AsyncMock,
@@ -338,17 +329,8 @@ async def test_smart_decision_maker_parameter_validation():
             model=llm_module.LlmModel.GPT4O,
             credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
             retry=2,  # Set retry to 2 for testing
-            agent_mode_max_iterations=0,
         )
 
-        # Create execution context
-
-        mock_execution_context = ExecutionContext(safe_mode=False)
-
-        # Create a mock execution processor for tests
-
-        mock_execution_processor = MagicMock()
-
         # Should raise ValueError after retries due to typo'd parameter name
         with pytest.raises(ValueError) as exc_info:
             outputs = {}
@@ -360,9 +342,6 @@ async def test_smart_decision_maker_parameter_validation():
                 graph_exec_id="test-exec-id",
                 node_exec_id="test-node-exec-id",
                 user_id="test-user-id",
-                graph_version=1,
-                execution_context=mock_execution_context,
-                execution_processor=mock_execution_processor,
             ):
                 outputs[output_name] = output_data
 
@@ -389,6 +368,8 @@ async def test_smart_decision_maker_parameter_validation():
     mock_response_missing_required.reasoning = None
     mock_response_missing_required.raw_response = {"role": "assistant", "content": None}
 
+    from unittest.mock import AsyncMock
+
     with patch(
         "backend.blocks.llm.llm_call",
         new_callable=AsyncMock,
@@ -404,17 +385,8 @@ async def test_smart_decision_maker_parameter_validation():
             prompt="Search for keywords",
             model=llm_module.LlmModel.GPT4O,
             credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
-            agent_mode_max_iterations=0,
         )
 
-        # Create execution context
-
-        mock_execution_context = ExecutionContext(safe_mode=False)
-
-        # Create a mock execution processor for tests
-
-        mock_execution_processor = MagicMock()
-
         # Should raise ValueError due to missing required parameter
         with pytest.raises(ValueError) as exc_info:
             outputs = {}
@@ -426,9 +398,6 @@ async def test_smart_decision_maker_parameter_validation():
                 graph_exec_id="test-exec-id",
                 node_exec_id="test-node-exec-id",
                 user_id="test-user-id",
-                graph_version=1,
-                execution_context=mock_execution_context,
-                execution_processor=mock_execution_processor,
             ):
                 outputs[output_name] = output_data
 
@@ -449,6 +418,8 @@ async def test_smart_decision_maker_parameter_validation():
     mock_response_valid.reasoning = None
     mock_response_valid.raw_response = {"role": "assistant", "content": None}
 
+    from unittest.mock import AsyncMock
+
     with patch(
         "backend.blocks.llm.llm_call",
         new_callable=AsyncMock,
@@ -464,19 +435,10 @@ async def test_smart_decision_maker_parameter_validation():
             prompt="Search for keywords",
             model=llm_module.LlmModel.GPT4O,
             credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
-            agent_mode_max_iterations=0,
         )
 
         # Should succeed - optional parameter missing is OK
         outputs = {}
-        # Create execution context
-
-        mock_execution_context = ExecutionContext(safe_mode=False)
-
-        # Create a mock execution processor for tests
-
-        mock_execution_processor = MagicMock()
-
         async for output_name, output_data in block.run(
             input_data,
             credentials=llm_module.TEST_CREDENTIALS,
@@ -485,9 +447,6 @@ async def test_smart_decision_maker_parameter_validation():
             graph_exec_id="test-exec-id",
             node_exec_id="test-node-exec-id",
             user_id="test-user-id",
-            graph_version=1,
-            execution_context=mock_execution_context,
-            execution_processor=mock_execution_processor,
         ):
             outputs[output_name] = output_data
 
@@ -513,6 +472,8 @@ async def test_smart_decision_maker_parameter_validation():
     mock_response_all_params.reasoning = None
     mock_response_all_params.raw_response = {"role": "assistant", "content": None}
 
+    from unittest.mock import AsyncMock
+
     with patch(
         "backend.blocks.llm.llm_call",
         new_callable=AsyncMock,
@@ -528,19 +489,10 @@ async def test_smart_decision_maker_parameter_validation():
             prompt="Search for keywords",
             model=llm_module.LlmModel.GPT4O,
             credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
-            agent_mode_max_iterations=0,
         )
 
         # Should succeed with all parameters
         outputs = {}
-        # Create execution context
-
-        mock_execution_context = ExecutionContext(safe_mode=False)
-
-        # Create a mock execution processor for tests
-
-        mock_execution_processor = MagicMock()
-
         async for output_name, output_data in block.run(
             input_data,
             credentials=llm_module.TEST_CREDENTIALS,
@@ -549,9 +501,6 @@ async def test_smart_decision_maker_parameter_validation():
             graph_exec_id="test-exec-id",
             node_exec_id="test-node-exec-id",
             user_id="test-user-id",
-            graph_version=1,
-            execution_context=mock_execution_context,
-            execution_processor=mock_execution_processor,
         ):
             outputs[output_name] = output_data
 
@@ -564,6 +513,8 @@ async def test_smart_decision_maker_parameter_validation():
 @pytest.mark.asyncio
 async def test_smart_decision_maker_raw_response_conversion():
     """Test that SmartDecisionMaker correctly handles different raw_response types with retry mechanism."""
+    from unittest.mock import MagicMock, patch
+
     import backend.blocks.llm as llm_module
     from backend.blocks.smart_decision_maker import SmartDecisionMakerBlock
 
@@ -633,6 +584,7 @@ async def test_smart_decision_maker_raw_response_conversion():
     )
 
     # Mock llm_call to return different responses on different calls
+    from unittest.mock import AsyncMock
 
     with patch(
         "backend.blocks.llm.llm_call", new_callable=AsyncMock
@@ -651,19 +603,10 @@ async def test_smart_decision_maker_raw_response_conversion():
             model=llm_module.LlmModel.GPT4O,
             credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
             retry=2,
-            agent_mode_max_iterations=0,
         )
 
         # Should succeed after retry, demonstrating our helper function works
         outputs = {}
-        # Create execution context
-
-        mock_execution_context = ExecutionContext(safe_mode=False)
-
-        # Create a mock execution processor for tests
-
-        mock_execution_processor = MagicMock()
-
         async for output_name, output_data in block.run(
             input_data,
             credentials=llm_module.TEST_CREDENTIALS,
@@ -672,9 +615,6 @@ async def test_smart_decision_maker_raw_response_conversion():
             graph_exec_id="test-exec-id",
             node_exec_id="test-node-exec-id",
             user_id="test-user-id",
-            graph_version=1,
-            execution_context=mock_execution_context,
-            execution_processor=mock_execution_processor,
         ):
             outputs[output_name] = output_data
 
@@ -710,6 +650,8 @@ async def test_smart_decision_maker_raw_response_conversion():
         "I'll help you with that."  # Ollama returns string
     )
 
+    from unittest.mock import AsyncMock
+
     with patch(
         "backend.blocks.llm.llm_call",
         new_callable=AsyncMock,
@@ -724,18 +666,9 @@ async def test_smart_decision_maker_raw_response_conversion():
             prompt="Simple prompt",
             model=llm_module.LlmModel.GPT4O,
             credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
-            agent_mode_max_iterations=0,
         )
 
         outputs = {}
-        # Create execution context
-
-        mock_execution_context = ExecutionContext(safe_mode=False)
-
-        # Create a mock execution processor for tests
-
-        mock_execution_processor = MagicMock()
-
         async for output_name, output_data in block.run(
             input_data,
             credentials=llm_module.TEST_CREDENTIALS,
@@ -744,9 +677,6 @@ async def test_smart_decision_maker_raw_response_conversion():
             graph_exec_id="test-exec-id",
             node_exec_id="test-node-exec-id",
             user_id="test-user-id",
-            graph_version=1,
-            execution_context=mock_execution_context,
-            execution_processor=mock_execution_processor,
         ):
             outputs[output_name] = output_data
 
@@ -766,6 +696,8 @@ async def test_smart_decision_maker_raw_response_conversion():
         "content": "Test response",
     }  # Dict format
 
+    from unittest.mock import AsyncMock
+
     with patch(
         "backend.blocks.llm.llm_call",
         new_callable=AsyncMock,
@@ -780,18 +712,9 @@ async def test_smart_decision_maker_raw_response_conversion():
             prompt="Another test",
             model=llm_module.LlmModel.GPT4O,
             credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
-            agent_mode_max_iterations=0,
         )
 
         outputs = {}
-        # Create execution context
-
-        mock_execution_context = ExecutionContext(safe_mode=False)
-
-        # Create a mock execution processor for tests
-
-        mock_execution_processor = MagicMock()
-
         async for output_name, output_data in block.run(
             input_data,
             credentials=llm_module.TEST_CREDENTIALS,
@@ -800,260 +723,8 @@ async def test_smart_decision_maker_raw_response_conversion():
             graph_exec_id="test-exec-id",
             node_exec_id="test-node-exec-id",
             user_id="test-user-id",
-            graph_version=1,
-            execution_context=mock_execution_context,
-            execution_processor=mock_execution_processor,
         ):
             outputs[output_name] = output_data
 
         assert "finished" in outputs
         assert outputs["finished"] == "Test response"
-
-
-@pytest.mark.asyncio
-async def test_smart_decision_maker_agent_mode():
-    """Test that agent mode executes tools directly and loops until finished."""
-    import backend.blocks.llm as llm_module
-    from backend.blocks.smart_decision_maker import SmartDecisionMakerBlock
-
-    block = SmartDecisionMakerBlock()
-
-    # Mock tool call that requires multiple iterations
-    mock_tool_call_1 = MagicMock()
-    mock_tool_call_1.id = "call_1"
-    mock_tool_call_1.function.name = "search_keywords"
-    mock_tool_call_1.function.arguments = (
-        '{"query": "test", "max_keyword_difficulty": 50}'
-    )
-
-    mock_response_1 = MagicMock()
-    mock_response_1.response = None
-    mock_response_1.tool_calls = [mock_tool_call_1]
-    mock_response_1.prompt_tokens = 50
-    mock_response_1.completion_tokens = 25
-    mock_response_1.reasoning = "Using search tool"
-    mock_response_1.raw_response = {
-        "role": "assistant",
-        "content": None,
-        "tool_calls": [{"id": "call_1", "type": "function"}],
-    }
-
-    # Final response with no tool calls (finished)
-    mock_response_2 = MagicMock()
-    mock_response_2.response = "Task completed successfully"
-    mock_response_2.tool_calls = []
-    mock_response_2.prompt_tokens = 30
-    mock_response_2.completion_tokens = 15
-    mock_response_2.reasoning = None
-    mock_response_2.raw_response = {
-        "role": "assistant",
-        "content": "Task completed successfully",
-    }
-
-    # Mock the LLM call to return different responses on each iteration
-    llm_call_mock = AsyncMock()
-    llm_call_mock.side_effect = [mock_response_1, mock_response_2]
-
-    # Mock tool node signatures
-    mock_tool_signatures = [
-        {
-            "type": "function",
-            "function": {
-                "name": "search_keywords",
-                "_sink_node_id": "test-sink-node-id",
-                "_field_mapping": {},
-                "parameters": {
-                    "properties": {
-                        "query": {"type": "string"},
-                        "max_keyword_difficulty": {"type": "integer"},
-                    },
-                    "required": ["query", "max_keyword_difficulty"],
-                },
-            },
-        }
-    ]
-
-    # Mock database and execution components
-    mock_db_client = AsyncMock()
-    mock_node = MagicMock()
-    mock_node.block_id = "test-block-id"
-    mock_db_client.get_node.return_value = mock_node
-
-    # Mock upsert_execution_input to return proper NodeExecutionResult and input data
-    mock_node_exec_result = MagicMock()
-    mock_node_exec_result.node_exec_id = "test-tool-exec-id"
-    mock_input_data = {"query": "test", "max_keyword_difficulty": 50}
-    mock_db_client.upsert_execution_input.return_value = (
-        mock_node_exec_result,
-        mock_input_data,
-    )
-
-    # No longer need mock_execute_node since we use execution_processor.on_node_execution
-
-    with patch("backend.blocks.llm.llm_call", llm_call_mock), patch.object(
-        block, "_create_tool_node_signatures", return_value=mock_tool_signatures
-    ), patch(
-        "backend.blocks.smart_decision_maker.get_database_manager_async_client",
-        return_value=mock_db_client,
-    ), patch(
-        "backend.executor.manager.async_update_node_execution_status",
-        new_callable=AsyncMock,
-    ), patch(
-        "backend.integrations.creds_manager.IntegrationCredentialsManager"
-    ):
-
-        # Create a mock execution context
-
-        mock_execution_context = ExecutionContext(
-            safe_mode=False,
-        )
-
-        # Create a mock execution processor for agent mode tests
-
-        mock_execution_processor = AsyncMock()
-        # Configure the execution processor mock with required attributes
-        mock_execution_processor.running_node_execution = defaultdict(MagicMock)
-        mock_execution_processor.execution_stats = MagicMock()
-        mock_execution_processor.execution_stats_lock = threading.Lock()
-
-        # Mock the on_node_execution method to return successful stats
-        mock_node_stats = MagicMock()
-        mock_node_stats.error = None  # No error
-        mock_execution_processor.on_node_execution = AsyncMock(
-            return_value=mock_node_stats
-        )
-
-        # Mock the get_execution_outputs_by_node_exec_id method
-        mock_db_client.get_execution_outputs_by_node_exec_id.return_value = {
-            "result": {"status": "success", "data": "search completed"}
-        }
-
-        # Test agent mode with max_iterations = 3
-        input_data = SmartDecisionMakerBlock.Input(
-            prompt="Complete this task using tools",
-            model=llm_module.LlmModel.GPT4O,
-            credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
-            agent_mode_max_iterations=3,  # Enable agent mode with 3 max iterations
-        )
-
-        outputs = {}
-        async for output_name, output_data in block.run(
-            input_data,
-            credentials=llm_module.TEST_CREDENTIALS,
-            graph_id="test-graph-id",
-            node_id="test-node-id",
-            graph_exec_id="test-exec-id",
-            node_exec_id="test-node-exec-id",
-            user_id="test-user-id",
-            graph_version=1,
-            execution_context=mock_execution_context,
-            execution_processor=mock_execution_processor,
-        ):
-            outputs[output_name] = output_data
-
-        # Verify agent mode behavior
-        assert "tool_functions" in outputs  # tool_functions is yielded in both modes
-        assert "finished" in outputs
-        assert outputs["finished"] == "Task completed successfully"
-        assert "conversations" in outputs
-
-        # Verify the conversation includes tool responses
-        conversations = outputs["conversations"]
-        assert len(conversations) > 2  # Should have multiple conversation entries
-
-        # Verify LLM was called twice (once for tool call, once for finish)
-        assert llm_call_mock.call_count == 2
-
-        # Verify tool was executed via execution processor
-        assert mock_execution_processor.on_node_execution.call_count == 1
-
-
-@pytest.mark.asyncio
-async def test_smart_decision_maker_traditional_mode_default():
-    """Test that default behavior (agent_mode_max_iterations=0) works as traditional mode."""
-    import backend.blocks.llm as llm_module
-    from backend.blocks.smart_decision_maker import SmartDecisionMakerBlock
-
-    block = SmartDecisionMakerBlock()
-
-    # Mock tool call
-    mock_tool_call = MagicMock()
-    mock_tool_call.function.name = "search_keywords"
-    mock_tool_call.function.arguments = (
-        '{"query": "test", "max_keyword_difficulty": 50}'
-    )
-
-    mock_response = MagicMock()
-    mock_response.response = None
-    mock_response.tool_calls = [mock_tool_call]
-    mock_response.prompt_tokens = 50
-    mock_response.completion_tokens = 25
-    mock_response.reasoning = None
-    mock_response.raw_response = {"role": "assistant", "content": None}
-
-    mock_tool_signatures = [
-        {
-            "type": "function",
-            "function": {
-                "name": "search_keywords",
-                "_sink_node_id": "test-sink-node-id",
-                "_field_mapping": {},
-                "parameters": {
-                    "properties": {
-                        "query": {"type": "string"},
-                        "max_keyword_difficulty": {"type": "integer"},
-                    },
-                    "required": ["query", "max_keyword_difficulty"],
-                },
-            },
-        }
-    ]
-
-    with patch(
-        "backend.blocks.llm.llm_call",
-        new_callable=AsyncMock,
-        return_value=mock_response,
-    ), patch.object(
-        block, "_create_tool_node_signatures", return_value=mock_tool_signatures
-    ):
-
-        # Test default behavior (traditional mode)
-        input_data = SmartDecisionMakerBlock.Input(
-            prompt="Test prompt",
-            model=llm_module.LlmModel.GPT4O,
-            credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
-            agent_mode_max_iterations=0,  # Traditional mode
-        )
-
-        # Create execution context
-
-        mock_execution_context = ExecutionContext(safe_mode=False)
-
-        # Create a mock execution processor for tests
-
-        mock_execution_processor = MagicMock()
-
-        outputs = {}
-        async for output_name, output_data in block.run(
-            input_data,
-            credentials=llm_module.TEST_CREDENTIALS,
-            graph_id="test-graph-id",
-            node_id="test-node-id",
-            graph_exec_id="test-exec-id",
-            node_exec_id="test-node-exec-id",
-            user_id="test-user-id",
-            graph_version=1,
-            execution_context=mock_execution_context,
-            execution_processor=mock_execution_processor,
-        ):
-            outputs[output_name] = output_data
-
-        # Verify traditional mode behavior
-        assert (
-            "tool_functions" in outputs
-        )  # Should yield tool_functions in traditional mode
-        assert (
-            "tools_^_test-sink-node-id_~_query" in outputs
-        )  # Should yield individual tool parameters
-        assert "tools_^_test-sink-node-id_~_max_keyword_difficulty" in outputs
-        assert "conversations" in outputs

autogpt_platform/backend/backend/blocks/test/test_smart_decision_maker_dynamic_fields.py

@@ -1,7 +1,7 @@
 """Comprehensive tests for SmartDecisionMakerBlock dynamic field handling."""
 
 import json
-from unittest.mock import AsyncMock, MagicMock, Mock, patch
+from unittest.mock import AsyncMock, Mock, patch
 
 import pytest
 
@@ -308,47 +308,10 @@ async def test_output_yielding_with_dynamic_fields():
     ) as mock_llm:
         mock_llm.return_value = mock_response
 
-        # Mock the database manager to avoid HTTP calls during tool execution
-        with patch(
-            "backend.blocks.smart_decision_maker.get_database_manager_async_client"
-        ) as mock_db_manager, patch.object(
+        # Mock the function signature creation
+        with patch.object(
             block, "_create_tool_node_signatures", new_callable=AsyncMock
         ) as mock_sig:
-            # Set up the mock database manager
-            mock_db_client = AsyncMock()
-            mock_db_manager.return_value = mock_db_client
-
-            # Mock the node retrieval
-            mock_target_node = Mock()
-            mock_target_node.id = "test-sink-node-id"
-            mock_target_node.block_id = "CreateDictionaryBlock"
-            mock_target_node.block = Mock()
-            mock_target_node.block.name = "Create Dictionary"
-            mock_db_client.get_node.return_value = mock_target_node
-
-            # Mock the execution result creation
-            mock_node_exec_result = Mock()
-            mock_node_exec_result.node_exec_id = "mock-node-exec-id"
-            mock_final_input_data = {
-                "values_#_name": "Alice",
-                "values_#_age": 30,
-                "values_#_email": "alice@example.com",
-            }
-            mock_db_client.upsert_execution_input.return_value = (
-                mock_node_exec_result,
-                mock_final_input_data,
-            )
-
-            # Mock the output retrieval
-            mock_outputs = {
-                "values_#_name": "Alice",
-                "values_#_age": 30,
-                "values_#_email": "alice@example.com",
-            }
-            mock_db_client.get_execution_outputs_by_node_exec_id.return_value = (
-                mock_outputs
-            )
-
             mock_sig.return_value = [
                 {
                     "type": "function",
@@ -374,16 +337,10 @@ async def test_output_yielding_with_dynamic_fields():
                 prompt="Create a user dictionary",
                 credentials=llm.TEST_CREDENTIALS_INPUT,
                 model=llm.LlmModel.GPT4O,
-                agent_mode_max_iterations=0,  # Use traditional mode to test output yielding
             )
 
             # Run the block
             outputs = {}
-            from backend.data.execution import ExecutionContext
-
-            mock_execution_context = ExecutionContext(safe_mode=False)
-            mock_execution_processor = MagicMock()
-
             async for output_name, output_value in block.run(
                 input_data,
                 credentials=llm.TEST_CREDENTIALS,
@@ -392,9 +349,6 @@ async def test_output_yielding_with_dynamic_fields():
                 graph_exec_id="test_exec",
                 node_exec_id="test_node_exec",
                 user_id="test_user",
-                graph_version=1,
-                execution_context=mock_execution_context,
-                execution_processor=mock_execution_processor,
             ):
                 outputs[output_name] = output_value
 
@@ -557,108 +511,45 @@ async def test_validation_errors_dont_pollute_conversation():
                 }
             ]
 
-            # Mock the database manager to avoid HTTP calls during tool execution
-            with patch(
-                "backend.blocks.smart_decision_maker.get_database_manager_async_client"
-            ) as mock_db_manager:
-                # Set up the mock database manager for agent mode
-                mock_db_client = AsyncMock()
-                mock_db_manager.return_value = mock_db_client
+            # Create input data
+            from backend.blocks import llm
 
-                # Mock the node retrieval
-                mock_target_node = Mock()
-                mock_target_node.id = "test-sink-node-id"
-                mock_target_node.block_id = "TestBlock"
-                mock_target_node.block = Mock()
-                mock_target_node.block.name = "Test Block"
-                mock_db_client.get_node.return_value = mock_target_node
+            input_data = block.input_schema(
+                prompt="Test prompt",
+                credentials=llm.TEST_CREDENTIALS_INPUT,
+                model=llm.LlmModel.GPT4O,
+                retry=3,  # Allow retries
+            )
 
-                # Mock the execution result creation
-                mock_node_exec_result = Mock()
-                mock_node_exec_result.node_exec_id = "mock-node-exec-id"
-                mock_final_input_data = {"correct_param": "value"}
-                mock_db_client.upsert_execution_input.return_value = (
-                    mock_node_exec_result,
-                    mock_final_input_data,
-                )
+            # Run the block
+            outputs = {}
+            async for output_name, output_value in block.run(
+                input_data,
+                credentials=llm.TEST_CREDENTIALS,
+                graph_id="test_graph",
+                node_id="test_node",
+                graph_exec_id="test_exec",
+                node_exec_id="test_node_exec",
+                user_id="test_user",
+            ):
+                outputs[output_name] = output_value
 
-                # Mock the output retrieval
-                mock_outputs = {"correct_param": "value"}
-                mock_db_client.get_execution_outputs_by_node_exec_id.return_value = (
-                    mock_outputs
-                )
+            # Verify we had 2 LLM calls (initial + retry)
+            assert call_count == 2
 
-                # Create input data
-                from backend.blocks import llm
+            # Check the final conversation output
+            final_conversation = outputs.get("conversations", [])
 
-                input_data = block.input_schema(
-                    prompt="Test prompt",
-                    credentials=llm.TEST_CREDENTIALS_INPUT,
-                    model=llm.LlmModel.GPT4O,
-                    retry=3,  # Allow retries
-                    agent_mode_max_iterations=1,
-                )
+            # The final conversation should NOT contain the validation error message
+            error_messages = [
+                msg
+                for msg in final_conversation
+                if msg.get("role") == "user"
+                and "parameter errors" in msg.get("content", "")
+            ]
+            assert (
+                len(error_messages) == 0
+            ), "Validation error leaked into final conversation"
 
-                # Run the block
-                outputs = {}
-                from backend.data.execution import ExecutionContext
-
-                mock_execution_context = ExecutionContext(safe_mode=False)
-
-                # Create a proper mock execution processor for agent mode
-                from collections import defaultdict
-
-                mock_execution_processor = AsyncMock()
-                mock_execution_processor.execution_stats = MagicMock()
-                mock_execution_processor.execution_stats_lock = MagicMock()
-
-                # Create a mock NodeExecutionProgress for the sink node
-                mock_node_exec_progress = MagicMock()
-                mock_node_exec_progress.add_task = MagicMock()
-                mock_node_exec_progress.pop_output = MagicMock(
-                    return_value=None
-                )  # No outputs to process
-
-                # Set up running_node_execution as a defaultdict that returns our mock for any key
-                mock_execution_processor.running_node_execution = defaultdict(
-                    lambda: mock_node_exec_progress
-                )
-
-                # Mock the on_node_execution method that gets called during tool execution
-                mock_node_stats = MagicMock()
-                mock_node_stats.error = None
-                mock_execution_processor.on_node_execution.return_value = (
-                    mock_node_stats
-                )
-
-                async for output_name, output_value in block.run(
-                    input_data,
-                    credentials=llm.TEST_CREDENTIALS,
-                    graph_id="test_graph",
-                    node_id="test_node",
-                    graph_exec_id="test_exec",
-                    node_exec_id="test_node_exec",
-                    user_id="test_user",
-                    graph_version=1,
-                    execution_context=mock_execution_context,
-                    execution_processor=mock_execution_processor,
-                ):
-                    outputs[output_name] = output_value
-
-                # Verify we had at least 1 LLM call
-                assert call_count >= 1
-
-                # Check the final conversation output
-                final_conversation = outputs.get("conversations", [])
-
-                # The final conversation should NOT contain validation error messages
-                # Even if retries don't happen in agent mode, we should not leak errors
-                error_messages = [
-                    msg
-                    for msg in final_conversation
-                    if msg.get("role") == "user"
-                    and "parameter errors" in msg.get("content", "")
-                ]
-                assert (
-                    len(error_messages) == 0
-                ), "Validation error leaked into final conversation"
+            # The final conversation should only have the successful response
+            assert final_conversation[-1]["content"] == "valid"

autogpt_platform/backend/backend/data/execution.py

@@ -5,7 +5,6 @@ from enum import Enum
 from multiprocessing import Manager
 from queue import Empty
 from typing import (
-    TYPE_CHECKING,
     Annotated,
     Any,
     AsyncGenerator,
@@ -66,9 +65,6 @@ from .includes import (
 )
 from .model import CredentialsMetaInput, GraphExecutionStats, NodeExecutionStats
 
-if TYPE_CHECKING:
-    pass
-
 T = TypeVar("T")
 
 logger = logging.getLogger(__name__)
@@ -840,30 +836,6 @@ async def upsert_execution_output(
     await AgentNodeExecutionInputOutput.prisma().create(data=data)
 
 
-async def get_execution_outputs_by_node_exec_id(
-    node_exec_id: str,
-) -> dict[str, Any]:
-    """
-    Get all execution outputs for a specific node execution ID.
-
-    Args:
-        node_exec_id: The node execution ID to get outputs for
-
-    Returns:
-        Dictionary mapping output names to their data values
-    """
-    outputs = await AgentNodeExecutionInputOutput.prisma().find_many(
-        where={"referencedByOutputExecId": node_exec_id}
-    )
-
-    result = {}
-    for output in outputs:
-        if output.data is not None:
-            result[output.name] = type_utils.convert(output.data, JsonValue)
-
-    return result
-
-
 async def update_graph_execution_start_time(
     graph_exec_id: str,
 ) -> GraphExecution | None:

autogpt_platform/backend/backend/data/human_review.py

@@ -100,7 +100,7 @@ async def get_or_create_human_review(
         return None
     else:
         return ReviewResult(
-            data=review.payload,
+            data=review.payload if review.status == ReviewStatus.APPROVED else None,
             status=review.status,
             message=review.reviewMessage or "",
             processed=review.processed,

autogpt_platform/backend/backend/executor/database.py

@@ -13,7 +13,6 @@ from backend.data.execution import (
     get_block_error_stats,
     get_child_graph_executions,
     get_execution_kv_data,
-    get_execution_outputs_by_node_exec_id,
     get_frequently_executed_graphs,
     get_graph_execution_meta,
     get_graph_executions,
@@ -148,7 +147,6 @@ class DatabaseManager(AppService):
     update_graph_execution_stats = _(update_graph_execution_stats)
     upsert_execution_input = _(upsert_execution_input)
     upsert_execution_output = _(upsert_execution_output)
-    get_execution_outputs_by_node_exec_id = _(get_execution_outputs_by_node_exec_id)
     get_execution_kv_data = _(get_execution_kv_data)
     set_execution_kv_data = _(set_execution_kv_data)
     get_block_error_stats = _(get_block_error_stats)
@@ -279,7 +277,6 @@ class DatabaseManagerAsyncClient(AppServiceClient):
     get_user_integrations = d.get_user_integrations
     upsert_execution_input = d.upsert_execution_input
     upsert_execution_output = d.upsert_execution_output
-    get_execution_outputs_by_node_exec_id = d.get_execution_outputs_by_node_exec_id
     update_graph_execution_stats = d.update_graph_execution_stats
     update_node_execution_status = d.update_node_execution_status
     update_node_execution_status_batch = d.update_node_execution_status_batch

autogpt_platform/backend/backend/executor/manager.py

@@ -133,8 +133,9 @@ def execute_graph(
     cluster_lock: ClusterLock,
 ):
     """Execute graph using thread-local ExecutionProcessor instance"""
-    processor: ExecutionProcessor = _tls.processor
-    return processor.on_graph_execution(graph_exec_entry, cancel_event, cluster_lock)
+    return _tls.processor.on_graph_execution(
+        graph_exec_entry, cancel_event, cluster_lock
+    )
 
