fix(frontend): improve waitlist error display for users not on allowlist

- Updated EmailNotAllowedModal with clear waitlist CTA and helpful messaging - Added "Join Waitlist" button that opens https://agpt.co/waitlist - Fixed OAuth provider signup/login to properly display waitlist modal - Enhanced auth-code-error page to detect and display waitlist errors - Added helpful guidance about checking email and Discord support link - Consistent waitlist error handling across all auth flows Fixes OPEN-2794 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
2026-01-12 16:48:06 -05:00 · 2025-10-17 12:37:03 -05:00
1030 changed files with 18231 additions and 121690 deletions
--- a/.github/copilot-instructions.md
+++ b/.github/copilot-instructions.md
@@ -12,7 +12,6 @@ This file provides comprehensive onboarding information for GitHub Copilot codin
 - **Infrastructure** - Docker configurations, CI/CD, and development tools

 **Primary Languages & Frameworks:**
-
 - **Backend**: Python 3.10-3.13, FastAPI, Prisma ORM, PostgreSQL, RabbitMQ
 - **Frontend**: TypeScript, Next.js 15, React, Tailwind CSS, Radix UI
 - **Development**: Docker, Poetry, pnpm, Playwright, Storybook
@@ -24,17 +23,15 @@ This file provides comprehensive onboarding information for GitHub Copilot codin
 **Always run these commands in the correct directory and in this order:**

 1. **Initial Setup** (required once):
-
   ```bash
   # Clone and enter repository
   git clone <repo> && cd AutoGPT
-
+   
   # Start all services (database, redis, rabbitmq, clamav)
   cd autogpt_platform && docker compose --profile local up deps --build --detach
   ```

 2. **Backend Setup** (always run before backend development):
-
   ```bash
   cd autogpt_platform/backend
   poetry install                    # Install dependencies
@@ -51,7 +48,6 @@ This file provides comprehensive onboarding information for GitHub Copilot codin
 ### Runtime Requirements

 **Critical:** Always ensure Docker services are running before starting development:
-
 ```bash
 cd autogpt_platform && docker compose --profile local up deps --build --detach
 ```
@@ -62,7 +58,6 @@ cd autogpt_platform && docker compose --profile local up deps --build --detach
 ### Development Commands

 **Backend Development:**
-
 ```bash
 cd autogpt_platform/backend
 poetry run serve                     # Start development server (port 8000)
@@ -73,7 +68,6 @@ poetry run lint                      # Lint code (ruff) - run after format
 ```

 **Frontend Development:**
-
 ```bash
 cd autogpt_platform/frontend
 pnpm dev                            # Start development server (port 3000) - use for active development
@@ -87,27 +81,23 @@ pnpm storybook                      # Start component development server
 ### Testing Strategy

 **Backend Tests:**
-
 - **Block Tests**: `poetry run pytest backend/blocks/test/test_block.py -xvs` (validates all blocks)
 - **Specific Block**: `poetry run pytest 'backend/blocks/test/test_block.py::test_available_blocks[BlockName]' -xvs`
 - **Snapshot Tests**: Use `--snapshot-update` when output changes, always review with `git diff`

 **Frontend Tests:**
-
 - **E2E Tests**: Always run `pnpm dev` before `pnpm test` (Playwright requires running instance)
 - **Component Tests**: Use Storybook for isolated component development

 ### Critical Validation Steps

 **Before committing changes:**
-
 1. Run `poetry run format` (backend) and `pnpm format` (frontend)
 2. Ensure all tests pass in modified areas
 3. Verify Docker services are still running
 4. Check that database migrations apply cleanly

 **Common Issues & Workarounds:**
-
 - **Prisma issues**: Run `poetry run prisma generate` after schema changes
 - **Permission errors**: Ensure Docker has proper permissions
 - **Port conflicts**: Check the `docker-compose.yml` file for the current list of exposed ports. You can list all mapped ports with:
@@ -118,7 +108,6 @@ pnpm storybook                      # Start component development server
 ### Core Architecture

 **AutoGPT Platform** (`autogpt_platform/`):
-
 - `backend/` - FastAPI server with async support
  - `backend/backend/` - Core API logic
  - `backend/blocks/` - Agent execution blocks
@@ -132,7 +121,6 @@ pnpm storybook                      # Start component development server
 - `docker-compose.yml` - Development stack orchestration

 **Key Configuration Files:**
-
 - `pyproject.toml` - Python dependencies and tooling
 - `package.json` - Node.js dependencies and scripts
 - `schema.prisma` - Database schema and migrations
@@ -142,13 +130,12 @@ pnpm storybook                      # Start component development server
 ### Security & Middleware

 **Cache Protection**: Backend includes middleware preventing sensitive data caching in browsers/proxies
-**Authentication**: JWT-based with native authentication
+**Authentication**: JWT-based with Supabase integration
 **User ID Validation**: All data access requires user ID checks - verify this for any `data/*.py` changes

 ### Development Workflow

 **GitHub Actions**: Multiple CI/CD workflows in `.github/workflows/`
-
 - `platform-backend-ci.yml` - Backend testing and validation
 - `platform-frontend-ci.yml` - Frontend testing and validation
 - `platform-fullstack-ci.yml` - End-to-end integration tests
@@ -159,23 +146,20 @@ pnpm storybook                      # Start component development server
 ### Key Source Files

 **Backend Entry Points:**
-
 - `backend/backend/server/server.py` - FastAPI application setup
 - `backend/backend/data/` - Database models and user management
 - `backend/blocks/` - Agent execution blocks and logic

 **Frontend Entry Points:**
-
 - `frontend/src/app/layout.tsx` - Root application layout
 - `frontend/src/app/page.tsx` - Home page
- `frontend/src/lib/auth/` - Authentication client
+- `frontend/src/lib/supabase/` - Authentication and database client

-**Protected Routes**: Update `frontend/middleware.ts` when adding protected routes
+**Protected Routes**: Update `frontend/lib/supabase/middleware.ts` when adding protected routes

 ### Agent Block System

 Agents are built using a visual block-based system where each block performs a single action. Blocks are defined in `backend/blocks/` and must include:
-
 - Block definition with input/output schemas
 - Execution logic with proper error handling
 - Tests validating functionality
@@ -183,7 +167,6 @@ Agents are built using a visual block-based system where each block performs a s
 ### Database & ORM

 **Prisma ORM** with PostgreSQL backend including pgvector for embeddings:
-
 - Schema in `schema.prisma`
 - Migrations in `backend/migrations/`
 - Always run `prisma migrate dev` and `prisma generate` after schema changes
@@ -191,15 +174,13 @@ Agents are built using a visual block-based system where each block performs a s
 ## Environment Configuration

 ### Configuration Files Priority Order
-
 1. **Backend**: `/backend/.env.default` → `/backend/.env` (user overrides)
-2. **Frontend**: `/frontend/.env.default` → `/frontend/.env` (user overrides)
-3. **Platform**: `/.env.default` (shared) → `/.env` (user overrides)
+2. **Frontend**: `/frontend/.env.default` → `/frontend/.env` (user overrides)  
+3. **Platform**: `/.env.default` (Supabase/shared) → `/.env` (user overrides)
 4. Docker Compose `environment:` sections override file-based config
 5. Shell environment variables have highest precedence

 ### Docker Environment Setup
-
 - All services use hardcoded defaults (no `${VARIABLE}` substitutions)
 - The `env_file` directive loads variables INTO containers at runtime
 - Backend/Frontend services use YAML anchors for consistent configuration
@@ -208,7 +189,6 @@ Agents are built using a visual block-based system where each block performs a s
 ## Advanced Development Patterns

 ### Adding New Blocks
-
 1. Create file in `/backend/backend/blocks/`
 2. Inherit from `Block` base class with input/output schemas
 3. Implement `run` method with proper error handling
@@ -218,7 +198,6 @@ Agents are built using a visual block-based system where each block performs a s
 7. Consider how inputs/outputs connect with other blocks in graph editor

 ### API Development
-
 1. Update routes in `/backend/backend/server/routers/`
 2. Add/update Pydantic models in same directory
 3. Write tests alongside route files
@@ -226,76 +205,21 @@ Agents are built using a visual block-based system where each block performs a s
 5. Run `poetry run test` to verify changes

 ### Frontend Development
-
-**📖 Complete Frontend Guide**: See `autogpt_platform/frontend/CONTRIBUTING.md` and `autogpt_platform/frontend/.cursorrules` for comprehensive patterns and conventions.
-
-**Quick Reference:**
-
-**Component Structure:**
-
- Separate render logic from data/behavior
- Structure: `ComponentName/ComponentName.tsx` + `useComponentName.ts` + `helpers.ts`
- Exception: Small components (3-4 lines of logic) can be inline
- Render-only components can be direct files without folders
-
-**Data Fetching:**
-
- Use generated API hooks from `@/app/api/__generated__/endpoints/`
- Generated via Orval from backend OpenAPI spec
- Pattern: `use{Method}{Version}{OperationName}`
- Example: `useGetV2ListLibraryAgents`
- Regenerate with: `pnpm generate:api`
- **Never** use deprecated `BackendAPI` or `src/lib/autogpt-server-api/*`
-
-**Code Conventions:**
-
- Use function declarations for components and handlers (not arrow functions)
- Only arrow functions for small inline lambdas (map, filter, etc.)
- Components: `PascalCase`, Hooks: `camelCase` with `use` prefix
- No barrel files or `index.ts` re-exports
- Minimal comments (code should be self-documenting)
-
-**Styling:**
-
- Use Tailwind CSS utilities only
- Use design system components from `src/components/` (atoms, molecules, organisms)
- Never use `src/components/__legacy__/*`
- Only use Phosphor Icons (`@phosphor-icons/react`)
- Prefer design tokens over hardcoded values
-
-**Error Handling:**
-
- Render errors: Use `<ErrorCard />` component
- Mutation errors: Display with toast notifications
- Manual exceptions: Use `Sentry.captureException()`
- Global error boundaries already configured
-
-**Testing:**
-
- Add/update Storybook stories for UI components (`pnpm storybook`)
- Run Playwright E2E tests with `pnpm test`
- Verify in Chromatic after PR
-
-**Architecture:**
-
- Default to client components ("use client")
- Server components only for SEO or extreme TTFB needs
- Use React Query for server state (via generated hooks)
- Co-locate UI state in components/hooks
+1. Components in `/frontend/src/components/`
+2. Use existing UI components from `/frontend/src/components/ui/`
+3. Add Storybook stories for component development
+4. Test user-facing features with Playwright E2E tests
+5. Update protected routes in middleware when needed

 ### Security Guidelines
-
 **Cache Protection Middleware** (`/backend/backend/server/middleware/security.py`):
-
 - Default: Disables caching for ALL endpoints with `Cache-Control: no-store, no-cache, must-revalidate, private`
 - Uses allow list approach for cacheable paths (static assets, health checks, public pages)
 - Prevents sensitive data caching in browsers/proxies
 - Add new cacheable endpoints to `CACHEABLE_PATHS`

 ### CI/CD Alignment
-
 The repository has comprehensive CI workflows that test:
-
 - **Backend**: Python 3.11-3.13, services (Redis/RabbitMQ/ClamAV), Prisma migrations, Poetry lock validation
 - **Frontend**: Node.js 21, pnpm, Playwright with Docker Compose stack, API schema validation
 - **Integration**: Full-stack type checking and E2E testing
@@ -305,7 +229,6 @@ Match these patterns when developing locally - the copilot setup environment mir
 ## Collaboration with Other AI Assistants

 This repository is actively developed with assistance from Claude (via CLAUDE.md files). When working on this codebase:
-
 - Check for existing CLAUDE.md files that provide additional context
 - Follow established patterns and conventions already in the codebase
 - Maintain consistency with existing code style and architecture
@@ -314,9 +237,8 @@ This repository is actively developed with assistance from Claude (via CLAUDE.md
 ## Trust These Instructions

 These instructions are comprehensive and tested. Only perform additional searches if:
-
 1. Information here is incomplete for your specific task
 2. You encounter errors not covered by the workarounds
 3. You need to understand implementation details not covered above

-For detailed platform development patterns, refer to `autogpt_platform/CLAUDE.md` and `AGENTS.md` in the repository root.
+For detailed platform development patterns, refer to `autogpt_platform/CLAUDE.md` and `AGENTS.md` in the repository root.
--- a/.github/workflows/claude-dependabot.yml
+++ b/.github/workflows/claude-dependabot.yml
@@ -80,7 +80,7 @@ jobs:
      - name: Set up Node.js
        uses: actions/setup-node@v4
        with:
-          node-version: "22"
+          node-version: "21"

      - name: Enable corepack
        run: corepack enable
@@ -144,7 +144,11 @@ jobs:
            "rabbitmq:management"
            "clamav/clamav-debian:latest"
            "busybox:latest"
-            "pgvector/pgvector:pg18"
+            "kong:2.8.1"
+            "supabase/gotrue:v2.170.0"
+            "supabase/postgres:15.8.1.049"
+            "supabase/postgres-meta:v0.86.1"
+            "supabase/studio:20250224-d10db0f"
          )
          
          # Check if any cached tar files exist (more reliable than cache-hit)
--- a/.github/workflows/claude.yml
+++ b/.github/workflows/claude.yml
@@ -44,12 +44,6 @@ jobs:
        with:
          fetch-depth: 1

-      - name: Free Disk Space (Ubuntu)
-        uses: jlumbroso/free-disk-space@v1.3.1
-        with:
-          large-packages: false  # slow
-          docker-images: false  # limited benefit
-
      # Backend Python/Poetry setup (mirrors platform-backend-ci.yml)
      - name: Set up Python
        uses: actions/setup-python@v5
@@ -96,7 +90,7 @@ jobs:
      - name: Set up Node.js
        uses: actions/setup-node@v4
        with:
-          node-version: "22"
+          node-version: "21"

      - name: Enable corepack
        run: corepack enable
@@ -160,7 +154,11 @@ jobs:
            "rabbitmq:management"
            "clamav/clamav-debian:latest"
            "busybox:latest"
-            "pgvector/pgvector:pg18"
+            "kong:2.8.1"
+            "supabase/gotrue:v2.170.0"
+            "supabase/postgres:15.8.1.049"
+            "supabase/postgres-meta:v0.86.1"
+            "supabase/studio:20250224-d10db0f"
          )
          
          # Check if any cached tar files exist (more reliable than cache-hit)
--- a/.github/workflows/copilot-setup-steps.yml
+++ b/.github/workflows/copilot-setup-steps.yml
@@ -78,7 +78,7 @@ jobs:
      - name: Set up Node.js
        uses: actions/setup-node@v4
        with:
-          node-version: "22"
+          node-version: "21"

      - name: Enable corepack
        run: corepack enable
@@ -142,7 +142,11 @@ jobs:
            "rabbitmq:management"
            "clamav/clamav-debian:latest"
            "busybox:latest"
-            "pgvector/pgvector:pg18"
+            "kong:2.8.1"
+            "supabase/gotrue:v2.170.0"
+            "supabase/postgres:15.8.1.049"
+            "supabase/postgres-meta:v0.86.1"
+            "supabase/studio:20250224-d10db0f"
          )
          
          # Check if any cached tar files exist (more reliable than cache-hit)
@@ -295,4 +299,4 @@ jobs:
          echo "✅ AutoGPT Platform development environment setup complete!"
          echo "🚀 Ready for development with Docker services running"
          echo "📝 Backend server: poetry run serve (port 8000)"
-          echo "🌐 Frontend server: pnpm dev (port 3000)"
+          echo "🌐 Frontend server: pnpm dev (port 3000)"
--- a/.github/workflows/platform-backend-ci.yml
+++ b/.github/workflows/platform-backend-ci.yml
@@ -2,13 +2,13 @@ name: AutoGPT Platform - Backend CI

 on:
  push:
-    branches: [master, dev, ci-test*, native-auth]
+    branches: [master, dev, ci-test*]
    paths:
      - ".github/workflows/platform-backend-ci.yml"
      - "autogpt_platform/backend/**"
      - "autogpt_platform/autogpt_libs/**"
  pull_request:
-    branches: [master, dev, release-*, native-auth]
+    branches: [master, dev, release-*]
    paths:
      - ".github/workflows/platform-backend-ci.yml"
      - "autogpt_platform/backend/**"
@@ -36,19 +36,6 @@ jobs:
    runs-on: ubuntu-latest

    services:
-      postgres:
-        image: pgvector/pgvector:pg18
-        ports:
-          - 5432:5432
-        env:
-          POSTGRES_USER: postgres
-          POSTGRES_PASSWORD: your-super-secret-and-long-postgres-password
-          POSTGRES_DB: postgres
-        options: >-
-          --health-cmd "pg_isready -U postgres"
-          --health-interval 5s
-          --health-timeout 5s
-          --health-retries 10
      redis:
        image: redis:latest
        ports:
@@ -91,6 +78,11 @@ jobs:
        with:
          python-version: ${{ matrix.python-version }}

+      - name: Setup Supabase
+        uses: supabase/setup-cli@v1
+        with:
+          version: 1.178.1
+
      - id: get_date
        name: Get date
        run: echo "date=$(date +'%Y-%m-%d')" >> $GITHUB_OUTPUT
@@ -144,6 +136,16 @@ jobs:
      - 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
+        # 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..."
@@ -176,8 +178,8 @@ jobs:
      - name: Run Database Migrations
        run: poetry run prisma migrate dev --name updates
        env:
-          DATABASE_URL: postgresql://postgres:your-super-secret-and-long-postgres-password@localhost:5432/postgres
-          DIRECT_URL: postgresql://postgres:your-super-secret-and-long-postgres-password@localhost:5432/postgres
+          DATABASE_URL: ${{ steps.supabase.outputs.DB_URL }}
+          DIRECT_URL: ${{ steps.supabase.outputs.DB_URL }}

      - id: lint
        name: Run Linter
@@ -193,9 +195,11 @@ jobs:
        if: success() || (failure() && steps.lint.outcome == 'failure')
        env:
          LOG_LEVEL: ${{ runner.debug && 'DEBUG' || 'INFO' }}
-          DATABASE_URL: postgresql://postgres:your-super-secret-and-long-postgres-password@localhost:5432/postgres
-          DIRECT_URL: postgresql://postgres:your-super-secret-and-long-postgres-password@localhost:5432/postgres
-          JWT_SECRET: your-super-secret-jwt-token-with-at-least-32-characters-long
+          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=" # DO NOT USE IN PRODUCTION!!
--- a/.github/workflows/platform-frontend-ci.yml
+++ b/.github/workflows/platform-frontend-ci.yml
@@ -2,21 +2,16 @@ name: AutoGPT Platform - Frontend CI

 on:
  push:
-    branches: [master, dev, native-auth]
+    branches: [master, dev]
    paths:
      - ".github/workflows/platform-frontend-ci.yml"
      - "autogpt_platform/frontend/**"
  pull_request:
-    branches: [master, dev, native-auth]
    paths:
      - ".github/workflows/platform-frontend-ci.yml"
      - "autogpt_platform/frontend/**"
  merge_group:

-concurrency:
-  group: ${{ github.workflow }}-${{ github.event_name == 'merge_group' && format('merge-queue-{0}', github.ref) || format('{0}-{1}', github.ref, github.event.pull_request.number || github.sha) }}
-  cancel-in-progress: ${{ github.event_name == 'pull_request' }}
-
 defaults:
  run:
    shell: bash
@@ -35,7 +30,7 @@ jobs:
      - name: Set up Node.js
        uses: actions/setup-node@v4
        with:
-          node-version: "22.18.0"
+          node-version: "21"

      - name: Enable corepack
        run: corepack enable
@@ -67,7 +62,7 @@ jobs:
      - name: Set up Node.js
        uses: actions/setup-node@v4
        with:
-          node-version: "22.18.0"
+          node-version: "21"

      - name: Enable corepack
        run: corepack enable
@@ -102,7 +97,7 @@ jobs:
      - name: Set up Node.js
        uses: actions/setup-node@v4
        with:
-          node-version: "22.18.0"
+          node-version: "21"

      - name: Enable corepack
        run: corepack enable
@@ -143,12 +138,12 @@ jobs:
      - name: Set up Node.js
        uses: actions/setup-node@v4
        with:
-          node-version: "22.18.0"
+          node-version: "21"

      - name: Enable corepack
        run: corepack enable

-      - name: Copy default platform .env
+      - name: Copy default supabase .env
        run: |
          cp ../.env.default ../.env

--- a/.github/workflows/platform-fullstack-ci.yml
+++ b/.github/workflows/platform-fullstack-ci.yml
@@ -1,22 +1,17 @@
-name: AutoGPT Platform - Fullstack CI
+name: AutoGPT Platform - Frontend CI

 on:
  push:
-    branches: [master, dev, native-auth]
+    branches: [master, dev]
    paths:
      - ".github/workflows/platform-fullstack-ci.yml"
      - "autogpt_platform/**"
  pull_request:
-    branches: [master, dev, native-auth]
    paths:
      - ".github/workflows/platform-fullstack-ci.yml"
      - "autogpt_platform/**"
  merge_group:

-concurrency:
-  group: ${{ github.workflow }}-${{ github.event_name == 'merge_group' && format('merge-queue-{0}', github.ref) || github.head_ref && format('pr-{0}', github.event.pull_request.number) || github.sha }}
-  cancel-in-progress: ${{ github.event_name == 'pull_request' }}
-
 defaults:
  run:
    shell: bash
@@ -35,7 +30,7 @@ jobs:
      - name: Set up Node.js
        uses: actions/setup-node@v4
        with:
-          node-version: "22.18.0"
+          node-version: "21"

      - name: Enable corepack
        run: corepack enable
@@ -59,20 +54,35 @@ jobs:
  types:
    runs-on: ubuntu-latest
    needs: setup
-    timeout-minutes: 10
+    strategy:
+      fail-fast: false

    steps:
      - name: Checkout repository
        uses: actions/checkout@v4
+        with:
+          submodules: recursive

      - name: Set up Node.js
        uses: actions/setup-node@v4
        with:
-          node-version: "22.18.0"
+          node-version: "21"

      - name: Enable corepack
        run: corepack enable

+      - name: Copy default supabase .env
+        run: |
+          cp ../.env.default ../.env
+
+      - name: Copy backend .env
+        run: |
+          cp ../backend/.env.default ../backend/.env
+
+      - name: Run docker compose
+        run: |
+          docker compose -f ../docker-compose.yml --profile local --profile deps_backend up -d
+
      - name: Restore dependencies cache
        uses: actions/cache@v4
        with:
@@ -87,12 +97,36 @@ jobs:
      - name: Setup .env
        run: cp .env.default .env

+      - name: Wait for services to be ready
+        run: |
+          echo "Waiting for rest_server to be ready..."
+          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 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
+        run: pnpm generate:api:force
+
+      - name: Check for API schema changes
+        run: |
+          if ! git diff --exit-code src/app/api/openapi.json; then
+            echo "❌ API schema changes detected in src/app/api/openapi.json"
+            echo ""
+            echo "The openapi.json file has been modified after running 'pnpm generate:api-all'."
+            echo "This usually means changes have been made in the BE endpoints without updating the Frontend."
+            echo "The API schema is now out of sync with the Front-end queries."
+            echo ""
+            echo "To fix this:"
+            echo "1. Pull the backend 'docker compose pull && docker compose up -d --build --force-recreate'"
+            echo "2. Run 'pnpm generate:api' locally"
+            echo "3. Run 'pnpm types' locally"
+            echo "4. Fix any TypeScript errors that may have been introduced"
+            echo "5. Commit and push your changes"
+            echo ""
+            exit 1
+          else
+            echo "✅ No API schema changes detected"
+          fi

      - name: Run Typescript checks
        run: pnpm types
-
-    env:
-      CI: true
-      PLAIN_OUTPUT: True
--- a/.github/workflows/repo-close-stale-issues.yml
+++ b/.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: >
--- a/.github/workflows/repo-pr-label.yml
+++ b/.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
--- a/.gitignore
+++ b/.gitignore
@@ -178,4 +178,3 @@ autogpt_platform/backend/settings.py
 *.ign.*
 .test-contents
 .claude/settings.local.json
-/autogpt_platform/backend/logs
--- a/AGENTS.md
+++ b/AGENTS.md
@@ -49,5 +49,5 @@ Use conventional commit messages for all commits (e.g. `feat(backend): add API`)
 - Keep out-of-scope changes under 20% of the PR.
 - Ensure PR descriptions are complete.
 - For changes touching `data/*.py`, validate user ID checks or explain why not needed.
- If adding protected frontend routes, update `frontend/lib/auth/helpers.ts`.
+- If adding protected frontend routes, update `frontend/lib/supabase/middleware.ts`.
 - Use the linear ticket branch structure if given codex/open-1668-resume-dropped-runs
--- a/autogpt_platform/.env.default
+++ b/autogpt_platform/.env.default
@@ -5,6 +5,12 @@

 POSTGRES_PASSWORD=your-super-secret-and-long-postgres-password
 JWT_SECRET=your-super-secret-jwt-token-with-at-least-32-characters-long
+ANON_KEY=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyAgCiAgICAicm9sZSI6ICJhbm9uIiwKICAgICJpc3MiOiAic3VwYWJhc2UtZGVtbyIsCiAgICAiaWF0IjogMTY0MTc2OTIwMCwKICAgICJleHAiOiAxNzk5NTM1NjAwCn0.dc_X5iR_VP_qT0zsiyj_I_OZ2T9FtRU2BBNWN8Bu4GE
+SERVICE_ROLE_KEY=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyAgCiAgICAicm9sZSI6ICJzZXJ2aWNlX3JvbGUiLAogICAgImlzcyI6ICJzdXBhYmFzZS1kZW1vIiwKICAgICJpYXQiOiAxNjQxNzY5MjAwLAogICAgImV4cCI6IDE3OTk1MzU2MDAKfQ.DaYlNEoUrrEn2Ig7tqibS-PHK5vgusbcbo7X36XVt4Q
+DASHBOARD_USERNAME=supabase
+DASHBOARD_PASSWORD=this_password_is_insecure_and_should_be_updated
+SECRET_KEY_BASE=UpNVntn3cDxHJpq99YMc1T1AQgQpc8kfYTuRgBiYa15BLrx8etQoXz3gZv1/u2oq
+VAULT_ENC_KEY=your-encryption-key-32-chars-min


 ############
@@ -18,31 +24,100 @@ POSTGRES_PORT=5432


 ############
-# Auth - Native authentication configuration
+# Supavisor -- Database pooler
+############
+POOLER_PROXY_PORT_TRANSACTION=6543
+POOLER_DEFAULT_POOL_SIZE=20
+POOLER_MAX_CLIENT_CONN=100
+POOLER_TENANT_ID=your-tenant-id
+
+
+############
+# API Proxy - Configuration for the Kong Reverse proxy.
 ############

+KONG_HTTP_PORT=8000
+KONG_HTTPS_PORT=8443
+
+
+############
+# API - Configuration for PostgREST.
+############
+
+PGRST_DB_SCHEMAS=public,storage,graphql_public
+
+
+############
+# Auth - Configuration for the GoTrue authentication server.
+############
+
+## General
 SITE_URL=http://localhost:3000
+ADDITIONAL_REDIRECT_URLS=
+JWT_EXPIRY=3600
+DISABLE_SIGNUP=false
+API_EXTERNAL_URL=http://localhost:8000

-# JWT token configuration
-ACCESS_TOKEN_EXPIRE_MINUTES=15
-REFRESH_TOKEN_EXPIRE_DAYS=7
-JWT_ISSUER=autogpt-platform
+## Mailer Config
+MAILER_URLPATHS_CONFIRMATION="/auth/v1/verify"
+MAILER_URLPATHS_INVITE="/auth/v1/verify"
+MAILER_URLPATHS_RECOVERY="/auth/v1/verify"
+MAILER_URLPATHS_EMAIL_CHANGE="/auth/v1/verify"

-# Google OAuth (optional)
-GOOGLE_CLIENT_ID=
-GOOGLE_CLIENT_SECRET=
+## Email auth
+ENABLE_EMAIL_SIGNUP=true
+ENABLE_EMAIL_AUTOCONFIRM=false
+SMTP_ADMIN_EMAIL=admin@example.com
+SMTP_HOST=supabase-mail
+SMTP_PORT=2500
+SMTP_USER=fake_mail_user
+SMTP_PASS=fake_mail_password
+SMTP_SENDER_NAME=fake_sender
+ENABLE_ANONYMOUS_USERS=false
+
+## Phone auth
+ENABLE_PHONE_SIGNUP=true
+ENABLE_PHONE_AUTOCONFIRM=true


 ############
-# Email configuration (optional)
+# Studio - Configuration for the Dashboard
 ############

-SMTP_HOST=
-SMTP_PORT=587
-SMTP_USER=
-SMTP_PASS=
-SMTP_FROM_EMAIL=noreply@example.com
+STUDIO_DEFAULT_ORGANIZATION=Default Organization
+STUDIO_DEFAULT_PROJECT=Default Project

+STUDIO_PORT=3000
+# replace if you intend to use Studio outside of localhost
+SUPABASE_PUBLIC_URL=http://localhost:8000
+
+# Enable webp support
+IMGPROXY_ENABLE_WEBP_DETECTION=true
+
+# Add your OpenAI API key to enable SQL Editor Assistant
+OPENAI_API_KEY=
+
+
+############
+# Functions - Configuration for Functions
+############
+# NOTE: VERIFY_JWT applies to all functions. Per-function VERIFY_JWT is not supported yet.
+FUNCTIONS_VERIFY_JWT=false
+
+
+############
+# Logs - Configuration for Logflare
+# Please refer to https://supabase.com/docs/reference/self-hosting-analytics/introduction
+############
+
+LOGFLARE_LOGGER_BACKEND_API_KEY=your-super-secret-and-long-logflare-key
+
+# Change vector.toml sinks to reflect this change
+LOGFLARE_API_KEY=your-super-secret-and-long-logflare-key

 # Docker socket location - this value will differ depending on your OS
 DOCKER_SOCKET_LOCATION=/var/run/docker.sock
+
+# Google Cloud Project details
+GOOGLE_PROJECT_ID=GOOGLE_PROJECT_ID
+GOOGLE_PROJECT_NUMBER=GOOGLE_PROJECT_NUMBER
--- a/autogpt_platform/CLAUDE.md
+++ b/autogpt_platform/CLAUDE.md
@@ -63,9 +63,6 @@ poetry run pytest path/to/test.py --snapshot-update
 # Install dependencies
 cd frontend && pnpm i

-# Generate API client from OpenAPI spec
-pnpm generate:api
-
 # Start development server
 pnpm dev