 
 T = TypeVar("T")
@@ -142,8 +143,8 @@ T = TypeVar("T")
 
 async def execute_node(
     node: Node,
+    creds_manager: IntegrationCredentialsManager,
     data: NodeExecutionEntry,
-    execution_processor: "ExecutionProcessor",
     execution_stats: NodeExecutionStats | None = None,
     nodes_input_masks: Optional[NodesInputMasks] = None,
 ) -> BlockOutput:
@@ -168,7 +169,6 @@ async def execute_node(
     node_id = data.node_id
     node_block = node.block
     execution_context = data.execution_context
-    creds_manager = execution_processor.creds_manager
 
     log_metadata = LogMetadata(
         logger=_logger,
@@ -212,7 +212,6 @@ async def execute_node(
         "node_exec_id": node_exec_id,
         "user_id": user_id,
         "execution_context": execution_context,
-        "execution_processor": execution_processor,
     }
 
     # Last-minute fetch credentials + acquire a system-wide read-write lock to prevent
@@ -609,8 +608,8 @@ class ExecutionProcessor:
 
             async for output_name, output_data in execute_node(
                 node=node,
+                creds_manager=self.creds_manager,
                 data=node_exec,
-                execution_processor=self,
                 execution_stats=stats,
                 nodes_input_masks=nodes_input_masks,
             ):
@@ -861,17 +860,12 @@ class ExecutionProcessor:
         execution_stats_lock = threading.Lock()
 
         # State holders ----------------------------------------------------
-        self.running_node_execution: dict[str, NodeExecutionProgress] = defaultdict(
+        running_node_execution: dict[str, NodeExecutionProgress] = defaultdict(
             NodeExecutionProgress
         )
-        self.running_node_evaluation: dict[str, Future] = {}
-        self.execution_stats = execution_stats
-        self.execution_stats_lock = execution_stats_lock
+        running_node_evaluation: dict[str, Future] = {}
         execution_queue = ExecutionQueue[NodeExecutionEntry]()
 
-        running_node_execution = self.running_node_execution
-        running_node_evaluation = self.running_node_evaluation
-
         try:
             if db_client.get_credits(graph_exec.user_id) <= 0:
                 raise InsufficientBalanceError(

autogpt_platform/backend/backend/integrations/embeddings.py

@@ -1,157 +0,0 @@
-"""
-Embedding service for generating text embeddings using OpenAI.
-
-Used for vector-based semantic search in the store.
-"""
-
-import functools
-import logging
-from typing import Optional
-
-import openai
-
-from backend.util.settings import Settings
-
-logger = logging.getLogger(__name__)
-
-# Model configuration
-# Using text-embedding-3-small (1536 dimensions) for compatibility with pgvector indexes
-# pgvector IVFFlat/HNSW indexes have dimension limits (2000 for IVFFlat, varies for HNSW)
-EMBEDDING_MODEL = "text-embedding-3-small"
-EMBEDDING_DIMENSIONS = 1536
-
-# Input validation limits
-# OpenAI text-embedding-3-large supports up to 8191 tokens (~32k chars)
-# We set a conservative limit to prevent abuse
-MAX_TEXT_LENGTH = 10000  # characters
-MAX_BATCH_SIZE = 100  # maximum texts per batch request
-
-
-class EmbeddingService:
-    """Service for generating text embeddings using OpenAI.
-
-    The service can be created without an API key - the key is validated
-    only when the client property is first accessed. This allows the service
-    to be instantiated at module load time without requiring configuration.
-    """
-
-    def __init__(self, api_key: Optional[str] = None):
-        settings = Settings()
-        self.api_key = (
-            api_key
-            or settings.secrets.openai_internal_api_key
-            or settings.secrets.openai_api_key
-        )
-
-    @functools.cached_property
-    def client(self) -> openai.AsyncOpenAI:
-        """Lazily create the OpenAI client, raising if no API key is configured."""
-        if not self.api_key:
-            raise ValueError(
-                "OpenAI API key not configured. "
-                "Set OPENAI_API_KEY or OPENAI_INTERNAL_API_KEY environment variable."
-            )
-        return openai.AsyncOpenAI(api_key=self.api_key)
-
-    async def generate_embedding(self, text: str) -> list[float]:
-        """
-        Generate embedding for a single text string.
-
-        Args:
-            text: The text to generate an embedding for.
-
-        Returns:
-            A list of floats representing the embedding vector.
-
-        Raises:
-            ValueError: If the text is empty or exceeds maximum length.
-            openai.APIError: If the OpenAI API call fails.
-        """
-        # Input validation
-        if not text or not text.strip():
-            raise ValueError("Text cannot be empty")
-        if len(text) > MAX_TEXT_LENGTH:
-            raise ValueError(
-                f"Text exceeds maximum length of {MAX_TEXT_LENGTH} characters"
-            )
-
-        response = await self.client.embeddings.create(
-            model=EMBEDDING_MODEL,
-            input=text,
-            dimensions=EMBEDDING_DIMENSIONS,
-        )
-        if not response.data:
-            raise ValueError("OpenAI API returned empty embedding data")
-        return response.data[0].embedding
-
-    async def generate_embeddings(self, texts: list[str]) -> list[list[float]]:
-        """
-        Generate embeddings for multiple texts (batch).
-
-        Args:
-            texts: List of texts to generate embeddings for.
-
-        Returns:
-            List of embedding vectors, one per input text.
-
-        Raises:
-            ValueError: If any text is invalid or batch size exceeds limit.
-            openai.APIError: If the OpenAI API call fails.
-        """
-        # Input validation
-        if not texts:
-            raise ValueError("Texts list cannot be empty")
-        if len(texts) > MAX_BATCH_SIZE:
-            raise ValueError(f"Batch size exceeds maximum of {MAX_BATCH_SIZE} texts")
-        for i, text in enumerate(texts):
-            if not text or not text.strip():
-                raise ValueError(f"Text at index {i} cannot be empty")
-            if len(text) > MAX_TEXT_LENGTH:
-                raise ValueError(
-                    f"Text at index {i} exceeds maximum length of {MAX_TEXT_LENGTH} characters"
-                )
-
-        response = await self.client.embeddings.create(
-            model=EMBEDDING_MODEL,
-            input=texts,
-            dimensions=EMBEDDING_DIMENSIONS,
-        )
-        # Sort by index to ensure correct ordering
-        sorted_data = sorted(response.data, key=lambda x: x.index)
-        return [item.embedding for item in sorted_data]
-
-
-def create_search_text(name: str, sub_heading: str, description: str) -> str:
-    """
-    Combine fields into searchable text for embedding.
-
-    This creates a single text string from the agent's name, sub-heading,
-    and description, which is then converted to an embedding vector.
-
-    Args:
-        name: The agent name.
-        sub_heading: The agent sub-heading/tagline.
-        description: The agent description.
-
-    Returns:
-        A single string combining all non-empty fields.
-    """
-    parts = [name or "", sub_heading or "", description or ""]
-    # filter(None, parts) removes empty strings since empty string is falsy
-    return " ".join(filter(None, parts)).strip()
-
-
-@functools.cache
-def get_embedding_service() -> EmbeddingService:
-    """
-    Get or create the embedding service singleton.
-
-    Uses functools.cache for thread-safe lazy initialization.
-
-    Returns:
-        The shared EmbeddingService instance.
-
-    Raises:
-        ValueError: If OpenAI API key is not configured (when generating embeddings).
-    """
-    return EmbeddingService()

autogpt_platform/backend/backend/integrations/embeddings_test.py

@@ -1,235 +0,0 @@
-"""Tests for the embedding service.
-
-This module tests:
-- create_search_text utility function
-- EmbeddingService input validation
-- EmbeddingService API interaction (mocked)
-"""
-
-from unittest.mock import AsyncMock, MagicMock, patch
-
-import pytest
-
-from backend.integrations.embeddings import (
-    EMBEDDING_DIMENSIONS,
-    MAX_BATCH_SIZE,
-    MAX_TEXT_LENGTH,
-    EmbeddingService,
-    create_search_text,
-)
-
-
-class TestCreateSearchText:
-    """Tests for the create_search_text utility function."""
-
-    def test_combines_all_fields(self):
-        result = create_search_text("Agent Name", "A cool agent", "Does amazing things")
-        assert result == "Agent Name A cool agent Does amazing things"
-
-    def test_handles_empty_name(self):
-        result = create_search_text("", "Sub heading", "Description")
-        assert result == "Sub heading Description"
-
-    def test_handles_empty_sub_heading(self):
-        result = create_search_text("Name", "", "Description")
-        assert result == "Name Description"
-
-    def test_handles_empty_description(self):
-        result = create_search_text("Name", "Sub heading", "")
-        assert result == "Name Sub heading"
-
-    def test_handles_all_empty(self):
-        result = create_search_text("", "", "")
-        assert result == ""
-
-    def test_handles_none_values(self):
-        # The function expects strings but should handle None gracefully
-        result = create_search_text(None, None, None)  # type: ignore
-        assert result == ""
-
-    def test_preserves_content_strips_outer_whitespace(self):
-        # The function joins parts and strips the outer result
-        # Internal whitespace in each part is preserved
-        result = create_search_text("  Name  ", "  Sub  ", "  Desc  ")
-        # Each part is joined with space, then outer strip applied
-        assert result == "Name     Sub     Desc"
-
-    def test_handles_only_whitespace(self):
-        # Parts that are only whitespace become empty after filter
-        result = create_search_text("   ", "   ", "   ")
-        assert result == ""
-
-
-class TestEmbeddingServiceValidation:
-    """Tests for EmbeddingService input validation."""
-
-    @pytest.fixture
-    def mock_settings(self):
-        """Mock settings with a test API key."""
-        with patch("backend.integrations.embeddings.Settings") as mock:
-            mock_instance = MagicMock()
-            mock_instance.secrets.openai_internal_api_key = "test-api-key"
-            mock_instance.secrets.openai_api_key = ""
-            mock.return_value = mock_instance
-            yield mock
-
-    @pytest.fixture
-    def service(self, mock_settings):
-        """Create an EmbeddingService instance with mocked settings."""
-        service = EmbeddingService()
-        # Inject a mock client by setting the cached_property directly
-        service.__dict__["client"] = MagicMock()
-        return service
-
-    def test_client_access_requires_api_key(self):
-        """Test that accessing client fails without an API key."""
-        with patch("backend.integrations.embeddings.Settings") as mock:
-            mock_instance = MagicMock()
-            mock_instance.secrets.openai_internal_api_key = ""
-            mock_instance.secrets.openai_api_key = ""
-            mock.return_value = mock_instance
-
-            # Service creation should succeed
-            service = EmbeddingService()
-
-            # But accessing client should fail
-            with pytest.raises(ValueError, match="OpenAI API key not configured"):
-                _ = service.client
-
-    def test_init_accepts_explicit_api_key(self):
-        """Test that explicit API key overrides settings."""
-        with patch("backend.integrations.embeddings.Settings") as mock:
-            mock_instance = MagicMock()
-            mock_instance.secrets.openai_internal_api_key = ""
-            mock_instance.secrets.openai_api_key = ""
-            mock.return_value = mock_instance
-
-            with patch("backend.integrations.embeddings.openai.AsyncOpenAI"):
-                service = EmbeddingService(api_key="explicit-key")
-                assert service.api_key == "explicit-key"
-
-    @pytest.mark.asyncio
-    async def test_generate_embedding_empty_text(self, service):
-        """Test that empty text raises ValueError."""
-        with pytest.raises(ValueError, match="Text cannot be empty"):
-            await service.generate_embedding("")
-
-    @pytest.mark.asyncio
-    async def test_generate_embedding_whitespace_only(self, service):
-        """Test that whitespace-only text raises ValueError."""
-        with pytest.raises(ValueError, match="Text cannot be empty"):
-            await service.generate_embedding("   ")
-
-    @pytest.mark.asyncio
-    async def test_generate_embedding_exceeds_max_length(self, service):
-        """Test that text exceeding max length raises ValueError."""
-        long_text = "a" * (MAX_TEXT_LENGTH + 1)
-        with pytest.raises(ValueError, match="exceeds maximum length"):
-            await service.generate_embedding(long_text)
-
-    @pytest.mark.asyncio
-    async def test_generate_embeddings_empty_list(self, service):
-        """Test that empty list raises ValueError."""
-        with pytest.raises(ValueError, match="Texts list cannot be empty"):
-            await service.generate_embeddings([])
-
-    @pytest.mark.asyncio
-    async def test_generate_embeddings_exceeds_batch_size(self, service):
-        """Test that batch exceeding max size raises ValueError."""
-        texts = ["text"] * (MAX_BATCH_SIZE + 1)
-        with pytest.raises(ValueError, match="Batch size exceeds maximum"):
-            await service.generate_embeddings(texts)
-
-    @pytest.mark.asyncio
-    async def test_generate_embeddings_empty_text_in_batch(self, service):
-        """Test that empty text in batch raises ValueError with index."""
-        with pytest.raises(ValueError, match="Text at index 1 cannot be empty"):
-            await service.generate_embeddings(["valid", "", "also valid"])
-
-    @pytest.mark.asyncio
-    async def test_generate_embeddings_long_text_in_batch(self, service):
-        """Test that long text in batch raises ValueError with index."""
-        long_text = "a" * (MAX_TEXT_LENGTH + 1)
-        with pytest.raises(ValueError, match="Text at index 2 exceeds maximum length"):
-            await service.generate_embeddings(["short", "also short", long_text])
-
-
-class TestEmbeddingServiceAPI:
-    """Tests for EmbeddingService API interaction."""
-
-    @pytest.fixture
-    def mock_openai_client(self):
-        """Create a mock OpenAI client."""
-        mock_client = MagicMock()
-        mock_client.embeddings = MagicMock()
-        return mock_client
-
-    @pytest.fixture
-    def service_with_mock_client(self, mock_openai_client):
-        """Create an EmbeddingService with a mocked OpenAI client."""
-        with patch("backend.integrations.embeddings.Settings") as mock_settings:
-            mock_instance = MagicMock()
-            mock_instance.secrets.openai_internal_api_key = "test-key"
-            mock_instance.secrets.openai_api_key = ""
-            mock_settings.return_value = mock_instance
-
-            service = EmbeddingService()
-            # Inject mock client by setting the cached_property directly
-            service.__dict__["client"] = mock_openai_client
-            return service, mock_openai_client
-
-    @pytest.mark.asyncio
-    async def test_generate_embedding_success(self, service_with_mock_client):
-        """Test successful embedding generation."""
-        service, mock_client = service_with_mock_client
-
-        # Create mock response
-        mock_embedding = [0.1] * EMBEDDING_DIMENSIONS
-        mock_response = MagicMock()
-        mock_response.data = [MagicMock(embedding=mock_embedding)]
-        mock_client.embeddings.create = AsyncMock(return_value=mock_response)
-
-        result = await service.generate_embedding("test text")
-
-        assert result == mock_embedding
-        mock_client.embeddings.create.assert_called_once()
-
-    @pytest.mark.asyncio
-    async def test_generate_embeddings_success(self, service_with_mock_client):
-        """Test successful batch embedding generation."""
-        service, mock_client = service_with_mock_client
-
-        # Create mock response with multiple embeddings
-        mock_embeddings = [[0.1] * EMBEDDING_DIMENSIONS, [0.2] * EMBEDDING_DIMENSIONS]
-        mock_response = MagicMock()
-        mock_response.data = [
-            MagicMock(embedding=mock_embeddings[0], index=0),
-            MagicMock(embedding=mock_embeddings[1], index=1),
-        ]
-        mock_client.embeddings.create = AsyncMock(return_value=mock_response)
-
-        result = await service.generate_embeddings(["text1", "text2"])
-
-        assert result == mock_embeddings
-        mock_client.embeddings.create.assert_called_once()
-
-    @pytest.mark.asyncio
-    async def test_generate_embeddings_preserves_order(self, service_with_mock_client):
-        """Test that batch embeddings are returned in correct order even if API returns out of order."""
-        service, mock_client = service_with_mock_client
-
-        # Create mock response with embeddings out of order
-        mock_embeddings = [[0.1] * EMBEDDING_DIMENSIONS, [0.2] * EMBEDDING_DIMENSIONS]
-        mock_response = MagicMock()
-        # Return in reverse order
-        mock_response.data = [
-            MagicMock(embedding=mock_embeddings[1], index=1),
-            MagicMock(embedding=mock_embeddings[0], index=0),
-        ]
-        mock_client.embeddings.create = AsyncMock(return_value=mock_response)
-
-        result = await service.generate_embeddings(["text1", "text2"])
-
-        # Should be sorted by index
-        assert result[0] == mock_embeddings[0]
-        assert result[1] == mock_embeddings[1]

autogpt_platform/backend/backend/server/v2/chat/service.py

@@ -1,4 +1,3 @@
-import functools
 import logging
 from collections.abc import AsyncGenerator
 from datetime import UTC, datetime
@@ -33,12 +32,7 @@ from backend.util.exceptions import NotFoundError
 logger = logging.getLogger(__name__)
 
 config = backend.server.v2.chat.config.ChatConfig()
-
-
-@functools.cache
-def get_openai_client() -> AsyncOpenAI:
-    """Lazily create the OpenAI client singleton."""
-    return AsyncOpenAI(api_key=config.api_key, base_url=config.base_url)
+client = AsyncOpenAI(api_key=config.api_key, base_url=config.base_url)
 
 
 async def create_chat_session(
@@ -361,7 +355,7 @@ async def _stream_chat_chunks(
             logger.info("Creating OpenAI chat completion stream...")
 
             # Create the stream with proper types
-            stream = await get_openai_client().chat.completions.create(
+            stream = await client.chat.completions.create(
                 model=model,
                 messages=session.to_openai_messages(),
                 tools=tools,

autogpt_platform/backend/backend/server/v2/executions/review/routes.py

@@ -134,14 +134,18 @@ async def process_review_action(
     # Build review decisions map
     review_decisions = {}
     for review in request.reviews:
-        review_status = (
-            ReviewStatus.APPROVED if review.approved else ReviewStatus.REJECTED
-        )
-        review_decisions[review.node_exec_id] = (
-            review_status,
-            review.reviewed_data,
-            review.message,
-        )
+        if review.approved:
+            review_decisions[review.node_exec_id] = (
+                ReviewStatus.APPROVED,
+                review.reviewed_data,
+                review.message,
+            )
+        else:
+            review_decisions[review.node_exec_id] = (
+                ReviewStatus.REJECTED,
+                None,
+                review.message,
+            )
 
     # Process all reviews
     updated_reviews = await process_all_reviews_for_execution(

autogpt_platform/backend/backend/server/v2/store/backfill_embeddings.py

@@ -1,168 +0,0 @@
-"""
-Script to backfill embeddings for existing store listing versions.
-
-This script should be run after the migration to add the embedding column
-to populate embeddings for all existing store listing versions.
-
-Usage:
-    poetry run python -m backend.server.v2.store.backfill_embeddings
-    poetry run python -m backend.server.v2.store.backfill_embeddings --dry-run
-    poetry run python -m backend.server.v2.store.backfill_embeddings --batch-size 25
-"""
-
-import argparse
-import asyncio
-import logging
-import sys
-
-from backend.data.db import connect, disconnect, query_raw_with_schema
-from backend.integrations.embeddings import create_search_text, get_embedding_service
-
-logging.basicConfig(
-    level=logging.INFO,
-    format="%(asctime)s - %(levelname)s - %(message)s",
-)
-logger = logging.getLogger(__name__)
-
-# Default batch size for processing
-DEFAULT_BATCH_SIZE = 50
-
-# Delay between batches to avoid rate limits (seconds)
-BATCH_DELAY_SECONDS = 1.0
-
-
-async def backfill_embeddings(
-    dry_run: bool = False,
-    batch_size: int = DEFAULT_BATCH_SIZE,
-) -> tuple[int, int]:
-    """
-    Backfill embeddings for all store listing versions without embeddings.
-
-    Args:
-        dry_run: If True, don't make any changes, just report what would be done.
-        batch_size: Number of versions to process in each batch.
-
-    Returns:
-        Tuple of (processed_count, error_count)
-    """
-    await connect()
-
-    try:
-        embedding_service = get_embedding_service()
-
-        # Get all versions without embeddings
-        versions = await query_raw_with_schema(
-            """
-            SELECT id, name, "subHeading", description
-            FROM {schema_prefix}"StoreListingVersion"
-            WHERE embedding IS NULL
-            ORDER BY "createdAt" DESC
-            """
-        )
-
-        total = len(versions)
-        logger.info(f"Found {total} versions without embeddings")
-
-        if dry_run:
-            logger.info("Dry run mode - no changes will be made")
-            return (0, 0)
-
-        if total == 0:
-            logger.info("No versions need embeddings")
-            return (0, 0)
-
-        processed = 0
-        errors = 0
-
-        for i in range(0, total, batch_size):
-            batch = versions[i : i + batch_size]
-            batch_num = (i // batch_size) + 1
-            total_batches = (total + batch_size - 1) // batch_size
-
-            logger.info(f"Processing batch {batch_num}/{total_batches}")
-
-            for version in batch:
-                version_id = version["id"]
-                try:
-                    search_text = create_search_text(
-                        version["name"] or "",
-                        version["subHeading"] or "",
-                        version["description"] or "",
-                    )
-
-                    if not search_text:
-                        logger.warning(f"Skipping {version_id} - no searchable text")
-                        continue
-
-                    embedding = await embedding_service.generate_embedding(search_text)
-                    embedding_str = "[" + ",".join(map(str, embedding)) + "]"
-
-                    await query_raw_with_schema(
-                        """
-                        UPDATE {schema_prefix}"StoreListingVersion"
-                        SET embedding = $1::vector
-                        WHERE id = $2
-                        """,
-                        embedding_str,
-                        version_id,
-                    )
-
-                    processed += 1
-
-                except Exception as e:
-                    logger.error(f"Error processing {version_id}: {e}")
-                    errors += 1
-
-            logger.info(f"Progress: {processed}/{total} processed, {errors} errors")
-
-            # Rate limit: wait between batches to avoid hitting API limits
-            if i + batch_size < total:
-                await asyncio.sleep(BATCH_DELAY_SECONDS)
-
-        logger.info(f"Backfill complete: {processed} processed, {errors} errors")
-        return (processed, errors)
-
-    finally:
-        await disconnect()
-
-
-def main():
-    parser = argparse.ArgumentParser(
-        description="Backfill embeddings for store listing versions"
-    )
-    parser.add_argument(
-        "--dry-run",
-        action="store_true",
-        help="Don't make any changes, just report what would be done",
-    )
-    parser.add_argument(
-        "--batch-size",
-        type=int,
-        default=DEFAULT_BATCH_SIZE,
-        help=f"Number of versions to process in each batch (default: {DEFAULT_BATCH_SIZE})",
-    )
-
-    args = parser.parse_args()
-
-    try:
-        processed, errors = asyncio.run(
-            backfill_embeddings(dry_run=args.dry_run, batch_size=args.batch_size)
-        )
-
-        if errors > 0:
-            logger.warning(f"Completed with {errors} errors")
-            sys.exit(1)
-        else:
-            logger.info("Completed successfully")
-            sys.exit(0)
-
-    except KeyboardInterrupt:
-        logger.info("Interrupted by user")
-        sys.exit(130)
-    except Exception as e:
-        logger.error(f"Fatal error: {e}")
-        sys.exit(1)
-
-
-if __name__ == "__main__":
-    main()

autogpt_platform/backend/backend/server/v2/store/cache.py

@@ -27,9 +27,8 @@ async def _get_cached_store_agents(
     category: str | None,
     page: int,
     page_size: int,
-    filter_mode: Literal["strict", "permissive", "combined"] = "permissive",
 ):
-    """Cached helper to get store agents with hybrid search support."""
+    """Cached helper to get store agents."""
     return await backend.server.v2.store.db.get_store_agents(
         featured=featured,
         creators=[creator] if creator else None,
@@ -38,7 +37,6 @@ async def _get_cached_store_agents(
         category=category,
         page=page,
         page_size=page_size,
-        filter_mode=filter_mode,
     )
 
 

autogpt_platform/backend/backend/server/v2/store/db.py

@@ -26,7 +26,6 @@ from backend.data.notifications import (
     AgentRejectionData,
     NotificationEventModel,
 )
-from backend.integrations.embeddings import create_search_text, get_embedding_service
 from backend.notifications.notifications import queue_notification_async
 from backend.util.exceptions import DatabaseError
 from backend.util.settings import Settings
@@ -39,25 +38,6 @@ settings = Settings()
 DEFAULT_ADMIN_NAME = "AutoGPT Admin"
 DEFAULT_ADMIN_EMAIL = "admin@autogpt.co"
 
-# Minimum similarity threshold for vector search results
-# Cosine similarity ranges from -1 to 1, where 1 is identical
-# 0.4 filters loosely related results while keeping semantically relevant ones
-VECTOR_SEARCH_SIMILARITY_THRESHOLD = 0.4
-
-# Minimum relevance threshold for BM25 full-text search results
-# ts_rank_cd returns values typically in range 0-1 (can exceed 1 for exact matches)
-# 0.05 allows partial keyword matches
-BM25_RELEVANCE_THRESHOLD = 0.05
-
-# RRF constant (k) - standard value that balances influence of top vs lower ranks
-# Higher k values reduce the influence of high-ranking items
-RRF_K = 60
-
-# Minimum RRF score threshold for combined mode
-# Filters out results that rank poorly across all signals
-# For reference: rank #1 in all = ~0.041, rank #100 in all = ~0.016
-RRF_SCORE_THRESHOLD = 0.02
-
 
 async def get_store_agents(
     featured: bool = False,
@@ -67,223 +47,64 @@ async def get_store_agents(
     category: str | None = None,
     page: int = 1,
     page_size: int = 20,
-    filter_mode: Literal["strict", "permissive", "combined"] = "permissive",
 ) -> backend.server.v2.store.model.StoreAgentsResponse:
     """
-    Get PUBLIC store agents from the StoreAgent view.
-
-    When search_query is provided, uses hybrid search combining:
-    - BM25 full-text search (lexical matching via PostgreSQL tsvector)
-    - Vector semantic similarity (meaning-based matching via pgvector)
-    - Popularity signal (run counts as PageRank proxy)
-
-    Results are ranked using Reciprocal Rank Fusion (RRF).
-
-    Args:
-        featured: Filter to only show featured agents.
-        creators: Filter agents by creator usernames.
-        sorted_by: Sort agents by "runs", "rating", "name", or "updated_at".
-        search_query: Search query for hybrid search.
-        category: Filter agents by category.
-        page: Page number for pagination.
-        page_size: Number of agents per page.
-        filter_mode: Controls how results are filtered when searching:
-            - "strict": Must match BOTH BM25 AND vector thresholds
-            - "permissive": Must match EITHER BM25 OR vector threshold
-            - "combined": No threshold filtering, rely on RRF score (default)
-
-    Returns:
-        StoreAgentsResponse with paginated list of agents.
+    Get PUBLIC store agents from the StoreAgent view
     """
     logger.debug(
-        f"Getting store agents. featured={featured}, creators={creators}, "
-        f"sorted_by={sorted_by}, search={search_query}, category={category}, "
-        f"page={page}, filter_mode={filter_mode}"
+        f"Getting store agents. featured={featured}, creators={creators}, sorted_by={sorted_by}, search={search_query}, category={category}, page={page}"
     )
 
     try:
-        # If search_query is provided, use hybrid search (BM25 + vector + popularity)
+        # If search_query is provided, use full-text search
         if search_query:
             offset = (page - 1) * page_size
 
-            # Try to generate embedding for vector search
-            # Falls back to BM25-only if embedding service is not available
-            query_embedding: list[float] | None = None
-            try:
-                embedding_service = get_embedding_service()
-                query_embedding = await embedding_service.generate_embedding(
-                    search_query
-                )
-            except (ValueError, Exception) as e:
-                # Embedding service not configured or failed - use BM25 only
-                logger.warning(f"Embedding generation failed, using BM25 only: {e}")
+            # Whitelist allowed order_by columns
+            ALLOWED_ORDER_BY = {
+                "rating": "rating DESC, rank DESC",
+                "runs": "runs DESC, rank DESC",
+                "name": "agent_name ASC, rank ASC",
+                "updated_at": "updated_at DESC, rank DESC",
+            }
 
-            # Convert embedding to PostgreSQL array format (or None for BM25-only)
-            embedding_str = (
-                "[" + ",".join(map(str, query_embedding)) + "]"
-                if query_embedding
-                else None
-            )
+            # Validate and get order clause
+            if sorted_by and sorted_by in ALLOWED_ORDER_BY:
+                order_by_clause = ALLOWED_ORDER_BY[sorted_by]
+            else:
+                order_by_clause = "updated_at DESC, rank DESC"
 
             # Build WHERE conditions and parameters list
-            # When embedding is not available (no OpenAI key), $1 will be NULL
             where_parts: list[str] = []
-            params: list[typing.Any] = [embedding_str]  # $1 - query embedding (or NULL)
+            params: list[typing.Any] = [search_query]  # $1 - search term
             param_index = 2  # Start at $2 for next parameter
 
             # Always filter for available agents
             where_parts.append("is_available = true")
 
-            # Require search signals to be present
-            if embedding_str is None:
-                # No embedding available - require BM25 search only
-                where_parts.append("search IS NOT NULL")
-            elif filter_mode == "strict":
-                # Strict mode: require both embedding AND search to be available
-                where_parts.append("embedding IS NOT NULL")
-                where_parts.append("search IS NOT NULL")
-            else:
-                # Permissive/combined: require at least one signal
-                where_parts.append("(embedding IS NOT NULL OR search IS NOT NULL)")
-
             if featured:
                 where_parts.append("featured = true")
 
-            if creators:
+            if creators and creators:
                 # Use ANY with array parameter
                 where_parts.append(f"creator_username = ANY(${param_index})")
                 params.append(creators)
                 param_index += 1
 
-            if category:
+            if category and category:
                 where_parts.append(f"${param_index} = ANY(categories)")
                 params.append(category)
                 param_index += 1
 
-            # Add search query for BM25
-            params.append(search_query)
-            bm25_query_param = f"${param_index}"
-            param_index += 1
-
             sql_where_clause: str = " AND ".join(where_parts) if where_parts else "1=1"
 
-            # Build score filter based on filter_mode
-            # This filter is applied BEFORE RRF ranking in the filtered_agents CTE
-            if embedding_str is None:
-                # No embedding - filter only on BM25 score
-                score_filter = f"bm25_score >= {BM25_RELEVANCE_THRESHOLD}"
-            elif filter_mode == "strict":
-                score_filter = f"""
-                    bm25_score >= {BM25_RELEVANCE_THRESHOLD}
-                    AND vector_score >= {VECTOR_SEARCH_SIMILARITY_THRESHOLD}
-                """
-            elif filter_mode == "permissive":
-                score_filter = f"""
-                    bm25_score >= {BM25_RELEVANCE_THRESHOLD}
-                    OR vector_score >= {VECTOR_SEARCH_SIMILARITY_THRESHOLD}
-                """
-            else:  # combined - no pre-filtering on individual scores
-                score_filter = "1=1"
-
-            # RRF score filter is applied AFTER ranking to filter irrelevant results
-            rrf_score_filter = f"rrf_score >= {RRF_SCORE_THRESHOLD}"
-
-            # Build ORDER BY clause - sorted_by takes precedence, rrf_score as secondary
-            if sorted_by == "rating":
-                order_by_clause = "rating DESC, rrf_score DESC"
-            elif sorted_by == "runs":
-                order_by_clause = "runs DESC, rrf_score DESC"
-            elif sorted_by == "name":
-                order_by_clause = "agent_name ASC, rrf_score DESC"
-            elif sorted_by == "updated_at":
-                order_by_clause = "updated_at DESC, rrf_score DESC"
-            else:
-                # Default: order by RRF relevance score
-                order_by_clause = "rrf_score DESC, updated_at DESC"
-
             # Add pagination params
             params.extend([page_size, offset])
             limit_param = f"${param_index}"
             offset_param = f"${param_index + 1}"
 
-            # Hybrid search SQL with Reciprocal Rank Fusion (RRF)
-            # CTEs: scored_agents -> filtered_agents -> ranked_agents -> rrf_scored
+            # Execute full-text search query with parameterized values
             sql_query = f"""
-                WITH scored_agents AS (
-                    SELECT
-                        slug,
-                        agent_name,
-                        agent_image,
-                        creator_username,
-                        creator_avatar,
-                        sub_heading,
-                        description,
-                        runs,
-                        rating,
-                        categories,
-                        featured,
-                        is_available,
-                        updated_at,
-                        -- BM25 score using ts_rank_cd (covers density normalization)
-                        COALESCE(
-                            ts_rank_cd(
-                                search,
-                                plainto_tsquery('english', {bm25_query_param}),
-                                32  -- normalization: divide by document length
-                            ),
-                            0
-                        ) AS bm25_score,
-                        -- Vector similarity score (cosine: 1 - distance)
-                        -- Returns 0 when query embedding ($1) is NULL (no OpenAI key)
-                        CASE
-                            WHEN $1 IS NOT NULL AND embedding IS NOT NULL
-                            THEN 1 - (embedding <=> $1::vector)
-                            ELSE 0
-                        END AS vector_score,
-                        -- Popularity score (log-normalized run count)
-                        CASE
-                            WHEN runs > 0
-                            THEN LN(runs + 1)
-                            ELSE 0
-                        END AS popularity_score
-                    FROM {{schema_prefix}}"StoreAgent"
-                    WHERE {sql_where_clause}
-                ),
-                max_popularity AS (
-                    SELECT GREATEST(MAX(popularity_score), 1) AS max_pop
-                    FROM scored_agents
-                ),
-                normalized_agents AS (
-                    SELECT
-                        sa.*,
-                        -- Normalize popularity to [0, 1] range
-                        sa.popularity_score / mp.max_pop AS norm_popularity_score
-                    FROM scored_agents sa
-                    CROSS JOIN max_popularity mp
-                ),
-                filtered_agents AS (
-                    SELECT *
-                    FROM normalized_agents
-                    WHERE {score_filter}
-                ),
-                ranked_agents AS (
-                    SELECT
-                        *,
-                        ROW_NUMBER() OVER (ORDER BY bm25_score DESC NULLS LAST) AS bm25_rank,
-                        ROW_NUMBER() OVER (ORDER BY vector_score DESC NULLS LAST) AS vector_rank,
-                        ROW_NUMBER() OVER (ORDER BY norm_popularity_score DESC NULLS LAST) AS popularity_rank
-                    FROM filtered_agents
-                ),
-                rrf_scored AS (
-                    SELECT
-                        *,
-                        -- RRF formula with weighted contributions
-                        -- BM25 and vector get full weight, popularity gets 0.5x weight
-                        (1.0 / ({RRF_K} + bm25_rank)) +
-                        (1.0 / ({RRF_K} + vector_rank)) +
-                        (0.5 / ({RRF_K} + popularity_rank)) AS rrf_score
-                    FROM ranked_agents
-                )
                 SELECT
                     slug,
                     agent_name,
@@ -298,79 +119,25 @@ async def get_store_agents(
                     featured,
                     is_available,
                     updated_at,
-                    rrf_score
-                FROM rrf_scored
-                WHERE {rrf_score_filter}
+                    ts_rank_cd(search, query) AS rank
+                FROM {{schema_prefix}}"StoreAgent",
+                    plainto_tsquery('english', $1) AS query
+                WHERE {sql_where_clause}
+                    AND search @@ query
                 ORDER BY {order_by_clause}
                 LIMIT {limit_param} OFFSET {offset_param}
             """
 
-            # Count query (without pagination) - requires same CTE structure because:
-            # 1. RRF scoring requires computing ranks across ALL matching results
-            # 2. The rrf_score_filter threshold must be applied consistently
-            # Note: This is inherent to RRF - there's no way to count without ranking
+            # Count query for pagination - only uses search term parameter
             count_query = f"""
-                WITH scored_agents AS (
-                    SELECT
-                        runs,
-                        COALESCE(
-                            ts_rank_cd(
-                                search,
-                                plainto_tsquery('english', {bm25_query_param}),
-                                32
-                            ),
-                            0
-                        ) AS bm25_score,
-                        CASE
-                            WHEN $1 IS NOT NULL AND embedding IS NOT NULL
-                            THEN 1 - (embedding <=> $1::vector)
-                            ELSE 0
-                        END AS vector_score,
-                        CASE
-                            WHEN runs > 0
-                            THEN LN(runs + 1)
-                            ELSE 0
-                        END AS popularity_score
-                    FROM {{schema_prefix}}"StoreAgent"
-                    WHERE {sql_where_clause}
-                ),
-                max_popularity AS (
-                    SELECT GREATEST(MAX(popularity_score), 1) AS max_pop
-                    FROM scored_agents
-                ),
-                normalized_agents AS (
-                    SELECT
-                        sa.*,
-                        sa.popularity_score / mp.max_pop AS norm_popularity_score
-                    FROM scored_agents sa
-                    CROSS JOIN max_popularity mp
-                ),
-                filtered_agents AS (
-                    SELECT *
-                    FROM normalized_agents
-                    WHERE {score_filter}
-                ),
-                ranked_agents AS (
-                    SELECT
-                        *,
-                        ROW_NUMBER() OVER (ORDER BY bm25_score DESC NULLS LAST) AS bm25_rank,
-                        ROW_NUMBER() OVER (ORDER BY vector_score DESC NULLS LAST) AS vector_rank,
-                        ROW_NUMBER() OVER (ORDER BY norm_popularity_score DESC NULLS LAST) AS popularity_rank
-                    FROM filtered_agents
-                ),
-                rrf_scored AS (
-                    SELECT
-                        (1.0 / ({RRF_K} + bm25_rank)) +
-                        (1.0 / ({RRF_K} + vector_rank)) +
-                        (0.5 / ({RRF_K} + popularity_rank)) AS rrf_score
-                    FROM ranked_agents
-                )
                 SELECT COUNT(*) as count
-                FROM rrf_scored
-                WHERE {rrf_score_filter}
+                FROM {{schema_prefix}}"StoreAgent",
+                    plainto_tsquery('english', $1) AS query
+                WHERE {sql_where_clause}
+                    AND search @@ query
             """
 
-            # Execute queries
+            # Execute both queries with parameters
             agents = await query_raw_with_schema(sql_query, *params)
 
             # For count, use params without pagination (last 2 params)
@@ -488,56 +255,6 @@ async def log_search_term(search_query: str):
         logger.error(f"Error logging search term: {e}")
 
 
-async def _generate_and_store_embedding(
-    store_listing_version_id: str,
-    name: str,
-    sub_heading: str,
-    description: str,
-) -> None:
-    """
-    Generate and store embedding for a store listing version.
-
-    This creates a vector embedding from the agent's name, sub_heading, and
-    description, which is used for semantic search.
-
-    Args:
-        store_listing_version_id: The ID of the store listing version.
-        name: The agent name.
-        sub_heading: The agent sub-heading/tagline.
-        description: The agent description.
-    """
-    try:
-        embedding_service = get_embedding_service()
-        search_text = create_search_text(name, sub_heading, description)
-
-        if not search_text:
-            logger.warning(
-                f"No searchable text for version {store_listing_version_id}, "
-                "skipping embedding generation"
-            )
-            return
-
-        embedding = await embedding_service.generate_embedding(search_text)
-        embedding_str = "[" + ",".join(map(str, embedding)) + "]"
-
-        await query_raw_with_schema(
-            """
-            UPDATE {schema_prefix}"StoreListingVersion"
-            SET embedding = $1::vector
-            WHERE id = $2
-            """,
-            embedding_str,
-            store_listing_version_id,
-        )
-        logger.debug(f"Generated embedding for version {store_listing_version_id}")
-    except Exception as e:
-        # Log error but don't fail the whole operation
-        # Embeddings can be generated later via backfill
-        logger.error(
-            f"Failed to generate embedding for {store_listing_version_id}: {e}"
-        )
-
-
 async def get_store_agent_details(
     username: str, agent_name: str
 ) -> backend.server.v2.store.model.StoreAgentDetails:
@@ -1088,12 +805,6 @@ async def create_store_submission(
             else None
         )
 
-        # Generate embedding for semantic search
-        if store_listing_version_id:
-            await _generate_and_store_embedding(
-                store_listing_version_id, name, sub_heading, description
-            )
-
         logger.debug(f"Created store listing for agent {agent_id}")
         # Return submission details
         return backend.server.v2.store.model.StoreSubmission(
@@ -1259,12 +970,6 @@ async def edit_store_submission(
 
             if not updated_version:
                 raise DatabaseError("Failed to update store listing version")
-
-            # Regenerate embedding with updated content
-            await _generate_and_store_embedding(
-                store_listing_version_id, name, sub_heading, description
-            )
-
             return backend.server.v2.store.model.StoreSubmission(
                 agent_id=current_version.agentGraphId,
                 agent_version=current_version.agentGraphVersion,
@@ -1397,12 +1102,6 @@ async def create_store_version(
         logger.debug(
             f"Created new version for listing {store_listing_id} of agent {agent_id}"
         )
-
-        # Generate embedding for semantic search
-        await _generate_and_store_embedding(
-            new_version.id, name, sub_heading, description
-        )
-
         # Return submission details
         return backend.server.v2.store.model.StoreSubmission(
             agent_id=agent_id,

autogpt_platform/backend/backend/server/v2/store/db_test.py

@@ -405,347 +405,3 @@ async def test_get_store_agents_search_category_array_injection():
     # Verify the query executed without error
     # Category should be parameterized, preventing SQL injection
     assert isinstance(result.agents, list)
-
-
-# Hybrid search tests (BM25 + vector + popularity with RRF ranking)
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_get_store_agents_hybrid_search_mocked(mocker):
-    """Test hybrid search uses embedding service and executes query safely."""
-    from backend.integrations.embeddings import EMBEDDING_DIMENSIONS
-
-    # Mock embedding service
-    mock_embedding = [0.1] * EMBEDDING_DIMENSIONS
-    mock_embedding_service = mocker.MagicMock()
-    mock_embedding_service.generate_embedding = mocker.AsyncMock(
-        return_value=mock_embedding
-    )
-    mocker.patch(
-        "backend.server.v2.store.db.get_embedding_service",
-        mocker.MagicMock(return_value=mock_embedding_service),
-    )
-
-    # Mock query_raw_with_schema to return empty results
-    mocker.patch(
-        "backend.server.v2.store.db.query_raw_with_schema",
-        mocker.AsyncMock(side_effect=[[], [{"count": 0}]]),
-    )
-
-    # Call function with search query
-    result = await db.get_store_agents(search_query="test query")
-
-    # Verify embedding service was called
-    mock_embedding_service.generate_embedding.assert_called_once_with("test query")
-
-    # Verify results
-    assert isinstance(result.agents, list)
-    assert len(result.agents) == 0
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_get_store_agents_hybrid_search_with_results(mocker):
-    """Test hybrid search returns properly formatted results with RRF scoring."""
-    from backend.integrations.embeddings import EMBEDDING_DIMENSIONS
-
-    # Mock embedding service
-    mock_embedding = [0.1] * EMBEDDING_DIMENSIONS
-    mock_embedding_service = mocker.MagicMock()
-    mock_embedding_service.generate_embedding = mocker.AsyncMock(
-        return_value=mock_embedding
-    )
-    mocker.patch(
-        "backend.server.v2.store.db.get_embedding_service",
-        mocker.MagicMock(return_value=mock_embedding_service),
-    )
-
-    # Mock query results (hybrid search returns rrf_score instead of similarity)
-    mock_agents = [
-        {
-            "slug": "test-agent",
-            "agent_name": "Test Agent",
-            "agent_image": ["image.jpg"],
-            "creator_username": "creator",
-            "creator_avatar": "avatar.jpg",
-            "sub_heading": "Test heading",
-            "description": "Test description",
-            "runs": 10,
-            "rating": 4.5,
-            "categories": ["test"],
-            "featured": False,
-            "is_available": True,
-            "updated_at": datetime.now(),
-            "rrf_score": 0.048,  # RRF score from combined rankings
-        }
-    ]
-    mock_count = [{"count": 1}]
-
-    mocker.patch(
-        "backend.server.v2.store.db.query_raw_with_schema",
-        mocker.AsyncMock(side_effect=[mock_agents, mock_count]),
-    )
-
-    # Call function with search query
-    result = await db.get_store_agents(search_query="test query")
-
-    # Verify results
-    assert len(result.agents) == 1
-    assert result.agents[0].slug == "test-agent"
-    assert result.agents[0].agent_name == "Test Agent"
-    assert result.pagination.total_items == 1
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_get_store_agents_hybrid_search_with_filters(mocker):
-    """Test hybrid search works correctly with additional filters."""
-    from backend.integrations.embeddings import EMBEDDING_DIMENSIONS
-
-    # Mock embedding service
-    mock_embedding = [0.1] * EMBEDDING_DIMENSIONS
-    mock_embedding_service = mocker.MagicMock()
-    mock_embedding_service.generate_embedding = mocker.AsyncMock(
-        return_value=mock_embedding
-    )
-    mocker.patch(
-        "backend.server.v2.store.db.get_embedding_service",
-        mocker.MagicMock(return_value=mock_embedding_service),
-    )
-
-    # Mock query_raw_with_schema
-    mock_query = mocker.patch(
-        "backend.server.v2.store.db.query_raw_with_schema",
-        mocker.AsyncMock(side_effect=[[], [{"count": 0}]]),
-    )
-
-    # Call function with search query and filters
-    await db.get_store_agents(
-        search_query="test query",
-        featured=True,
-        creators=["creator1", "creator2"],
-        category="AI",
-    )
-
-    # Verify query was called with parameterized values
-    # First call is the main query, second is count
-    assert mock_query.call_count == 2
-
-    # Check that the SQL query includes proper parameterization
-    first_call_args = mock_query.call_args_list[0]
-    sql_query = first_call_args[0][0]
-
-    # Verify key elements of hybrid search query
-    assert "embedding <=> $1::vector" in sql_query  # Vector search
-    assert "ts_rank_cd" in sql_query  # BM25 search
-    assert "rrf_score" in sql_query  # RRF ranking
-    assert "featured = true" in sql_query
-    assert "creator_username = ANY($" in sql_query
-    assert "= ANY(categories)" in sql_query
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_get_store_agents_hybrid_search_strict_filter_mode(mocker):
-    """Test hybrid search with strict filter mode requires both BM25 and vector matches."""
-    from backend.integrations.embeddings import EMBEDDING_DIMENSIONS
-
-    # Mock embedding service
-    mock_embedding = [0.1] * EMBEDDING_DIMENSIONS
-    mock_embedding_service = mocker.MagicMock()
-    mock_embedding_service.generate_embedding = mocker.AsyncMock(
-        return_value=mock_embedding
-    )
-    mocker.patch(
-        "backend.server.v2.store.db.get_embedding_service",
-        mocker.MagicMock(return_value=mock_embedding_service),
-    )
-
-    # Mock query_raw_with_schema
-    mock_query = mocker.patch(
-        "backend.server.v2.store.db.query_raw_with_schema",
-        mocker.AsyncMock(side_effect=[[], [{"count": 0}]]),
-    )
-
-    # Call function with strict filter mode
-    await db.get_store_agents(search_query="test query", filter_mode="strict")
-
-    # Check that the SQL query includes strict filtering conditions
-    first_call_args = mock_query.call_args_list[0]
-    sql_query = first_call_args[0][0]
-
-    # Strict mode requires both embedding AND search to be present
-    assert "embedding IS NOT NULL" in sql_query
-    assert "search IS NOT NULL" in sql_query
-    # Strict score filter requires both thresholds to be met
-    assert "bm25_score >=" in sql_query
-    assert "AND vector_score >=" in sql_query
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_get_store_agents_hybrid_search_permissive_filter_mode(mocker):
-    """Test hybrid search with permissive filter mode requires either BM25 or vector match."""
-    from backend.integrations.embeddings import EMBEDDING_DIMENSIONS
-
-    # Mock embedding service
-    mock_embedding = [0.1] * EMBEDDING_DIMENSIONS
-    mock_embedding_service = mocker.MagicMock()
-    mock_embedding_service.generate_embedding = mocker.AsyncMock(
-        return_value=mock_embedding
-    )
-    mocker.patch(
-        "backend.server.v2.store.db.get_embedding_service",
-        mocker.MagicMock(return_value=mock_embedding_service),
-    )
-
-    # Mock query_raw_with_schema
-    mock_query = mocker.patch(
-        "backend.server.v2.store.db.query_raw_with_schema",
-        mocker.AsyncMock(side_effect=[[], [{"count": 0}]]),
-    )
-
-    # Call function with permissive filter mode
-    await db.get_store_agents(search_query="test query", filter_mode="permissive")
-
-    # Check that the SQL query includes permissive filtering conditions
-    first_call_args = mock_query.call_args_list[0]
-    sql_query = first_call_args[0][0]
-
-    # Permissive mode requires at least one signal
-    assert "(embedding IS NOT NULL OR search IS NOT NULL)" in sql_query
-    # Permissive score filter requires either threshold to be met
-    assert "OR vector_score >=" in sql_query
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_get_store_agents_hybrid_search_combined_filter_mode(mocker):
-    """Test hybrid search with combined filter mode (default) filters by RRF score."""
-    from backend.integrations.embeddings import EMBEDDING_DIMENSIONS
-
-    # Mock embedding service
-    mock_embedding = [0.1] * EMBEDDING_DIMENSIONS
-    mock_embedding_service = mocker.MagicMock()
-    mock_embedding_service.generate_embedding = mocker.AsyncMock(
-        return_value=mock_embedding
-    )
-    mocker.patch(
-        "backend.server.v2.store.db.get_embedding_service",
-        mocker.MagicMock(return_value=mock_embedding_service),
-    )
-
-    # Mock query_raw_with_schema
-    mock_query = mocker.patch(
-        "backend.server.v2.store.db.query_raw_with_schema",
-        mocker.AsyncMock(side_effect=[[], [{"count": 0}]]),
-    )
-
-    # Call function with combined filter mode (default)
-    await db.get_store_agents(search_query="test query", filter_mode="combined")
-
-    # Check that the SQL query includes combined filtering
-    first_call_args = mock_query.call_args_list[0]
-    sql_query = first_call_args[0][0]
-
-    # Combined mode requires at least one signal
-    assert "(embedding IS NOT NULL OR search IS NOT NULL)" in sql_query
-    # Combined mode uses "1=1" as pre-filter (no individual score filtering)
-    # But applies RRF score threshold to filter irrelevant results
-    assert "rrf_score" in sql_query
-    assert "rrf_score >=" in sql_query  # RRF threshold filter applied
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_generate_and_store_embedding_success(mocker):
-    """Test that embedding generation and storage works correctly."""
-    from backend.integrations.embeddings import EMBEDDING_DIMENSIONS
-
-    # Mock embedding service
-    mock_embedding = [0.1] * EMBEDDING_DIMENSIONS
-    mock_embedding_service = mocker.MagicMock()
-    mock_embedding_service.generate_embedding = mocker.AsyncMock(
-        return_value=mock_embedding
-    )
-    mocker.patch(
-        "backend.server.v2.store.db.get_embedding_service",
-        mocker.MagicMock(return_value=mock_embedding_service),
-    )
-
-    # Mock query_raw_with_schema
-    mock_query = mocker.patch(
-        "backend.server.v2.store.db.query_raw_with_schema",
-        mocker.AsyncMock(return_value=[]),
-    )
-
-    # Call the internal function
-    await db._generate_and_store_embedding(
-        store_listing_version_id="version-123",
-        name="Test Agent",
-        sub_heading="A test agent",
-        description="Does testing",
-    )
-
-    # Verify embedding service was called with combined text
-    mock_embedding_service.generate_embedding.assert_called_once_with(
-        "Test Agent A test agent Does testing"
-    )
-
-    # Verify database update was called
-    mock_query.assert_called_once()
-    call_args = mock_query.call_args
-    assert "UPDATE" in call_args[0][0]
-    assert "embedding = $1::vector" in call_args[0][0]
-    assert call_args[0][2] == "version-123"
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_generate_and_store_embedding_empty_text(mocker):
-    """Test that embedding is not generated for empty text."""
-    # Mock embedding service
-    mock_embedding_service = mocker.MagicMock()
-    mock_embedding_service.generate_embedding = mocker.AsyncMock()
-    mocker.patch(
-        "backend.server.v2.store.db.get_embedding_service",
-        mocker.MagicMock(return_value=mock_embedding_service),
-    )
-
-    # Mock query_raw_with_schema
-    mock_query = mocker.patch(
-        "backend.server.v2.store.db.query_raw_with_schema",
-        mocker.AsyncMock(return_value=[]),
-    )
-
-    # Call with empty fields
-    await db._generate_and_store_embedding(
-        store_listing_version_id="version-123",
-        name="",
-        sub_heading="",
-        description="",
-    )
-
-    # Verify embedding service was NOT called
-    mock_embedding_service.generate_embedding.assert_not_called()
-
-    # Verify database was NOT updated
-    mock_query.assert_not_called()
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_generate_and_store_embedding_handles_error(mocker):
-    """Test that embedding generation errors don't crash the operation."""
-    # Mock embedding service to raise an error
-    mock_embedding_service = mocker.MagicMock()
-    mock_embedding_service.generate_embedding = mocker.AsyncMock(
-        side_effect=Exception("API error")
-    )
-    mocker.patch(
-        "backend.server.v2.store.db.get_embedding_service",
-        mocker.MagicMock(return_value=mock_embedding_service),
-    )
-
-    # Call should not raise - errors are logged but not propagated
-    await db._generate_and_store_embedding(
-        store_listing_version_id="version-123",
-        name="Test Agent",
-        sub_heading="A test agent",
-        description="Does testing",
-    )
-
-    # Verify embedding service was called (and failed)
-    mock_embedding_service.generate_embedding.assert_called_once()

autogpt_platform/backend/backend/server/v2/store/model.py

@@ -1,5 +1,4 @@
 import datetime
-from enum import Enum
 from typing import List
 
 import prisma.enums
@@ -8,19 +7,6 @@ import pydantic
 from backend.util.models import Pagination
 
 
-class SearchFilterMode(str, Enum):
-    """How to combine BM25 and vector search results for filtering.
-
-    - STRICT: Must pass BOTH BM25 AND vector similarity thresholds
-    - PERMISSIVE: Must pass EITHER BM25 OR vector similarity threshold
-    - COMBINED: No pre-filtering, only the combined RRF score matters (default)
-    """
-
-    STRICT = "strict"
-    PERMISSIVE = "permissive"
-    COMBINED = "combined"
-
-
 class MyAgent(pydantic.BaseModel):
     agent_id: str
     agent_version: int

autogpt_platform/backend/backend/server/v2/store/routes.py

@@ -99,30 +99,18 @@ async def get_agents(
     category: str | None = None,
     page: int = 1,
     page_size: int = 20,
-    filter_mode: Literal["strict", "permissive", "combined"] = "permissive",
 ):
     """
     Get a paginated list of agents from the store with optional filtering and sorting.
 