@@ -78,23 +75,12 @@ pnpm storybook
 # Build production
 pnpm build

-# Format and lint
-pnpm format
-
 # Type checking
 pnpm types
 ```

-**📖 Complete Guide**: See `/frontend/CONTRIBUTING.md` and `/frontend/.cursorrules` for comprehensive frontend patterns.
+We have a components library in autogpt_platform/frontend/src/components/atoms that should be used when adding new pages and components. 

-**Key Frontend Conventions:**
-
- Separate render logic from data/behavior in components
- Use generated API hooks from `@/app/api/__generated__/endpoints/`
- Use function declarations (not arrow functions) for components/handlers
- Use design system components from `src/components/` (atoms, molecules, organisms)
- Only use Phosphor Icons
- Never use `src/components/__legacy__/*` or deprecated `BackendAPI`

 ## Architecture Overview

@@ -109,16 +95,11 @@ pnpm types

 ### Frontend Architecture

- **Framework**: Next.js 15 App Router (client-first approach)
- **Data Fetching**: Type-safe generated API hooks via Orval + React Query
- **State Management**: React Query for server state, co-located UI state in components/hooks
- **Component Structure**: Separate render logic (`.tsx`) from business logic (`use*.ts` hooks)
+- **Framework**: Next.js App Router with React Server Components
+- **State Management**: React hooks + Supabase client for real-time updates
 - **Workflow Builder**: Visual graph editor using @xyflow/react
- **UI Components**: shadcn/ui (Radix UI primitives) with Tailwind CSS styling
- **Icons**: Phosphor Icons only
+- **UI Components**: Radix UI primitives with Tailwind CSS styling
 - **Feature Flags**: LaunchDarkly integration
- **Error Handling**: ErrorCard for render errors, toast for mutations, Sentry for exceptions
- **Testing**: Playwright for E2E, Storybook for component development

 ### Key Concepts

@@ -172,7 +153,6 @@ Key models (defined in `/backend/schema.prisma`):
 **Adding a new block:**

 Follow the comprehensive [Block SDK Guide](../../../docs/content/platform/block-sdk-guide.md) which covers:
-
 - Provider configuration with `ProviderBuilder`
 - Block schema definition
 - Authentication (API keys, OAuth, webhooks)
@@ -180,7 +160,6 @@ Follow the comprehensive [Block SDK Guide](../../../docs/content/platform/block-
 - File organization

 Quick steps:
-
 1. Create new file in `/backend/backend/blocks/`
 2. Configure provider using `ProviderBuilder` in `_config.py`
 3. Inherit from `Block` base class
@@ -192,8 +171,6 @@ Quick steps:
 Note: when making many new blocks analyze the interfaces for each of these blocks and picture if they would go well together in a graph based editor or would they struggle to connect productively?
 ex: do the inputs and outputs tie well together?

-If you get any pushback or hit complex block conditions check the new_blocks guide in the docs.
-
 **Modifying the API:**

 1. Update route in `/backend/backend/server/routers/`
@@ -203,20 +180,10 @@ If you get any pushback or hit complex block conditions check the new_blocks gui

 **Frontend feature development:**

-See `/frontend/CONTRIBUTING.md` for complete patterns. Quick reference:
-
-1. **Pages**: Create in `src/app/(platform)/feature-name/page.tsx`
-   - Add `usePageName.ts` hook for logic
-   - Put sub-components in local `components/` folder
-2. **Components**: Structure as `ComponentName/ComponentName.tsx` + `useComponentName.ts` + `helpers.ts`
-   - Use design system components from `src/components/` (atoms, molecules, organisms)
-   - Never use `src/components/__legacy__/*`
-3. **Data fetching**: Use generated API hooks from `@/app/api/__generated__/endpoints/`
-   - Regenerate with `pnpm generate:api`
-   - Pattern: `use{Method}{Version}{OperationName}`
-4. **Styling**: Tailwind CSS only, use design tokens, Phosphor Icons only
-5. **Testing**: Add Storybook stories for new components, Playwright for E2E
-6. **Code conventions**: Function declarations (not arrow functions) for components/handlers
+1. Components go in `/frontend/src/components/`
+2. Use existing UI components from `/frontend/src/components/ui/`
+3. Add Storybook stories for new components
+4. Test with Playwright if user-facing

 ### Security Implementation

--- a/autogpt_platform/Makefile
+++ b/autogpt_platform/Makefile
@@ -1,6 +1,6 @@
-.PHONY: start-core stop-core logs-core format lint migrate run-backend run-frontend load-store-agents
+.PHONY: start-core stop-core logs-core format lint migrate run-backend run-frontend

-# Run just PostgreSQL + Redis + RabbitMQ + ClamAV
+# Run just Supabase + Redis + RabbitMQ
 start-core:
 	docker compose up -d deps

@@ -8,11 +8,6 @@ start-core:
 stop-core:
 	docker compose stop deps

-reset-db:
-	rm -rf db/docker/volumes/db/data
-	cd backend && poetry run prisma migrate deploy
-	cd backend && poetry run prisma generate
-	
 # View logs for core services
 logs-core:
 	docker compose logs -f deps
@@ -40,22 +35,13 @@ run-backend:
 run-frontend:
 	cd frontend && pnpm dev

-test-data:
-	cd backend && poetry run python test/test_data_creator.py
-
-load-store-agents:
-	cd backend && poetry run load-store-agents
-
 help:
 	@echo "Usage: make <target>"
 	@echo "Targets:"
-	@echo "  start-core - Start just the core services (PostgreSQL, Redis, RabbitMQ, ClamAV) in background"
+	@echo "  start-core - Start just the core services (Supabase, Redis, RabbitMQ) in background"
 	@echo "  stop-core - Stop the core services"
-	@echo "  reset-db - Reset the database by deleting the volume"
 	@echo "  logs-core - Tail the logs for core services"
 	@echo "  format - Format & lint backend (Python) and frontend (TypeScript) code"
 	@echo "  migrate - Run backend database migrations"
 	@echo "  run-backend - Run the backend FastAPI server"
-	@echo "  run-frontend - Run the frontend Next.js development server"
-	@echo "  test-data - Run the test data creator"
-	@echo "  load-store-agents - Load store agents from agents/ folder into test database"
+	@echo "  run-frontend - Run the frontend Next.js development server"
--- a/autogpt_platform/autogpt_libs/autogpt_libs/api_key/keysmith.py
+++ b/autogpt_platform/autogpt_libs/autogpt_libs/api_key/keysmith.py
@@ -57,9 +57,6 @@ class APIKeySmith:

    def hash_key(self, raw_key: str) -> tuple[str, str]:
        """Migrate a legacy hash to secure hash format."""
-        if not raw_key.startswith(self.PREFIX):
-            raise ValueError("Key without 'agpt_' prefix would fail validation")
-
        salt = self._generate_salt()
        hash = self._hash_key_with_salt(raw_key, salt)
        return hash, salt.hex()
--- a/autogpt_platform/autogpt_libs/autogpt_libs/auth/init.py
+++ b/autogpt_platform/autogpt_libs/autogpt_libs/auth/init.py
@@ -1,10 +1,5 @@
 from .config import verify_settings
-from .dependencies import (
-    get_optional_user_id,
-    get_user_id,
-    requires_admin_user,
-    requires_user,
-)
+from .dependencies import get_user_id, requires_admin_user, requires_user
 from .helpers import add_auth_responses_to_openapi
 from .models import User

@@ -13,7 +8,6 @@ __all__ = [
    "get_user_id",
    "requires_admin_user",
    "requires_user",
-    "get_optional_user_id",
    "add_auth_responses_to_openapi",
    "User",
 ]
--- a/autogpt_platform/autogpt_libs/autogpt_libs/auth/config.py
+++ b/autogpt_platform/autogpt_libs/autogpt_libs/auth/config.py
@@ -16,37 +16,17 @@ ALGO_RECOMMENDATION = (
    "We highly recommend using an asymmetric algorithm such as ES256, "
    "because when leaked, a shared secret would allow anyone to "
    "forge valid tokens and impersonate users. "
-    "More info: https://pyjwt.readthedocs.io/en/stable/algorithms.html"
+    "More info: https://supabase.com/docs/guides/auth/signing-keys#choosing-the-right-signing-algorithm"  # noqa
 )


 class Settings:
    def __init__(self):
-        # JWT verification key (public key for asymmetric, shared secret for symmetric)
        self.JWT_VERIFY_KEY: str = os.getenv(
            "JWT_VERIFY_KEY", os.getenv("SUPABASE_JWT_SECRET", "")
        ).strip()
-
-        # JWT signing key (private key for asymmetric, shared secret for symmetric)
-        # Falls back to JWT_VERIFY_KEY for symmetric algorithms like HS256
-        self.JWT_SIGN_KEY: str = os.getenv("JWT_SIGN_KEY", self.JWT_VERIFY_KEY).strip()
-
        self.JWT_ALGORITHM: str = os.getenv("JWT_SIGN_ALGORITHM", "HS256").strip()

-        # Token expiration settings
-        self.ACCESS_TOKEN_EXPIRE_MINUTES: int = int(
-            os.getenv("ACCESS_TOKEN_EXPIRE_MINUTES", "15")
-        )
-        self.REFRESH_TOKEN_EXPIRE_DAYS: int = int(
-            os.getenv("REFRESH_TOKEN_EXPIRE_DAYS", "7")
-        )
-
-        # JWT issuer claim
-        self.JWT_ISSUER: str = os.getenv("JWT_ISSUER", "autogpt-platform").strip()
-
-        # JWT audience claim
-        self.JWT_AUDIENCE: str = os.getenv("JWT_AUDIENCE", "authenticated").strip()
-
        self.validate()

    def validate(self):
--- a/autogpt_platform/autogpt_libs/autogpt_libs/auth/dependencies.py
+++ b/autogpt_platform/autogpt_libs/autogpt_libs/auth/dependencies.py
@@ -4,53 +4,11 @@ FastAPI dependency functions for JWT-based authentication and authorization.
 These are the high-level dependency functions used in route definitions.
 """

-import logging
-
 import fastapi
-from fastapi.security import HTTPAuthorizationCredentials, HTTPBearer

 from .jwt_utils import get_jwt_payload, verify_user
 from .models import User

-optional_bearer = HTTPBearer(auto_error=False)
-
-# Header name for admin impersonation
-IMPERSONATION_HEADER_NAME = "X-Act-As-User-Id"
-
-logger = logging.getLogger(__name__)
-
-
-def get_optional_user_id(
-    credentials: HTTPAuthorizationCredentials | None = fastapi.Security(
-        optional_bearer
-    ),
-) -> str | None:
-    """
-    Attempts to extract the user ID ("sub" claim) from a Bearer JWT if provided.
-
-    This dependency allows for both authenticated and anonymous access. If a valid bearer token is
-    supplied, it parses the JWT and extracts the user ID. If the token is missing or invalid, it returns None,
-    treating the request as anonymous.
-
-    Args:
-        credentials: Optional HTTPAuthorizationCredentials object from FastAPI Security dependency.
-
-    Returns:
-        The user ID (str) extracted from the JWT "sub" claim, or None if no valid token is present.
-    """
-    if not credentials:
-        return None
-
-    try:
-        # Parse JWT token to get user ID
-        from autogpt_libs.auth.jwt_utils import parse_jwt_token
-
-        payload = parse_jwt_token(credentials.credentials)
-        return payload.get("sub")
-    except Exception as e:
-        logger.debug(f"Auth token validation failed (anonymous access): {e}")
-        return None
-

 async def requires_user(jwt_payload: dict = fastapi.Security(get_jwt_payload)) -> User:
    """
@@ -74,44 +32,16 @@ async def requires_admin_user(
    return verify_user(jwt_payload, admin_only=True)


-async def get_user_id(
-    request: fastapi.Request, jwt_payload: dict = fastapi.Security(get_jwt_payload)
-) -> str:
+async def get_user_id(jwt_payload: dict = fastapi.Security(get_jwt_payload)) -> str:
    """
    FastAPI dependency that returns the ID of the authenticated user.

-    Supports admin impersonation via X-Act-As-User-Id header:
-    - If the header is present and user is admin, returns the impersonated user ID
-    - Otherwise returns the authenticated user's own ID
-    - Logs all impersonation actions for audit trail
-
    Raises:
        HTTPException: 401 for authentication failures or missing user ID
-        HTTPException: 403 if non-admin tries to use impersonation
    """
-    # Get the authenticated user's ID from JWT
    user_id = jwt_payload.get("sub")
    if not user_id:
        raise fastapi.HTTPException(
            status_code=401, detail="User ID not found in token"
        )
-
-    # Check for admin impersonation header
-    impersonate_header = request.headers.get(IMPERSONATION_HEADER_NAME, "").strip()
-    if impersonate_header:
-        # Verify the authenticated user is an admin
-        authenticated_user = verify_user(jwt_payload, admin_only=False)
-        if authenticated_user.role != "admin":
-            raise fastapi.HTTPException(
-                status_code=403, detail="Only admin users can impersonate other users"
-            )
-
-        # Log the impersonation for audit trail
-        logger.info(
-            f"Admin impersonation: {authenticated_user.user_id} ({authenticated_user.email}) "
-            f"acting as user {impersonate_header} for requesting {request.method} {request.url}"
-        )
-
-        return impersonate_header
-
    return user_id
--- a/autogpt_platform/autogpt_libs/autogpt_libs/auth/dependencies_test.py
+++ b/autogpt_platform/autogpt_libs/autogpt_libs/auth/dependencies_test.py
@@ -4,10 +4,9 @@ Tests the full authentication flow from HTTP requests to user validation.
 """

 import os
-from unittest.mock import Mock

 import pytest
-from fastapi import FastAPI, HTTPException, Request, Security
+from fastapi import FastAPI, HTTPException, Security
 from fastapi.testclient import TestClient
 from pytest_mock import MockerFixture

@@ -46,7 +45,6 @@ class TestAuthDependencies:
        """Create a test client."""
        return TestClient(app)

-    @pytest.mark.asyncio
    async def test_requires_user_with_valid_jwt_payload(self, mocker: MockerFixture):
        """Test requires_user with valid JWT payload."""
        jwt_payload = {"sub": "user-123", "role": "user", "email": "user@example.com"}
@@ -60,7 +58,6 @@ class TestAuthDependencies:
        assert user.user_id == "user-123"
        assert user.role == "user"

-    @pytest.mark.asyncio
    async def test_requires_user_with_admin_jwt_payload(self, mocker: MockerFixture):
        """Test requires_user accepts admin users."""
        jwt_payload = {
@@ -76,7 +73,6 @@ class TestAuthDependencies:
        assert user.user_id == "admin-456"
        assert user.role == "admin"

-    @pytest.mark.asyncio
    async def test_requires_user_missing_sub(self):
        """Test requires_user with missing user ID."""
        jwt_payload = {"role": "user", "email": "user@example.com"}
@@ -86,7 +82,6 @@ class TestAuthDependencies:
        assert exc_info.value.status_code == 401
        assert "User ID not found" in exc_info.value.detail

-    @pytest.mark.asyncio
    async def test_requires_user_empty_sub(self):
        """Test requires_user with empty user ID."""
        jwt_payload = {"sub": "", "role": "user"}
@@ -95,7 +90,6 @@ class TestAuthDependencies:
            await requires_user(jwt_payload)
        assert exc_info.value.status_code == 401

-    @pytest.mark.asyncio
    async def test_requires_admin_user_with_admin(self, mocker: MockerFixture):
        """Test requires_admin_user with admin role."""
        jwt_payload = {
@@ -111,7 +105,6 @@ class TestAuthDependencies:
        assert user.user_id == "admin-789"
        assert user.role == "admin"

-    @pytest.mark.asyncio
    async def test_requires_admin_user_with_regular_user(self):
        """Test requires_admin_user rejects regular users."""
        jwt_payload = {"sub": "user-123", "role": "user", "email": "user@example.com"}
@@ -121,7 +114,6 @@ class TestAuthDependencies:
        assert exc_info.value.status_code == 403
        assert "Admin access required" in exc_info.value.detail

-    @pytest.mark.asyncio
    async def test_requires_admin_user_missing_role(self):
        """Test requires_admin_user with missing role."""
        jwt_payload = {"sub": "user-123", "email": "user@example.com"}
@@ -129,40 +121,31 @@ class TestAuthDependencies:
        with pytest.raises(KeyError):
            await requires_admin_user(jwt_payload)

-    @pytest.mark.asyncio
    async def test_get_user_id_with_valid_payload(self, mocker: MockerFixture):
        """Test get_user_id extracts user ID correctly."""
-        request = Mock(spec=Request)
-        request.headers = {}
        jwt_payload = {"sub": "user-id-xyz", "role": "user"}

        mocker.patch(
            "autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
        )
-        user_id = await get_user_id(request, jwt_payload)
+        user_id = await get_user_id(jwt_payload)
        assert user_id == "user-id-xyz"

-    @pytest.mark.asyncio
    async def test_get_user_id_missing_sub(self):
        """Test get_user_id with missing user ID."""
-        request = Mock(spec=Request)
-        request.headers = {}
        jwt_payload = {"role": "user"}

        with pytest.raises(HTTPException) as exc_info:
-            await get_user_id(request, jwt_payload)
+            await get_user_id(jwt_payload)
        assert exc_info.value.status_code == 401
        assert "User ID not found" in exc_info.value.detail

-    @pytest.mark.asyncio
    async def test_get_user_id_none_sub(self):
        """Test get_user_id with None user ID."""
-        request = Mock(spec=Request)
-        request.headers = {}
        jwt_payload = {"sub": None, "role": "user"}

        with pytest.raises(HTTPException) as exc_info:
-            await get_user_id(request, jwt_payload)
+            await get_user_id(jwt_payload)
        assert exc_info.value.status_code == 401


@@ -187,7 +170,6 @@ class TestAuthDependenciesIntegration:

        return _create_token

-    @pytest.mark.asyncio
    async def test_endpoint_auth_enabled_no_token(self):
        """Test endpoints require token when auth is enabled."""
        app = FastAPI()
@@ -202,7 +184,6 @@ class TestAuthDependenciesIntegration:
        response = client.get("/test")
        assert response.status_code == 401

-    @pytest.mark.asyncio
    async def test_endpoint_with_valid_token(self, create_token):
        """Test endpoint with valid JWT token."""
        app = FastAPI()
@@ -222,7 +203,6 @@ class TestAuthDependenciesIntegration:
        assert response.status_code == 200
        assert response.json()["user_id"] == "test-user"

-    @pytest.mark.asyncio
    async def test_admin_endpoint_requires_admin_role(self, create_token):
        """Test admin endpoint rejects non-admin users."""
        app = FastAPI()
@@ -260,7 +240,6 @@ class TestAuthDependenciesIntegration:
 class TestAuthDependenciesEdgeCases:
    """Edge case tests for authentication dependencies."""

-    @pytest.mark.asyncio
    async def test_dependency_with_complex_payload(self):
        """Test dependencies handle complex JWT payloads."""
        complex_payload = {
@@ -284,7 +263,6 @@ class TestAuthDependenciesEdgeCases:
        admin = await requires_admin_user(complex_payload)
        assert admin.role == "admin"

-    @pytest.mark.asyncio
    async def test_dependency_with_unicode_in_payload(self):
        """Test dependencies handle unicode in JWT payloads."""
        unicode_payload = {
@@ -298,7 +276,6 @@ class TestAuthDependenciesEdgeCases:
        assert "😀" in user.user_id
        assert user.email == "测试@example.com"

-    @pytest.mark.asyncio
    async def test_dependency_with_null_values(self):
        """Test dependencies handle null values in payload."""
        null_payload = {
@@ -313,7 +290,6 @@ class TestAuthDependenciesEdgeCases:
        assert user.user_id == "user-123"
        assert user.email is None

-    @pytest.mark.asyncio
    async def test_concurrent_requests_isolation(self):
        """Test that concurrent requests don't interfere with each other."""
        payload1 = {"sub": "user-1", "role": "user"}
@@ -338,7 +314,6 @@ class TestAuthDependenciesEdgeCases:
            ({"sub": "user", "role": "user"}, "Admin access required", True),
        ],
    )
-    @pytest.mark.asyncio
    async def test_dependency_error_cases(
        self, payload, expected_error: str, admin_only: bool
    ):
@@ -350,7 +325,6 @@ class TestAuthDependenciesEdgeCases:
            verify_user(payload, admin_only=admin_only)
        assert expected_error in exc_info.value.detail

-    @pytest.mark.asyncio
    async def test_dependency_valid_user(self):
        """Test valid user case for dependency."""
        # Import verify_user to test it directly since dependencies use FastAPI Security
@@ -359,196 +333,3 @@ class TestAuthDependenciesEdgeCases:
        # Valid case
        user = verify_user({"sub": "user", "role": "user"}, admin_only=False)
        assert user.user_id == "user"
-
-
-class TestAdminImpersonation:
-    """Test suite for admin user impersonation functionality."""
-
-    @pytest.mark.asyncio
-    async def test_admin_impersonation_success(self, mocker: MockerFixture):
-        """Test admin successfully impersonating another user."""
-        request = Mock(spec=Request)
-        request.headers = {"X-Act-As-User-Id": "target-user-123"}
-        jwt_payload = {
-            "sub": "admin-456",
-            "role": "admin",
-            "email": "admin@example.com",
-        }
-
-        # Mock verify_user to return admin user data
-        mock_verify_user = mocker.patch("autogpt_libs.auth.dependencies.verify_user")
-        mock_verify_user.return_value = Mock(
-            user_id="admin-456", email="admin@example.com", role="admin"
-        )
-
-        # Mock logger to verify audit logging
-        mock_logger = mocker.patch("autogpt_libs.auth.dependencies.logger")
-
-        mocker.patch(
-            "autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
-        )
-
-        user_id = await get_user_id(request, jwt_payload)
-
-        # Should return the impersonated user ID
-        assert user_id == "target-user-123"
-
-        # Should log the impersonation attempt
-        mock_logger.info.assert_called_once()
-        log_call = mock_logger.info.call_args[0][0]
-        assert "Admin impersonation:" in log_call
-        assert "admin@example.com" in log_call
-        assert "target-user-123" in log_call
-
-    @pytest.mark.asyncio
-    async def test_non_admin_impersonation_attempt(self, mocker: MockerFixture):
-        """Test non-admin user attempting impersonation returns 403."""
-        request = Mock(spec=Request)
-        request.headers = {"X-Act-As-User-Id": "target-user-123"}
-        jwt_payload = {
-            "sub": "regular-user",
-            "role": "user",
-            "email": "user@example.com",
-        }
-
-        # Mock verify_user to return regular user data
-        mock_verify_user = mocker.patch("autogpt_libs.auth.dependencies.verify_user")
-        mock_verify_user.return_value = Mock(
-            user_id="regular-user", email="user@example.com", role="user"
-        )
-
-        mocker.patch(
-            "autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
-        )
-
-        with pytest.raises(HTTPException) as exc_info:
-            await get_user_id(request, jwt_payload)
-
-        assert exc_info.value.status_code == 403
-        assert "Only admin users can impersonate other users" in exc_info.value.detail
-
-    @pytest.mark.asyncio
-    async def test_impersonation_empty_header(self, mocker: MockerFixture):
-        """Test impersonation with empty header falls back to regular user ID."""
-        request = Mock(spec=Request)
-        request.headers = {"X-Act-As-User-Id": ""}
-        jwt_payload = {
-            "sub": "admin-456",
-            "role": "admin",
-            "email": "admin@example.com",
-        }
-
-        mocker.patch(
-            "autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
-        )
-
-        user_id = await get_user_id(request, jwt_payload)
-
-        # Should fall back to the admin's own user ID
-        assert user_id == "admin-456"
-
-    @pytest.mark.asyncio
-    async def test_impersonation_missing_header(self, mocker: MockerFixture):
-        """Test normal behavior when impersonation header is missing."""
-        request = Mock(spec=Request)
-        request.headers = {}  # No impersonation header
-        jwt_payload = {
-            "sub": "admin-456",
-            "role": "admin",
-            "email": "admin@example.com",
-        }
-
-        mocker.patch(
-            "autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
-        )
-
-        user_id = await get_user_id(request, jwt_payload)
-
-        # Should return the admin's own user ID
-        assert user_id == "admin-456"
-
-    @pytest.mark.asyncio
-    async def test_impersonation_audit_logging_details(self, mocker: MockerFixture):
-        """Test that impersonation audit logging includes all required details."""
-        request = Mock(spec=Request)
-        request.headers = {"X-Act-As-User-Id": "victim-user-789"}
-        jwt_payload = {
-            "sub": "admin-999",
-            "role": "admin",
-            "email": "superadmin@company.com",
-        }
-
-        # Mock verify_user to return admin user data
-        mock_verify_user = mocker.patch("autogpt_libs.auth.dependencies.verify_user")
-        mock_verify_user.return_value = Mock(
-            user_id="admin-999", email="superadmin@company.com", role="admin"
-        )
-
-        # Mock logger to capture audit trail
-        mock_logger = mocker.patch("autogpt_libs.auth.dependencies.logger")
-
-        mocker.patch(
-            "autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
-        )
-
-        user_id = await get_user_id(request, jwt_payload)
-
-        # Verify all audit details are logged
-        assert user_id == "victim-user-789"
-        mock_logger.info.assert_called_once()
-
-        log_message = mock_logger.info.call_args[0][0]
-        assert "Admin impersonation:" in log_message
-        assert "superadmin@company.com" in log_message
-        assert "victim-user-789" in log_message
-
-    @pytest.mark.asyncio
-    async def test_impersonation_header_case_sensitivity(self, mocker: MockerFixture):
-        """Test that impersonation header is case-sensitive."""
-        request = Mock(spec=Request)
-        # Use wrong case - should not trigger impersonation
-        request.headers = {"x-act-as-user-id": "target-user-123"}
-        jwt_payload = {
-            "sub": "admin-456",
-            "role": "admin",
-            "email": "admin@example.com",
-        }
-
-        mocker.patch(
-            "autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
-        )
-
-        user_id = await get_user_id(request, jwt_payload)
-
-        # Should fall back to admin's own ID (header case mismatch)
-        assert user_id == "admin-456"
-
-    @pytest.mark.asyncio
-    async def test_impersonation_with_whitespace_header(self, mocker: MockerFixture):
-        """Test impersonation with whitespace in header value."""
-        request = Mock(spec=Request)
-        request.headers = {"X-Act-As-User-Id": "  target-user-123  "}
-        jwt_payload = {
-            "sub": "admin-456",
-            "role": "admin",
-            "email": "admin@example.com",
-        }
-
-        # Mock verify_user to return admin user data
-        mock_verify_user = mocker.patch("autogpt_libs.auth.dependencies.verify_user")
-        mock_verify_user.return_value = Mock(
-            user_id="admin-456", email="admin@example.com", role="admin"
-        )
-
-        # Mock logger
-        mock_logger = mocker.patch("autogpt_libs.auth.dependencies.logger")
-
-        mocker.patch(
-            "autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
-        )
-
-        user_id = await get_user_id(request, jwt_payload)
-
-        # Should strip whitespace and impersonate successfully
-        assert user_id == "target-user-123"
-        mock_logger.info.assert_called_once()
--- a/autogpt_platform/autogpt_libs/autogpt_libs/auth/jwt_utils.py
+++ b/autogpt_platform/autogpt_libs/autogpt_libs/auth/jwt_utils.py
@@ -1,8 +1,4 @@
-import hashlib
 import logging
-import secrets
-import uuid
-from datetime import datetime, timedelta, timezone
 from typing import Any

 import jwt
@@ -20,57 +16,6 @@ bearer_jwt_auth = HTTPBearer(
 )


-def create_access_token(
-    user_id: str,
-    email: str,
-    role: str = "authenticated",
-    email_verified: bool = False,
-) -> str:
-    """
-    Generate a new JWT access token.
-
-    :param user_id: The user's unique identifier
-    :param email: The user's email address
-    :param role: The user's role (default: "authenticated")
-    :param email_verified: Whether the user's email is verified
-    :return: Encoded JWT token
-    """
-    settings = get_settings()
-    now = datetime.now(timezone.utc)
-
-    payload = {
-        "sub": user_id,
-        "email": email,
-        "role": role,
-        "email_verified": email_verified,
-        "aud": settings.JWT_AUDIENCE,
-        "iss": settings.JWT_ISSUER,
-        "iat": now,
-        "exp": now + timedelta(minutes=settings.ACCESS_TOKEN_EXPIRE_MINUTES),
-        "jti": str(uuid.uuid4()),  # Unique token ID
-    }
-
-    return jwt.encode(payload, settings.JWT_SIGN_KEY, algorithm=settings.JWT_ALGORITHM)
-
-
-def create_refresh_token() -> tuple[str, str]:
-    """
-    Generate a new refresh token.
-
-    Returns a tuple of (raw_token, hashed_token).
-    The raw token should be sent to the client.
-    The hashed token should be stored in the database.
-    """
-    raw_token = secrets.token_urlsafe(64)
-    hashed_token = hashlib.sha256(raw_token.encode()).hexdigest()
-    return raw_token, hashed_token
-
-
-def hash_token(token: str) -> str:
-    """Hash a token using SHA-256."""
-    return hashlib.sha256(token.encode()).hexdigest()
-
-
 async def get_jwt_payload(
    credentials: HTTPAuthorizationCredentials | None = Security(bearer_jwt_auth),
 ) -> dict[str, Any]:
@@ -107,19 +52,11 @@ def parse_jwt_token(token: str) -> dict[str, Any]:
    """
    settings = get_settings()
    try:
-        # Build decode options
-        options = {
-            "verify_aud": True,
-            "verify_iss": bool(settings.JWT_ISSUER),
-        }
-
        payload = jwt.decode(
            token,
            settings.JWT_VERIFY_KEY,
            algorithms=[settings.JWT_ALGORITHM],
-            audience=settings.JWT_AUDIENCE,
-            issuer=settings.JWT_ISSUER if settings.JWT_ISSUER else None,
-            options=options,
+            audience="authenticated",
        )
        return payload
    except jwt.ExpiredSignatureError:
--- a/autogpt_platform/autogpt_libs/autogpt_libs/auth/models.py
+++ b/autogpt_platform/autogpt_libs/autogpt_libs/auth/models.py
@@ -11,7 +11,6 @@ class User:
    email: str
    phone_number: str
    role: str
-    email_verified: bool = False

    @classmethod
    def from_payload(cls, payload):
@@ -19,6 +18,5 @@ class User:
            user_id=payload["sub"],
            email=payload.get("email", ""),
            phone_number=payload.get("phone", ""),
-            role=payload.get("role", "authenticated"),
-            email_verified=payload.get("email_verified", False),
+            role=payload["role"],
        )
--- a/autogpt_platform/autogpt_libs/autogpt_libs/logging/config.py
+++ b/autogpt_platform/autogpt_libs/autogpt_libs/logging/config.py
@@ -94,36 +94,42 @@ def configure_logging(force_cloud_logging: bool = False) -> None:
    config = LoggingConfig()
    log_handlers: list[logging.Handler] = []

-    structured_logging = config.enable_cloud_logging or force_cloud_logging
-
    # Console output handlers
-    if not structured_logging:
-        stdout = logging.StreamHandler(stream=sys.stdout)
-        stdout.setLevel(config.level)
-        stdout.addFilter(BelowLevelFilter(logging.WARNING))
-        if config.level == logging.DEBUG:
-            stdout.setFormatter(AGPTFormatter(DEBUG_LOG_FORMAT))
-        else:
-            stdout.setFormatter(AGPTFormatter(SIMPLE_LOG_FORMAT))
+    stdout = logging.StreamHandler(stream=sys.stdout)
+    stdout.setLevel(config.level)
+    stdout.addFilter(BelowLevelFilter(logging.WARNING))
+    if config.level == logging.DEBUG:
+        stdout.setFormatter(AGPTFormatter(DEBUG_LOG_FORMAT))
+    else:
+        stdout.setFormatter(AGPTFormatter(SIMPLE_LOG_FORMAT))

-        stderr = logging.StreamHandler()
-        stderr.setLevel(logging.WARNING)
-        if config.level == logging.DEBUG:
-            stderr.setFormatter(AGPTFormatter(DEBUG_LOG_FORMAT))
-        else:
-            stderr.setFormatter(AGPTFormatter(SIMPLE_LOG_FORMAT))
+    stderr = logging.StreamHandler()
+    stderr.setLevel(logging.WARNING)
+    if config.level == logging.DEBUG:
+        stderr.setFormatter(AGPTFormatter(DEBUG_LOG_FORMAT))
+    else:
+        stderr.setFormatter(AGPTFormatter(SIMPLE_LOG_FORMAT))

-        log_handlers += [stdout, stderr]
+    log_handlers += [stdout, stderr]

    # Cloud logging setup
-    else:
-        # Use Google Cloud Structured Log Handler. Log entries are printed to stdout
-        # in a JSON format which is automatically picked up by Google Cloud Logging.
-        from google.cloud.logging.handlers import StructuredLogHandler
+    if config.enable_cloud_logging or force_cloud_logging:
+        import google.cloud.logging
+        from google.cloud.logging.handlers import CloudLoggingHandler
+        from google.cloud.logging_v2.handlers.transports import (
+            BackgroundThreadTransport,
+        )

-        structured_log_handler = StructuredLogHandler(stream=sys.stdout)
-        structured_log_handler.setLevel(config.level)
-        log_handlers.append(structured_log_handler)
+        client = google.cloud.logging.Client()
+        # Use BackgroundThreadTransport to prevent blocking the main thread
+        # and deadlocks when gRPC calls to Google Cloud Logging hang
+        cloud_handler = CloudLoggingHandler(
+            client,
+            name="autogpt_logs",
+            transport=BackgroundThreadTransport,
+        )
+        cloud_handler.setLevel(config.level)
+        log_handlers.append(cloud_handler)

    # File logging setup
    if config.enable_file_logging:
@@ -179,13 +185,7 @@ def configure_logging(force_cloud_logging: bool = False) -> None:

    # Configure the root logger
    logging.basicConfig(
-        format=(
-            "%(levelname)s  %(message)s"
-            if structured_logging
-            else (
-                DEBUG_LOG_FORMAT if config.level == logging.DEBUG else SIMPLE_LOG_FORMAT
-            )
-        ),
+        format=DEBUG_LOG_FORMAT if config.level == logging.DEBUG else SIMPLE_LOG_FORMAT,
        level=config.level,
        handlers=log_handlers,
    )
--- a/autogpt_platform/autogpt_libs/autogpt_libs/utils/cache.py
+++ b/autogpt_platform/autogpt_libs/autogpt_libs/utils/cache.py
@@ -0,0 +1,339 @@
+import asyncio
+import inspect
+import logging
+import threading
+import time
+from functools import wraps
+from typing import (
+    Any,
+    Callable,
+    ParamSpec,
+    Protocol,
+    TypeVar,
+    cast,
+    runtime_checkable,
+)
+
+P = ParamSpec("P")
+R = TypeVar("R")
+R_co = TypeVar("R_co", covariant=True)
+
+logger = logging.getLogger(__name__)
+
+
+def _make_hashable_key(
+    args: tuple[Any, ...], kwargs: dict[str, Any]
+) -> tuple[Any, ...]:
+    """
+    Convert args and kwargs into a hashable cache key.
+
+    Handles unhashable types like dict, list, set by converting them to
+    their sorted string representations.
+    """
+
+    def make_hashable(obj: Any) -> Any:
+        """Recursively convert an object to a hashable representation."""
+        if isinstance(obj, dict):
+            # Sort dict items to ensure consistent ordering
+            return (
+                "__dict__",
+                tuple(sorted((k, make_hashable(v)) for k, v in obj.items())),
+            )
+        elif isinstance(obj, (list, tuple)):
+            return ("__list__", tuple(make_hashable(item) for item in obj))
+        elif isinstance(obj, set):
+            return ("__set__", tuple(sorted(make_hashable(item) for item in obj)))
+        elif hasattr(obj, "__dict__"):
+            # Handle objects with __dict__ attribute
+            return ("__obj__", obj.__class__.__name__, make_hashable(obj.__dict__))
+        else:
+            # For basic hashable types (str, int, bool, None, etc.)
+            try:
+                hash(obj)
+                return obj
+            except TypeError:
+                # Fallback: convert to string representation
+                return ("__str__", str(obj))
+
+    hashable_args = tuple(make_hashable(arg) for arg in args)
+    hashable_kwargs = tuple(sorted((k, make_hashable(v)) for k, v in kwargs.items()))
+    return (hashable_args, hashable_kwargs)
+
+
+@runtime_checkable