-    When search_query is provided, uses hybrid search combining:
-    - BM25 full-text search (lexical matching)
-    - Vector semantic similarity (meaning-based matching)
-    - Popularity signal (run counts)
-
-    Results are ranked using Reciprocal Rank Fusion (RRF).
-
     Args:
         featured (bool, optional): Filter to only show featured agents. Defaults to False.
         creator (str | None, optional): Filter agents by creator username. Defaults to None.
         sorted_by (str | None, optional): Sort agents by "runs" or "rating". Defaults to None.
-        search_query (str | None, optional): Search agents by name, subheading and description.
+        search_query (str | None, optional): Search agents by name, subheading and description. Defaults to None.
         category (str | None, optional): Filter agents by category. Defaults to None.
         page (int, optional): Page number for pagination. Defaults to 1.
         page_size (int, optional): Number of agents per page. Defaults to 20.
-        filter_mode (str, optional): Controls result filtering when searching:
-            - "strict": Must match BOTH BM25 AND vector thresholds
-            - "permissive": Must match EITHER BM25 OR vector threshold
-            - "combined": No threshold filtering, rely on RRF score (default)
 
     Returns:
         StoreAgentsResponse: Paginated list of agents matching the filters
@@ -156,7 +144,6 @@ async def get_agents(
         category=category,
         page=page,
         page_size=page_size,
-        filter_mode=filter_mode,
     )
     return agents
 

autogpt_platform/backend/backend/server/v2/store/routes_test.py

@@ -65,7 +65,6 @@ def test_get_agents_defaults(
         category=None,
         page=1,
         page_size=20,
-        filter_mode="permissive",
     )
 
 
@@ -113,7 +112,6 @@ def test_get_agents_featured(
         category=None,
         page=1,
         page_size=20,
-        filter_mode="permissive",
     )
 
 
@@ -161,7 +159,6 @@ def test_get_agents_by_creator(
         category=None,
         page=1,
         page_size=20,
-        filter_mode="permissive",
     )
 
 
@@ -209,7 +206,6 @@ def test_get_agents_sorted(
         category=None,
         page=1,
         page_size=20,
-        filter_mode="permissive",
     )
 
 
@@ -257,7 +253,6 @@ def test_get_agents_search(
         category=None,
         page=1,
         page_size=20,
-        filter_mode="permissive",
     )
 
 
@@ -304,7 +299,6 @@ def test_get_agents_category(
         category="test-category",
         page=1,
         page_size=20,
-        filter_mode="permissive",
     )
 
 
@@ -354,7 +348,6 @@ def test_get_agents_pagination(
         category=None,
         page=2,
         page_size=5,
-        filter_mode="permissive",
     )
 
 

autogpt_platform/backend/backend/util/prompt.py

@@ -5,13 +5,6 @@ from tiktoken import encoding_for_model
 
 from backend.util import json
 
-# ---------------------------------------------------------------------------#
-#  CONSTANTS                                                                 #
-# ---------------------------------------------------------------------------#
-
-# Message prefixes for important system messages that should be protected during compression
-MAIN_OBJECTIVE_PREFIX = "[Main Objective Prompt]: "
-
 # ---------------------------------------------------------------------------#
 #  INTERNAL UTILITIES                                                         #
 # ---------------------------------------------------------------------------#
@@ -70,55 +63,6 @@ def _msg_tokens(msg: dict, enc) -> int:
     return WRAPPER + content_tokens + tool_call_tokens
 
 
-def _is_tool_message(msg: dict) -> bool:
-    """Check if a message contains tool calls or results that should be protected."""
-    content = msg.get("content")
-
-    # Check for Anthropic-style tool messages
-    if isinstance(content, list) and any(
-        isinstance(item, dict) and item.get("type") in ("tool_use", "tool_result")
-        for item in content
-    ):
-        return True
-
-    # Check for OpenAI-style tool calls in the message
-    if "tool_calls" in msg or msg.get("role") == "tool":
-        return True
-
-    return False
-
-
-def _is_objective_message(msg: dict) -> bool:
-    """Check if a message contains objective/system prompts that should be absolutely protected."""
-    content = msg.get("content", "")
-    if isinstance(content, str):
-        # Protect any message with the main objective prefix
-        return content.startswith(MAIN_OBJECTIVE_PREFIX)
-    return False
-
-
-def _truncate_tool_message_content(msg: dict, enc, max_tokens: int) -> None:
-    """
-    Carefully truncate tool message content while preserving tool structure.
-    Only truncates tool_result content, leaves tool_use intact.
-    """
-    content = msg.get("content")
-    if not isinstance(content, list):
-        return
-
-    for item in content:
-        # Only process tool_result items, leave tool_use blocks completely intact
-        if not (isinstance(item, dict) and item.get("type") == "tool_result"):
-            continue
-
-        result_content = item.get("content", "")
-        if (
-            isinstance(result_content, str)
-            and _tok_len(result_content, enc) > max_tokens
-        ):
-            item["content"] = _truncate_middle_tokens(result_content, enc, max_tokens)
-
-
 def _truncate_middle_tokens(text: str, enc, max_tok: int) -> str:
     """
     Return *text* shortened to ≈max_tok tokens by keeping the head & tail
@@ -196,21 +140,13 @@ def compress_prompt(
         return sum(_msg_tokens(m, enc) for m in msgs)
 
     original_token_count = total_tokens()
-
     if original_token_count + reserve <= target_tokens:
         return msgs
 
     # ---- STEP 0 : normalise content --------------------------------------
     # Convert non-string payloads to strings so token counting is coherent.
-    for i, m in enumerate(msgs):
+    for m in msgs[1:-1]:  # keep the first & last intact
         if not isinstance(m.get("content"), str) and m.get("content") is not None:
-            if _is_tool_message(m):
-                continue
-
-            # Keep first and last messages intact (unless they're tool messages)
-            if i == 0 or i == len(msgs) - 1:
-                continue
-
             # Reasonable 20k-char ceiling prevents pathological blobs
             content_str = json.dumps(m["content"], separators=(",", ":"))
             if len(content_str) > 20_000:
@@ -221,45 +157,34 @@ def compress_prompt(
     cap = start_cap
     while total_tokens() + reserve > target_tokens and cap >= floor_cap:
         for m in msgs[1:-1]:  # keep first & last intact
-            if _is_tool_message(m):
-                # For tool messages, only truncate tool result content, preserve structure
-                _truncate_tool_message_content(m, enc, cap)
-                continue
-
-            if _is_objective_message(m):
-                # Never truncate objective messages - they contain the core task
-                continue
-
-            content = m.get("content") or ""
-            if _tok_len(content, enc) > cap:
-                m["content"] = _truncate_middle_tokens(content, enc, cap)
+            if _tok_len(m.get("content") or "", enc) > cap:
+                m["content"] = _truncate_middle_tokens(m["content"], enc, cap)
         cap //= 2  # tighten the screw
 
     # ---- STEP 2 : middle-out deletion -----------------------------------
     while total_tokens() + reserve > target_tokens and len(msgs) > 2:
-        # Identify all deletable messages (not first/last, not tool messages, not objective messages)
-        deletable_indices = []
-        for i in range(1, len(msgs) - 1):  # Skip first and last
-            if not _is_tool_message(msgs[i]) and not _is_objective_message(msgs[i]):
-                deletable_indices.append(i)
-
-        if not deletable_indices:
-            break  # nothing more we can drop
-
-        # Delete from center outward - find the index closest to center
         centre = len(msgs) // 2
-        to_delete = min(deletable_indices, key=lambda i: abs(i - centre))
-        del msgs[to_delete]
+        # Build a symmetrical centre-out index walk: centre, centre+1, centre-1, ...
+        order = [centre] + [
+            i
+            for pair in zip(range(centre + 1, len(msgs) - 1), range(centre - 1, 0, -1))
+            for i in pair
+        ]
+        removed = False
+        for i in order:
+            msg = msgs[i]
+            if "tool_calls" in msg or msg.get("role") == "tool":
+                continue  # protect tool shells
+            del msgs[i]
+            removed = True
+            break
+        if not removed:  # nothing more we can drop
+            break
 
     # ---- STEP 3 : final safety-net trim on first & last ------------------
     cap = start_cap
     while total_tokens() + reserve > target_tokens and cap >= floor_cap:
         for idx in (0, -1):  # first and last
-            if _is_tool_message(msgs[idx]):
-                # For tool messages at first/last position, truncate tool result content only
-                _truncate_tool_message_content(msgs[idx], enc, cap)
-                continue
-
             text = msgs[idx].get("content") or ""
             if _tok_len(text, enc) > cap:
                 msgs[idx]["content"] = _truncate_middle_tokens(text, enc, cap)

autogpt_platform/backend/migrations/20251204121212_add_vector_search/migration.sql

@@ -1,87 +0,0 @@
--- Migration: Replace full-text search with pgvector-based vector search
--- This migration:
--- 1. Enables the pgvector extension
--- 2. Drops the StoreAgent view (depends on search column)
--- 3. Removes the full-text search infrastructure (trigger, function, tsvector column)
--- 4. Adds a vector embedding column for semantic search
--- 5. Creates an index for fast vector similarity search
--- 6. Recreates the StoreAgent view with the embedding column
-
--- Enable pgvector extension
-CREATE EXTENSION IF NOT EXISTS vector;
-
--- First drop the view that depends on the search column
-DROP VIEW IF EXISTS "StoreAgent";
-
--- Add embedding column for vector search (1536 dimensions for text-embedding-3-small)
-ALTER TABLE "StoreListingVersion"
-ADD COLUMN IF NOT EXISTS "embedding" vector(1536);
-
--- Create IVFFlat index for fast similarity search
--- Using cosine distance (vector_cosine_ops) which is standard for text embeddings
--- lists = 100 is appropriate for datasets under 1M rows
-CREATE INDEX IF NOT EXISTS idx_store_listing_version_embedding
-ON "StoreListingVersion"
-USING ivfflat (embedding vector_cosine_ops)
-WITH (lists = 100);
-
--- Recreate StoreAgent view WITHOUT search column, WITH embedding column
-CREATE OR REPLACE VIEW "StoreAgent" AS
-WITH latest_versions AS (
-    SELECT
-        "storeListingId",
-        MAX(version) AS max_version
-    FROM "StoreListingVersion"
-    WHERE "submissionStatus" = 'APPROVED'
-    GROUP BY "storeListingId"
-),
-agent_versions AS (
-    SELECT
-        "storeListingId",
-        array_agg(DISTINCT version::text ORDER BY version::text) AS versions
-    FROM "StoreListingVersion"
-    WHERE "submissionStatus" = 'APPROVED'
-    GROUP BY "storeListingId"
-)
-SELECT
-    sl.id AS listing_id,
-    slv.id AS "storeListingVersionId",
-    slv."createdAt" AS updated_at,
-    sl.slug,
-    COALESCE(slv.name, '') AS agent_name,
-    slv."videoUrl" AS agent_video,
-    slv."agentOutputDemoUrl" AS agent_output_demo,
-    COALESCE(slv."imageUrls", ARRAY[]::text[]) AS agent_image,
-    slv."isFeatured" AS featured,
-    p.username AS creator_username,
-    p."avatarUrl" AS creator_avatar,
-    slv."subHeading" AS sub_heading,
-    slv.description,
-    slv.categories,
-    slv.search,
-    slv.embedding,
-    COALESCE(ar.run_count, 0::bigint) AS runs,
-    COALESCE(rs.avg_rating, 0.0)::double precision AS rating,
-    COALESCE(av.versions, ARRAY[slv.version::text]) AS versions,
-    COALESCE(sl."useForOnboarding", false) AS "useForOnboarding",
-    slv."isAvailable" AS is_available
-FROM "StoreListing" sl
-JOIN latest_versions lv
-    ON sl.id = lv."storeListingId"
-JOIN "StoreListingVersion" slv
-    ON slv."storeListingId" = lv."storeListingId"
-   AND slv.version = lv.max_version
-   AND slv."submissionStatus" = 'APPROVED'
-JOIN "AgentGraph" a
-    ON slv."agentGraphId" = a.id
-    AND slv."agentGraphVersion" = a.version
-LEFT JOIN "Profile" p
-    ON sl."owningUserId" = p."userId"
-LEFT JOIN "mv_review_stats" rs
-    ON sl.id = rs."storeListingId"
-LEFT JOIN "mv_agent_run_counts" ar
-    ON a.id = ar."agentGraphId"
-LEFT JOIN agent_versions av
-    ON sl.id = av."storeListingId"
-WHERE sl."isDeleted" = false
-  AND sl."hasApprovedVersion" = true;

autogpt_platform/backend/migrations/20251218183537_add_hybrid_search/migration.sql

@@ -1,35 +0,0 @@
--- Migration: Add hybrid search infrastructure (BM25 + vector + popularity)
--- This migration:
--- 1. Creates/updates the tsvector trigger with weighted fields
--- 2. Adds GIN index for full-text search performance
--- 3. Backfills existing records with tsvector data
-
--- Create or replace the trigger function with WEIGHTED tsvector
--- Weight A = name (highest priority), B = subHeading, C = description
-CREATE OR REPLACE FUNCTION update_tsvector_column() RETURNS TRIGGER AS $$
-BEGIN
-  NEW.search := setweight(to_tsvector('english', COALESCE(NEW.name, '')), 'A') ||
-                setweight(to_tsvector('english', COALESCE(NEW."subHeading", '')), 'B') ||
-                setweight(to_tsvector('english', COALESCE(NEW.description, '')), 'C');
-  RETURN NEW;
-END;
-$$ LANGUAGE plpgsql;
-
--- Drop and recreate trigger to ensure it's active with the updated function
-DROP TRIGGER IF EXISTS "update_tsvector" ON "StoreListingVersion";
-CREATE TRIGGER "update_tsvector"
-BEFORE INSERT OR UPDATE OF name, "subHeading", description ON "StoreListingVersion"
-FOR EACH ROW
-EXECUTE FUNCTION update_tsvector_column();
-
--- Create GIN index for full-text search performance
-CREATE INDEX IF NOT EXISTS idx_store_listing_version_search_gin
-ON "StoreListingVersion" USING GIN (search);
-
--- Backfill existing records with weighted tsvector
-UPDATE "StoreListingVersion"
-SET search = setweight(to_tsvector('english', COALESCE(name, '')), 'A') ||
-             setweight(to_tsvector('english', COALESCE("subHeading", '')), 'B') ||
-             setweight(to_tsvector('english', COALESCE(description, '')), 'C')
-WHERE search IS NULL
-   OR search = ''::tsvector;

autogpt_platform/backend/pyproject.toml

@@ -138,4 +138,3 @@ filterwarnings = [
 
 [tool.ruff]
 target-version = "py310"
-

autogpt_platform/backend/schema.prisma

@@ -727,7 +727,7 @@ view StoreAgent {
   sub_heading      String
   description      String
   categories       String[]
-  embedding        Unsupported("vector(1536)")?
+  search           Unsupported("tsvector")? @default(dbgenerated("''::tsvector"))
   runs             Int
   rating           Float
   versions         String[]
@@ -863,11 +863,7 @@ model StoreListingVersion {
   // Old versions can be made unavailable by the author if desired
   isAvailable Boolean @default(true)
 
-  // Full-text search tsvector column
-  search Unsupported("tsvector")?
-
-  // Vector embedding for semantic search
-  embedding Unsupported("vector(1536)")?
+  search Unsupported("tsvector")? @default(dbgenerated("''::tsvector"))
 
   // Version workflow state
   submissionStatus SubmissionStatus @default(DRAFT)

autogpt_platform/frontend/next.config.mjs

@@ -3,14 +3,6 @@ import { withSentryConfig } from "@sentry/nextjs";
 /** @type {import('next').NextConfig} */
 const nextConfig = {
   productionBrowserSourceMaps: true,
-  experimental: {
-    serverActions: {
-      bodySizeLimit: "256mb",
-    },
-    // Increase body size limit for API routes (file uploads) - 256MB to match backend limit
-    proxyClientMaxBodySize: "256mb",
-    middlewareClientMaxBodySize: "256mb",
-  },
   images: {
     domains: [
       // We dont need to maintain alphabetical order here

autogpt_platform/frontend/package.json

@@ -137,8 +137,9 @@
     "concurrently": "9.2.1",
     "cross-env": "10.1.0",
     "eslint": "8.57.1",
-    "eslint-config-next": "15.5.7",
+    "eslint-config-next": "15.5.2",
     "eslint-plugin-storybook": "9.1.5",
+    "import-in-the-middle": "1.14.2",
     "msw": "2.11.6",
     "msw-storybook-addon": "2.0.6",
     "orval": "7.13.0",
@@ -158,4 +159,3 @@
   },
   "packageManager": "pnpm@10.20.0+sha512.cf9998222162dd85864d0a8102e7892e7ba4ceadebbf5a31f9c2fce48dfce317a9c53b9f6464d1ef9042cba2e02ae02a9f7c143a2b438cd93c91840f0192b9dd"
 }
-

autogpt_platform/frontend/pnpm-lock.yaml

@@ -331,11 +331,14 @@ importers:
         specifier: 8.57.1
         version: 8.57.1
       eslint-config-next:
-        specifier: 15.5.7
-        version: 15.5.7(eslint@8.57.1)(typescript@5.9.3)
+        specifier: 15.5.2
+        version: 15.5.2(eslint@8.57.1)(typescript@5.9.3)
       eslint-plugin-storybook:
         specifier: 9.1.5
         version: 9.1.5(eslint@8.57.1)(storybook@9.1.5(@testing-library/dom@10.4.1)(msw@2.11.6(@types/node@24.10.0)(typescript@5.9.3))(prettier@3.6.2))(typescript@5.9.3)
+      import-in-the-middle:
+        specifier: 1.14.2
+        version: 1.14.2
       msw:
         specifier: 2.11.6
         version: 2.11.6(@types/node@24.10.0)(typescript@5.9.3)
@@ -983,15 +986,12 @@ packages:
   '@date-fns/tz@1.4.1':
     resolution: {integrity: sha512-P5LUNhtbj6YfI3iJjw5EL9eUAG6OitD0W3fWQcpQjDRc/QIsL0tRNuO1PcDvPccWL1fSTXXdE1ds+l95DV/OFA==}
 
-  '@emnapi/core@1.7.1':
-    resolution: {integrity: sha512-o1uhUASyo921r2XtHYOHy7gdkGLge8ghBEQHMWmyJFoXlpU58kIrhhN3w26lpQb6dspetweapMn2CSNwQ8I4wg==}
+  '@emnapi/core@1.5.0':
+    resolution: {integrity: sha512-sbP8GzB1WDzacS8fgNPpHlp6C9VZe+SJP3F90W9rLemaQj2PzIuTEl1qDOYQf58YIpyjViI24y9aPWCjEzY2cg==}
 
   '@emnapi/runtime@1.5.0':
     resolution: {integrity: sha512-97/BJ3iXHww3djw6hYIfErCZFee7qCtrneuLa20UXFCOTCfBM2cvQHjWJ2EG0s0MtdNwInarqCTz35i4wWXHsQ==}
 
-  '@emnapi/runtime@1.7.1':
-    resolution: {integrity: sha512-PVtJr5CmLwYAU9PZDMITZoR5iAOShYREoR45EyyLrbntV50mdePTgUn4AmOw90Ifcj+x2kRjdzr1HP3RrNiHGA==}
-
   '@emnapi/wasi-threads@1.1.0':
     resolution: {integrity: sha512-WI0DdZ8xFSbgMjR1sFsKABJ/C5OnRrjT06JXbZKexJGrDuPTzZdDYfFlsgcCXCyf+suG5QU2e/y1Wo2V/OapLQ==}
 
@@ -1329,10 +1329,6 @@ packages:
     resolution: {integrity: sha512-CCZCDJuduB9OUkFkY2IgppNZMi2lBQgD2qzwXkEia16cge2pijY/aXi96CJMquDMn3nJdlPV1A5KrJEXwfLNzQ==}
     engines: {node: ^12.0.0 || ^14.0.0 || >=16.0.0}
 
-  '@eslint-community/regexpp@4.12.2':
-    resolution: {integrity: sha512-EriSTlt5OC9/7SXkRSCAhfSxxoSUgBm33OH+IkwbdpgoqsSsUg7y3uh+IICI/Qg4BBWr3U2i39RpmycbxMq4ew==}
-    engines: {node: ^12.0.0 || ^14.0.0 || >=16.0.0}
-
   '@eslint/eslintrc@2.1.4':
     resolution: {integrity: sha512-269Z39MS6wVJtsoUl10L60WdkhJVdPG24Q4eZTH3nnF6lpvSShEK3wQjDX9JRWAUPvPh7COouPpU9IrqaZFvtQ==}
     engines: {node: ^12.22.0 || ^14.17.0 || >=16.0.0}
@@ -1609,8 +1605,8 @@ packages:
   '@next/env@15.4.10':
     resolution: {integrity: sha512-knhmoJ0Vv7VRf6pZEPSnciUG1S4bIhWx+qTYBW/AjxEtlzsiNORPk8sFDCEvqLfmKuey56UB9FL1UdHEV3uBrg==}
 
-  '@next/eslint-plugin-next@15.5.7':
-    resolution: {integrity: sha512-DtRU2N7BkGr8r+pExfuWHwMEPX5SD57FeA6pxdgCHODo+b/UgIgjE+rgWKtJAbEbGhVZ2jtHn4g3wNhWFoNBQQ==}
+  '@next/eslint-plugin-next@15.5.2':
+    resolution: {integrity: sha512-lkLrRVxcftuOsJNhWatf1P2hNVfh98k/omQHrCEPPriUypR6RcS13IvLdIrEvkm9AH2Nu2YpR5vLqBuy6twH3Q==}
 
   '@next/swc-darwin-arm64@15.4.8':
     resolution: {integrity: sha512-Pf6zXp7yyQEn7sqMxur6+kYcywx5up1J849psyET7/8pG2gQTVMjU3NzgIt8SeEP5to3If/SaWmaA6H6ysBr1A==}
@@ -2626,8 +2622,8 @@ packages:
   '@rtsao/scc@1.1.0':
     resolution: {integrity: sha512-zt6OdqaDoOnJ1ZYsCYGt9YmWzDXl4vQdKTyJev62gFhRGKdx7mcT54V9KIjg+d2wi9EXsPvAPKe7i7WjfVWB8g==}
 
-  '@rushstack/eslint-patch@1.15.0':
-    resolution: {integrity: sha512-ojSshQPKwVvSMR8yT2L/QtUkV5SXi/IfDiJ4/8d6UbTPjiHVmxZzUAzGD8Tzks1b9+qQkZa0isUOvYObedITaw==}
+  '@rushstack/eslint-patch@1.12.0':
+    resolution: {integrity: sha512-5EwMtOqvJMMa3HbmxLlF74e+3/HhwBTMcvt3nqVJgGCozO6hzIPOBlwm8mGVNR9SN2IJpxSnlxczyDjcn7qIyw==}
 
   '@scarf/scarf@1.4.0':
     resolution: {integrity: sha512-xxeapPiUXdZAE3che6f3xogoJPeZgig6omHEy1rIY5WVsB3H2BHNnZH+gHG6x91SCWyQCzWGsuL2Hh3ClO5/qQ==}
@@ -3101,8 +3097,8 @@ packages:
     peerDependencies:
       '@testing-library/dom': '>=7.21.4'
 
-  '@tybys/wasm-util@0.10.1':
-    resolution: {integrity: sha512-9tTaPJLSiejZKx+Bmog4uSubteqTvFrVrURwkmHixBo0G4seD0zUxp98E1DzUBJxLQ3NPwXrGKDiVjwx/DpPsg==}
+  '@tybys/wasm-util@0.10.0':
+    resolution: {integrity: sha512-VyyPYFlOMNylG45GoAe0xDoLwWuowvf92F9kySqzYh8vmYm7D2u4iUJKa1tOUpS70Ku13ASrOkS4ScXFsTaCNQ==}
 
   '@types/aria-query@5.0.4':
     resolution: {integrity: sha512-rfT93uj5s0PRL7EzccGMs3brplhcrghnDoV26NqKhCAS1hVo+WdNsPvE/yb6ilfr5hi2MEk6d5EWJTKdxg8jVw==}
@@ -3292,16 +3288,16 @@ packages:
   '@types/ws@8.18.1':
     resolution: {integrity: sha512-ThVF6DCVhA8kUGy+aazFQ4kXQ7E1Ty7A3ypFOe0IcJV8O/M511G99AW24irKrW56Wt44yG9+ij8FaqoBGkuBXg==}
 
-  '@typescript-eslint/eslint-plugin@8.48.1':
-    resolution: {integrity: sha512-X63hI1bxl5ohelzr0LY5coufyl0LJNthld+abwxpCoo6Gq+hSqhKwci7MUWkXo67mzgUK6YFByhmaHmUcuBJmA==}
+  '@typescript-eslint/eslint-plugin@8.43.0':
+    resolution: {integrity: sha512-8tg+gt7ENL7KewsKMKDHXR1vm8tt9eMxjJBYINf6swonlWgkYn5NwyIgXpbbDxTNU5DgpDFfj95prcTq2clIQQ==}
     engines: {node: ^18.18.0 || ^20.9.0 || >=21.1.0}
     peerDependencies:
-      '@typescript-eslint/parser': ^8.48.1
+      '@typescript-eslint/parser': ^8.43.0
       eslint: ^8.57.0 || ^9.0.0
       typescript: '>=4.8.4 <6.0.0'
 
-  '@typescript-eslint/parser@8.48.1':
-    resolution: {integrity: sha512-PC0PDZfJg8sP7cmKe6L3QIL8GZwU5aRvUFedqSIpw3B+QjRSUZeeITC2M5XKeMXEzL6wccN196iy3JLwKNvDVA==}
+  '@typescript-eslint/parser@8.43.0':
+    resolution: {integrity: sha512-B7RIQiTsCBBmY+yW4+ILd6mF5h1FUwJsVvpqkrgpszYifetQ2Ke+Z4u6aZh0CblkUGIdR59iYVyXqqZGkZ3aBw==}
     engines: {node: ^18.18.0 || ^20.9.0 || >=21.1.0}
     peerDependencies:
       eslint: ^8.57.0 || ^9.0.0
@@ -3319,12 +3315,6 @@ packages:
     peerDependencies:
       typescript: '>=4.8.4 <6.0.0'
 
-  '@typescript-eslint/project-service@8.48.1':
-    resolution: {integrity: sha512-HQWSicah4s9z2/HifRPQ6b6R7G+SBx64JlFQpgSSHWPKdvCZX57XCbszg/bapbRsOEv42q5tayTYcEFpACcX1w==}
-    engines: {node: ^18.18.0 || ^20.9.0 || >=21.1.0}
-    peerDependencies:
-      typescript: '>=4.8.4 <6.0.0'
-
   '@typescript-eslint/scope-manager@8.43.0':
     resolution: {integrity: sha512-daSWlQ87ZhsjrbMLvpuuMAt3y4ba57AuvadcR7f3nl8eS3BjRc8L9VLxFLk92RL5xdXOg6IQ+qKjjqNEimGuAg==}
     engines: {node: ^18.18.0 || ^20.9.0 || >=21.1.0}
@@ -3333,10 +3323,6 @@ packages:
     resolution: {integrity: sha512-LF4b/NmGvdWEHD2H4MsHD8ny6JpiVNDzrSZr3CsckEgCbAGZbYM4Cqxvi9L+WqDMT+51Ozy7lt2M+d0JLEuBqA==}
     engines: {node: ^18.18.0 || ^20.9.0 || >=21.1.0}
 
-  '@typescript-eslint/scope-manager@8.48.1':
-    resolution: {integrity: sha512-rj4vWQsytQbLxC5Bf4XwZ0/CKd362DkWMUkviT7DCS057SK64D5lH74sSGzhI6PDD2HCEq02xAP9cX68dYyg1w==}
-    engines: {node: ^18.18.0 || ^20.9.0 || >=21.1.0}
-
   '@typescript-eslint/tsconfig-utils@8.43.0':
     resolution: {integrity: sha512-ALC2prjZcj2YqqL5X/bwWQmHA2em6/94GcbB/KKu5SX3EBDOsqztmmX1kMkvAJHzxk7TazKzJfFiEIagNV3qEA==}
     engines: {node: ^18.18.0 || ^20.9.0 || >=21.1.0}
@@ -3349,14 +3335,8 @@ packages:
     peerDependencies:
       typescript: '>=4.8.4 <6.0.0'
 
-  '@typescript-eslint/tsconfig-utils@8.48.1':
-    resolution: {integrity: sha512-k0Jhs4CpEffIBm6wPaCXBAD7jxBtrHjrSgtfCjUvPp9AZ78lXKdTR8fxyZO5y4vWNlOvYXRtngSZNSn+H53Jkw==}
-    engines: {node: ^18.18.0 || ^20.9.0 || >=21.1.0}
-    peerDependencies:
-      typescript: '>=4.8.4 <6.0.0'
-
-  '@typescript-eslint/type-utils@8.48.1':
-    resolution: {integrity: sha512-1jEop81a3LrJQLTf/1VfPQdhIY4PlGDBc/i67EVWObrtvcziysbLN3oReexHOM6N3jyXgCrkBsZpqwH0hiDOQg==}
+  '@typescript-eslint/type-utils@8.43.0':
+    resolution: {integrity: sha512-qaH1uLBpBuBBuRf8c1mLJ6swOfzCXryhKND04Igr4pckzSEW9JX5Aw9AgW00kwfjWJF0kk0ps9ExKTfvXfw4Qg==}
     engines: {node: ^18.18.0 || ^20.9.0 || >=21.1.0}
     peerDependencies:
       eslint: ^8.57.0 || ^9.0.0
@@ -3370,10 +3350,6 @@ packages:
     resolution: {integrity: sha512-lNCWCbq7rpg7qDsQrd3D6NyWYu+gkTENkG5IKYhUIcxSb59SQC/hEQ+MrG4sTgBVghTonNWq42bA/d4yYumldQ==}
     engines: {node: ^18.18.0 || ^20.9.0 || >=21.1.0}
 
-  '@typescript-eslint/types@8.48.1':
-    resolution: {integrity: sha512-+fZ3LZNeiELGmimrujsDCT4CRIbq5oXdHe7chLiW8qzqyPMnn1puNstCrMNVAqwcl2FdIxkuJ4tOs/RFDBVc/Q==}
-    engines: {node: ^18.18.0 || ^20.9.0 || >=21.1.0}
-
   '@typescript-eslint/typescript-estree@8.43.0':
     resolution: {integrity: sha512-7Vv6zlAhPb+cvEpP06WXXy/ZByph9iL6BQRBDj4kmBsW98AqEeQHlj/13X+sZOrKSo9/rNKH4Ul4f6EICREFdw==}
     engines: {node: ^18.18.0 || ^20.9.0 || >=21.1.0}
@@ -3386,12 +3362,6 @@ packages:
     peerDependencies:
       typescript: '>=4.8.4 <6.0.0'
 