+class CachedFunction(Protocol[P, R_co]):
+    """Protocol for cached functions with cache management methods."""
+
+    def cache_clear(self) -> None:
+        """Clear all cached entries."""
+        return None
+
+    def cache_info(self) -> dict[str, int | None]:
+        """Get cache statistics."""
+        return {}
+
+    def cache_delete(self, *args: P.args, **kwargs: P.kwargs) -> bool:
+        """Delete a specific cache entry by its arguments. Returns True if entry existed."""
+        return False
+
+    def __call__(self, *args: P.args, **kwargs: P.kwargs) -> R_co:
+        """Call the cached function."""
+        return None  # type: ignore
+
+
+def cached(
+    *,
+    maxsize: int = 128,
+    ttl_seconds: int | None = None,
+) -> Callable[[Callable], CachedFunction]:
+    """
+    Thundering herd safe cache decorator for both sync and async functions.
+
+    Uses double-checked locking to prevent multiple threads/coroutines from
+    executing the expensive operation simultaneously during cache misses.
+
+    Args:
+        func: The function to cache (when used without parentheses)
+        maxsize: Maximum number of cached entries
+        ttl_seconds: Time to live in seconds. If None, entries never expire
+
+    Returns:
+        Decorated function or decorator
+
+    Example:
+        @cache()  # Default: maxsize=128, no TTL
+        def expensive_sync_operation(param: str) -> dict:
+            return {"result": param}
+
+        @cache()  # Works with async too
+        async def expensive_async_operation(param: str) -> dict:
+            return {"result": param}
+
+        @cache(maxsize=1000, ttl_seconds=300)  # Custom maxsize and TTL
+        def another_operation(param: str) -> dict:
+            return {"result": param}
+    """
+
+    def decorator(target_func):
+        # Cache storage and per-event-loop locks
+        cache_storage = {}
+        _event_loop_locks = {}  # Maps event loop to its asyncio.Lock
+
+        if inspect.iscoroutinefunction(target_func):
+
+            def _get_cache_lock():
+                """Get or create an asyncio.Lock for the current event loop."""
+                try:
+                    loop = asyncio.get_running_loop()
+                except RuntimeError:
+                    # No event loop, use None as default key
+                    loop = None
+
+                if loop not in _event_loop_locks:
+                    return _event_loop_locks.setdefault(loop, asyncio.Lock())
+                return _event_loop_locks[loop]
+
+            @wraps(target_func)
+            async def async_wrapper(*args: P.args, **kwargs: P.kwargs):
+                key = _make_hashable_key(args, kwargs)
+                current_time = time.time()
+
+                # Fast path: check cache without lock
+                if key in cache_storage:
+                    if ttl_seconds is None:
+                        logger.debug(f"Cache hit for {target_func.__name__}")
+                        return cache_storage[key]
+                    else:
+                        cached_data = cache_storage[key]
+                        if isinstance(cached_data, tuple):
+                            result, timestamp = cached_data
+                            if current_time - timestamp < ttl_seconds:
+                                logger.debug(f"Cache hit for {target_func.__name__}")
+                                return result
+
+                # Slow path: acquire lock for cache miss/expiry
+                async with _get_cache_lock():
+                    # Double-check: another coroutine might have populated cache
+                    if key in cache_storage:
+                        if ttl_seconds is None:
+                            return cache_storage[key]
+                        else:
+                            cached_data = cache_storage[key]
+                            if isinstance(cached_data, tuple):
+                                result, timestamp = cached_data
+                                if current_time - timestamp < ttl_seconds:
+                                    return result
+
+                    # Cache miss - execute function
+                    logger.debug(f"Cache miss for {target_func.__name__}")
+                    result = await target_func(*args, **kwargs)
+
+                    # Store result
+                    if ttl_seconds is None:
+                        cache_storage[key] = result
+                    else:
+                        cache_storage[key] = (result, current_time)
+
+                    # Cleanup if needed
+                    if len(cache_storage) > maxsize:
+                        cutoff = maxsize // 2
+                        oldest_keys = (
+                            list(cache_storage.keys())[:-cutoff] if cutoff > 0 else []
+                        )
+                        for old_key in oldest_keys:
+                            cache_storage.pop(old_key, None)
+
+                    return result
+
+            wrapper = async_wrapper
+
+        else:
+            # Sync function with threading.Lock
+            cache_lock = threading.Lock()
+
+            @wraps(target_func)
+            def sync_wrapper(*args: P.args, **kwargs: P.kwargs):
+                key = _make_hashable_key(args, kwargs)
+                current_time = time.time()
+
+                # Fast path: check cache without lock
+                if key in cache_storage:
+                    if ttl_seconds is None:
+                        logger.debug(f"Cache hit for {target_func.__name__}")
+                        return cache_storage[key]
+                    else:
+                        cached_data = cache_storage[key]
+                        if isinstance(cached_data, tuple):
+                            result, timestamp = cached_data
+                            if current_time - timestamp < ttl_seconds:
+                                logger.debug(f"Cache hit for {target_func.__name__}")
+                                return result
+
+                # Slow path: acquire lock for cache miss/expiry
+                with cache_lock:
+                    # Double-check: another thread might have populated cache
+                    if key in cache_storage:
+                        if ttl_seconds is None:
+                            return cache_storage[key]
+                        else:
+                            cached_data = cache_storage[key]
+                            if isinstance(cached_data, tuple):
+                                result, timestamp = cached_data
+                                if current_time - timestamp < ttl_seconds:
+                                    return result
+
+                    # Cache miss - execute function
+                    logger.debug(f"Cache miss for {target_func.__name__}")
+                    result = target_func(*args, **kwargs)
+
+                    # Store result
+                    if ttl_seconds is None:
+                        cache_storage[key] = result
+                    else:
+                        cache_storage[key] = (result, current_time)
+
+                    # Cleanup if needed
+                    if len(cache_storage) > maxsize:
+                        cutoff = maxsize // 2
+                        oldest_keys = (
+                            list(cache_storage.keys())[:-cutoff] if cutoff > 0 else []
+                        )
+                        for old_key in oldest_keys:
+                            cache_storage.pop(old_key, None)
+
+                    return result
+
+            wrapper = sync_wrapper
+
+        # Add cache management methods
+        def cache_clear() -> None:
+            cache_storage.clear()
+
+        def cache_info() -> dict[str, int | None]:
+            return {
+                "size": len(cache_storage),
+                "maxsize": maxsize,
+                "ttl_seconds": ttl_seconds,
+            }
+
+        def cache_delete(*args, **kwargs) -> bool:
+            """Delete a specific cache entry. Returns True if entry existed."""
+            key = _make_hashable_key(args, kwargs)
+            if key in cache_storage:
+                del cache_storage[key]
+                return True
+            return False
+
+        setattr(wrapper, "cache_clear", cache_clear)
+        setattr(wrapper, "cache_info", cache_info)
+        setattr(wrapper, "cache_delete", cache_delete)
+
+        return cast(CachedFunction, wrapper)
+
+    return decorator
+
+
+def thread_cached(func):
+    """
+    Thread-local cache decorator for both sync and async functions.
+
+    Each thread gets its own cache, which is useful for request-scoped caching
+    in web applications where you want to cache within a single request but
+    not across requests.
+
+    Args:
+        func: The function to cache
+
+    Returns:
+        Decorated function with thread-local caching
+
+    Example:
+        @thread_cached
+        def expensive_operation(param: str) -> dict:
+            return {"result": param}
+
+        @thread_cached  # Works with async too
+        async def expensive_async_operation(param: str) -> dict:
+            return {"result": param}
+    """
+    thread_local = threading.local()
+
+    def _clear():
+        if hasattr(thread_local, "cache"):
+            del thread_local.cache
+
+    if inspect.iscoroutinefunction(func):
+
+        @wraps(func)
+        async def async_wrapper(*args, **kwargs):
+            cache = getattr(thread_local, "cache", None)
+            if cache is None:
+                cache = thread_local.cache = {}
+            key = _make_hashable_key(args, kwargs)
+            if key not in cache:
+                cache[key] = await func(*args, **kwargs)
+            return cache[key]
+
+        setattr(async_wrapper, "clear_cache", _clear)
+        return async_wrapper
+
+    else:
+
+        @wraps(func)
+        def sync_wrapper(*args, **kwargs):
+            cache = getattr(thread_local, "cache", None)
+            if cache is None:
+                cache = thread_local.cache = {}
+            key = _make_hashable_key(args, kwargs)
+            if key not in cache:
+                cache[key] = func(*args, **kwargs)
+            return cache[key]
+
+        setattr(sync_wrapper, "clear_cache", _clear)
+        return sync_wrapper
+
+
+def clear_thread_cache(func: Callable) -> None:
+    """Clear thread-local cache for a function."""
+    if clear := getattr(func, "clear_cache", None):
+        clear()
--- a/autogpt_platform/autogpt_libs/autogpt_libs/utils/cache_test.py
+++ b/autogpt_platform/autogpt_libs/autogpt_libs/utils/cache_test.py
@@ -16,7 +16,7 @@ from unittest.mock import Mock

 import pytest

-from backend.util.cache import cached, clear_thread_cache, thread_cached
+from autogpt_libs.utils.cache import cached, clear_thread_cache, thread_cached


 class TestThreadCached:
@@ -332,7 +332,7 @@ class TestCache:
        """Test basic sync caching functionality."""
        call_count = 0

-        @cached(ttl_seconds=300)
+        @cached()
        def expensive_sync_function(x: int, y: int = 0) -> int:
            nonlocal call_count
            call_count += 1
@@ -358,7 +358,7 @@ class TestCache:
        """Test basic async caching functionality."""
        call_count = 0

-        @cached(ttl_seconds=300)
+        @cached()
        async def expensive_async_function(x: int, y: int = 0) -> int:
            nonlocal call_count
            call_count += 1
@@ -385,7 +385,7 @@ class TestCache:
        call_count = 0
        results = []

-        @cached(ttl_seconds=300)
+        @cached()
        def slow_function(x: int) -> int:
            nonlocal call_count
            call_count += 1
@@ -412,7 +412,7 @@ class TestCache:
        """Test that concurrent async calls don't cause thundering herd."""
        call_count = 0

-        @cached(ttl_seconds=300)
+        @cached()
        async def slow_async_function(x: int) -> int:
            nonlocal call_count
            call_count += 1
@@ -508,7 +508,7 @@ class TestCache:
        """Test cache clearing functionality."""
        call_count = 0

-        @cached(ttl_seconds=300)
+        @cached()
        def clearable_function(x: int) -> int:
            nonlocal call_count
            call_count += 1
@@ -537,7 +537,7 @@ class TestCache:
        """Test cache clearing functionality with async function."""
        call_count = 0

-        @cached(ttl_seconds=300)
+        @cached()
        async def async_clearable_function(x: int) -> int:
            nonlocal call_count
            call_count += 1
@@ -567,7 +567,7 @@ class TestCache:
        """Test that cached async functions return actual results, not coroutines."""
        call_count = 0

-        @cached(ttl_seconds=300)
+        @cached()
        async def async_result_function(x: int) -> str:
            nonlocal call_count
            call_count += 1
@@ -593,7 +593,7 @@ class TestCache:
        """Test selective cache deletion functionality."""
        call_count = 0

-        @cached(ttl_seconds=300)
+        @cached()
        def deletable_function(x: int) -> int:
            nonlocal call_count
            call_count += 1
@@ -636,7 +636,7 @@ class TestCache:
        """Test selective cache deletion functionality with async function."""
        call_count = 0

-        @cached(ttl_seconds=300)
+        @cached()
        async def async_deletable_function(x: int) -> int:
            nonlocal call_count
            call_count += 1
@@ -674,450 +674,3 @@ class TestCache:
        # Try to delete non-existent entry
        was_deleted = async_deletable_function.cache_delete(99)
        assert was_deleted is False
-
-
-class TestSharedCache:
-    """Tests for shared_cache (Redis-backed) functionality."""
-
-    def test_sync_shared_cache_basic(self):
-        """Test basic shared cache functionality with sync function."""
-        call_count = 0
-
-        @cached(ttl_seconds=30, shared_cache=True)
-        def shared_sync_function(x: int, y: int = 0) -> int:
-            nonlocal call_count
-            call_count += 1
-            return x + y
-
-        # Clear any existing cache
-        shared_sync_function.cache_clear()
-
-        # First call
-        result1 = shared_sync_function(10, 20)
-        assert result1 == 30
-        assert call_count == 1
-
-        # Second call - should use Redis cache
-        result2 = shared_sync_function(10, 20)
-        assert result2 == 30
-        assert call_count == 1
-
-        # Different args - should call function again
-        result3 = shared_sync_function(15, 25)
-        assert result3 == 40
-        assert call_count == 2
-
-        # Cleanup
-        shared_sync_function.cache_clear()
-
-    @pytest.mark.asyncio
-    async def test_async_shared_cache_basic(self):
-        """Test basic shared cache functionality with async function."""
-        call_count = 0
-
-        @cached(ttl_seconds=30, shared_cache=True)
-        async def shared_async_function(x: int, y: int = 0) -> int:
-            nonlocal call_count
-            call_count += 1
-            await asyncio.sleep(0.01)
-            return x + y
-
-        # Clear any existing cache
-        shared_async_function.cache_clear()
-
-        # First call
-        result1 = await shared_async_function(10, 20)
-        assert result1 == 30
-        assert call_count == 1
-
-        # Second call - should use Redis cache
-        result2 = await shared_async_function(10, 20)
-        assert result2 == 30
-        assert call_count == 1
-
-        # Different args - should call function again
-        result3 = await shared_async_function(15, 25)
-        assert result3 == 40
-        assert call_count == 2
-
-        # Cleanup
-        shared_async_function.cache_clear()
-
-    def test_shared_cache_ttl_refresh(self):
-        """Test TTL refresh functionality with shared cache."""
-        call_count = 0
-
-        @cached(ttl_seconds=2, shared_cache=True, refresh_ttl_on_get=True)
-        def ttl_refresh_function(x: int) -> int:
-            nonlocal call_count
-            call_count += 1
-            return x * 10
-
-        # Clear any existing cache
-        ttl_refresh_function.cache_clear()
-
-        # First call
-        result1 = ttl_refresh_function(3)
-        assert result1 == 30
-        assert call_count == 1
-
-        # Wait 1 second
-        time.sleep(1)
-
-        # Second call - should refresh TTL and use cache
-        result2 = ttl_refresh_function(3)
-        assert result2 == 30
-        assert call_count == 1
-
-        # Wait another 1.5 seconds (total 2.5s from first call, 1.5s from second)
-        time.sleep(1.5)
-
-        # Third call - TTL should have been refreshed, so still cached
-        result3 = ttl_refresh_function(3)
-        assert result3 == 30
-        assert call_count == 1
-
-        # Wait 2.1 seconds - now it should expire
-        time.sleep(2.1)
-
-        # Fourth call - should call function again
-        result4 = ttl_refresh_function(3)
-        assert result4 == 30
-        assert call_count == 2
-
-        # Cleanup
-        ttl_refresh_function.cache_clear()
-
-    def test_shared_cache_without_ttl_refresh(self):
-        """Test that TTL doesn't refresh when refresh_ttl_on_get=False."""
-        call_count = 0
-
-        @cached(ttl_seconds=2, shared_cache=True, refresh_ttl_on_get=False)
-        def no_ttl_refresh_function(x: int) -> int:
-            nonlocal call_count
-            call_count += 1
-            return x * 10
-
-        # Clear any existing cache
-        no_ttl_refresh_function.cache_clear()
-
-        # First call
-        result1 = no_ttl_refresh_function(4)
-        assert result1 == 40
-        assert call_count == 1
-
-        # Wait 1 second
-        time.sleep(1)
-
-        # Second call - should use cache but NOT refresh TTL
-        result2 = no_ttl_refresh_function(4)
-        assert result2 == 40
-        assert call_count == 1
-
-        # Wait another 1.1 seconds (total 2.1s from first call)
-        time.sleep(1.1)
-
-        # Third call - should have expired
-        result3 = no_ttl_refresh_function(4)
-        assert result3 == 40
-        assert call_count == 2
-
-        # Cleanup
-        no_ttl_refresh_function.cache_clear()
-
-    def test_shared_cache_complex_objects(self):
-        """Test caching complex objects with shared cache (pickle serialization)."""
-        call_count = 0
-
-        @cached(ttl_seconds=30, shared_cache=True)
-        def complex_object_function(x: int) -> dict:
-            nonlocal call_count
-            call_count += 1
-            return {
-                "number": x,
-                "squared": x**2,
-                "nested": {"list": [1, 2, x], "tuple": (x, x * 2)},
-                "string": f"value_{x}",
-            }
-
-        # Clear any existing cache
-        complex_object_function.cache_clear()
-
-        # First call
-        result1 = complex_object_function(5)
-        assert result1["number"] == 5
-        assert result1["squared"] == 25
-        assert result1["nested"]["list"] == [1, 2, 5]
-        assert call_count == 1
-
-        # Second call - should use cache
-        result2 = complex_object_function(5)
-        assert result2 == result1
-        assert call_count == 1
-
-        # Cleanup
-        complex_object_function.cache_clear()
-
-    def test_shared_cache_info(self):
-        """Test cache_info for shared cache."""
-
-        @cached(ttl_seconds=30, shared_cache=True)
-        def info_shared_function(x: int) -> int:
-            return x * 2
-
-        # Clear any existing cache
-        info_shared_function.cache_clear()
-
-        # Check initial info
-        info = info_shared_function.cache_info()
-        assert info["size"] == 0
-        assert info["maxsize"] is None  # Redis manages size
-        assert info["ttl_seconds"] == 30
-
-        # Add some entries
-        info_shared_function(1)
-        info_shared_function(2)
-        info_shared_function(3)
-
-        info = info_shared_function.cache_info()
-        assert info["size"] == 3
-
-        # Cleanup
-        info_shared_function.cache_clear()
-
-    def test_shared_cache_delete(self):
-        """Test selective deletion with shared cache."""
-        call_count = 0
-
-        @cached(ttl_seconds=30, shared_cache=True)
-        def delete_shared_function(x: int) -> int:
-            nonlocal call_count
-            call_count += 1
-            return x * 3
-
-        # Clear any existing cache
-        delete_shared_function.cache_clear()
-
-        # Add entries
-        delete_shared_function(1)
-        delete_shared_function(2)
-        delete_shared_function(3)
-        assert call_count == 3
-
-        # Verify cached
-        delete_shared_function(1)
-        delete_shared_function(2)
-        assert call_count == 3
-
-        # Delete specific entry
-        was_deleted = delete_shared_function.cache_delete(2)
-        assert was_deleted is True
-
-        # Entry for x=2 should be gone
-        delete_shared_function(2)
-        assert call_count == 4
-
-        # Others should still be cached
-        delete_shared_function(1)
-        delete_shared_function(3)
-        assert call_count == 4
-
-        # Try to delete non-existent
-        was_deleted = delete_shared_function.cache_delete(99)
-        assert was_deleted is False
-
-        # Cleanup
-        delete_shared_function.cache_clear()
-
-    @pytest.mark.asyncio
-    async def test_async_shared_cache_thundering_herd(self):
-        """Test that shared cache prevents thundering herd for async functions."""
-        call_count = 0
-
-        @cached(ttl_seconds=30, shared_cache=True)
-        async def shared_slow_function(x: int) -> int:
-            nonlocal call_count
-            call_count += 1
-            await asyncio.sleep(0.1)
-            return x * x
-
-        # Clear any existing cache
-        shared_slow_function.cache_clear()
-
-        # Launch multiple concurrent tasks
-        tasks = [shared_slow_function(8) for _ in range(10)]
-        results = await asyncio.gather(*tasks)
-
-        # All should return same result
-        assert all(r == 64 for r in results)
-        # Only one should have executed
-        assert call_count == 1
-
-        # Cleanup
-        shared_slow_function.cache_clear()
-
-    def test_shared_cache_clear_pattern(self):
-        """Test pattern-based cache clearing (Redis feature)."""
-
-        @cached(ttl_seconds=30, shared_cache=True)
-        def pattern_function(category: str, item: int) -> str:
-            return f"{category}_{item}"
-
-        # Clear any existing cache
-        pattern_function.cache_clear()
-
-        # Add various entries
-        pattern_function("fruit", 1)
-        pattern_function("fruit", 2)
-        pattern_function("vegetable", 1)
-        pattern_function("vegetable", 2)
-
-        info = pattern_function.cache_info()
-        assert info["size"] == 4
-
-        # Note: Pattern clearing with wildcards requires specific Redis scan
-        # implementation. The current code clears by pattern but needs
-        # adjustment for partial matching. For now, test full clear.
-        pattern_function.cache_clear()
-        info = pattern_function.cache_info()
-        assert info["size"] == 0
-
-    def test_shared_vs_local_cache_isolation(self):
-        """Test that shared and local caches are isolated."""
-        shared_count = 0
-        local_count = 0
-
-        @cached(ttl_seconds=30, shared_cache=True)
-        def shared_function(x: int) -> int:
-            nonlocal shared_count
-            shared_count += 1
-            return x * 2
-
-        @cached(ttl_seconds=30, shared_cache=False)
-        def local_function(x: int) -> int:
-            nonlocal local_count
-            local_count += 1
-            return x * 2
-
-        # Clear caches
-        shared_function.cache_clear()
-        local_function.cache_clear()
-
-        # Call both with same args
-        shared_result = shared_function(5)
-        local_result = local_function(5)
-
-        assert shared_result == local_result == 10
-        assert shared_count == 1
-        assert local_count == 1
-
-        # Call again - both should use their respective caches
-        shared_function(5)
-        local_function(5)
-        assert shared_count == 1
-        assert local_count == 1
-
-        # Clear only shared cache
-        shared_function.cache_clear()
-
-        # Shared should recompute, local should still use cache
-        shared_function(5)
-        local_function(5)
-        assert shared_count == 2
-        assert local_count == 1
-
-        # Cleanup
-        shared_function.cache_clear()
-        local_function.cache_clear()
-
-    @pytest.mark.asyncio
-    async def test_shared_cache_concurrent_different_keys(self):
-        """Test that concurrent calls with different keys work correctly."""
-        call_counts = {}
-
-        @cached(ttl_seconds=30, shared_cache=True)
-        async def multi_key_function(key: str) -> str:
-            if key not in call_counts:
-                call_counts[key] = 0
-            call_counts[key] += 1
-            await asyncio.sleep(0.05)
-            return f"result_{key}"
-
-        # Clear cache
-        multi_key_function.cache_clear()
-
-        # Launch concurrent tasks with different keys
-        keys = ["a", "b", "c", "d", "e"]
-        tasks = []
-        for key in keys:
-            # Multiple calls per key
-            tasks.extend([multi_key_function(key) for _ in range(3)])
-
-        results = await asyncio.gather(*tasks)
-
-        # Verify results
-        for i, key in enumerate(keys):
-            expected = f"result_{key}"
-            # Each key appears 3 times in results
-            key_results = results[i * 3 : (i + 1) * 3]
-            assert all(r == expected for r in key_results)
-
-        # Each key should only be computed once
-        for key in keys:
-            assert call_counts[key] == 1
-
-        # Cleanup
-        multi_key_function.cache_clear()
-
-    def test_shared_cache_performance_comparison(self):
-        """Compare performance of shared vs local cache."""
-        import statistics
-
-        shared_times = []
-        local_times = []
-
-        @cached(ttl_seconds=30, shared_cache=True)
-        def shared_perf_function(x: int) -> int:
-            time.sleep(0.01)  # Simulate work
-            return x * 2
-
-        @cached(ttl_seconds=30, shared_cache=False)
-        def local_perf_function(x: int) -> int:
-            time.sleep(0.01)  # Simulate work
-            return x * 2
-
-        # Clear caches
-        shared_perf_function.cache_clear()
-        local_perf_function.cache_clear()
-
-        # Warm up both caches
-        for i in range(5):
-            shared_perf_function(i)
-            local_perf_function(i)
-
-        # Measure cache hit times
-        for i in range(5):
-            # Shared cache hit
-            start = time.time()
-            shared_perf_function(i)
-            shared_times.append(time.time() - start)
-
-            # Local cache hit
-            start = time.time()
-            local_perf_function(i)
-            local_times.append(time.time() - start)
-
-        # Local cache should be faster (no Redis round-trip)
-        avg_shared = statistics.mean(shared_times)
-        avg_local = statistics.mean(local_times)
-
-        print(f"Avg shared cache hit time: {avg_shared:.6f}s")
-        print(f"Avg local cache hit time: {avg_local:.6f}s")
-
-        # Local should be significantly faster for cache hits
-        # Redis adds network latency even for cache hits
-        assert avg_local < avg_shared
-
-        # Cleanup
-        shared_perf_function.cache_clear()
-        local_perf_function.cache_clear()
--- a/autogpt_platform/autogpt_libs/poetry.lock
+++ b/autogpt_platform/autogpt_libs/poetry.lock
@@ -48,21 +48,6 @@ files = [
    {file = "async_timeout-5.0.1.tar.gz", hash = "sha256:d9321a7a3d5a6a5e187e824d2fa0793ce379a202935782d555d6e9d2735677d3"},
 ]

-[[package]]
-name = "authlib"
-version = "1.6.6"
-description = "The ultimate Python library in building OAuth and OpenID Connect servers and clients."
-optional = false
-python-versions = ">=3.9"
-groups = ["main"]
-files = [
-    {file = "authlib-1.6.6-py2.py3-none-any.whl", hash = "sha256:7d9e9bc535c13974313a87f53e8430eb6ea3d1cf6ae4f6efcd793f2e949143fd"},
-    {file = "authlib-1.6.6.tar.gz", hash = "sha256:45770e8e056d0f283451d9996fbb59b70d45722b45d854d58f32878d0a40c38e"},
-]
-
-[package.dependencies]
-cryptography = "*"
-
 [[package]]
 name = "backports-asyncio-runner"
 version = "1.2.0"
@@ -76,71 +61,6 @@ files = [
    {file = "backports_asyncio_runner-1.2.0.tar.gz", hash = "sha256:a5aa7b2b7d8f8bfcaa2b57313f70792df84e32a2a746f585213373f900b42162"},
 ]

-[[package]]
-name = "bcrypt"
-version = "4.3.0"
-description = "Modern password hashing for your software and your servers"
-optional = false
-python-versions = ">=3.8"
-groups = ["main"]
-files = [
-    {file = "bcrypt-4.3.0-cp313-cp313t-macosx_10_12_universal2.whl", hash = "sha256:f01e060f14b6b57bbb72fc5b4a83ac21c443c9a2ee708e04a10e9192f90a6281"},
-    {file = "bcrypt-4.3.0-cp313-cp313t-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c5eeac541cefd0bb887a371ef73c62c3cd78535e4887b310626036a7c0a817bb"},
-    {file = "bcrypt-4.3.0-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:59e1aa0e2cd871b08ca146ed08445038f42ff75968c7ae50d2fdd7860ade2180"},
-    {file = "bcrypt-4.3.0-cp313-cp313t-manylinux_2_28_aarch64.whl", hash = "sha256:0042b2e342e9ae3d2ed22727c1262f76cc4f345683b5c1715f0250cf4277294f"},
-    {file = "bcrypt-4.3.0-cp313-cp313t-manylinux_2_28_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:74a8d21a09f5e025a9a23e7c0fd2c7fe8e7503e4d356c0a2c1486ba010619f09"},
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-
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-tests = ["pytest (>=3.2.1,!=3.3.0)"]
-typecheck = ["mypy"]
-
 [[package]]
 name = "cachetools"
 version = "5.5.2"
@@ -539,6 +459,21 @@ ssh = ["bcrypt (>=3.1.5)"]
 test = ["certifi (>=2024)", "cryptography-vectors (==45.0.6)", "pretend (>=0.7)", "pytest (>=7.4.0)", "pytest-benchmark (>=4.0)", "pytest-cov (>=2.10.1)", "pytest-xdist (>=3.5.0)"]
 test-randomorder = ["pytest-randomly"]

+[[package]]
+name = "deprecation"
+version = "2.1.0"
+description = "A library to handle automated deprecations"
+optional = false
+python-versions = "*"
+groups = ["main"]
+files = [
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+]
+
+[package.dependencies]
+packaging = "*"
+
 [[package]]
 name = "exceptiongroup"
 version = "1.3.0"
@@ -760,6 +695,23 @@ protobuf = ">=3.20.2,<4.21.1 || >4.21.1,<4.21.2 || >4.21.2,<4.21.3 || >4.21.3,<4
 [package.extras]
 grpc = ["grpcio (>=1.44.0,<2.0.0)"]

+[[package]]
+name = "gotrue"
+version = "2.12.3"
+description = "Python Client Library for Supabase Auth"
+optional = false
+python-versions = "<4.0,>=3.9"
+groups = ["main"]
+files = [
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+    {file = "gotrue-2.12.3.tar.gz", hash = "sha256:f874cf9d0b2f0335bfbd0d6e29e3f7aff79998cd1c14d2ad814db8c06cee3852"},
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+
+[package.dependencies]
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+pydantic = ">=1.10,<3"
+pyjwt = ">=2.10.1,<3.0.0"
+
 [[package]]
 name = "grpc-google-iam-v1"
 version = "0.14.2"
@@ -870,6 +822,94 @@ files = [
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 ]

+[[package]]
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+version = "4.2.0"
+description = "Pure-Python HTTP/2 protocol implementation"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
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+    {file = "h2-4.2.0.tar.gz", hash = "sha256:c8a52129695e88b1a0578d8d2cc6842bbd79128ac685463b887ee278126ad01f"},
+]
+
+[package.dependencies]
+hpack = ">=4.1,<5"
+hyperframe = ">=6.1,<7"
+
+[[package]]
+name = "hpack"
+version = "4.1.0"
+description = "Pure-Python HPACK header encoding"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
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+    {file = "hpack-4.1.0.tar.gz", hash = "sha256:ec5eca154f7056aa06f196a557655c5b009b382873ac8d1e66e79e87535f1dca"},
+]
+
+[[package]]
+name = "httpcore"
+version = "1.0.9"
+description = "A minimal low-level HTTP client."
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
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+    {file = "httpcore-1.0.9.tar.gz", hash = "sha256:6e34463af53fd2ab5d807f399a9b45ea31c3dfa2276f15a2c3f00afff6e176e8"},
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+h11 = ">=0.16"
+
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+asyncio = ["anyio (>=4.0,<5.0)"]
+http2 = ["h2 (>=3,<5)"]
+socks = ["socksio (==1.*)"]
+trio = ["trio (>=0.22.0,<1.0)"]
+
+[[package]]
+name = "httpx"
+version = "0.28.1"
+description = "The next generation HTTP client."
+optional = false
+python-versions = ">=3.8"
+groups = ["main"]
+files = [
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+    {file = "httpx-0.28.1.tar.gz", hash = "sha256:75e98c5f16b0f35b567856f597f06ff2270a374470a5c2392242528e3e3e42fc"},
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+certifi = "*"
+h2 = {version = ">=3,<5", optional = true, markers = "extra == \"http2\""}
+httpcore = "==1.*"
+idna = "*"
+
+[package.extras]
+brotli = ["brotli ; platform_python_implementation == \"CPython\"", "brotlicffi ; platform_python_implementation != \"CPython\""]
+cli = ["click (==8.*)", "pygments (==2.*)", "rich (>=10,<14)"]
+http2 = ["h2 (>=3,<5)"]
+socks = ["socksio (==1.*)"]
+zstd = ["zstandard (>=0.18.0)"]
+
+[[package]]
+name = "hyperframe"
+version = "6.1.0"
+description = "Pure-Python HTTP/2 framing"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
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+    {file = "hyperframe-6.1.0.tar.gz", hash = "sha256:f630908a00854a7adeabd6382b43923a4c4cd4b821fcb527e6ab9e15382a3b08"},
+]
+
 [[package]]
 name = "idna"
 version = "3.10"
@@ -996,7 +1036,7 @@ version = "25.0"
 description = "Core utilities for Python packages"
 optional = false
 python-versions = ">=3.8"
-groups = ["dev"]
+groups = ["main", "dev"]
 files = [
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@@ -1018,6 +1058,24 @@ files = [
 dev = ["pre-commit", "tox"]
 testing = ["coverage", "pytest", "pytest-benchmark"]

+[[package]]
+name = "postgrest"
+version = "1.1.1"
+description = "PostgREST client for Python. This library provides an ORM interface to PostgREST."
+optional = false
+python-versions = "<4.0,>=3.9"
+groups = ["main"]
+files = [
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+    {file = "postgrest-1.1.1.tar.gz", hash = "sha256:f3bb3e8c4602775c75c844a31f565f5f3dd584df4d36d683f0b67d01a86be322"},
+]
+
+[package.dependencies]
+deprecation = ">=2.1.0,<3.0.0"
+httpx = {version = ">=0.26,<0.29", extras = ["http2"]}
+pydantic = ">=1.9,<3.0"
+strenum = {version = ">=0.4.9,<0.5.0", markers = "python_version < \"3.11\""}
+
 [[package]]
 name = "proto-plus"
 version = "1.26.1"
@@ -1404,6 +1462,21 @@ pytest = ">=6.2.5"
 [package.extras]
 dev = ["pre-commit", "pytest-asyncio", "tox"]

+[[package]]
+name = "python-dateutil"
+version = "2.9.0.post0"
+description = "Extensions to the standard Python datetime module"
+optional = false
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
+groups = ["main"]
+files = [
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+    {file = "python_dateutil-2.9.0.post0-py2.py3-none-any.whl", hash = "sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427"},
+]
+
+[package.dependencies]
+six = ">=1.5"
+
 [[package]]
 name = "python-dotenv"
 version = "1.1.1"
@@ -1419,6 +1492,22 @@ files = [
 [package.extras]
 cli = ["click (>=5.0)"]

+[[package]]
+name = "realtime"
+version = "2.5.3"
+description = ""
+optional = false
+python-versions = "<4.0,>=3.9"
+groups = ["main"]
+files = [
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+    {file = "realtime-2.5.3.tar.gz", hash = "sha256:0587594f3bc1c84bf007ff625075b86db6528843e03250dc84f4f2808be3d99a"},
+]
+
+[package.dependencies]
+typing-extensions = ">=4.14.0,<5.0.0"
+websockets = ">=11,<16"
+
 [[package]]
 name = "redis"
 version = "6.2.0"
@@ -1517,6 +1606,18 @@ files = [
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 ]