-  '@typescript-eslint/typescript-estree@8.48.1':
-    resolution: {integrity: sha512-/9wQ4PqaefTK6POVTjJaYS0bynCgzh6ClJHGSBj06XEHjkfylzB+A3qvyaXnErEZSaxhIo4YdyBgq6j4RysxDg==}
-    engines: {node: ^18.18.0 || ^20.9.0 || >=21.1.0}
-    peerDependencies:
-      typescript: '>=4.8.4 <6.0.0'
-
   '@typescript-eslint/utils@8.43.0':
     resolution: {integrity: sha512-S1/tEmkUeeswxd0GGcnwuVQPFWo8NzZTOMxCvw8BX7OMxnNae+i8Tm7REQen/SwUIPoPqfKn7EaZ+YLpiB3k9g==}
     engines: {node: ^18.18.0 || ^20.9.0 || >=21.1.0}
@@ -3406,13 +3376,6 @@ packages:
       eslint: ^8.57.0 || ^9.0.0
       typescript: '>=4.8.4 <6.0.0'
 
-  '@typescript-eslint/utils@8.48.1':
-    resolution: {integrity: sha512-fAnhLrDjiVfey5wwFRwrweyRlCmdz5ZxXz2G/4cLn0YDLjTapmN4gcCsTBR1N2rWnZSDeWpYtgLDsJt+FpmcwA==}
-    engines: {node: ^18.18.0 || ^20.9.0 || >=21.1.0}
-    peerDependencies:
-      eslint: ^8.57.0 || ^9.0.0
-      typescript: '>=4.8.4 <6.0.0'
-
   '@typescript-eslint/visitor-keys@8.43.0':
     resolution: {integrity: sha512-T+S1KqRD4sg/bHfLwrpF/K3gQLBM1n7Rp7OjjikjTEssI2YJzQpi5WXoynOaQ93ERIuq3O8RBTOUYDKszUCEHw==}
     engines: {node: ^18.18.0 || ^20.9.0 || >=21.1.0}
@@ -3421,10 +3384,6 @@ packages:
     resolution: {integrity: sha512-tUFMXI4gxzzMXt4xpGJEsBsTox0XbNQ1y94EwlD/CuZwFcQP79xfQqMhau9HsRc/J0cAPA/HZt1dZPtGn9V/7w==}
     engines: {node: ^18.18.0 || ^20.9.0 || >=21.1.0}
 
-  '@typescript-eslint/visitor-keys@8.48.1':
-    resolution: {integrity: sha512-BmxxndzEWhE4TIEEMBs8lP3MBWN3jFPs/p6gPm/wkv02o41hI6cq9AuSmGAaTTHPtA1FTi2jBre4A9rm5ZmX+Q==}
-    engines: {node: ^18.18.0 || ^20.9.0 || >=21.1.0}
-
   '@ungap/structured-clone@1.3.0':
     resolution: {integrity: sha512-WmoN8qaIAo7WTYWbAZuG8PYEhn5fkz7dZrqTBZ7dtt//lL2Gwms1IcnQ5yHqjDfX8Ft5j4YzDM23f87zBfDe9g==}
 
@@ -4626,8 +4585,8 @@ packages:
     resolution: {integrity: sha512-/veY75JbMK4j1yjvuUxuVsiS/hr/4iHs9FTT6cgTexxdE0Ly/glccBAkloH/DofkjRbZU3bnoj38mOmhkZ0lHw==}
     engines: {node: '>=12'}
 
-  eslint-config-next@15.5.7:
-    resolution: {integrity: sha512-nU/TRGHHeG81NeLW5DeQT5t6BDUqbpsNQTvef1ld/tqHT+/zTx60/TIhKnmPISTTe++DVo+DLxDmk4rnwHaZVw==}
+  eslint-config-next@15.5.2:
+    resolution: {integrity: sha512-3hPZghsLupMxxZ2ggjIIrat/bPniM2yRpsVPVM40rp8ZMzKWOJp2CGWn7+EzoV2ddkUr5fxNfHpF+wU1hGt/3g==}
     peerDependencies:
       eslint: ^7.23.0 || ^8.0.0 || ^9.0.0
       typescript: '>=3.3.1'
@@ -4959,10 +4918,6 @@ packages:
     peerDependencies:
       next: '>=13.2.0'
 
-  generator-function@2.0.1:
-    resolution: {integrity: sha512-SFdFmIJi+ybC0vjlHN0ZGVGHc3lgE0DxPAT0djjVg+kjOnSqclqmj0KQ7ykTOLP6YxoqOvuAODGdcHJn+43q3g==}
-    engines: {node: '>= 0.4'}
-
   gensync@1.0.0-beta.2:
     resolution: {integrity: sha512-3hN7NaskYvMDLQY55gnW3NQ+mesEAepTqlg+VEbj7zzqEMBVNhzcGYYeqFo/TlYz6eQiFcp1HcsCZO+nGgS8zg==}
     engines: {node: '>=6.9.0'}
@@ -4991,8 +4946,8 @@ packages:
     resolution: {integrity: sha512-w9UMqWwJxHNOvoNzSJ2oPF5wvYcvP7jUvYzhp67yEhTi17ZDBBC1z9pTdGuzjD+EFIqLSYRweZjqfiPzQ06Ebg==}
     engines: {node: '>= 0.4'}
 
-  get-tsconfig@4.13.0:
-    resolution: {integrity: sha512-1VKTZJCwBrvbd+Wn3AOgQP/2Av+TfTCOlE4AcRJE72W1ksZXbAx8PPBR9RzgTeSPzlPMHrbANMH3LbltH73wxQ==}
+  get-tsconfig@4.10.1:
+    resolution: {integrity: sha512-auHyJ4AgMz7vgS8Hp3N6HXSmlMdUyhSUrfBF16w153rxtLIEOE+HGqaBppczZvnHLqQJfiHotCYpNhl0lUROFQ==}
 
   github-slugger@2.0.0:
     resolution: {integrity: sha512-IaOQ9puYtjrkq7Y0Ygl9KDZnrf/aiUJYUpVf89y8kyaxbRG7Y1SrX/jaumrv81vc61+kiMempujsM3Yw7w5qcw==}
@@ -5213,6 +5168,9 @@ packages:
     resolution: {integrity: sha512-TR3KfrTZTYLPB6jUjfx6MF9WcWrHL9su5TObK4ZkYgBdWKPOFoSoQIdEuTuR82pmtxH2spWG9h6etwfr1pLBqQ==}
     engines: {node: '>=6'}
 
+  import-in-the-middle@1.14.2:
+    resolution: {integrity: sha512-5tCuY9BV8ujfOpwtAGgsTx9CGUapcFMEEyByLv1B+v2+6DhAcw+Zr0nhQT7uwaZ7DiourxFEscghOR8e1aPLQw==}
+
   import-in-the-middle@2.0.0:
     resolution: {integrity: sha512-yNZhyQYqXpkT0AKq3F3KLasUSK4fHvebNH5hOsKQw2dhGSALvQ4U0BqUc5suziKvydO5u5hgN2hy1RJaho8U5A==}
 
@@ -5324,10 +5282,6 @@ packages:
     resolution: {integrity: sha512-nPUB5km40q9e8UfN/Zc24eLlzdSf9OfKByBw9CIdw4H1giPMeA0OIJvbchsCu4npfI2QcMVBsGEBHKZ7wLTWmQ==}
     engines: {node: '>= 0.4'}
 
-  is-generator-function@1.1.2:
-    resolution: {integrity: sha512-upqt1SkGkODW9tsGNG5mtXTXtECizwtS2kA161M+gJPc1xdb/Ax629af6YrTwcOeQHbewrPNlE5Dx7kzvXTizA==}
-    engines: {node: '>= 0.4'}
-
   is-glob@4.0.3:
     resolution: {integrity: sha512-xelSayHH36ZgE7ZWhli7pW34hNbNl8Ojv5KVmkJD4hBdD3th8Tfk9vYasLM+mXWOZhFkgZfxhLSnrwRr4elSSg==}
     engines: {node: '>=0.10.0'}
@@ -5949,8 +5903,8 @@ packages:
     engines: {node: ^10 || ^12 || ^13.7 || ^14 || >=15.0.1}
     hasBin: true
 
-  napi-postinstall@0.3.4:
-    resolution: {integrity: sha512-PHI5f1O0EP5xJ9gQmFGMS6IZcrVvTjpXjz7Na41gTE7eE2hK11lg04CECCYEEjdc17EV4DO+fkGEtt7TpTaTiQ==}
+  napi-postinstall@0.3.3:
+    resolution: {integrity: sha512-uTp172LLXSxuSYHv/kou+f6KW3SMppU9ivthaVTXian9sOt3XM/zHYHpRZiLgQoxeWfYUnslNWQHF1+G71xcow==}
     engines: {node: ^12.20.0 || ^14.18.0 || >=16.0.0}
     hasBin: true
 
@@ -6815,11 +6769,6 @@ packages:
     engines: {node: '>= 0.4'}
     hasBin: true
 
-  resolve@1.22.11:
-    resolution: {integrity: sha512-RfqAvLnMl313r7c9oclB1HhUEAezcpLjz95wFH4LVuhk9JF/r22qmVP9AMmOU4vMX7Q8pN8jwNg/CSpdFnMjTQ==}
-    engines: {node: '>= 0.4'}
-    hasBin: true
-
   resolve@1.22.8:
     resolution: {integrity: sha512-oKWePCxqpd6FlLvGV1VU0x7bkPmmCNolxzjMf4NczoDnQcIWrAF+cPtZn5i6n+RfD2d9i0tzpKnG6Yk168yIyw==}
     hasBin: true
@@ -7909,7 +7858,7 @@ snapshots:
       '@babel/helper-plugin-utils': 7.27.1
       debug: 4.4.3
       lodash.debounce: 4.0.8
-      resolve: 1.22.11
+      resolve: 1.22.10
     transitivePeerDependencies:
       - supports-color
 
@@ -8601,7 +8550,7 @@ snapshots:
 
   '@date-fns/tz@1.4.1': {}
 
-  '@emnapi/core@1.7.1':
+  '@emnapi/core@1.5.0':
     dependencies:
       '@emnapi/wasi-threads': 1.1.0
       tslib: 2.8.1
@@ -8612,11 +8561,6 @@ snapshots:
       tslib: 2.8.1
     optional: true
 
-  '@emnapi/runtime@1.7.1':
-    dependencies:
-      tslib: 2.8.1
-    optional: true
-
   '@emnapi/wasi-threads@1.1.0':
     dependencies:
       tslib: 2.8.1
@@ -8795,8 +8739,6 @@ snapshots:
 
   '@eslint-community/regexpp@4.12.1': {}
 
-  '@eslint-community/regexpp@4.12.2': {}
-
   '@eslint/eslintrc@2.1.4':
     dependencies:
       ajv: 6.12.6
@@ -9054,16 +8996,16 @@ snapshots:
 
   '@napi-rs/wasm-runtime@0.2.12':
     dependencies:
-      '@emnapi/core': 1.7.1
-      '@emnapi/runtime': 1.7.1
-      '@tybys/wasm-util': 0.10.1
+      '@emnapi/core': 1.5.0
+      '@emnapi/runtime': 1.5.0
+      '@tybys/wasm-util': 0.10.0
     optional: true
 
   '@neoconfetti/react@1.0.0': {}
 
   '@next/env@15.4.10': {}
 
-  '@next/eslint-plugin-next@15.5.7':
+  '@next/eslint-plugin-next@15.5.2':
     dependencies:
       fast-glob: 3.3.1
 
@@ -10173,7 +10115,7 @@ snapshots:
 
   '@rtsao/scc@1.1.0': {}
 
-  '@rushstack/eslint-patch@1.15.0': {}
+  '@rushstack/eslint-patch@1.12.0': {}
 
   '@scarf/scarf@1.4.0': {}
 
@@ -10925,7 +10867,7 @@ snapshots:
     dependencies:
       '@testing-library/dom': 10.4.1
 
-  '@tybys/wasm-util@0.10.1':
+  '@tybys/wasm-util@0.10.0':
     dependencies:
       tslib: 2.8.1
     optional: true
@@ -11123,14 +11065,14 @@ snapshots:
     dependencies:
       '@types/node': 24.10.0
 
-  '@typescript-eslint/eslint-plugin@8.48.1(@typescript-eslint/parser@8.48.1(eslint@8.57.1)(typescript@5.9.3))(eslint@8.57.1)(typescript@5.9.3)':
+  '@typescript-eslint/eslint-plugin@8.43.0(@typescript-eslint/parser@8.43.0(eslint@8.57.1)(typescript@5.9.3))(eslint@8.57.1)(typescript@5.9.3)':
     dependencies:
-      '@eslint-community/regexpp': 4.12.2
-      '@typescript-eslint/parser': 8.48.1(eslint@8.57.1)(typescript@5.9.3)
-      '@typescript-eslint/scope-manager': 8.48.1
-      '@typescript-eslint/type-utils': 8.48.1(eslint@8.57.1)(typescript@5.9.3)
-      '@typescript-eslint/utils': 8.48.1(eslint@8.57.1)(typescript@5.9.3)
-      '@typescript-eslint/visitor-keys': 8.48.1
+      '@eslint-community/regexpp': 4.12.1
+      '@typescript-eslint/parser': 8.43.0(eslint@8.57.1)(typescript@5.9.3)
+      '@typescript-eslint/scope-manager': 8.43.0
+      '@typescript-eslint/type-utils': 8.43.0(eslint@8.57.1)(typescript@5.9.3)
+      '@typescript-eslint/utils': 8.43.0(eslint@8.57.1)(typescript@5.9.3)
+      '@typescript-eslint/visitor-keys': 8.43.0
       eslint: 8.57.1
       graphemer: 1.4.0
       ignore: 7.0.5
@@ -11140,12 +11082,12 @@ snapshots:
     transitivePeerDependencies:
       - supports-color
 
-  '@typescript-eslint/parser@8.48.1(eslint@8.57.1)(typescript@5.9.3)':
+  '@typescript-eslint/parser@8.43.0(eslint@8.57.1)(typescript@5.9.3)':
     dependencies:
-      '@typescript-eslint/scope-manager': 8.48.1
-      '@typescript-eslint/types': 8.48.1
-      '@typescript-eslint/typescript-estree': 8.48.1(typescript@5.9.3)
-      '@typescript-eslint/visitor-keys': 8.48.1
+      '@typescript-eslint/scope-manager': 8.43.0
+      '@typescript-eslint/types': 8.43.0
+      '@typescript-eslint/typescript-estree': 8.43.0(typescript@5.9.3)
+      '@typescript-eslint/visitor-keys': 8.43.0
       debug: 4.4.3
       eslint: 8.57.1
       typescript: 5.9.3
@@ -11155,7 +11097,7 @@ snapshots:
   '@typescript-eslint/project-service@8.43.0(typescript@5.9.3)':
     dependencies:
       '@typescript-eslint/tsconfig-utils': 8.43.0(typescript@5.9.3)
-      '@typescript-eslint/types': 8.48.1
+      '@typescript-eslint/types': 8.43.0
       debug: 4.4.3
       typescript: 5.9.3
     transitivePeerDependencies:
@@ -11164,16 +11106,7 @@ snapshots:
   '@typescript-eslint/project-service@8.46.2(typescript@5.9.3)':
     dependencies:
       '@typescript-eslint/tsconfig-utils': 8.46.2(typescript@5.9.3)
-      '@typescript-eslint/types': 8.48.1
-      debug: 4.4.3
-      typescript: 5.9.3
-    transitivePeerDependencies:
-      - supports-color
-
-  '@typescript-eslint/project-service@8.48.1(typescript@5.9.3)':
-    dependencies:
-      '@typescript-eslint/tsconfig-utils': 8.48.1(typescript@5.9.3)
-      '@typescript-eslint/types': 8.48.1
+      '@typescript-eslint/types': 8.46.2
       debug: 4.4.3
       typescript: 5.9.3
     transitivePeerDependencies:
@@ -11189,11 +11122,6 @@ snapshots:
       '@typescript-eslint/types': 8.46.2
       '@typescript-eslint/visitor-keys': 8.46.2
 
-  '@typescript-eslint/scope-manager@8.48.1':
-    dependencies:
-      '@typescript-eslint/types': 8.48.1
-      '@typescript-eslint/visitor-keys': 8.48.1
-
   '@typescript-eslint/tsconfig-utils@8.43.0(typescript@5.9.3)':
     dependencies:
       typescript: 5.9.3
@@ -11202,15 +11130,11 @@ snapshots:
     dependencies:
       typescript: 5.9.3
 
-  '@typescript-eslint/tsconfig-utils@8.48.1(typescript@5.9.3)':
+  '@typescript-eslint/type-utils@8.43.0(eslint@8.57.1)(typescript@5.9.3)':
     dependencies:
-      typescript: 5.9.3
-
-  '@typescript-eslint/type-utils@8.48.1(eslint@8.57.1)(typescript@5.9.3)':
-    dependencies:
-      '@typescript-eslint/types': 8.48.1
-      '@typescript-eslint/typescript-estree': 8.48.1(typescript@5.9.3)
-      '@typescript-eslint/utils': 8.48.1(eslint@8.57.1)(typescript@5.9.3)
+      '@typescript-eslint/types': 8.43.0
+      '@typescript-eslint/typescript-estree': 8.43.0(typescript@5.9.3)
+      '@typescript-eslint/utils': 8.43.0(eslint@8.57.1)(typescript@5.9.3)
       debug: 4.4.3
       eslint: 8.57.1
       ts-api-utils: 2.1.0(typescript@5.9.3)
@@ -11222,8 +11146,6 @@ snapshots:
 
   '@typescript-eslint/types@8.46.2': {}
 
-  '@typescript-eslint/types@8.48.1': {}
-
   '@typescript-eslint/typescript-estree@8.43.0(typescript@5.9.3)':
     dependencies:
       '@typescript-eslint/project-service': 8.43.0(typescript@5.9.3)
@@ -11234,7 +11156,7 @@ snapshots:
       fast-glob: 3.3.3
       is-glob: 4.0.3
       minimatch: 9.0.5
-      semver: 7.7.3
+      semver: 7.7.2
       ts-api-utils: 2.1.0(typescript@5.9.3)
       typescript: 5.9.3
     transitivePeerDependencies:
@@ -11256,21 +11178,6 @@ snapshots:
     transitivePeerDependencies:
       - supports-color
 
-  '@typescript-eslint/typescript-estree@8.48.1(typescript@5.9.3)':
-    dependencies:
-      '@typescript-eslint/project-service': 8.48.1(typescript@5.9.3)
-      '@typescript-eslint/tsconfig-utils': 8.48.1(typescript@5.9.3)
-      '@typescript-eslint/types': 8.48.1
-      '@typescript-eslint/visitor-keys': 8.48.1
-      debug: 4.4.3
-      minimatch: 9.0.5
-      semver: 7.7.3
-      tinyglobby: 0.2.15
-      ts-api-utils: 2.1.0(typescript@5.9.3)
-      typescript: 5.9.3
-    transitivePeerDependencies:
-      - supports-color
-
   '@typescript-eslint/utils@8.43.0(eslint@8.57.1)(typescript@5.9.3)':
     dependencies:
       '@eslint-community/eslint-utils': 4.9.0(eslint@8.57.1)
@@ -11293,17 +11200,6 @@ snapshots:
     transitivePeerDependencies:
       - supports-color
 
-  '@typescript-eslint/utils@8.48.1(eslint@8.57.1)(typescript@5.9.3)':
-    dependencies:
-      '@eslint-community/eslint-utils': 4.9.0(eslint@8.57.1)
-      '@typescript-eslint/scope-manager': 8.48.1
-      '@typescript-eslint/types': 8.48.1
-      '@typescript-eslint/typescript-estree': 8.48.1(typescript@5.9.3)
-      eslint: 8.57.1
-      typescript: 5.9.3
-    transitivePeerDependencies:
-      - supports-color
-
   '@typescript-eslint/visitor-keys@8.43.0':
     dependencies:
       '@typescript-eslint/types': 8.43.0
@@ -11314,11 +11210,6 @@ snapshots:
       '@typescript-eslint/types': 8.46.2
       eslint-visitor-keys: 4.2.1
 
-  '@typescript-eslint/visitor-keys@8.48.1':
-    dependencies:
-      '@typescript-eslint/types': 8.48.1
-      eslint-visitor-keys: 4.2.1
-
   '@ungap/structured-clone@1.3.0': {}
 
   '@unrs/resolver-binding-android-arm-eabi@1.11.1':
@@ -12641,16 +12532,16 @@ snapshots:
 
   escape-string-regexp@5.0.0: {}
 
-  eslint-config-next@15.5.7(eslint@8.57.1)(typescript@5.9.3):
+  eslint-config-next@15.5.2(eslint@8.57.1)(typescript@5.9.3):
     dependencies:
-      '@next/eslint-plugin-next': 15.5.7
-      '@rushstack/eslint-patch': 1.15.0
-      '@typescript-eslint/eslint-plugin': 8.48.1(@typescript-eslint/parser@8.48.1(eslint@8.57.1)(typescript@5.9.3))(eslint@8.57.1)(typescript@5.9.3)
-      '@typescript-eslint/parser': 8.48.1(eslint@8.57.1)(typescript@5.9.3)
+      '@next/eslint-plugin-next': 15.5.2
+      '@rushstack/eslint-patch': 1.12.0
+      '@typescript-eslint/eslint-plugin': 8.43.0(@typescript-eslint/parser@8.43.0(eslint@8.57.1)(typescript@5.9.3))(eslint@8.57.1)(typescript@5.9.3)
+      '@typescript-eslint/parser': 8.43.0(eslint@8.57.1)(typescript@5.9.3)
       eslint: 8.57.1
       eslint-import-resolver-node: 0.3.9
       eslint-import-resolver-typescript: 3.10.1(eslint-plugin-import@2.32.0)(eslint@8.57.1)
-      eslint-plugin-import: 2.32.0(@typescript-eslint/parser@8.48.1(eslint@8.57.1)(typescript@5.9.3))(eslint-import-resolver-typescript@3.10.1)(eslint@8.57.1)
+      eslint-plugin-import: 2.32.0(@typescript-eslint/parser@8.43.0(eslint@8.57.1)(typescript@5.9.3))(eslint-import-resolver-typescript@3.10.1)(eslint@8.57.1)
       eslint-plugin-jsx-a11y: 6.10.2(eslint@8.57.1)
       eslint-plugin-react: 7.37.5(eslint@8.57.1)
       eslint-plugin-react-hooks: 5.2.0(eslint@8.57.1)
@@ -12665,7 +12556,7 @@ snapshots:
     dependencies:
       debug: 3.2.7
       is-core-module: 2.16.1
-      resolve: 1.22.11
+      resolve: 1.22.10
     transitivePeerDependencies:
       - supports-color
 
@@ -12674,28 +12565,28 @@ snapshots:
       '@nolyfill/is-core-module': 1.0.39
       debug: 4.4.3
       eslint: 8.57.1
-      get-tsconfig: 4.13.0
+      get-tsconfig: 4.10.1
       is-bun-module: 2.0.0
       stable-hash: 0.0.5
       tinyglobby: 0.2.15
       unrs-resolver: 1.11.1
     optionalDependencies:
-      eslint-plugin-import: 2.32.0(@typescript-eslint/parser@8.48.1(eslint@8.57.1)(typescript@5.9.3))(eslint-import-resolver-typescript@3.10.1)(eslint@8.57.1)
+      eslint-plugin-import: 2.32.0(@typescript-eslint/parser@8.43.0(eslint@8.57.1)(typescript@5.9.3))(eslint-import-resolver-typescript@3.10.1)(eslint@8.57.1)
     transitivePeerDependencies:
       - supports-color
 
-  eslint-module-utils@2.12.1(@typescript-eslint/parser@8.48.1(eslint@8.57.1)(typescript@5.9.3))(eslint-import-resolver-node@0.3.9)(eslint-import-resolver-typescript@3.10.1)(eslint@8.57.1):
+  eslint-module-utils@2.12.1(@typescript-eslint/parser@8.43.0(eslint@8.57.1)(typescript@5.9.3))(eslint-import-resolver-node@0.3.9)(eslint-import-resolver-typescript@3.10.1)(eslint@8.57.1):
     dependencies:
       debug: 3.2.7
     optionalDependencies:
-      '@typescript-eslint/parser': 8.48.1(eslint@8.57.1)(typescript@5.9.3)
+      '@typescript-eslint/parser': 8.43.0(eslint@8.57.1)(typescript@5.9.3)
       eslint: 8.57.1
       eslint-import-resolver-node: 0.3.9
       eslint-import-resolver-typescript: 3.10.1(eslint-plugin-import@2.32.0)(eslint@8.57.1)
     transitivePeerDependencies:
       - supports-color
 
-  eslint-plugin-import@2.32.0(@typescript-eslint/parser@8.48.1(eslint@8.57.1)(typescript@5.9.3))(eslint-import-resolver-typescript@3.10.1)(eslint@8.57.1):
+  eslint-plugin-import@2.32.0(@typescript-eslint/parser@8.43.0(eslint@8.57.1)(typescript@5.9.3))(eslint-import-resolver-typescript@3.10.1)(eslint@8.57.1):
     dependencies:
       '@rtsao/scc': 1.1.0
       array-includes: 3.1.9
@@ -12706,7 +12597,7 @@ snapshots:
       doctrine: 2.1.0
       eslint: 8.57.1
       eslint-import-resolver-node: 0.3.9
-      eslint-module-utils: 2.12.1(@typescript-eslint/parser@8.48.1(eslint@8.57.1)(typescript@5.9.3))(eslint-import-resolver-node@0.3.9)(eslint-import-resolver-typescript@3.10.1)(eslint@8.57.1)
+      eslint-module-utils: 2.12.1(@typescript-eslint/parser@8.43.0(eslint@8.57.1)(typescript@5.9.3))(eslint-import-resolver-node@0.3.9)(eslint-import-resolver-typescript@3.10.1)(eslint@8.57.1)
       hasown: 2.0.2
       is-core-module: 2.16.1
       is-glob: 4.0.3
@@ -12718,7 +12609,7 @@ snapshots:
       string.prototype.trimend: 1.0.9
       tsconfig-paths: 3.15.0
     optionalDependencies:
-      '@typescript-eslint/parser': 8.48.1(eslint@8.57.1)(typescript@5.9.3)
+      '@typescript-eslint/parser': 8.43.0(eslint@8.57.1)(typescript@5.9.3)
     transitivePeerDependencies:
       - eslint-import-resolver-typescript
       - eslint-import-resolver-webpack
@@ -13067,8 +12958,6 @@ snapshots:
     dependencies:
       next: 15.4.10(@babel/core@7.28.4)(@opentelemetry/api@1.9.0)(@playwright/test@1.56.1)(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
 
-  generator-function@2.0.1: {}
-
   gensync@1.0.0-beta.2: {}
 
   get-caller-file@2.0.5: {}
@@ -13101,7 +12990,7 @@ snapshots:
       es-errors: 1.3.0
       get-intrinsic: 1.3.0
 
-  get-tsconfig@4.13.0:
+  get-tsconfig@4.10.1:
     dependencies:
       resolve-pkg-maps: 1.0.0
 
@@ -13385,6 +13274,13 @@ snapshots:
       parent-module: 1.0.1
       resolve-from: 4.0.0
 
+  import-in-the-middle@1.14.2:
+    dependencies:
+      acorn: 8.15.0
+      acorn-import-attributes: 1.9.5(acorn@8.15.0)
+      cjs-module-lexer: 1.4.3
+      module-details-from-path: 1.0.4
+
   import-in-the-middle@2.0.0:
     dependencies:
       acorn: 8.15.0
@@ -13461,7 +13357,7 @@ snapshots:
 
   is-bun-module@2.0.0:
     dependencies:
-      semver: 7.7.3
+      semver: 7.7.2
 
   is-callable@1.2.7: {}
 
@@ -13499,14 +13395,6 @@ snapshots:
       has-tostringtag: 1.0.2
       safe-regex-test: 1.1.0
 
-  is-generator-function@1.1.2:
-    dependencies:
-      call-bound: 1.0.4
-      generator-function: 2.0.1
-      get-proto: 1.0.1
-      has-tostringtag: 1.0.2
-      safe-regex-test: 1.1.0
-
   is-glob@4.0.3:
     dependencies:
       is-extglob: 2.1.1
@@ -14327,7 +14215,7 @@ snapshots:
 
   nanoid@3.3.11: {}
 
-  napi-postinstall@0.3.4: {}
+  napi-postinstall@0.3.3: {}
 
   natural-compare@1.4.0: {}
 
@@ -15297,12 +15185,6 @@ snapshots:
       path-parse: 1.0.7
       supports-preserve-symlinks-flag: 1.0.0
 