+[[package]]
+name = "six"
+version = "1.17.0"
+description = "Python 2 and 3 compatibility utilities"
+optional = false
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
+groups = ["main"]
+files = [
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+    {file = "six-1.17.0.tar.gz", hash = "sha256:ff70335d468e7eb6ec65b95b99d3a2836546063f63acc5171de367e834932a81"},
+]
+
 [[package]]
 name = "sniffio"
 version = "1.3.1"
@@ -1548,6 +1649,76 @@ typing-extensions = {version = ">=4.10.0", markers = "python_version < \"3.13\""
 [package.extras]
 full = ["httpx (>=0.27.0,<0.29.0)", "itsdangerous", "jinja2", "python-multipart (>=0.0.18)", "pyyaml"]

+[[package]]
+name = "storage3"
+version = "0.12.0"
+description = "Supabase Storage client for Python."
+optional = false
+python-versions = "<4.0,>=3.9"
+groups = ["main"]
+files = [
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+httpx = {version = ">=0.26,<0.29", extras = ["http2"]}
+python-dateutil = ">=2.8.2,<3.0.0"
+
+[[package]]
+name = "strenum"
+version = "0.4.15"
+description = "An Enum that inherits from str."
+optional = false
+python-versions = "*"
+groups = ["main"]
+files = [
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+
+[package.extras]
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+release = ["twine"]
+test = ["pylint", "pytest", "pytest-black", "pytest-cov", "pytest-pylint"]
+
+[[package]]
+name = "supabase"
+version = "2.16.0"
+description = "Supabase client for Python."
+optional = false
+python-versions = "<4.0,>=3.9"
+groups = ["main"]
+files = [
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+httpx = ">=0.26,<0.29"
+postgrest = ">0.19,<1.2"
+realtime = ">=2.4.0,<2.6.0"
+storage3 = ">=0.10,<0.13"
+supafunc = ">=0.9,<0.11"
+
+[[package]]
+name = "supafunc"
+version = "0.10.1"
+description = "Library for Supabase Functions"
+optional = false
+python-versions = "<4.0,>=3.9"
+groups = ["main"]
+files = [
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+
+[package.dependencies]
+httpx = {version = ">=0.26,<0.29", extras = ["http2"]}
+strenum = ">=0.4.15,<0.5.0"
+
 [[package]]
 name = "tomli"
 version = "2.2.1"
@@ -1656,6 +1827,85 @@ typing-extensions = {version = ">=4.0", markers = "python_version < \"3.11\""}
 [package.extras]
 standard = ["colorama (>=0.4) ; sys_platform == \"win32\"", "httptools (>=0.6.3)", "python-dotenv (>=0.13)", "pyyaml (>=5.1)", "uvloop (>=0.15.1) ; sys_platform != \"win32\" and sys_platform != \"cygwin\" and platform_python_implementation != \"PyPy\"", "watchfiles (>=0.13)", "websockets (>=10.4)"]

+[[package]]
+name = "websockets"
+version = "15.0.1"
+description = "An implementation of the WebSocket Protocol (RFC 6455 & 7692)"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
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+    {file = "websockets-15.0.1.tar.gz", hash = "sha256:82544de02076bafba038ce055ee6412d68da13ab47f0c60cab827346de828dee"},
+]
+
 [[package]]
 name = "zipp"
 version = "3.23.0"
@@ -1679,4 +1929,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.10,<4.0"
-content-hash = "de209c97aa0feb29d669a20e4422d51bdf3a0872ec37e85ce9b88ce726fcee7a"
+content-hash = "0c40b63c3c921846cf05ccfb4e685d4959854b29c2c302245f9832e20aac6954"
--- a/autogpt_platform/autogpt_libs/pyproject.toml
+++ b/autogpt_platform/autogpt_libs/pyproject.toml
@@ -18,8 +18,7 @@ pydantic = "^2.11.7"
 pydantic-settings = "^2.10.1"
 pyjwt = { version = "^2.10.1", extras = ["crypto"] }
 redis = "^6.2.0"
-bcrypt = "^4.1.0"
-authlib = "^1.3.0"
+supabase = "^2.16.0"
 uvicorn = "^0.35.0"

 [tool.poetry.group.dev.dependencies]
--- a/autogpt_platform/backend/.env.default
+++ b/autogpt_platform/backend/.env.default
@@ -27,15 +27,10 @@ REDIS_PORT=6379
 RABBITMQ_DEFAULT_USER=rabbitmq_user_default
 RABBITMQ_DEFAULT_PASS=k0VMxyIJF9S35f3x2uaw5IWAl6Y536O7

-# JWT Authentication
-# Generate a secure random key: python -c "import secrets; print(secrets.token_urlsafe(32))"
-JWT_SIGN_KEY=your-super-secret-jwt-token-with-at-least-32-characters-long
+# Supabase Authentication
+SUPABASE_URL=http://localhost:8000
+SUPABASE_SERVICE_ROLE_KEY=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyAgCiAgICAicm9sZSI6ICJzZXJ2aWNlX3JvbGUiLAogICAgImlzcyI6ICJzdXBhYmFzZS1kZW1vIiwKICAgICJpYXQiOiAxNjQxNzY5MjAwLAogICAgImV4cCI6IDE3OTk1MzU2MDAKfQ.DaYlNEoUrrEn2Ig7tqibS-PHK5vgusbcbo7X36XVt4Q
 JWT_VERIFY_KEY=your-super-secret-jwt-token-with-at-least-32-characters-long
-JWT_SIGN_ALGORITHM=HS256
-ACCESS_TOKEN_EXPIRE_MINUTES=15
-REFRESH_TOKEN_EXPIRE_DAYS=7
-JWT_ISSUER=autogpt-platform
-JWT_AUDIENCE=authenticated

 ## ===== REQUIRED SECURITY KEYS ===== ##
 # Generate using: from cryptography.fernet import Fernet;Fernet.generate_key().decode()
@@ -139,6 +134,13 @@ POSTMARK_WEBHOOK_TOKEN=
 # Error Tracking
 SENTRY_DSN=

+# Cloudflare Turnstile (CAPTCHA) Configuration
+# Get these from the Cloudflare Turnstile dashboard: https://dash.cloudflare.com/?to=/:account/turnstile
+# This is the backend secret key
+TURNSTILE_SECRET_KEY=
+# This is the verify URL
+TURNSTILE_VERIFY_URL=https://challenges.cloudflare.com/turnstile/v0/siteverify
+
 # Feature Flags
 LAUNCH_DARKLY_SDK_KEY=

--- a/autogpt_platform/backend/.gitignore
+++ b/autogpt_platform/backend/.gitignore
@@ -18,6 +18,3 @@ load-tests/results/
 load-tests/*.json
 load-tests/*.log
 load-tests/node_modules/*
-
-# Migration backups (contain user data)
-migration_backups/
--- a/autogpt_platform/backend/Dockerfile
+++ b/autogpt_platform/backend/Dockerfile
@@ -47,7 +47,6 @@ RUN poetry install --no-ansi --no-root

 # Generate Prisma client
 COPY autogpt_platform/backend/schema.prisma ./
-COPY autogpt_platform/backend/backend/data/partial_types.py ./backend/data/partial_types.py
 RUN poetry run prisma generate

 FROM debian:13-slim AS server_dependencies
@@ -93,7 +92,6 @@ FROM server_dependencies AS migrate

 # Migration stage only needs schema and migrations - much lighter than full backend
 COPY autogpt_platform/backend/schema.prisma /app/autogpt_platform/backend/
-COPY autogpt_platform/backend/backend/data/partial_types.py /app/autogpt_platform/backend/backend/data/partial_types.py
 COPY autogpt_platform/backend/migrations /app/autogpt_platform/backend/migrations

 FROM server_dependencies AS server
--- a/autogpt_platform/backend/agents/StoreAgent_rows.csv
+++ b/autogpt_platform/backend/agents/StoreAgent_rows.csv
@@ -1,242 +0,0 @@
-listing_id,storeListingVersionId,slug,agent_name,agent_video,agent_image,featured,sub_heading,description,categories,useForOnboarding,is_available
-6e60a900-9d7d-490e-9af2-a194827ed632,d85882b8-633f-44ce-a315-c20a8c123d19,flux-ai-image-generator,Flux AI Image Generator,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/ca154dd1-140e-454c-91bd-2d8a00de3f08.jpg"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/577d995d-bc38-40a9-a23f-1f30f5774bdb.jpg"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/415db1b7-115c-43ab-bd6c-4e9f7ef95be1.jpg""]",false,Transform ideas into breathtaking images,"Transform ideas into breathtaking images with this AI-powered Image Generator. Using cutting-edge Flux AI technology, the tool crafts highly detailed, photorealistic visuals from simple text prompts. Perfect for artists, marketers, and content creators, this generator produces unique images tailored to user specifications. From fantastical scenes to lifelike portraits, users can unleash creativity with professional-quality results in seconds. Easy to use and endlessly versatile, bring imagination to life with the AI Image Generator today!","[""creative""]",false,true
-f11fc6e9-6166-4676-ac5d-f07127b270c1,c775f60d-b99f-418b-8fe0-53172258c3ce,youtube-transcription-scraper,YouTube Transcription Scraper,https://youtu.be/H8S3pU68lGE,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/65bce54b-0124-4b0d-9e3e-f9b89d0dc99e.jpg""]",false,Fetch the transcriptions from the most popular YouTube videos in your chosen topic,"Effortlessly gather transcriptions from multiple YouTube videos with this agent. It scrapes and compiles video transcripts into a clean, organized list, making it easy to extract insights, quotes, or content from various sources in one go. Ideal for researchers, content creators, and marketers looking to quickly analyze or repurpose video content.","[""writing""]",false,true
-17908889-b599-4010-8e4f-bed19b8f3446,6e16e65a-ad34-4108-b4fd-4a23fced5ea2,business-ownerceo-finder,Decision Maker Lead Finder,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/1020d94e-b6a2-4fa7-bbdf-2c218b0de563.jpg""]",false,Contact CEOs today,"Find the key decision-makers you need, fast.
-
-This agent identifies business owners or CEOs of local companies in any area you choose. Simply enter what kind of businesses you’re looking for and where, and it will:
-
-* Search the area and gather public information
-* Return names, roles, and contact details when available
-* Provide smart Google search suggestions if details aren’t found
-
-Perfect for:
-
-* B2B sales teams seeking verified leads
-* Recruiters sourcing local talent
-* Researchers looking to connect with business leaders
-
-Save hours of manual searching and get straight to the people who matter most.","[""business""]",true,true
-72beca1d-45ea-4403-a7ce-e2af168ee428,415b7352-0dc6-4214-9d87-0ad3751b711d,smart-meeting-brief,Smart Meeting Prep,https://youtu.be/9ydZR2hkxaY,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/2f116ce1-63ae-4d39-a5cd-f514defc2b97.png"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/0a71a60a-2263-4f12-9836-9c76ab49f155.png"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/95327695-9184-403c-907a-a9d3bdafa6a5.png"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/2bc77788-790b-47d4-8a61-ce97b695e9f5.png""]",true,Business meeting briefings delivered daily,"Never walk into a meeting unprepared again. Every day at 4 pm, the Smart Meeting Prep Agent scans your calendar for tomorrow's external meetings. It reviews your past email exchanges, researches each participant's background and role, and compiles the insights into a concise briefing, so you can close your workday ready for tomorrow's calls.
-
-How It Works
-1. At 4 pm, the agent scans your calendar and identifies external meetings scheduled for the next day.
-2. It reviews recent email threads with each participant to surface key relationship history and communication context.
-3. It conducts online research to gather publicly available information on roles, company backgrounds, and relevant professional data.
-4. It produces a unified briefing for each participant, including past exchange highlights, profile notes, and strategic conversation points.","[""personal""]",true,true
-9fa5697a-617b-4fae-aea0-7dbbed279976,b8ceb480-a7a2-4c90-8513-181a49f7071f,automated-support-ai,Automated Support Agent,https://youtu.be/nBMfu_5sgDA,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/ed56febc-2205-4179-9e7e-505d8500b66c.png""]",true,Automate up to 80 percent of inbound support emails,"Overview:
-Support teams spend countless hours on basic tickets. This agent automates repetitive customer support tasks. It reads incoming requests, researches your knowledge base, and responds automatically when confident. When unsure, it escalates to a human for final resolution.
-
-How it Works:
-New support emails are routed to the agent.
-The agent checks internal documentation for answers.
-It measures confidence in the answer found and either replies directly or escalates to a human.
-
-Business Value:
-Automating the easy 80 percent of support tickets allows your team to focus on high-value, complex customer issues, improving efficiency and response times.","[""business""]",false,true
-2bdac92b-a12c-4131-bb46-0e3b89f61413,31daf49d-31d3-476b-aa4c-099abc59b458,unspirational-poster-maker,Unspirational Poster Maker,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/6a490dac-27e5-405f-a4c4-8d1c55b85060.jpg"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/d343fbb5-478c-4e38-94df-4337293b61f1.jpg""]",false,Because adulting is hard,"This witty AI agent generates hilariously relatable ""motivational"" posters that tackle the everyday struggles of procrastination, overthinking, and workplace chaos with a blend of absurdity and sarcasm. From goldfish facing impossible tasks to cats in existential crises, The Unspirational Poster Maker designs tongue-in-cheek graphics and captions that mock productivity clichés and embrace our collective struggles to ""get it together."" Perfect for adding a touch of humour to the workday, these posters remind us that sometimes, all we can do is laugh at the chaos.","[""creative""]",false,true
-9adf005e-2854-4cc7-98cf-f7103b92a7b7,a03b0d8c-4751-43d6-a54e-c3b7856ba4e3,ai-shortform-video-generator-create-viral-ready-content,AI Video Generator,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/8d2670b9-fea5-4966-a597-0a4511bffdc3.png"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/aabe8aec-0110-4ce7-a259-4f86fe8fe07d.png""]",false,Create Viral-Ready Shorts Content in Seconds,"OVERVIEW
-Transform any trending headline or broad topic into a polished, vertical short-form video in a single run.  
-The agent automates research, scriptwriting, metadata creation, and Revid.ai rendering, returning one ready-to-publish MP4 plus its title, script and hashtags.
-
-HOW IT WORKS
-1. Input a topic or an exact news headline.  
-2. The agent fetches live search results and selects the most engaging related story.  
-3. Key facts are summarised into concise research notes.  
-4. Claude writes a 30–35 second script with visual cues, a three-second hook, tension loops, and a call-to-action.  
-5. GPT-4o generates an eye-catching title and one or two discoverability hashtags.  
-6. The script is sent to a state-of-the-art AI video generator to render a single 9:16 MP4 (default: 720 p, 30 fps, voice “Brian”, style “movingImage”, music “Bladerunner 2049”).  
-   – All voice, style and resolution settings can be adjusted in the Builder before you press ""Run"".  
-7. Output delivered: Title, Script, Hashtags, Video URL.
-
-KEY USE CASES
- Broad-topic explainers (e.g. “Artificial Intelligence” or “Climate Tech”).  
- Real-time newsjacking with a specific breaking headline.  
- Product-launch spotlights and quick event recaps while interest is high.  
-
-BUSINESS VALUE
- One-click speed: from idea to finished video in minutes.  
- Consistent brand look: Revid presets keep voice, style and aspect ratio on spec.  
- No-code workflow: marketers create social video without design or development queues.  
- Cloud convenience: Auto-GPT Cloud users are pre-configured with all required keys.  
-  Self-hosted users simply add OpenAI, Anthropic, Perplexity (OpenRouter/Jina) and Revid keys once.
-
-IMPORTANT NOTES
- The agent outputs exactly one video per execution. Run it again for additional shorts.  
- Video rendering time varies; AI-generated footage may take several minutes.","[""writing""]",false,true
-864e48ef-fee5-42c1-b6a4-2ae139db9fc1,55d40473-0f31-4ada-9e40-d3a7139fcbd4,automated-blog-writer,Automated SEO Blog Writer,https://youtu.be/nKcDCbDVobs,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/2dd5f95b-5b30-4bf8-a11b-bac776c5141a.jpg""]",true,"Automate research, writing, and publishing for high-ranking blog posts","Scale your blog with a fully automated content engine. The Automated SEO Blog Writer learns your brand voice, finds high-demand keywords, and creates SEO-optimized articles that attract organic traffic and boost visibility.
-
-How it works:
-
-1. Share your pitch, website, and values.
-2. The agent studies your site and uncovers proven SEO opportunities.
-3. It spends two hours researching and drafting each post.
-4. You set the cadence—publishing runs on autopilot.
-
-Business value: Consistently publish research-backed, optimized posts that build domain authority, rankings, and thought leadership while you focus on what matters most.
-
-Use cases:
-• Founders: Keep your blog active with no time drain.
-• Agencies: Deliver scalable SEO content for clients.
-• Strategists: Automate execution, focus on strategy.
-• Marketers: Drive steady organic growth.
-• Local businesses: Capture nearby search traffic.","[""writing""]",false,true
-6046f42e-eb84-406f-bae0-8e052064a4fa,a548e507-09a7-4b30-909c-f63fcda10fff,lead-finder-local-businesses,Lead Finder,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/abd6605f-d5f8-426b-af36-052e8ba5044f.webp""]",false,Auto-Prospect Like a Pro,"Turbo-charge your local lead generation with the AutoGPT Marketplace’s top Google Maps prospecting agent. “Lead Finder: Local Businesses” delivers verified, ready-to-contact prospects in any niche and city—so you can focus on closing, not searching.
-
-**WHAT IT DOES**
-• Searches Google Maps via the official API (no scraping)
-• Prompts like “dentists in Chicago” or “coffee shops near me”
-• Returns: Name, Website, Rating, Reviews, **Phone & Address**
-• Exports instantly to your CRM, sheet, or outreach workflow
-
-**WHY YOU’LL LOVE IT**
-✓ Hyper-targeted leads in minutes
-✓ Unlimited searches & locations
-✓ Zero CAPTCHAs or IP blocks
-✓ Works on AutoGPT Cloud or self-hosted (with your API key)
-✓ Cut prospecting time by 90%
-
-**PERFECT FOR**
-— Marketers & PPC agencies
-— SEO consultants & designers
-— SaaS founders & sales teams
-
-Stop scrolling directories—start filling your pipeline. Start now and let AI prospect while you profit.
-
-→ Click *Add to Library* and own your market today.","[""business""]",true,true
-f623c862-24e9-44fc-8ce8-d8282bb51ad2,eafa21d3-bf14-4f63-a97f-a5ee41df83b3,linkedin-post-generator,LinkedIn Post Generator,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/297f6a8e-81a8-43e2-b106-c7ad4a5662df.png"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/fceebdc1-aef6-4000-97fc-4ef587f56bda.png""]",false,Auto‑craft LinkedIn gold,"Create research‑driven, high‑impact LinkedIn posts in minutes. This agent searches YouTube for the best videos on your chosen topic, pulls their transcripts, and distils the most valuable insights into a polished post ready for your company page or personal feed.  
-
-FEATURES  
-• Automated YouTube research – discovers and analyses top‑ranked videos so you don’t have to  