-  resolve@1.22.11:
-    dependencies:
-      is-core-module: 2.16.1
-      path-parse: 1.0.7
-      supports-preserve-symlinks-flag: 1.0.0
-
   resolve@1.22.8:
     dependencies:
       is-core-module: 2.16.1
@@ -16114,7 +15996,7 @@ snapshots:
 
   unrs-resolver@1.11.1:
     dependencies:
-      napi-postinstall: 0.3.4
+      napi-postinstall: 0.3.3
     optionalDependencies:
       '@unrs/resolver-binding-android-arm-eabi': 1.11.1
       '@unrs/resolver-binding-android-arm64': 1.11.1
@@ -16342,7 +16224,7 @@ snapshots:
       is-async-function: 2.1.1
       is-date-object: 1.1.0
       is-finalizationregistry: 1.1.1
-      is-generator-function: 1.1.2
+      is-generator-function: 1.1.0
       is-regex: 1.2.1
       is-weakref: 1.1.1
       isarray: 2.0.5

autogpt_platform/frontend/src/app/(no-navbar)/onboarding/5-run/page.tsx

@@ -8,6 +8,7 @@ import {
   CardTitle,
 } from "@/components/__legacy__/ui/card";
 import { ErrorCard } from "@/components/molecules/ErrorCard/ErrorCard";
+import { InformationTooltip } from "@/components/molecules/InformationTooltip/InformationTooltip";
 import { CircleNotchIcon } from "@phosphor-icons/react/dist/ssr";
 import { Play } from "lucide-react";
 import OnboardingButton from "../components/OnboardingButton";
@@ -78,13 +79,20 @@ export default function Page() {
                 <CardContent className="flex flex-col gap-4">
                   {Object.entries(agent?.input_schema.properties || {}).map(
                     ([key, inputSubSchema]) => (
-                      <RunAgentInputs
-                        key={key}
-                        schema={inputSubSchema}
-                        value={onboarding.state?.agentInput?.[key]}
-                        placeholder={inputSubSchema.description}
-                        onChange={(value) => handleSetAgentInput(key, value)}
-                      />
+                      <div key={key} className="flex flex-col space-y-2">
+                        <label className="flex items-center gap-1 text-sm font-medium">
+                          {inputSubSchema.title || key}
+                          <InformationTooltip
+                            description={inputSubSchema.description}
+                          />
+                        </label>
+                        <RunAgentInputs
+                          schema={inputSubSchema}
+                          value={onboarding.state?.agentInput?.[key]}
+                          placeholder={inputSubSchema.description}
+                          onChange={(value) => handleSetAgentInput(key, value)}
+                        />
+                      </div>
                     ),
                   )}
                   <AgentOnboardingCredentials

autogpt_platform/frontend/src/app/(platform)/build/components/FlowEditor/nodes/CustomNode/CustomNode.tsx

@@ -106,11 +106,7 @@ export const CustomNode: React.FC<NodeProps<CustomNode>> = React.memo(
           />
           <NodeAdvancedToggle nodeId={nodeId} />
           {data.uiType != BlockUIType.OUTPUT && (
-            <OutputHandler
-              uiType={data.uiType}
-              outputSchema={outputSchema}
-              nodeId={nodeId}
-            />
+            <OutputHandler outputSchema={outputSchema} nodeId={nodeId} />
           )}
           <NodeDataRenderer nodeId={nodeId} />
         </div>

autogpt_platform/frontend/src/app/(platform)/build/components/FlowEditor/nodes/FormCreator.tsx

@@ -20,32 +20,17 @@ export const FormCreator = React.memo(
     className?: string;
   }) => {
     const updateNodeData = useNodeStore((state) => state.updateNodeData);
-
     const getHardCodedValues = useNodeStore(
       (state) => state.getHardCodedValues,
     );
-
     const handleChange = ({ formData }: any) => {
       if ("credentials" in formData && !formData.credentials?.id) {
         delete formData.credentials;
       }
-
-      const updatedValues =
-        uiType === BlockUIType.AGENT
-          ? {
-              ...getHardCodedValues(nodeId),
-              inputs: formData,
-            }
-          : formData;
-
-      updateNodeData(nodeId, { hardcodedValues: updatedValues });
+      updateNodeData(nodeId, { hardcodedValues: formData });
     };
 
-    const hardcodedValues = getHardCodedValues(nodeId);
-    const initialValues =
-      uiType === BlockUIType.AGENT
-        ? (hardcodedValues.inputs ?? {})
-        : hardcodedValues;
+    const initialValues = getHardCodedValues(nodeId);
 
     return (
       <div className={className}>

autogpt_platform/frontend/src/app/(platform)/build/components/FlowEditor/nodes/OutputHandler.tsx

@@ -14,16 +14,13 @@ import {
 import { useEdgeStore } from "@/app/(platform)/build/stores/edgeStore";
 import { getTypeDisplayInfo } from "./helpers";
 import { generateHandleId } from "../handlers/helpers";
-import { BlockUIType } from "../../types";
 
 export const OutputHandler = ({
   outputSchema,
   nodeId,
-  uiType,
 }: {
   outputSchema: RJSFSchema;
   nodeId: string;
-  uiType: BlockUIType;
 }) => {
   const { isOutputConnected } = useEdgeStore();
   const properties = outputSchema?.properties || {};
@@ -82,9 +79,7 @@ export const OutputHandler = ({
                 </Text>
 
                 <NodeHandle
-                  handleId={
-                    uiType === BlockUIType.AGENT ? key : generateHandleId(key)
-                  }
+                  handleId={generateHandleId(key)}
                   isConnected={isConnected}
                   side="right"
                 />

autogpt_platform/frontend/src/app/(platform)/build/components/NewControlPanel/NewBlockMenu/BlockMenuSearch/useBlockMenuSearch.ts

@@ -7,7 +7,6 @@ import { LibraryAgent } from "@/app/api/__generated__/models/libraryAgent";
 import { getV2GetSpecificAgent } from "@/app/api/__generated__/endpoints/store/store";
 import {
   getGetV2ListLibraryAgentsQueryKey,
-  getV2GetLibraryAgent,
   usePostV2AddMarketplaceAgent,
 } from "@/app/api/__generated__/endpoints/library/library";
 import {
@@ -152,12 +151,7 @@ export const useBlockMenuSearch = () => {
       });
 
       const libraryAgent = response.data as LibraryAgent;
-
-      const { data: libraryAgentDetails } = await getV2GetLibraryAgent(
-        libraryAgent.id,
-      );
-
-      addAgentToBuilder(libraryAgentDetails as LibraryAgent);
+      addAgentToBuilder(libraryAgent);
 
       toast({
         title: "Agent Added",

autogpt_platform/frontend/src/app/(platform)/build/components/NewControlPanel/NewBlockMenu/MarketplaceAgentsContent/useMarketplaceAgentsContent.ts

@@ -1,7 +1,6 @@
 import { getGetV2GetBuilderItemCountsQueryKey } from "@/app/api/__generated__/endpoints/default/default";
 import {
   getGetV2ListLibraryAgentsQueryKey,
-  getV2GetLibraryAgent,
   usePostV2AddMarketplaceAgent,
 } from "@/app/api/__generated__/endpoints/library/library";
 import {
@@ -106,16 +105,8 @@ export const useMarketplaceAgentsContent = () => {
         },
       });
 
-      // Here, libraryAgent has empty input and output schemas.
-      // Not updating the endpoint because this endpoint is used elsewhere.
-      // TODO: Create a new endpoint for builder specific to marketplace agents.
       const libraryAgent = response.data as LibraryAgent;
-
-      const { data: libraryAgentDetails } = await getV2GetLibraryAgent(
-        libraryAgent.id,
-      );
-
-      addAgentToBuilder(libraryAgentDetails as LibraryAgent);
+      addAgentToBuilder(libraryAgent);
 
       toast({
         title: "Agent Added",

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/AgentInputsReadOnly/AgentInputsReadOnly.tsx

@@ -1,11 +1,16 @@
 "use client";
 
 import type { LibraryAgent } from "@/app/api/__generated__/models/libraryAgent";
-import { Text } from "@/components/atoms/Text/Text";
-import type { CredentialsMetaInput } from "@/lib/autogpt-server-api/types";
+import type {
+  BlockIOSubSchema,
+  CredentialsMetaInput,
+} from "@/lib/autogpt-server-api/types";
 import { CredentialsInput } from "../CredentialsInputs/CredentialsInputs";
-import { RunAgentInputs } from "../RunAgentInputs/RunAgentInputs";
-import { getAgentCredentialsFields, getAgentInputFields } from "./helpers";
+import {
+  getAgentCredentialsFields,
+  getAgentInputFields,
+  renderValue,
+} from "./helpers";
 
 type Props = {
   agent: LibraryAgent;
@@ -23,23 +28,19 @@ export function AgentInputsReadOnly({
     getAgentCredentialsFields(agent),
   );
 
+  // Take actual input entries as leading; augment with schema from input fields.
+  // TODO: ensure consistent ordering.
   const inputEntries =
     inputs &&
-    Object.entries(inputs).map(([key, value]) => ({
-      key,
-      schema: inputFields[key],
-      value,
-    }));
+    Object.entries(inputs).map<[string, [BlockIOSubSchema | undefined, any]]>(
+      ([k, v]) => [k, [inputFields[k], v]],
+    );
 
   const hasInputs = inputEntries && inputEntries.length > 0;
   const hasCredentials = credentialInputs && credentialFieldEntries.length > 0;
 
   if (!hasInputs && !hasCredentials) {
-    return (
-      <Text variant="body" className="text-zinc-700">
-        No input for this run.
-      </Text>
-    );
+    return <div className="text-neutral-600">No input for this run.</div>;
   }
 
   return (
@@ -47,20 +48,16 @@ export function AgentInputsReadOnly({
       {/* Regular inputs */}
       {hasInputs && (
         <div className="flex flex-col gap-4">
-          {inputEntries.map(({ key, schema, value }) => {
-            if (!schema) return null;
-
-            return (
-              <RunAgentInputs
-                key={key}
-                schema={schema}
-                value={value}
-                placeholder={schema.description}
-                onChange={() => {}}
-                readOnly={true}
-              />
-            );
-          })}
+          {inputEntries.map(([key, [schema, value]]) => (
+            <div key={key} className="flex flex-col gap-1.5">
+              <label className="text-sm font-medium">
+                {schema?.title || key}
+              </label>
+              <p className="whitespace-pre-wrap break-words text-sm text-neutral-700">
+                {renderValue(value)}
+              </p>
+            </div>
+          ))}
         </div>
       )}
 

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/CredentialsInputs/components/CredentialRow/CredentialRow.tsx

@@ -62,15 +62,12 @@ export function CredentialRow({
       </div>
       <IconKey className="h-5 w-5 shrink-0 text-zinc-800" />
       <div className="flex min-w-0 flex-1 flex-nowrap items-center gap-4">
-        <Text
-          variant="body"
-          className="line-clamp-1 flex-[0_0_50%] text-ellipsis tracking-tight"
-        >
+        <Text variant="body" className="tracking-tight">
           {getCredentialDisplayName(credential, displayName)}
         </Text>
         <Text
           variant="large"
-          className="relative top-1 hidden flex-[0_0_40%] overflow-hidden truncate font-mono tracking-tight md:block"
+          className="relative top-1 font-mono tracking-tight"
         >
           {"*".repeat(MASKED_KEY_LENGTH)}
         </Text>

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/RunAgentInputs/RunAgentInputs.tsx

@@ -9,7 +9,6 @@ import { Button } from "@/components/atoms/Button/Button";
 import { FileInput } from "@/components/atoms/FileInput/FileInput";
 import { Switch } from "@/components/atoms/Switch/Switch";
 import { GoogleDrivePickerInput } from "@/components/contextual/GoogleDrivePicker/GoogleDrivePickerInput";
-import { InformationTooltip } from "@/components/molecules/InformationTooltip/InformationTooltip";
 import { TimePicker } from "@/components/molecules/TimePicker/TimePicker";
 import {
   BlockIOObjectSubSchema,
@@ -33,7 +32,6 @@ interface Props {
   value?: any;
   placeholder?: string;
   onChange: (value: any) => void;
-  readOnly?: boolean;
 }
 
 /**
@@ -46,7 +44,6 @@ export function RunAgentInputs({
   value,
   placeholder,
   onChange,
-  readOnly = false,
   ...props
 }: Props & React.HTMLAttributes<HTMLElement>) {
   const { handleUploadFile, uploadProgress } = useRunAgentInputs();
@@ -65,6 +62,7 @@ export function RunAgentInputs({
           id={`${baseId}-number`}
           label={schema.title ?? placeholder ?? "Number"}
           hideLabel
+          size="small"
           type="number"
           value={value ?? ""}
           placeholder={placeholder || "Enter number"}
@@ -82,6 +80,7 @@ export function RunAgentInputs({
           id={`${baseId}-textarea`}
           label={schema.title ?? placeholder ?? "Text"}
           hideLabel
+          size="small"
           type="textarea"
           rows={3}
           value={value ?? ""}
@@ -103,7 +102,7 @@ export function RunAgentInputs({
           value={value}
           onChange={onChange}
           className="w-full"
-          showRemoveButton={!readOnly}
+          showRemoveButton={false}
         />
       );
       break;
@@ -131,6 +130,7 @@ export function RunAgentInputs({
           id={`${baseId}-date`}
           label={schema.title ?? placeholder ?? "Date"}
           hideLabel
+          size="small"
           type="date"
           value={value ? format(value as Date, "yyyy-MM-dd") : ""}
           onChange={(e) => {
@@ -159,6 +159,7 @@ export function RunAgentInputs({
           id={`${baseId}-datetime`}
           label={schema.title ?? placeholder ?? "Date time"}
           hideLabel
+          size="small"
           type="datetime-local"
           value={value ?? ""}
           onChange={(e) => onChange((e.target as HTMLInputElement).value)}
@@ -193,6 +194,7 @@ export function RunAgentInputs({
             label={schema.title ?? placeholder ?? "Select"}
             hideLabel
             value={value ?? ""}
+            size="small"
             onValueChange={(val: string) => onChange(val)}
             placeholder={placeholder || "Select an option"}
             options={schema.enum
@@ -215,6 +217,7 @@ export function RunAgentInputs({
           items={allKeys.map((key) => ({
             value: key,
             label: _schema.properties[key]?.title ?? key,
+            size: "small",
           }))}
           selectedValues={selectedValues}
           onChange={(values: string[]) =>
@@ -333,6 +336,7 @@ export function RunAgentInputs({
           id={`${baseId}-text`}
           label={schema.title ?? placeholder ?? "Text"}
           hideLabel
+          size="small"
           type="text"
           value={value ?? ""}
           onChange={(e) => onChange((e.target as HTMLInputElement).value)}
@@ -343,17 +347,6 @@ export function RunAgentInputs({
   }
 
   return (
-    <div className="flex w-full flex-col gap-0 space-y-2">
-      <label className="large-medium flex items-center gap-1 font-medium">
-        {schema.title || placeholder}
-        <InformationTooltip description={schema.description} />
-      </label>
-      <div
-        className="no-drag relative flex w-full"
-        style={readOnly ? { pointerEvents: "none", opacity: 0.7 } : undefined}
-      >
-        {innerInputElement}
-      </div>
-    </div>
+    <div className="no-drag relative flex w-full">{innerInputElement}</div>
   );
 }

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/RunAgentModal/components/ModalRunSection/ModalRunSection.tsx

@@ -73,15 +73,22 @@ export function ModalRunSection() {
           title="Task Inputs"
           subtitle="Enter the information you want to provide to the agent for this task"
         >
+          {/* Regular inputs */}
           {inputFields.map(([key, inputSubSchema]) => (
-            <RunAgentInputs
-              key={key}
-              schema={inputSubSchema}
-              value={inputValues[key] ?? inputSubSchema.default}
-              placeholder={inputSubSchema.description}
-              onChange={(value) => setInputValue(key, value)}
-              data-testid={`agent-input-${key}`}
-            />
+            <div key={key} className="flex w-full flex-col gap-0 space-y-2">
+              <label className="flex items-center gap-1 text-sm font-medium">
+                {inputSubSchema.title || key}
+                <InformationTooltip description={inputSubSchema.description} />
+              </label>
+
+              <RunAgentInputs
+                schema={inputSubSchema}
+                value={inputValues[key] ?? inputSubSchema.default}
+                placeholder={inputSubSchema.description}
+                onChange={(value) => setInputValue(key, value)}
+                data-testid={`agent-input-${key}`}
+              />
+            </div>
           ))}
         </ModalSection>
       ) : null}

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/selected-views/SelectedRunView/SelectedRunView.tsx

@@ -4,19 +4,20 @@ import { AgentExecutionStatus } from "@/app/api/__generated__/models/agentExecut
 import type { LibraryAgent } from "@/app/api/__generated__/models/libraryAgent";
 import { LoadingSpinner } from "@/components/atoms/LoadingSpinner/LoadingSpinner";
 import { Text } from "@/components/atoms/Text/Text";
-import { ErrorCard } from "@/components/molecules/ErrorCard/ErrorCard";
-import { InformationTooltip } from "@/components/molecules/InformationTooltip/InformationTooltip";
 import {
-  ScrollableTabs,
-  ScrollableTabsContent,
-  ScrollableTabsList,
-  ScrollableTabsTrigger,
-} from "@/components/molecules/ScrollableTabs/ScrollableTabs";
+  Tooltip,
+  TooltipContent,
+  TooltipProvider,
+  TooltipTrigger,
+} from "@/components/atoms/Tooltip/BaseTooltip";
+import { ErrorCard } from "@/components/molecules/ErrorCard/ErrorCard";
 import { PendingReviewsList } from "@/components/organisms/PendingReviewsList/PendingReviewsList";
 import { usePendingReviewsForExecution } from "@/hooks/usePendingReviews";
 import { isLargeScreen, useBreakpoint } from "@/lib/hooks/useBreakpoint";
+import { InfoIcon } from "@phosphor-icons/react";
 import { useEffect } from "react";
 import { AgentInputsReadOnly } from "../../modals/AgentInputsReadOnly/AgentInputsReadOnly";
+import { AnchorLinksWrap } from "../AnchorLinksWrap";
 import { LoadingSelectedContent } from "../LoadingSelectedContent";
 import { RunDetailCard } from "../RunDetailCard/RunDetailCard";
 import { RunDetailHeader } from "../RunDetailHeader/RunDetailHeader";
@@ -27,6 +28,9 @@ import { SelectedRunActions } from "./components/SelectedRunActions/SelectedRunA
 import { WebhookTriggerSection } from "./components/WebhookTriggerSection";
 import { useSelectedRunView } from "./useSelectedRunView";
 
+const anchorStyles =
+  "border-b-2 border-transparent pb-1 text-sm font-medium text-slate-600 transition-colors hover:text-slate-900 hover:border-slate-900";
+
 interface Props {
   agent: LibraryAgent;
   runId: string;
@@ -61,6 +65,13 @@ export function SelectedRunView({
   const withSummary = run?.stats?.activity_status;
   const withReviews = run?.status === AgentExecutionStatus.REVIEW;
 
+  function scrollToSection(id: string) {
+    const element = document.getElementById(id);
+    if (element) {
+      element.scrollIntoView({ behavior: "smooth", block: "start" });
+    }
+  }
+
   if (responseError || httpError) {
     return (
       <ErrorCard
@@ -101,116 +112,118 @@ export function SelectedRunView({
                 />
               )}
 
-            <ScrollableTabs
-              defaultValue="output"
-              className="-mt-2 flex flex-col"
-            >
-              <ScrollableTabsList className="px-4">
-                {withSummary && (
-                  <ScrollableTabsTrigger value="summary">
-                    Summary
-                  </ScrollableTabsTrigger>
-                )}
-                <ScrollableTabsTrigger value="output">
-                  Output
-                </ScrollableTabsTrigger>
-                <ScrollableTabsTrigger value="input">
-                  Your input
-                </ScrollableTabsTrigger>
-                {withReviews && (
-                  <ScrollableTabsTrigger value="reviews">
-                    Reviews ({pendingReviews.length})
-                  </ScrollableTabsTrigger>
-                )}
-              </ScrollableTabsList>
-              <div className="my-6 flex flex-col gap-6">
-                {/* Summary Section */}
-                {withSummary && (
-                  <ScrollableTabsContent value="summary">
-                    <div className="scroll-mt-4">
-                      <RunDetailCard
-                        title={
-                          <div className="flex items-center gap-1">
-                            <Text variant="lead-semibold">Summary</Text>
-                            <InformationTooltip
-                              iconSize={20}
-                              description="This AI-generated summary describes how the agent handled your task. It's an experimental feature and may occasionally be inaccurate."
+            {/* Navigation Links */}
+            <AnchorLinksWrap>
+              {withSummary && (
+                <button
+                  onClick={() => scrollToSection("summary")}
+                  className={anchorStyles}
+                >
+                  Summary
+                </button>
+              )}
+              <button
+                onClick={() => scrollToSection("output")}
+                className={anchorStyles}
+              >
+                Output
+              </button>
+              <button
+                onClick={() => scrollToSection("input")}
+                className={anchorStyles}
+              >
+                Your input
+              </button>
+              {withReviews && (
+                <button
+                  onClick={() => scrollToSection("reviews")}
+                  className={anchorStyles}
+                >
+                  Reviews ({pendingReviews.length})
+                </button>
+              )}
+            </AnchorLinksWrap>
+
+            {/* Summary Section */}
+            {withSummary && (
+              <div id="summary" className="scroll-mt-4">
+                <RunDetailCard
+                  title={
+                    <div className="flex items-center gap-2">
+                      <Text variant="lead-semibold">Summary</Text>
+                      <TooltipProvider>
+                        <Tooltip>
+                          <TooltipTrigger asChild>
+                            <InfoIcon
+                              size={16}
+                              className="cursor-help text-neutral-500 hover:text-neutral-700"
                             />
-                          </div>
-                        }
-                      >
-                        <RunSummary run={run} />
-                      </RunDetailCard>
+                          </TooltipTrigger>
+                          <TooltipContent>
+                            <p className="max-w-xs">
+                              This AI-generated summary describes how the agent
+                              handled your task. It&apos;s an experimental
+                              feature and may occasionally be inaccurate.
+                            </p>
+                          </TooltipContent>
+                        </Tooltip>
+                      </TooltipProvider>
                     </div>
-                  </ScrollableTabsContent>
-                )}
-
-                {/* Output Section */}
-                <ScrollableTabsContent value="output">
-                  <div className="scroll-mt-4">
-                    <RunDetailCard title="Output">
-                      {isLoading ? (
-                        <div className="text-neutral-500">
-                          <LoadingSpinner />
-                        </div>
-                      ) : run && "outputs" in run ? (
-                        <RunOutputs outputs={run.outputs as any} />
-                      ) : (
-                        <Text variant="body" className="text-neutral-600">
-                          No output from this run.
-                        </Text>
-                      )}
-                    </RunDetailCard>
-                  </div>
-                </ScrollableTabsContent>
-
-                {/* Input Section */}
-                <ScrollableTabsContent value="input">
-                  <div id="input" className="scroll-mt-4">
-                    <RunDetailCard
-                      title={
-                        <div className="flex items-center gap-1">
-                          <Text variant="lead-semibold">Your input</Text>
-                          <InformationTooltip
-                            iconSize={20}
-                            description="This is the input that was provided to the agent for running this task."
-                          />
-                        </div>
-                      }
-                    >
-                      <AgentInputsReadOnly
-                        agent={agent}
-                        inputs={run?.inputs}
-                        credentialInputs={run?.credential_inputs}
-                      />
-                    </RunDetailCard>
-                  </div>
-                </ScrollableTabsContent>
-
-                {/* Reviews Section */}
-                {withReviews && (
-                  <ScrollableTabsContent value="reviews">
-                    <div className="scroll-mt-4">
-                      <RunDetailCard>
-                        {reviewsLoading ? (
-                          <LoadingSpinner size="small" />
-                        ) : pendingReviews.length > 0 ? (
-                          <PendingReviewsList
-                            reviews={pendingReviews}
-                            onReviewComplete={refetchReviews}
-                            emptyMessage="No pending reviews for this execution"
-                          />
-                        ) : (
-                          <Text variant="body" className="text-zinc-700">
-                            No pending reviews for this execution
-                          </Text>
-                        )}
-                      </RunDetailCard>
-                    </div>
-                  </ScrollableTabsContent>
-                )}
+                  }
+                >
+                  <RunSummary run={run} />
+                </RunDetailCard>
               </div>
-            </ScrollableTabs>
+            )}
+
+            {/* Output Section */}
+            <div id="output" className="scroll-mt-4">
+              <RunDetailCard title="Output">
+                {isLoading ? (
+                  <div className="text-neutral-500">
+                    <LoadingSpinner />
+                  </div>
+                ) : run && "outputs" in run ? (
+                  <RunOutputs outputs={run.outputs as any} />
+                ) : (
+                  <Text variant="body" className="text-neutral-600">
+                    No output from this run.
+                  </Text>
+                )}
+              </RunDetailCard>
+            </div>
+
+            {/* Input Section */}
+            <div id="input" className="scroll-mt-4">
+              <RunDetailCard title="Your input">
+                <AgentInputsReadOnly
+                  agent={agent}
+                  inputs={run?.inputs}
+                  credentialInputs={run?.credential_inputs}
+                />
+              </RunDetailCard>
+            </div>
+
+            {/* Reviews Section */}
+            {withReviews && (
+              <div id="reviews" className="scroll-mt-4">
+                <RunDetailCard>
+                  {reviewsLoading ? (
+                    <div className="text-neutral-500">Loading reviews…</div>
+                  ) : pendingReviews.length > 0 ? (
+                    <PendingReviewsList
+                      reviews={pendingReviews}
+                      onReviewComplete={refetchReviews}
+                      emptyMessage="No pending reviews for this execution"
+                    />
+                  ) : (
+                    <div className="text-neutral-600">
+                      No pending reviews for this execution
+                    </div>
+                  )}
+                </RunDetailCard>
+              </div>
+            )}
           </div>
         </SelectedViewLayout>
       </div>

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/selected-views/SelectedScheduleView/SelectedScheduleView.tsx

@@ -9,6 +9,7 @@ import { humanizeCronExpression } from "@/lib/cron-expression-utils";
 import { isLargeScreen, useBreakpoint } from "@/lib/hooks/useBreakpoint";
 import { formatInTimezone, getTimezoneDisplayName } from "@/lib/timezone-utils";
 import { AgentInputsReadOnly } from "../../modals/AgentInputsReadOnly/AgentInputsReadOnly";
+import { AnchorLinksWrap } from "../AnchorLinksWrap";
 import { LoadingSelectedContent } from "../LoadingSelectedContent";
 import { RunDetailCard } from "../RunDetailCard/RunDetailCard";
 import { RunDetailHeader } from "../RunDetailHeader/RunDetailHeader";
@@ -16,6 +17,9 @@ import { SelectedViewLayout } from "../SelectedViewLayout";
 import { SelectedScheduleActions } from "./components/SelectedScheduleActions";
 import { useSelectedScheduleView } from "./useSelectedScheduleView";
 
+const anchorStyles =
+  "border-b-2 border-transparent pb-1 text-sm font-medium text-slate-600 transition-colors hover:text-slate-900 hover:border-slate-900";
+
 interface Props {
   agent: LibraryAgent;
   scheduleId: string;
@@ -41,6 +45,13 @@ export function SelectedScheduleView({
   const breakpoint = useBreakpoint();
   const isLgScreenUp = isLargeScreen(breakpoint);
 
+  function scrollToSection(id: string) {
+    const element = document.getElementById(id);
+    if (element) {
+      element.scrollIntoView({ behavior: "smooth", block: "start" });
+    }
+  }
+
   if (error) {
     return (
       <ErrorCard
@@ -97,6 +108,22 @@ export function SelectedScheduleView({
               ) : null}
             </div>
 
+            {/* Navigation Links */}
+            <AnchorLinksWrap>
+              <button
+                onClick={() => scrollToSection("schedule")}
+                className={anchorStyles}
+              >
+                Schedule
+              </button>
+              <button
+                onClick={() => scrollToSection("input")}
+                className={anchorStyles}
+              >
+                Your input
+              </button>
+            </AnchorLinksWrap>
+
             {/* Schedule Section */}
             <div id="schedule" className="scroll-mt-4">
               <RunDetailCard title="Schedule">

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/selected-views/SelectedScheduleView/components/EditInputsModal/EditInputsModal.tsx

@@ -0,0 +1,84 @@
+"use client";
+
+import type { GraphExecutionJobInfo } from "@/app/api/__generated__/models/graphExecutionJobInfo";
+import type { LibraryAgent } from "@/app/api/__generated__/models/libraryAgent";
+import { Button } from "@/components/atoms/Button/Button";
+import { Text } from "@/components/atoms/Text/Text";
+import { Dialog } from "@/components/molecules/Dialog/Dialog";
+import { PencilSimpleIcon } from "@phosphor-icons/react";
+import { RunAgentInputs } from "../../../../modals/RunAgentInputs/RunAgentInputs";
+import { useEditInputsModal } from "./useEditInputsModal";
+
+type Props = {
+  agent: LibraryAgent;
+  schedule: GraphExecutionJobInfo;
+};
+
+export function EditInputsModal({ agent, schedule }: Props) {
+  const {
+    isOpen,
+    setIsOpen,
+    inputFields,
+    values,
+    setValues,
+    handleSave,
+    isSaving,
+  } = useEditInputsModal(agent, schedule);
+
+  return (
+    <Dialog
+      controlled={{ isOpen, set: setIsOpen }}
+      styling={{ maxWidth: "32rem" }}
+    >
+      <Dialog.Trigger>
+        <Button
+          variant="ghost"
+          size="small"
+          className="absolute -right-2 -top-2"
+        >
+          <PencilSimpleIcon className="size-4" /> Edit inputs
+        </Button>
+      </Dialog.Trigger>
+      <Dialog.Content>
+        <div className="flex flex-col gap-4">
+          <Text variant="h3">Edit inputs</Text>
+          <div className="flex flex-col gap-4">
+            {Object.entries(inputFields).map(([key, fieldSchema]) => (
+              <div key={key} className="flex flex-col gap-1.5">
+                <label className="text-sm font-medium">
+                  {fieldSchema?.title || key}
+                </label>
+                <RunAgentInputs
+                  schema={fieldSchema as any}
+                  value={values[key]}
+                  onChange={(v) => setValues((prev) => ({ ...prev, [key]: v }))}
+                />
+              </div>
+            ))}
+          </div>
+        </div>
+        <Dialog.Footer>
+          <div className="flex w-full justify-end gap-2">
+            <Button
+              variant="secondary"
+              size="small"
+              onClick={() => setIsOpen(false)}
+              className="min-w-32"
+            >
+              Cancel
+            </Button>
+            <Button
+              variant="primary"
+              size="small"
+              onClick={handleSave}
+              loading={isSaving}
+              className="min-w-32"
+            >
+              {isSaving ? "Saving…" : "Save"}
+            </Button>
+          </div>
+        </Dialog.Footer>
+      </Dialog.Content>
+    </Dialog>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/selected-views/SelectedScheduleView/components/EditInputsModal/useEditInputsModal.ts

@@ -0,0 +1,78 @@
+"use client";
+
+import { useMemo, useState } from "react";
+import { useQueryClient } from "@tanstack/react-query";
+import { getGetV1ListExecutionSchedulesForAGraphQueryKey } from "@/app/api/__generated__/endpoints/schedules/schedules";
+import type { LibraryAgent } from "@/app/api/__generated__/models/libraryAgent";
+import type { GraphExecutionJobInfo } from "@/app/api/__generated__/models/graphExecutionJobInfo";
+import { useToast } from "@/components/molecules/Toast/use-toast";
+
+function getAgentInputFields(agent: LibraryAgent): Record<string, any> {
+  const schema = agent.input_schema as unknown as {
+    properties?: Record<string, any>;
+  } | null;
+  if (!schema || !schema.properties) return {};
+  const properties = schema.properties as Record<string, any>;
+  const visibleEntries = Object.entries(properties).filter(
+    ([, sub]) => !sub?.hidden,
+  );
+  return Object.fromEntries(visibleEntries);
+}
+
+export function useEditInputsModal(
+  agent: LibraryAgent,
+  schedule: GraphExecutionJobInfo,
+) {
+  const queryClient = useQueryClient();
+  const { toast } = useToast();
+  const [isOpen, setIsOpen] = useState(false);
+  const [isSaving, setIsSaving] = useState(false);
+  const inputFields = useMemo(() => getAgentInputFields(agent), [agent]);
+  const [values, setValues] = useState<Record<string, any>>({
+    ...(schedule.input_data as Record<string, any>),
+  });
+
+  async function handleSave() {
+    setIsSaving(true);
+    try {
+      const res = await fetch(`/api/schedules/${schedule.id}`, {
+        method: "PATCH",
+        headers: { "Content-Type": "application/json" },
+        body: JSON.stringify({ inputs: values }),
+      });
+      if (!res.ok) {
+        let message = "Failed to update schedule inputs";
+        const data = await res.json();
+        message = data?.message || data?.detail || message;
+        throw new Error(message);
+      }
+
+      await queryClient.invalidateQueries({
+        queryKey: getGetV1ListExecutionSchedulesForAGraphQueryKey(
+          schedule.graph_id,
+        ),
+      });
+      toast({
+        title: "Schedule inputs updated",
+      });
+      setIsOpen(false);
+    } catch (error: any) {
+      toast({
+        title: "Failed to update schedule inputs",
+        description: error?.message || "An unexpected error occurred.",
+        variant: "destructive",
+      });
+    }
+    setIsSaving(false);
+  }
+
+  return {
+    isOpen,
+    setIsOpen,
+    inputFields,
+    values,
+    setValues,
+    handleSave,
+    isSaving,
+  } as const;
+}

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/selected-views/SelectedScheduleView/components/SelectedScheduleActions.tsx

@@ -25,10 +25,9 @@ export function SelectedScheduleActions({ agent, scheduleId }: Props) {
           <Button
             variant="icon"
             size="icon"
+            aria-label="Open in builder"
             as="NextLink"
             href={openInBuilderHref}
-            target="_blank"
-            aria-label="View scheduled task details"
           >
             <EyeIcon weight="bold" size={18} className="text-zinc-700" />
           </Button>

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/selected-views/SelectedTemplateView/SelectedTemplateView.tsx

@@ -4,6 +4,7 @@ import type { GraphExecutionMeta } from "@/app/api/__generated__/models/graphExe
 import type { LibraryAgent } from "@/app/api/__generated__/models/libraryAgent";
 import { Input } from "@/components/atoms/Input/Input";
 import { ErrorCard } from "@/components/molecules/ErrorCard/ErrorCard";
+import { InformationTooltip } from "@/components/molecules/InformationTooltip/InformationTooltip";
 import {
   getAgentCredentialsFields,
   getAgentInputFields,
@@ -137,13 +138,25 @@ export function SelectedTemplateView({
               <RunDetailCard title="Your Input">
                 <div className="flex flex-col gap-4">
                   {inputFields.map(([key, inputSubSchema]) => (
-                    <RunAgentInputs
+                    <div
                       key={key}
-                      schema={inputSubSchema}
-                      value={inputs[key] ?? inputSubSchema.default}
-                      placeholder={inputSubSchema.description}
-                      onChange={(value) => setInputValue(key, value)}
-                    />
+                      className="flex w-full flex-col gap-0 space-y-2"
+                    >
+                      <label className="flex items-center gap-1 text-sm font-medium">
+                        {inputSubSchema.title || key}
+                        {inputSubSchema.description && (
+                          <InformationTooltip
+                            description={inputSubSchema.description}
+                          />
+                        )}
+                      </label>
+                      <RunAgentInputs
+                        schema={inputSubSchema}
+                        value={inputs[key] ?? inputSubSchema.default}
+                        placeholder={inputSubSchema.description}
+                        onChange={(value) => setInputValue(key, value)}
+                      />
+                    </div>
                   ))}
                 </div>
               </RunDetailCard>

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/selected-views/SelectedTriggerView/SelectedTriggerView.tsx

@@ -3,6 +3,7 @@
 import type { LibraryAgent } from "@/app/api/__generated__/models/libraryAgent";
 import { Input } from "@/components/atoms/Input/Input";
 import { ErrorCard } from "@/components/molecules/ErrorCard/ErrorCard";
+import { InformationTooltip } from "@/components/molecules/InformationTooltip/InformationTooltip";
 import {
   getAgentCredentialsFields,
   getAgentInputFields,
@@ -130,13 +131,25 @@ export function SelectedTriggerView({
               <RunDetailCard title="Your Input">
                 <div className="flex flex-col gap-4">
                   {inputFields.map(([key, inputSubSchema]) => (
-                    <RunAgentInputs
+                    <div
                       key={key}
-                      schema={inputSubSchema}
-                      value={inputs[key] ?? inputSubSchema.default}
-                      placeholder={inputSubSchema.description}
-                      onChange={(value) => setInputValue(key, value)}
-                    />
+                      className="flex w-full flex-col gap-0 space-y-2"
+                    >
+                      <label className="flex items-center gap-1 text-sm font-medium">
+                        {inputSubSchema.title || key}
+                        {inputSubSchema.description && (
+                          <InformationTooltip
+                            description={inputSubSchema.description}
+                          />
+                        )}
+                      </label>
+                      <RunAgentInputs
+                        schema={inputSubSchema}
+                        value={inputs[key] ?? inputSubSchema.default}
+                        placeholder={inputSubSchema.description}
+                        onChange={(value) => setInputValue(key, value)}
+                      />
+                    </div>
                   ))}
                 </div>
               </RunDetailCard>

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/OldAgentLibraryView/components/agent-run-draft-view.tsx

@@ -680,20 +680,28 @@ export function AgentRunDraftView({
 
             {/* Regular inputs */}
             {Object.entries(agentInputFields).map(([key, inputSubSchema]) => (
-              <RunAgentInputs
-                key={key}
-                schema={inputSubSchema}
-                value={inputValues[key] ?? inputSubSchema.default}
-                placeholder={inputSubSchema.description}
-                onChange={(value) => {
-                  setInputValues((obj) => ({
-                    ...obj,
-                    [key]: value,
-                  }));
-                  setChangedPresetAttributes((prev) => prev.add("inputs"));
-                }}
-                data-testid={`agent-input-${key}`}
-              />
+              <div key={key} className="flex flex-col space-y-2">
+                <label className="flex items-center gap-1 text-sm font-medium">
+                  {inputSubSchema.title || key}
+                  <InformationTooltip
+                    description={inputSubSchema.description}
+                  />
+                </label>
+
+                <RunAgentInputs
+                  schema={inputSubSchema}
+                  value={inputValues[key] ?? inputSubSchema.default}
+                  placeholder={inputSubSchema.description}
+                  onChange={(value) => {
+                    setInputValues((obj) => ({
+                      ...obj,
+                      [key]: value,
+                    }));
+                    setChangedPresetAttributes((prev) => prev.add("inputs"));
+                  }}
+                  data-testid={`agent-input-${key}`}
+                />
+              </div>
             ))}
           </CardContent>
         </Card>

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

@@ -2690,7 +2690,7 @@
       "get": {
         "tags": ["v2", "store", "public"],
         "summary": "List store agents",
-        "description": "Get a paginated list of agents from the store with optional filtering and sorting.\n\nWhen search_query is provided, uses hybrid search combining:\n- BM25 full-text search (lexical matching)\n- Vector semantic similarity (meaning-based matching)\n- Popularity signal (run counts)\n\nResults are ranked using Reciprocal Rank Fusion (RRF).\n\nArgs:\n    featured (bool, optional): Filter to only show featured agents. Defaults to False.\n    creator (str | None, optional): Filter agents by creator username. Defaults to None.\n    sorted_by (str | None, optional): Sort agents by \"runs\" or \"rating\". Defaults to None.\n    search_query (str | None, optional): Search agents by name, subheading and description.\n    category (str | None, optional): Filter agents by category. Defaults to None.\n    page (int, optional): Page number for pagination. Defaults to 1.\n    page_size (int, optional): Number of agents per page. Defaults to 20.\n    filter_mode (str, optional): Controls result filtering when searching:\n        - \"strict\": Must match BOTH BM25 AND vector thresholds\n        - \"permissive\": Must match EITHER BM25 OR vector threshold\n        - \"combined\": No threshold filtering, rely on RRF score (default)\n\nReturns:\n    StoreAgentsResponse: Paginated list of agents matching the filters\n\nRaises:\n    HTTPException: If page or page_size are less than 1\n\nUsed for:\n- Home Page Featured Agents\n- Home Page Top Agents\n- Search Results\n- Agent Details - Other Agents By Creator\n- Agent Details - Similar Agents\n- Creator Details - Agents By Creator",
+        "description": "Get a paginated list of agents from the store with optional filtering and sorting.\n\nArgs:\n    featured (bool, optional): Filter to only show featured agents. Defaults to False.\n    creator (str | None, optional): Filter agents by creator username. Defaults to None.\n    sorted_by (str | None, optional): Sort agents by \"runs\" or \"rating\". Defaults to None.\n    search_query (str | None, optional): Search agents by name, subheading and description. Defaults to None.\n    category (str | None, optional): Filter agents by category. Defaults to None.\n    page (int, optional): Page number for pagination. Defaults to 1.\n    page_size (int, optional): Number of agents per page. Defaults to 20.\n\nReturns:\n    StoreAgentsResponse: Paginated list of agents matching the filters\n\nRaises:\n    HTTPException: If page or page_size are less than 1\n\nUsed for:\n- Home Page Featured Agents\n- Home Page Top Agents\n- Search Results\n- Agent Details - Other Agents By Creator\n- Agent Details - Similar Agents\n- Creator Details - Agents By Creator",
         "operationId": "getV2List store agents",
         "parameters": [
           {
@@ -2756,17 +2756,6 @@
             "in": "query",
             "required": false,
             "schema": { "type": "integer", "default": 20, "title": "Page Size" }
-          },
-          {
-            "name": "filter_mode",
-            "in": "query",
-            "required": false,
-            "schema": {
-              "enum": ["strict", "permissive", "combined"],
-              "type": "string",
-              "default": "permissive",
-              "title": "Filter Mode"
-            }
           }
         ],
         "responses": {

autogpt_platform/frontend/src/app/api/proxy/[...path]/route.ts

@@ -6,10 +6,6 @@ import {
 import { environment } from "@/services/environment";
 import { NextRequest, NextResponse } from "next/server";
 
-// Increase body size limit to 256MB to match backend file upload limit
-export const maxDuration = 300; // 5 minutes timeout for large uploads
-export const dynamic = "force-dynamic";
-
 function buildBackendUrl(path: string[], queryString: string): string {
   const backendPath = path.join("/");
   return `${environment.getAGPTServerBaseUrl()}/${backendPath}${queryString}`;

autogpt_platform/frontend/src/app/providers.tsx

@@ -1,33 +1,36 @@
 "use client";
 
-import { TooltipProvider } from "@/components/atoms/Tooltip/BaseTooltip";
-import { SentryUserTracker } from "@/components/monitor/SentryUserTracker";
+import { LaunchDarklyProvider } from "@/services/feature-flags/feature-flag-provider";
+import OnboardingProvider from "@/providers/onboarding/onboarding-provider";
 import { BackendAPIProvider } from "@/lib/autogpt-server-api/context";
 import { getQueryClient } from "@/lib/react-query/queryClient";
-import CredentialsProvider from "@/providers/agent-credentials/credentials-provider";
-import OnboardingProvider from "@/providers/onboarding/onboarding-provider";
-import { LaunchDarklyProvider } from "@/services/feature-flags/feature-flag-provider";
 import { QueryClientProvider } from "@tanstack/react-query";
-import { ThemeProvider, ThemeProviderProps } from "next-themes";
+import {
+  ThemeProvider as NextThemesProvider,
+  ThemeProviderProps,
+} from "next-themes";
 import { NuqsAdapter } from "nuqs/adapters/next/app";
+import { TooltipProvider } from "@/components/atoms/Tooltip/BaseTooltip";
+import CredentialsProvider from "@/providers/agent-credentials/credentials-provider";
+import { SentryUserTracker } from "@/components/monitor/SentryUserTracker";
 
 export function Providers({ children, ...props }: ThemeProviderProps) {
   const queryClient = getQueryClient();
   return (
     <QueryClientProvider client={queryClient}>
       <NuqsAdapter>
-        <BackendAPIProvider>
-          <SentryUserTracker />
-          <CredentialsProvider>
-            <LaunchDarklyProvider>
-              <OnboardingProvider>
-                <ThemeProvider forcedTheme="light" {...props}>
+        <NextThemesProvider {...props}>
+          <BackendAPIProvider>
+            <SentryUserTracker />
+            <CredentialsProvider>
+              <LaunchDarklyProvider>
+                <OnboardingProvider>
                   <TooltipProvider>{children}</TooltipProvider>
-                </ThemeProvider>
-              </OnboardingProvider>
-            </LaunchDarklyProvider>
-          </CredentialsProvider>
-        </BackendAPIProvider>
+                </OnboardingProvider>
+              </LaunchDarklyProvider>
+            </CredentialsProvider>
+          </BackendAPIProvider>
+        </NextThemesProvider>
       </NuqsAdapter>
     </QueryClientProvider>
   );

autogpt_platform/frontend/src/components/atoms/OverflowText/OverflowText.stories.tsx

@@ -1,157 +0,0 @@
-import type { Meta, StoryObj } from "@storybook/nextjs";
-import { OverflowText } from "./OverflowText";
-
-const meta: Meta<typeof OverflowText> = {
-  title: "Atoms/OverflowText",
-  component: OverflowText,
-  tags: ["autodocs"],
-  parameters: {
-    layout: "centered",
-    docs: {
-      description: {
-        component:
-          "Text component that automatically truncates overflowing content with ellipsis and shows a tooltip on hover when truncated. Supports both string and ReactNode values.",
-      },
-    },
-  },
-  argTypes: {
-    value: {
-      control: "text",
-      description: "The text content to display (string or ReactNode)",
-    },
-    className: {
-      control: "text",
-      description: "Additional CSS classes to customize styling",
-    },
-  },
-  args: {
-    value: "This is a sample text that may overflow",
-    className: "",
-  },
-};
-
-export default meta;
-type Story = StoryObj<typeof meta>;
-
-export const Default: Story = {
-  render: function DefaultOverflowText(args) {
-    return (
-      <div className="w-64">
-        <OverflowText {...args} />
-      </div>
-    );
-  },
-};
-
-export const ShortText: Story = {
-  args: {
-    value: "Short text",
-  },
-  render: function ShortTextStory(args) {
-    return (
-      <div className="w-64">
-        <OverflowText {...args} />
-      </div>
-    );
-  },
-};
-
-export const LongText: Story = {
-  args: {
-    value:
-      "This is a very long text that will definitely overflow and show a tooltip when you hover over it",
-  },
-  render: function LongTextStory(args) {
-    return (
-      <div className="w-64">
-        <OverflowText {...args} />
-      </div>
-    );
-  },
-};
-
-export const CustomStyling: Story = {
-  args: {
-    value: "Text with custom styling",
-    className: "text-lg font-semibold text-indigo-600",
-  },
-  render: function CustomStylingStory(args) {
-    return (
-      <div className="w-64">
-        <OverflowText {...args} />
-      </div>
-    );
-  },
-};
-
-export const WithReactNode: Story = {
-  args: {
-    value: (
-      <span>
-        Text with <strong>bold</strong> and <em>italic</em> content
-      </span>
-    ),
-  },
-  render: function WithReactNodeStory(args) {
-    return (
-      <div className="w-64">
-        <OverflowText {...args} />
-      </div>
-    );
-  },
-};
-
-export const DifferentWidths: Story = {
-  render: function DifferentWidthsStory() {
-    const longText =
-      "This text will truncate differently depending on the container width";
-    return (
-      <div className="flex flex-col gap-8">
-        <div className="flex flex-col gap-2">
-          <span className="text-xs text-zinc-500">Width: 200px</span>
-          <div className="w-[200px]">
-            <OverflowText value={longText} variant="body" />
-          </div>
-        </div>
-        <div className="flex flex-col gap-2">
-          <span className="text-xs text-zinc-500">Width: 300px</span>
-          <div className="w-[300px]">
-            <OverflowText value={longText} variant="body" />
-          </div>
-        </div>
-        <div className="flex flex-col gap-2">
-          <span className="text-xs text-zinc-500">Width: 400px</span>
-          <div className="w-[400px]">
-            <OverflowText value={longText} variant="body" />
-          </div>
-        </div>
-      </div>
-    );
-  },
-};
-
-export const FilePathExample: Story = {
-  args: {
-    value: "/very/long/path/to/a/file/that/might/overflow/in/the/ui.tsx",
-  },
-  render: function FilePathExampleStory(args) {
-    return (
-      <div className="w-64">
-        <OverflowText {...args} className="font-mono text-sm" />
-      </div>
-    );
-  },
-};
-
-export const URLExample: Story = {
-  args: {
-    value: "https://example.com/very/long/url/path/that/might/overflow",
-  },
-  render: function URLExampleStory(args) {
-    return (
-      <div className="w-64">
-        <OverflowText {...args} className="text-blue-600" />
-      </div>
-    );
-  },
-};

autogpt_platform/frontend/src/components/atoms/OverflowText/OverflowText.tsx

@@ -1,100 +0,0 @@
-import { Text, type TextProps } from "@/components/atoms/Text/Text";
-import {
-  Tooltip,
-  TooltipContent,
-  TooltipProvider,
-  TooltipTrigger,
-} from "@/components/atoms/Tooltip/BaseTooltip";
-import { cn } from "@/lib/utils";
-import type { ReactNode } from "react";
-import { useEffect, useRef, useState } from "react";
-
-interface Props extends Omit<TextProps, "children"> {
-  value: string | ReactNode;
-}
-
-export function OverflowText(props: Props) {
-  const elementRef = useRef<HTMLSpanElement | null>(null);
-  const [isTruncated, setIsTruncated] = useState(false);
-
-  function updateTruncation() {
-    const element = elementRef.current;
-
-    if (!element) {
-      return;
-    }
-
-    const hasOverflow = element.scrollWidth > element.clientWidth;
-
-    setIsTruncated(hasOverflow);
-  }
-
-  function setupResizeListener() {
-    function handleResize() {
-      updateTruncation();
-    }
-
-    window.addEventListener("resize", handleResize);
-
-    return function cleanupResizeListener() {
-      window.removeEventListener("resize", handleResize);
-    };
-  }
-
-  function setupObserver() {
-    const element = elementRef.current;
-
-    if (!element || typeof ResizeObserver === "undefined") {
-      return undefined;
-    }
-
-    function handleResizeObserver() {
-      updateTruncation();
-    }
-
-    const observer = new ResizeObserver(handleResizeObserver);
-
-    observer.observe(element);
-
-    return function disconnectObserver() {
-      observer.disconnect();
-    };
-  }
-
-  useEffect(() => {
-    if (typeof props.value === "string") updateTruncation();
-  }, [props.value]);
-
-  useEffect(setupResizeListener, []);
-  useEffect(setupObserver, []);
-
-  const { value, className, variant = "body", ...restProps } = props;
-
-  const content = (
-    <span
-      ref={elementRef}
-      className={cn(
-        "block min-w-0 overflow-hidden text-ellipsis whitespace-nowrap",
-      )}
-    >
-      <Text variant={variant} className={className} {...restProps}>
-        {value}
-      </Text>
-    </span>
-  );
-
-  if (isTruncated) {
-    return (
-      <TooltipProvider>
-        <Tooltip>
-          <TooltipTrigger asChild>{content}</TooltipTrigger>
-          <TooltipContent>
-            {typeof value === "string" ? <p>{value}</p> : value}
-          </TooltipContent>
-        </Tooltip>
-      </TooltipProvider>
-    );
-  }
-
-  return content;
-}

autogpt_platform/frontend/src/components/contextual/GoogleDrivePicker/GoogleDrivePickerInput.tsx

@@ -5,7 +5,7 @@ import { Cross2Icon } from "@radix-ui/react-icons";
 import React, { useCallback } from "react";
 import { GoogleDrivePicker } from "./GoogleDrivePicker";
 
-export interface Props {
+export interface GoogleDrivePickerInputProps {
   config: GoogleDrivePickerConfig;
   value: any;
   onChange: (value: any) => void;
@@ -21,7 +21,7 @@ export function GoogleDrivePickerInput({
   error,
   className,
   showRemoveButton = true,
-}: Props) {
+}: GoogleDrivePickerInputProps) {
   const [pickerError, setPickerError] = React.useState<string | null>(null);
   const isMultiSelect = config.multiselect || false;
   const hasAutoCredentials = !!config.auto_credentials;

autogpt_platform/frontend/src/components/layout/Navbar/components/MobileNavbar/components/MobileNavbarMenuItem.tsx

@@ -19,7 +19,7 @@ export function MobileNavbarMenuItem({
   onClick,
 }: Props) {
   const content = (
-    <div className="inline-flex w-full items-center justify-start gap-4 py-2 hover:rounded hover:bg-[#e0e0e0]">
+    <div className="inline-flex w-full items-center justify-start gap-4 hover:rounded hover:bg-[#e0e0e0]">
       {getAccountMenuOptionIcon(icon)}
       <div className="relative">
         <div

autogpt_platform/frontend/src/components/molecules/Dialog/components/styles.ts

@@ -3,7 +3,8 @@ const commonStyles = {
   title: "font-poppins text-md md:text-lg leading-none",
   overlay:
     "fixed inset-0 z-50 bg-stone-500/20 dark:bg-black/50 backdrop-blur-md animate-fade-in",
-  content: "bg-white p-6 fixed rounded-2xlarge flex flex-col z-50 w-full",
+  content:
+    "overflow-y-hidden bg-white p-6 fixed rounded-2xlarge flex flex-col z-50 w-full",
 };
 
 // Modal specific styles

autogpt_platform/frontend/src/components/molecules/InformationTooltip/InformationTooltip.tsx

@@ -9,20 +9,16 @@ import ReactMarkdown from "react-markdown";
 
 type Props = {
   description?: string;
-  iconSize?: number;
 };
 
-export function InformationTooltip({ description, iconSize = 24 }: Props) {
+export function InformationTooltip({ description }: Props) {
   if (!description) return null;
 
   return (
     <TooltipProvider delayDuration={400}>
       <Tooltip>
         <TooltipTrigger asChild>
-          <Info
-            className="rounded-full p-1 hover:bg-slate-50"
-            size={iconSize}
-          />
+          <Info className="rounded-full p-1 hover:bg-slate-50" size={24} />
         </TooltipTrigger>
         <TooltipContent>
           <ReactMarkdown

autogpt_platform/frontend/src/components/molecules/ScrollableTabs/ScrollableTabs.stories.tsx

@@ -1,437 +0,0 @@
-import type { Meta, StoryObj } from "@storybook/nextjs";
-import {
-  ScrollableTabs,
-  ScrollableTabsContent,
-  ScrollableTabsList,
-  ScrollableTabsTrigger,
-} from "./ScrollableTabs";
-
-const meta = {
-  title: "Molecules/ScrollableTabs",
-  component: ScrollableTabs,
-  parameters: {
-    layout: "fullscreen",
-  },
-  tags: ["autodocs"],
-  argTypes: {},
-} satisfies Meta<typeof ScrollableTabs>;
-
-export default meta;
-type Story = StoryObj<typeof meta>;
-
-function ScrollableTabsDemo() {
-  return (
-    <div className="flex flex-col gap-8 p-8">
-      <h2 className="text-2xl font-bold">ScrollableTabs Examples</h2>
-
-      <div className="space-y-6">
-        <div>
-          <h3 className="mb-4 text-lg font-semibold">
-            Short Content (Tabs Hidden)
-          </h3>
-          <div className="h-[300px] overflow-y-auto border border-zinc-200">
-            <ScrollableTabs defaultValue="tab1" className="h-full">
-              <ScrollableTabsList>
-                <ScrollableTabsTrigger value="tab1">
-                  Account
-                </ScrollableTabsTrigger>
-                <ScrollableTabsTrigger value="tab2">
-                  Password
-                </ScrollableTabsTrigger>
-                <ScrollableTabsTrigger value="tab3">
-                  Settings
-                </ScrollableTabsTrigger>
-              </ScrollableTabsList>
-              <ScrollableTabsContent value="tab1">
-                <div className="p-4 text-sm">
-                  Make changes to your account here. Click save when you&apos;re
-                  done.
-                </div>
-              </ScrollableTabsContent>
-              <ScrollableTabsContent value="tab2">
-                <div className="p-4 text-sm">
-                  Change your password here. After saving, you&apos;ll be logged
-                  out.
-                </div>
-              </ScrollableTabsContent>
-              <ScrollableTabsContent value="tab3">
-                <div className="p-4 text-sm">
-                  Update your preferences and settings here.
-                </div>
-              </ScrollableTabsContent>
-            </ScrollableTabs>
-          </div>
-        </div>
-
-        <div>
-          <h3 className="mb-4 text-lg font-semibold">
-            Long Content (Tabs Visible)
-          </h3>
-          <div className="h-[400px] overflow-y-auto border border-zinc-200">
-            <ScrollableTabs defaultValue="tab1" className="h-full">
-              <ScrollableTabsList>
-                <ScrollableTabsTrigger value="tab1">
-                  Account
-                </ScrollableTabsTrigger>
-                <ScrollableTabsTrigger value="tab2">
-                  Password
-                </ScrollableTabsTrigger>
-                <ScrollableTabsTrigger value="tab3">
-                  Settings
-                </ScrollableTabsTrigger>
-              </ScrollableTabsList>
-              <ScrollableTabsContent value="tab1">
-                <div className="p-8 text-sm">
-                  <h4 className="mb-4 text-lg font-semibold">
-                    Account Settings
-                  </h4>
-                  <p className="mb-4">
-                    Make changes to your account here. Click save when
-                    you&apos;re done.
-                  </p>
-                  <p className="mb-4">
-                    Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed
-                    do eiusmod tempor incididunt ut labore et dolore magna
-                    aliqua. Ut enim ad minim veniam, quis nostrud exercitation
-                    ullamco laboris.
-                  </p>
-                  <p className="mb-4">
-                    Duis aute irure dolor in reprehenderit in voluptate velit
-                    esse cillum dolore eu fugiat nulla pariatur. Excepteur sint
-                    occaecat cupidatat non proident.
-                  </p>
-                  <p>
-                    Sed ut perspiciatis unde omnis iste natus error sit
-                    voluptatem accusantium doloremque laudantium, totam rem
-                    aperiam.
-                  </p>
-                </div>
-              </ScrollableTabsContent>
-              <ScrollableTabsContent value="tab2">
-                <div className="p-8 text-sm">
-                  <h4 className="mb-4 text-lg font-semibold">
-                    Password Settings
-                  </h4>
-                  <p className="mb-4">
-                    Change your password here. After saving, you&apos;ll be
-                    logged out.
-                  </p>
-                  <p className="mb-4">
-                    At vero eos et accusamus et iusto odio dignissimos ducimus
-                    qui blanditiis praesentium voluptatum deleniti atque
-                    corrupti quos dolores et quas molestias excepturi sint
-                    occaecati cupiditate.
-                  </p>
-                  <p className="mb-4">
-                    Et harum quidem rerum facilis est et expedita distinctio.
-                    Nam libero tempore, cum soluta nobis est eligendi optio
-                    cumque nihil impedit quo minus.
-                  </p>
-                  <p>
-                    Temporibus autem quibusdam et aut officiis debitis aut rerum
-                    necessitatibus saepe eveniet ut et voluptates repudiandae
-                    sint.
-                  </p>
-                </div>
-              </ScrollableTabsContent>
-              <ScrollableTabsContent value="tab3">
-                <div className="p-8 text-sm">
-                  <h4 className="mb-4 text-lg font-semibold">
-                    General Settings
-                  </h4>
-                  <p className="mb-4">
-                    Update your preferences and settings here.
-                  </p>
-                  <p className="mb-4">
-                    Nemo enim ipsam voluptatem quia voluptas sit aspernatur aut
-                    odit aut fugit, sed quia consequuntur magni dolores eos qui
-                    ratione voluptatem sequi nesciunt.
-                  </p>
-                  <p className="mb-4">
-                    Neque porro quisquam est, qui dolorem ipsum quia dolor sit
-                    amet, consectetur, adipisci velit, sed quia non numquam eius
-                    modi tempora incidunt ut labore et dolore magnam aliquam
-                    quaerat voluptatem.
-                  </p>
-                  <p>
-                    Ut enim ad minima veniam, quis nostrum exercitationem ullam
-                    corporis suscipit laboriosam, nisi ut aliquid ex ea commodi
-                    consequatur.
-                  </p>
-                </div>
-              </ScrollableTabsContent>
-            </ScrollableTabs>
-          </div>
-        </div>
-
-        <div>
-          <h3 className="mb-4 text-lg font-semibold">Many Tabs</h3>
-          <div className="h-[500px] overflow-y-auto border border-zinc-200">
-            <ScrollableTabs defaultValue="overview" className="h-full">
-              <ScrollableTabsList>
-                <ScrollableTabsTrigger value="overview">
-                  Overview
-                </ScrollableTabsTrigger>
-                <ScrollableTabsTrigger value="analytics">
-                  Analytics
-                </ScrollableTabsTrigger>
-                <ScrollableTabsTrigger value="reports">
-                  Reports
-                </ScrollableTabsTrigger>
-                <ScrollableTabsTrigger value="notifications">
-                  Notifications
-                </ScrollableTabsTrigger>
-                <ScrollableTabsTrigger value="integrations">
-                  Integrations
-                </ScrollableTabsTrigger>
-                <ScrollableTabsTrigger value="billing">
-                  Billing
-                </ScrollableTabsTrigger>
-              </ScrollableTabsList>
-              <ScrollableTabsContent value="overview">
-                <div className="p-8 text-sm">
-                  <h4 className="mb-4 text-lg font-semibold">
-                    Dashboard Overview
-                  </h4>
-                  <p className="mb-4">
-                    Dashboard overview with key metrics and recent activity.
-                  </p>
-                  <p className="mb-4">
-                    Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed
-                    do eiusmod tempor incididunt ut labore et dolore magna
-                    aliqua.
-                  </p>
-                  <p>
-                    Ut enim ad minim veniam, quis nostrud exercitation ullamco
-                    laboris nisi ut aliquip ex ea commodo consequat.
-                  </p>
-                </div>
-              </ScrollableTabsContent>
-              <ScrollableTabsContent value="analytics">
-                <div className="p-8 text-sm">
-                  <h4 className="mb-4 text-lg font-semibold">Analytics</h4>
-                  <p className="mb-4">
-                    Detailed analytics and performance metrics.
-                  </p>
-                  <p className="mb-4">
-                    Duis aute irure dolor in reprehenderit in voluptate velit
-                    esse cillum dolore eu fugiat nulla pariatur.
-                  </p>
-                  <p>
-                    Excepteur sint occaecat cupidatat non proident, sunt in
-                    culpa qui officia deserunt mollit anim id est laborum.
-                  </p>
-                </div>
-              </ScrollableTabsContent>
-              <ScrollableTabsContent value="reports">
-                <div className="p-8 text-sm">
-                  <h4 className="mb-4 text-lg font-semibold">Reports</h4>
-                  <p className="mb-4">
-                    Generate and view reports for your account.
-                  </p>
-                  <p className="mb-4">
-                    Sed ut perspiciatis unde omnis iste natus error sit
-                    voluptatem accusantium doloremque laudantium.
-                  </p>
-                  <p>
-                    Totam rem aperiam, eaque ipsa quae ab illo inventore
-                    veritatis et quasi architecto beatae vitae dicta sunt
-                    explicabo.
-                  </p>
-                </div>
-              </ScrollableTabsContent>
-              <ScrollableTabsContent value="notifications">
-                <div className="p-8 text-sm">
-                  <h4 className="mb-4 text-lg font-semibold">Notifications</h4>
-                  <p className="mb-4">Manage your notification preferences.</p>
-                  <p className="mb-4">
-                    Nemo enim ipsam voluptatem quia voluptas sit aspernatur aut
-                    odit aut fugit.
-                  </p>
-                  <p>
-                    Sed quia consequuntur magni dolores eos qui ratione
-                    voluptatem sequi nesciunt.
-                  </p>
-                </div>
-              </ScrollableTabsContent>
-              <ScrollableTabsContent value="integrations">
-                <div className="p-8 text-sm">
-                  <h4 className="mb-4 text-lg font-semibold">Integrations</h4>
-                  <p className="mb-4">
-                    Connect and manage third-party integrations.
-                  </p>
-                  <p className="mb-4">
-                    Neque porro quisquam est, qui dolorem ipsum quia dolor sit
-                    amet.
-                  </p>
-                  <p>
-                    Consectetur, adipisci velit, sed quia non numquam eius modi
-                    tempora incidunt.
-                  </p>
-                </div>
-              </ScrollableTabsContent>
-              <ScrollableTabsContent value="billing">
-                <div className="p-8 text-sm">
-                  <h4 className="mb-4 text-lg font-semibold">Billing</h4>
-                  <p className="mb-4">
-                    View and manage your billing information.
-                  </p>
-                  <p className="mb-4">
-                    Ut enim ad minima veniam, quis nostrum exercitationem ullam
-                    corporis suscipit laboriosam.
-                  </p>
-                  <p>
-                    Nisi ut aliquid ex ea commodi consequatur? Quis autem vel
-                    eum iure reprehenderit qui in ea voluptate velit esse.
-                  </p>
-                </div>
-              </ScrollableTabsContent>
-            </ScrollableTabs>
-          </div>
-        </div>
-      </div>
-    </div>
-  );
-}
-
-export const Default = {
-  render: () => <ScrollableTabsDemo />,
-} satisfies Story;
-
-export const ShortContent = {
-  render: () => (
-    <div className="p-8">
-      <div className="h-[200px] overflow-y-auto border border-zinc-200">
-        <ScrollableTabs defaultValue="account" className="h-full">
-          <ScrollableTabsList>
-            <ScrollableTabsTrigger value="account">
-              Account
-            </ScrollableTabsTrigger>
-            <ScrollableTabsTrigger value="password">
-              Password
-            </ScrollableTabsTrigger>
-          </ScrollableTabsList>
-          <ScrollableTabsContent value="account">
-            <div className="p-4 text-sm">
-              Make changes to your account here. Click save when you&apos;re
-              done.
-            </div>
-          </ScrollableTabsContent>
-          <ScrollableTabsContent value="password">
-            <div className="p-4 text-sm">
-              Change your password here. After saving, you&apos;ll be logged
-              out.
-            </div>
-          </ScrollableTabsContent>
-        </ScrollableTabs>
-      </div>
-    </div>
-  ),
-} satisfies Story;
-
-export const LongContent = {
-  render: () => (
-    <div className="p-8">
-      <div className="h-[600px] overflow-y-auto border border-zinc-200">
-        <ScrollableTabs defaultValue="tab1" className="h-full">
-          <ScrollableTabsList>
-            <ScrollableTabsTrigger value="tab1">Account</ScrollableTabsTrigger>
-            <ScrollableTabsTrigger value="tab2">Password</ScrollableTabsTrigger>
-            <ScrollableTabsTrigger value="tab3">Settings</ScrollableTabsTrigger>
-          </ScrollableTabsList>
-          <ScrollableTabsContent value="tab1">
-            <div className="p-8 text-sm">
-              <h4 className="mb-4 text-lg font-semibold">Account Settings</h4>
-              <p className="mb-4">
-                Make changes to your account here. Click save when you&apos;re
-                done.
-              </p>
-              <p className="mb-4">
-                Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed do
-                eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut
-                enim ad minim veniam, quis nostrud exercitation ullamco laboris
-                nisi ut aliquip ex ea commodo consequat.
-              </p>
-              <p className="mb-4">
-                Duis aute irure dolor in reprehenderit in voluptate velit esse
-                cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat
-                cupidatat non proident, sunt in culpa qui officia deserunt
-                mollit anim id est laborum.
-              </p>
-              <p className="mb-4">
-                Sed ut perspiciatis unde omnis iste natus error sit voluptatem
-                accusantium doloremque laudantium, totam rem aperiam, eaque ipsa
-                quae ab illo inventore veritatis et quasi architecto beatae
-                vitae dicta sunt explicabo.
-              </p>
-              <p>
-                Nemo enim ipsam voluptatem quia voluptas sit aspernatur aut odit
-                aut fugit, sed quia consequuntur magni dolores eos qui ratione
-                voluptatem sequi nesciunt.
-              </p>
-            </div>
-          </ScrollableTabsContent>
-          <ScrollableTabsContent value="tab2">
-            <div className="p-8 text-sm">
-              <h4 className="mb-4 text-lg font-semibold">Password Settings</h4>
-              <p className="mb-4">
-                Change your password here. After saving, you&apos;ll be logged
-                out.
-              </p>
-              <p className="mb-4">
-                At vero eos et accusamus et iusto odio dignissimos ducimus qui
-                blanditiis praesentium voluptatum deleniti atque corrupti quos
-                dolores et quas molestias excepturi sint occaecati cupiditate
-                non provident.
-              </p>
-              <p className="mb-4">
-                Similique sunt in culpa qui officia deserunt mollitia animi, id
-                est laborum et dolorum fuga. Et harum quidem rerum facilis est
-                et expedita distinctio.
-              </p>
-              <p className="mb-4">
-                Nam libero tempore, cum soluta nobis est eligendi optio cumque
-                nihil impedit quo minus id quod maxime placeat facere possimus,
-                omnis voluptas assumenda est, omnis dolor repellendus.
-              </p>
-              <p>
-                Temporibus autem quibusdam et aut officiis debitis aut rerum
-                necessitatibus saepe eveniet ut et voluptates repudiandae sint
-                et molestiae non recusandae.
-              </p>
-            </div>
-          </ScrollableTabsContent>
-          <ScrollableTabsContent value="tab3">
-            <div className="p-8 text-sm">
-              <h4 className="mb-4 text-lg font-semibold">General Settings</h4>
-              <p className="mb-4">Update your preferences and settings here.</p>
-              <p className="mb-4">
-                Neque porro quisquam est, qui dolorem ipsum quia dolor sit amet,
-                consectetur, adipisci velit, sed quia non numquam eius modi
-                tempora incidunt ut labore et dolore magnam aliquam quaerat
-                voluptatem.
-              </p>
-              <p className="mb-4">
-                Ut enim ad minima veniam, quis nostrum exercitationem ullam
-                corporis suscipit laboriosam, nisi ut aliquid ex ea commodi
-                consequatur? Quis autem vel eum iure reprehenderit qui in ea
-                voluptate velit esse quam nihil molestiae consequatur.
-              </p>
-              <p className="mb-4">
-                Vel illum qui dolorem eum fugiat quo voluptas nulla pariatur? At
-                vero eos et accusamus et iusto odio dignissimos ducimus qui
-                blanditiis praesentium voluptatum deleniti atque corrupti quos
-                dolores.
-              </p>
-              <p>
-                Et quas molestias excepturi sint occaecati cupiditate non
-                provident, similique sunt in culpa qui officia deserunt mollitia
-                animi, id est laborum et dolorum fuga.
-              </p>
-            </div>
-          </ScrollableTabsContent>
-        </ScrollableTabs>
-      </div>
-    </div>
-  ),
-} satisfies Story;

autogpt_platform/frontend/src/components/molecules/ScrollableTabs/ScrollableTabs.tsx

@@ -1,59 +0,0 @@
-"use client";
-
-import { cn } from "@/lib/utils";
-import { Children } from "react";
-import { ScrollableTabsContent } from "./components/ScrollableTabsContent";
-import { ScrollableTabsList } from "./components/ScrollableTabsList";
-import { ScrollableTabsTrigger } from "./components/ScrollableTabsTrigger";
-import { ScrollableTabsContext } from "./context";
-import { findContentElements, findListElement } from "./helpers";
-import { useScrollableTabsInternal } from "./useScrollableTabs";
-
-interface Props {
-  children?: React.ReactNode;
-  className?: string;
-  defaultValue?: string;
-}
-
-export function ScrollableTabs({ children, className, defaultValue }: Props) {
-  const {
-    activeValue,
-    setActiveValue,
-    registerContent,
-    scrollToSection,
-    scrollContainer,
-    contentContainerRef,
-  } = useScrollableTabsInternal({ defaultValue });
-
-  const childrenArray = Children.toArray(children);
-  const listElement = findListElement(childrenArray);
-  const contentElements = findContentElements(childrenArray);
-
-  return (
-    <ScrollableTabsContext.Provider
-      value={{
-        activeValue,
-        setActiveValue,
-        registerContent,
-        scrollToSection,
-        scrollContainer,
-      }}
-    >
-      <div className={cn("relative flex flex-col", className)}>
-        {listElement}
-        <div
-          ref={(node) => {
-            if (contentContainerRef) {
-              contentContainerRef.current = node;
-            }
-          }}
-          className="max-h-[64rem] overflow-y-auto scrollbar-thin scrollbar-track-transparent scrollbar-thumb-zinc-300 dark:scrollbar-thumb-zinc-700"
-        >
-          <div className="min-h-full pb-[200px]">{contentElements}</div>
-        </div>
-      </div>
-    </ScrollableTabsContext.Provider>
-  );
-}
-
-export { ScrollableTabsContent, ScrollableTabsList, ScrollableTabsTrigger };

autogpt_platform/frontend/src/components/molecules/ScrollableTabs/components/ScrollableTabsContent.tsx

@@ -1,48 +0,0 @@
-"use client";
-
-import { cn } from "@/lib/utils";
-import * as React from "react";
-import { useScrollableTabs } from "../context";
-
-interface Props extends React.HTMLAttributes<HTMLDivElement> {
-  value: string;
-}
-
-export const ScrollableTabsContent = React.forwardRef<HTMLDivElement, Props>(
-  function ScrollableTabsContent(
-    { className, value, children, ...props },
-    ref,
-  ) {
-    const { registerContent } = useScrollableTabs();
-    const contentRef = React.useRef<HTMLDivElement>(null);
-
-    React.useEffect(() => {
-      if (contentRef.current) {
-        registerContent(value, contentRef.current);
-      }
-      return () => {
-        registerContent(value, null);
-      };
-    }, [value, registerContent]);
-
-    return (
-      <div
-        ref={(node) => {
-          if (typeof ref === "function") ref(node);
-          else if (ref) ref.current = node;
-          // eslint-disable-next-line @typescript-eslint/ban-ts-comment
-          // @ts-ignore
-          contentRef.current = node;
-        }}
-        data-scrollable-tab-content
-        data-value={value}
-        className={cn("focus-visible:outline-none", className)}
-        {...props}
-      >
-        {children}
-      </div>
-    );
-  },
-);
-
-ScrollableTabsContent.displayName = "ScrollableTabsContent";

autogpt_platform/frontend/src/components/molecules/ScrollableTabs/components/ScrollableTabsList.tsx

@@ -1,52 +0,0 @@
-"use client";
-
-import { cn } from "@/lib/utils";
-import * as React from "react";
-import { useScrollableTabs } from "../context";
-
-export const ScrollableTabsList = React.forwardRef<
-  HTMLDivElement,
-  React.HTMLAttributes<HTMLDivElement>
->(function ScrollableTabsList({ className, children, ...props }, ref) {
-  const { activeValue } = useScrollableTabs();
-  const [activeTabElement, setActiveTabElement] =
-    React.useState<HTMLElement | null>(null);
-
-  React.useEffect(() => {
-    const activeButton = Array.from(
-      document.querySelectorAll<HTMLElement>(
-        '[data-scrollable-tab-trigger][data-value="' + activeValue + '"]',
-      ),
-    )[0];
-
-    if (activeButton) {
-      setActiveTabElement(activeButton);
-    }
-  }, [activeValue]);
-
-  return (
-    <div className="relative" ref={ref}>
-      <div
-        className={cn(
-          "inline-flex w-full items-center justify-start border-b border-zinc-100",
-          className,
-        )}
-        {...props}
-      >
-        {children}
-      </div>
-      {activeTabElement && (
-        <div
-          className="transition-left transition-right absolute bottom-0 h-0.5 bg-purple-600 duration-200 ease-in-out"
-          style={{
-            left: activeTabElement.offsetLeft,
-            width: activeTabElement.offsetWidth,
-            willChange: "left, width",
-          }}
-        />
-      )}
-    </div>
-  );
-});
-
-ScrollableTabsList.displayName = "ScrollableTabsList";

autogpt_platform/frontend/src/components/molecules/ScrollableTabs/components/ScrollableTabsTrigger.tsx

@@ -1,53 +0,0 @@
-"use client";
-
-import { cn } from "@/lib/utils";
-import * as React from "react";
-import { useScrollableTabs } from "../context";
-
-interface Props extends React.ButtonHTMLAttributes<HTMLButtonElement> {
-  value: string;
-}
-
-export const ScrollableTabsTrigger = React.forwardRef<HTMLButtonElement, Props>(
-  function ScrollableTabsTrigger(
-    { className, value, children, ...props },
-    ref,
-  ) {
-    const { activeValue, scrollToSection } = useScrollableTabs();
-    const elementRef = React.useRef<HTMLButtonElement>(null);
-    const isActive = activeValue === value;
-
-    function handleClick(e: React.MouseEvent<HTMLButtonElement>) {
-      e.preventDefault();
-      e.stopPropagation();
-      scrollToSection(value);
-      props.onClick?.(e);
-    }
-
-    return (
-      <button
-        type="button"
-        ref={(node) => {
-          if (typeof ref === "function") ref(node);
-          else if (ref) ref.current = node;
-          // eslint-disable-next-line @typescript-eslint/ban-ts-comment
-          // @ts-ignore
-          elementRef.current = node;
-        }}
-        data-scrollable-tab-trigger
-        data-value={value}
-        onClick={handleClick}
-        className={cn(
-          "relative inline-flex items-center justify-center whitespace-nowrap px-3 py-3 font-sans text-[0.875rem] font-medium leading-[1.5rem] text-zinc-700 transition-all focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-neutral-400 focus-visible:ring-offset-2 disabled:pointer-events-none disabled:opacity-50",
-          isActive && "text-purple-600",
-          className,
-        )}
-        {...props}
-      >
-        {children}
-      </button>
-    );
-  },
-);
-
-ScrollableTabsTrigger.displayName = "ScrollableTabsTrigger";

autogpt_platform/frontend/src/components/molecules/ScrollableTabs/context.ts

@@ -1,22 +0,0 @@
-import * as React from "react";
-import { createContext, useContext } from "react";
-
-interface ScrollableTabsContextValue {
-  activeValue: string | null;
-  setActiveValue: React.Dispatch<React.SetStateAction<string | null>>;
-  registerContent: (value: string, element: HTMLElement | null) => void;
-  scrollToSection: (value: string) => void;
-  scrollContainer: HTMLElement | null;
-}
-
-export const ScrollableTabsContext = createContext<
-  ScrollableTabsContextValue | undefined
->(undefined);
-
-export function useScrollableTabs() {
-  const context = useContext(ScrollableTabsContext);
-  if (!context) {
-    throw new Error("useScrollableTabs must be used within a ScrollableTabs");
-  }
-  return context;
-}

autogpt_platform/frontend/src/components/molecules/ScrollableTabs/helpers.ts

@@ -1,48 +0,0 @@
-import * as React from "react";
-
-const HEADER_OFFSET = 100;
-
-export function calculateScrollPosition(
-  elementRect: DOMRect,
-  containerRect: DOMRect,
-  currentScrollTop: number,
-): number {
-  const elementTopRelativeToContainer =
-    elementRect.top - containerRect.top + currentScrollTop - HEADER_OFFSET;
-
-  return Math.max(0, elementTopRelativeToContainer);
-}
-
-function hasDisplayName(
-  type: unknown,
-  displayName: string,
-): type is { displayName: string } {
-  return (
-    typeof type === "object" &&
-    type !== null &&
-    "displayName" in type &&
-    (type as { displayName: unknown }).displayName === displayName
-  );
-}
-
-export function findListElement(
-  children: React.ReactNode[],
-): React.ReactElement | undefined {
-  return children.find(
-    (child) =>
-      React.isValidElement(child) &&
-      hasDisplayName(child.type, "ScrollableTabsList"),
-  ) as React.ReactElement | undefined;
-}
-
-export function findContentElements(
-  children: React.ReactNode[],
-): React.ReactNode[] {
-  return children.filter(
-    (child) =>
-      !(
-        React.isValidElement(child) &&
-        hasDisplayName(child.type, "ScrollableTabsList")
-      ),
-  );
-}

autogpt_platform/frontend/src/components/molecules/ScrollableTabs/useScrollableTabs.ts

@@ -1,60 +0,0 @@
-import { useCallback, useRef, useState } from "react";
-import { calculateScrollPosition } from "./helpers";
-
-interface Args {
-  defaultValue?: string;
-}
-
-export function useScrollableTabsInternal({ defaultValue }: Args) {
-  const [activeValue, setActiveValue] = useState<string | null>(
-    defaultValue || null,
-  );
-  const contentRefs = useRef<Map<string, HTMLElement>>(new Map());
-  const contentContainerRef = useRef<HTMLDivElement | null>(null);
-
-  function registerContent(value: string, element: HTMLElement | null) {
-    if (element) {
-      contentRefs.current.set(value, element);
-    } else {
-      contentRefs.current.delete(value);
-    }
-  }
-
-  function scrollToSection(value: string) {
-    const element = contentRefs.current.get(value);
-    const scrollContainer = contentContainerRef.current;
-    if (!element || !scrollContainer) return;
-
-    setActiveValue(value);
-
-    const containerRect = scrollContainer.getBoundingClientRect();
-    const elementRect = element.getBoundingClientRect();
-    const currentScrollTop = scrollContainer.scrollTop;
-    const scrollTop = calculateScrollPosition(
-      elementRect,
-      containerRect,
-      currentScrollTop,
-    );
-
-    const maxScrollTop =
-      scrollContainer.scrollHeight - scrollContainer.clientHeight;
-    const clampedScrollTop = Math.min(Math.max(0, scrollTop), maxScrollTop);
-
-    scrollContainer.scrollTo({
-      top: clampedScrollTop,
-      behavior: "smooth",
-    });
-  }
-
-  const memoizedRegisterContent = useCallback(registerContent, []);
-  const memoizedScrollToSection = useCallback(scrollToSection, []);
-
-  return {
-    activeValue,
-    setActiveValue,
-    registerContent: memoizedRegisterContent,
-    scrollToSection: memoizedScrollToSection,
-    scrollContainer: contentContainerRef.current,
-    contentContainerRef,
-  };
-}

autogpt_platform/frontend/src/components/renderers/input-renderer/fields/AnyOfField/AnyOfField.tsx

@@ -23,7 +23,6 @@ import {
   TooltipTrigger,
 } from "@/components/atoms/Tooltip/BaseTooltip";
 import { cn } from "@/lib/utils";
-import { BlockUIType } from "@/app/(platform)/build/components/types";
 
 type TypeOption = {
   type: string;
@@ -48,14 +47,7 @@ export const AnyOfField = ({
   onBlur,
   onFocus,
 }: FieldProps) => {
-  const handleId =
-    formContext.uiType === BlockUIType.AGENT
-      ? (idSchema.$id ?? "")
-          .split("_")
-          .filter((p) => p !== "root" && p !== "properties" && p.length > 0)
-          .join("_") || ""
-      : generateHandleId(idSchema.$id ?? "");
-
+  const handleId = generateHandleId(idSchema.$id ?? "");
   const updatedFormContexrt = { ...formContext, fromAnyOf: true };
 
   const { nodeId, showHandles = true } = updatedFormContexrt;

autogpt_platform/frontend/src/components/renderers/input-renderer/templates/FieldTemplate.tsx

@@ -58,15 +58,7 @@ const FieldTemplate: React.FC<FieldTemplateProps> = ({
 
   let handleId = null;
   if (!isArrayItem) {
-    if (uiType === BlockUIType.AGENT) {
-      const parts = fieldId.split("_");
-      const filtered = parts.filter(
-        (p) => p !== "root" && p !== "properties" && p.length > 0,
-      );
-      handleId = filtered.join("_") || "";
-    } else {
-      handleId = generateHandleId(fieldId);
-    }
+    handleId = generateHandleId(fieldId);
   } else {
     handleId = arrayFieldHandleId;
   }

autogpt_platform/frontend/src/lib/autogpt-server-api/client.ts

@@ -910,37 +910,7 @@ export default class BackendAPI {
             reject(new Error("Invalid JSON response"));
           }
         } else {
-          // Handle file size errors with user-friendly message
-          if (xhr.status === 413) {
-            reject(new Error("File is too large — max size is 256MB"));
-            return;
-          }
-
-          // Try to parse error response for better messages
-          let errorMessage = `Upload failed (${xhr.status})`;
-          try {
-            const errorData = JSON.parse(xhr.responseText);
-            if (errorData.detail) {
-              if (
-                typeof errorData.detail === "string" &&
-                errorData.detail.includes("exceeds the maximum")
-              ) {
-                const match = errorData.detail.match(
-                  /maximum allowed size of (\d+)MB/,
-                );
-                const maxSize = match ? match[1] : "256";
-                errorMessage = `File is too large — max size is ${maxSize}MB`;
-              } else if (typeof errorData.detail === "string") {
-                errorMessage = errorData.detail;
-              }
-            } else if (errorData.error) {
-              errorMessage = errorData.error;
-            }
-          } catch {
-            // Keep default message if parsing fails
-          }
-
-          reject(new Error(errorMessage));
+          reject(new Error(`HTTP ${xhr.status}: ${xhr.statusText}`));
         }
       });
 

autogpt_platform/frontend/src/lib/autogpt-server-api/helpers.ts

@@ -184,11 +184,6 @@ export function serializeRequestBody(
 }
 
 export async function parseApiError(response: Response): Promise<string> {
-  // Handle 413 Payload Too Large with user-friendly message
-  if (response.status === 413) {
-    return "File is too large — max size is 256MB";
-  }
-
   try {
     const errorData = await response.clone().json();
 
@@ -210,16 +205,6 @@ export async function parseApiError(response: Response): Promise<string> {
       return response.statusText; // Fallback to status text if no message
     }
 
-    // Check for file size error from backend
-    if (
-      typeof errorData.detail === "string" &&
-      errorData.detail.includes("exceeds the maximum")
-    ) {
-      const match = errorData.detail.match(/maximum allowed size of (\d+)MB/);
-      const maxSize = match ? match[1] : "256";
-      return `File is too large — max size is ${maxSize}MB`;
-    }
-
     return errorData.detail || errorData.error || response.statusText;
   } catch {
     return response.statusText;

autogpt_platform/frontend/tailwind.config.ts

@@ -1,10 +1,10 @@
-import scrollbar from "tailwind-scrollbar";
 import type { Config } from "tailwindcss";
 import tailwindcssAnimate from "tailwindcss-animate";
+import scrollbar from "tailwind-scrollbar";
 import { colors } from "./src/components/styles/colors";
 
 const config = {
-  darkMode: ["class", ".dark-mode"], // ignore dark: prefix classes for now until we fully support dark mode
+  darkMode: ["class"],
   content: ["./src/**/*.{ts,tsx}"],
   prefix: "",
   theme: {