-• AI‑curated synthesis – combines multiple transcripts into one authoritative narrative  
-• Full creative control – adjust style, tone, objective, opinion, clarity, target word count and number of videos  
-• LinkedIn‑optimised output – hook, 2‑3 key points, CTA, strategic line breaks, 3‑5 hashtags, no markdown  
-• One‑click publish – returns a ready‑to‑post text block (≤1 300 characters)  
-
-HOW IT WORKS  
-1. Enter a topic and your preferred writing parameters.  
-2. The agent builds a YouTube search, fetches the page, and extracts the top N video URLs.  
-3. It pulls each transcript, then feeds them—plus your settings—into Claude 3.5 Sonnet.  
-4. The model writes a concise, engaging post designed for maximum LinkedIn engagement.  
-
-USE CASES  
-• Thought‑leadership updates backed by fresh video research  
-• Rapid industry summaries after major events, webinars, or conferences  
-• Consistent LinkedIn content for busy founders, marketers, and creators  
-
-WHY YOU’LL LOVE IT  
-Save hours of manual research, avoid surface‑level hot‑takes, and publish posts that showcase real expertise—without the heavy lift.","[""writing""]",true,true
-7d4120ad-b6b3-4419-8bdb-7dd7d350ef32,e7bb29a1-23c7-4fee-aa3b-5426174b8c52,youtube-to-linkedin-post-converter,YouTube to LinkedIn Post Converter,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/f084b326-a708-4396-be51-7ba59ad2ef32.png""]",false,Transform Your YouTube Videos into Engaging LinkedIn Posts with AI,"WHAT IT DOES: 
-This agent converts YouTube video content into a LinkedIn post by analyzing the video's transcript. It provides you with a tailored post that reflects the core ideas, key takeaways, and tone of the original video, optimizing it for engagement on LinkedIn.
-
-HOW IT WORKS:
- You provide the URL to the YouTube video (required)
- You can choose the structure for the LinkedIn post (e.g., Personal Achievement Story, Lesson Learned, Thought Leadership, etc.)
- You can also select the tone (e.g., Inspirational, Analytical, Conversational, etc.)
- The transcript of the video is analyzed by the GPT-4 model and the Claude 3.5 Sonnet model
- The models extract key insights, memorable quotes, and the main points from the video
- You’ll receive a LinkedIn post, formatted according to your chosen structure and tone, optimized for professional engagement
-
-INPUTS:
- Source YouTube Video – Provide the URL to the YouTube video
- Structure – Choose the post format (e.g., Personal Achievement Story, Thought Leadership, etc.)
- Content – Specify the main message or idea of the post (e.g., Hot Take, Key Takeaways, etc.)
- Tone – Select the tone for the post (e.g., Conversational, Inspirational, etc.)
-
-OUTPUT:
- LinkedIn Post – A well-crafted, AI-generated LinkedIn post with a professional tone, based on the video content and your specified preferences
-
-Perfect for content creators, marketers, and professionals who want to repurpose YouTube videos for LinkedIn and boost their professional branding.","[""writing""]",false,true
-c61d6a83-ea48-4df8-b447-3da2d9fe5814,00fdd42c-a14c-4d19-a567-65374ea0e87f,personalized-morning-coffee-newsletter,Personal Newsletter,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/f4b38e4c-8166-4caf-9411-96c9c4c82d4c.png""]",false,Start your day with personalized AI newsletters that deliver credibility and context for every interest or mood.,"This Personal Newsletter Agent provides a bespoke daily digest on your favorite topics and tone. Whether you prefer industry insights, lighthearted reads, or breaking news, this agent crafts your own unique newsletter to keep you informed and entertained.
-
-
-How It Works
-1. Enter your favorite topics, industries, or areas of interest.
-2. Choose your tone—professional, casual, or humorous.
-3. Set your preferred delivery cadence: daily or weekly.
-4. The agent scans top sources and compiles 3–5 engaging stories, insights, and fun facts into a conversational newsletter.
-
-Skip the morning scroll and enjoy a thoughtfully curated newsletter designed just for you. Stay ahead of trends, spark creative ideas, and enjoy an effortless, informed start to your day.
-
-
-Use Cases
-• Executives: Get a daily digest of market updates and leadership insights.
-• Marketers: Receive curated creative trends and campaign inspiration.
-• Entrepreneurs: Stay updated on your industry without information overload.","[""research""]",true,true
-e2e49cfc-4a39-4d62-a6b3-c095f6d025ff,fc2c9976-0962-4625-a27b-d316573a9e7f,email-address-finder,Email Scout - Contact Finder Assistant,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/da8a690a-7a8b-4c1d-b6f8-e2f840c0205d.jpg"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/6a2ac25c-1609-4881-8140-e6da2421afb3.jpg"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/26179263-fe06-45bd-b6a0-0754660a0a46.jpg""]",false,Find contact details from name and location using AI search,"Finding someone's professional email address can be time-consuming and frustrating. Manual searching across multiple websites, social profiles, and business directories often leads to dead ends or outdated information.
-
-Email Scout automates this process by intelligently searching across publicly available sources when you provide a person's name and location. Simply input basic information like ""Tim Cook, USA"" or ""Sarah Smith, London"" and let the AI assistant do the work of finding potential contact details.
-
-Key Features:
- Quick search from just name and location
- Scans multiple public sources
- Automated AI-powered search process
- Easy to use with simple inputs
-
-Perfect for recruiters, business development professionals, researchers, and anyone needing to establish professional contact.
-
-Note: This tool searches only publicly available information. Search results depend on what contact information people have made public. Some searches may not yield results if the information isn't publicly accessible.","[""""]",false,true
-81bcc372-0922-4a36-bc35-f7b1e51d6939,e437cc95-e671-489d-b915-76561fba8c7f,ai-youtube-to-blog-converter,YouTube Video to SEO Blog Writer,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/239e5a41-2515-4e1c-96ef-31d0d37ecbeb.webp"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/c7d96966-786f-4be6-ad7d-3a51c84efc0e.png"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/0275a74c-e2c2-4e29-a6e4-3a616c3c35dd.png""]",false,One link. One click. One powerful blog post.,"Effortlessly transform your YouTube videos into high-quality, SEO-optimized blog posts.
-
-Your videos deserve a second life—in writing.  
-Make your content work twice as hard by repurposing it into engaging, searchable articles.
-
-Perfect for content creators, marketers, and bloggers, this tool analyzes video content and generates well-structured blog posts tailored to your tone, audience, and word count. Just paste a YouTube URL and let the AI handle the rest.
-
-FEATURES
-
-• CONTENT ANALYSIS  
-  Extracts key points from the video while preserving your message and intent.
-
-• CUSTOMIZABLE OUTPUT  
-  Select a tone that fits your audience: casual, professional, educational, or formal.
-
-• SEO OPTIMIZATION  
-  Automatically creates engaging titles and structured subheadings for better search visibility.
-
-• USER-FRIENDLY  
-  Repurpose your videos into written content to expand your reach and improve accessibility.
-
-Whether you're looking to grow your blog, boost SEO, or simply get more out of your content, the AI YouTube-to-Blog Converter makes it effortless.
-","[""writing""]",true,true
-5c3510d2-fc8b-4053-8e19-67f53c86eb1a,f2cc74bb-f43f-4395-9c35-ecb30b5b4fc9,ai-webpage-copy-improver,AI Webpage Copy Improver,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/d562d26f-5891-4b09-8859-fbb205972313.jpg""]",false,Boost Your Website's Search Engine Performance,"Elevate your web content with this powerful AI Webpage Copy Improver. Designed for marketers, SEO specialists, and web developers, this tool analyses and enhances website copy for maximum impact. Using advanced language models, it optimizes text for better clarity, SEO performance, and increased conversion rates. The AI examines your existing content, identifies areas for improvement, and generates refined copy that maintains your brand voice while boosting engagement. From homepage headlines to product descriptions, transform your web presence with AI-driven insights. Improve readability, incorporate targeted keywords, and craft compelling calls-to-action - all with the click of a button. Take your digital marketing to the next level with the AI Webpage Copy Improver.","[""marketing""]",true,true
-94d03bd3-7d44-4d47-b60c-edb2f89508d6,b6f6f0d3-49f4-4e3b-8155-ffe9141b32c0,domain-name-finder,Domain Name Finder,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/28545e09-b2b8-4916-b4c6-67f982510a78.jpeg""]",false,Instantly generate brand-ready domain names that are actually available,"Overview:
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-
-How It Works
-1. Input your product pitch, company name, or core keywords.
-2. The agent analyzes brand tone, audience, and industry context.
-3. It generates a list of unique, memorable domains that match your criteria.
-4. All names are pre-filtered for real-time availability, so you can register immediately.
-
-
-Business Value
-Save hours of guesswork and eliminate dead ends. Accelerate brand launches, startup naming, and campaign creation with ready-to-claim domains.
-
-
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-• Marketers: Test name options across campaigns with instant availability data.
-• Entrepreneurs: Validate ideas faster with instant domain options.","[""business""]",false,true
-7a831906-daab-426f-9d66-bcf98d869426,516d813b-d1bc-470f-add7-c63a4b2c2bad,ai-function,AI Function,,"[""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/620e8117-2ee1-4384-89e6-c2ef4ec3d9c9.webp"",""https://storage.googleapis.com/agpt-prod-website-artifacts/users/b3e41ea4-2f4c-4964-927c-fe682d857bad/images/476259e2-5a79-4a7b-8e70-deeebfca70d7.png""]",false,Never Code Again,"AI FUNCTION MAGIC  
-Your AI‑powered assistant for turning plain‑English descriptions into working Python functions.  
-
-HOW IT WORKS  
-1. Describe what the function should do.  
-2. Specify the inputs it needs.  
-3. Receive the generated Python code.  
-
-FEATURES  
- Effortless Function Generation: convert natural‑language specs into complete functions.  
- Customizable Inputs: define the parameters that matter to you.  
- Versatile Use Cases: simulate data, automate tasks, prototype ideas.  
- Seamless Integration: add the generated function directly to your codebase.  
-
-EXAMPLE  
-Request: “Create a function that generates 20 examples of fake people, each with a name, date of birth, job title, and age.”  
-Input parameter: number_of_people (default 20)  
-Result: a list of dictionaries such as  
-[  
-  { ""name"": ""Emma Martinez"",  ""date_of_birth"": ""1992‑11‑03"", ""job_title"": ""Data Analyst"",        ""age"": 32 },  
-  { ""name"": ""Liam O’Connor"",  ""date_of_birth"": ""1985‑07‑19"", ""job_title"": ""Marketing Manager"",  ""age"": 39 },  
-  …18 more entries…  
-]","[""development""]",false,true
--- a/autogpt_platform/backend/agents/agent_00fdd42c-a14c-4d19-a567-65374ea0e87f.json
+++ b/autogpt_platform/backend/agents/agent_00fdd42c-a14c-4d19-a567-65374ea0e87f.json
--- a/autogpt_platform/backend/agents/agent_31daf49d-31d3-476b-aa4c-099abc59b458.json
+++ b/autogpt_platform/backend/agents/agent_31daf49d-31d3-476b-aa4c-099abc59b458.json
@@ -1,590 +0,0 @@
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--- a/autogpt_platform/backend/agents/agent_415b7352-0dc6-4214-9d87-0ad3751b711d.json
+++ b/autogpt_platform/backend/agents/agent_415b7352-0dc6-4214-9d87-0ad3751b711d.json
--- a/autogpt_platform/backend/agents/agent_516d813b-d1bc-470f-add7-c63a4b2c2bad.json
+++ b/autogpt_platform/backend/agents/agent_516d813b-d1bc-470f-add7-c63a4b2c2bad.json
@@ -1,447 +0,0 @@
-{
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-          "source_name": "result",
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-          "sink_id": "26ff2973-3f9a-451d-b902-d45e5da0a7fe",
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-          "claude-3-haiku-20240307": "anthropic",
-          "claude-haiku-4-5-20251001": "anthropic",
-          "claude-opus-4-1-20250805": "anthropic",
-          "claude-opus-4-20250514": "anthropic",
-          "claude-opus-4-5-20251101": "anthropic",
-          "claude-sonnet-4-20250514": "anthropic",
-          "claude-sonnet-4-5-20250929": "anthropic",
-          "cohere/command-r-08-2024": "open_router",
-          "cohere/command-r-plus-08-2024": "open_router",
-          "deepseek/deepseek-chat": "open_router",
-          "deepseek/deepseek-r1-0528": "open_router",
-          "dolphin-mistral:latest": "ollama",
-          "google/gemini-2.0-flash-001": "open_router",
-          "google/gemini-2.0-flash-lite-001": "open_router",
-          "google/gemini-2.5-flash": "open_router",
-          "google/gemini-2.5-flash-lite-preview-06-17": "open_router",
-          "google/gemini-2.5-pro-preview-03-25": "open_router",
-          "google/gemini-3-pro-preview": "open_router",
-          "gpt-3.5-turbo": "openai",
-          "gpt-4-turbo": "openai",
-          "gpt-4.1-2025-04-14": "openai",
-          "gpt-4.1-mini-2025-04-14": "openai",
-          "gpt-4o": "openai",
-          "gpt-4o-mini": "openai",
-          "gpt-5-2025-08-07": "openai",
-          "gpt-5-chat-latest": "openai",
-          "gpt-5-mini-2025-08-07": "openai",
-          "gpt-5-nano-2025-08-07": "openai",
-          "gpt-5.1-2025-11-13": "openai",
-          "gryphe/mythomax-l2-13b": "open_router",
-          "llama-3.1-8b-instant": "groq",
-          "llama-3.3-70b-versatile": "groq",
-          "llama3": "ollama",
-          "llama3.1:405b": "ollama",
-          "llama3.2": "ollama",
-          "llama3.3": "ollama",
-          "meta-llama/Llama-3.2-3B-Instruct-Turbo": "aiml_api",
-          "meta-llama/Llama-3.3-70B-Instruct-Turbo": "aiml_api",
-          "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo": "aiml_api",
-          "meta-llama/llama-4-maverick": "open_router",
-          "meta-llama/llama-4-scout": "open_router",
-          "microsoft/wizardlm-2-8x22b": "open_router",
-          "mistralai/mistral-nemo": "open_router",
-          "moonshotai/kimi-k2": "open_router",
-          "nousresearch/hermes-3-llama-3.1-405b": "open_router",
-          "nousresearch/hermes-3-llama-3.1-70b": "open_router",
-          "nvidia/llama-3.1-nemotron-70b-instruct": "aiml_api",
-          "o1": "openai",
-          "o1-mini": "openai",
-          "o3-2025-04-16": "openai",
-          "o3-mini": "openai",
-          "openai/gpt-oss-120b": "open_router",
-          "openai/gpt-oss-20b": "open_router",
-          "perplexity/sonar": "open_router",
-          "perplexity/sonar-deep-research": "open_router",
-          "perplexity/sonar-pro": "open_router",
-          "qwen/qwen3-235b-a22b-thinking-2507": "open_router",
-          "qwen/qwen3-coder": "open_router",
-          "v0-1.0-md": "v0",
-          "v0-1.5-lg": "v0",
-          "v0-1.5-md": "v0",
-          "x-ai/grok-4": "open_router",
-          "x-ai/grok-4-fast": "open_router",
-          "x-ai/grok-4.1-fast": "open_router",
-          "x-ai/grok-code-fast-1": "open_router"
-        },
-        "discriminator_values": [
-          "o3-mini"
-        ]
-      }
-    },
-    "required": [
-      "openai_api_key_credentials"
-    ],
-    "title": "AIFunctionCredentialsInputSchema",
-    "type": "object"
-  }
-}
--- a/autogpt_platform/backend/agents/agent_55d40473-0f31-4ada-9e40-d3a7139fcbd4.json
+++ b/autogpt_platform/backend/agents/agent_55d40473-0f31-4ada-9e40-d3a7139fcbd4.json
--- a/autogpt_platform/backend/agents/agent_6e16e65a-ad34-4108-b4fd-4a23fced5ea2.json
+++ b/autogpt_platform/backend/agents/agent_6e16e65a-ad34-4108-b4fd-4a23fced5ea2.json
--- a/autogpt_platform/backend/agents/agent_a03b0d8c-4751-43d6-a54e-c3b7856ba4e3.json
+++ b/autogpt_platform/backend/agents/agent_a03b0d8c-4751-43d6-a54e-c3b7856ba4e3.json
--- a/autogpt_platform/backend/agents/agent_a548e507-09a7-4b30-909c-f63fcda10fff.json
+++ b/autogpt_platform/backend/agents/agent_a548e507-09a7-4b30-909c-f63fcda10fff.json
--- a/autogpt_platform/backend/agents/agent_b6f6f0d3-49f4-4e3b-8155-ffe9141b32c0.json
+++ b/autogpt_platform/backend/agents/agent_b6f6f0d3-49f4-4e3b-8155-ffe9141b32c0.json
--- a/autogpt_platform/backend/agents/agent_b8ceb480-a7a2-4c90-8513-181a49f7071f.json
+++ b/autogpt_platform/backend/agents/agent_b8ceb480-a7a2-4c90-8513-181a49f7071f.json
--- a/autogpt_platform/backend/agents/agent_c775f60d-b99f-418b-8fe0-53172258c3ce.json
+++ b/autogpt_platform/backend/agents/agent_c775f60d-b99f-418b-8fe0-53172258c3ce.json
--- a/autogpt_platform/backend/agents/agent_d85882b8-633f-44ce-a315-c20a8c123d19.json
+++ b/autogpt_platform/backend/agents/agent_d85882b8-633f-44ce-a315-c20a8c123d19.json
@@ -1,403 +0,0 @@
-{
-  "id": "ed2091cf-5b27-45a9-b3ea-42396f95b256",
-  "version": 12,
-  "is_active": true,
-  "name": "Flux AI Image Generator",
-  "description": "Transform ideas into breathtaking images with this AI-powered Image Generator. Using cutting-edge Flux AI technology, the tool crafts highly detailed, photorealistic visuals from simple text prompts. Perfect for artists, marketers, and content creators, this generator produces unique images tailored to user specifications. From fantastical scenes to lifelike portraits, users can unleash creativity with professional-quality results in seconds. Easy to use and endlessly versatile, bring imagination to life with the AI Image Generator today!",
-  "instructions": null,
-  "recommended_schedule_cron": null,
-  "nodes": [
-    {
-      "id": "7482c59d-725f-4686-82b9-0dfdc4e92316",
-      "block_id": "cc10ff7b-7753-4ff2-9af6-9399b1a7eddc",
-      "input_default": {
-        "text": "Press the \"Advanced\" toggle and input your replicate API key.\n\nYou can get one here:\nhttps://replicate.com/account/api-tokens\n"
-      },
-      "metadata": {
-        "position": {
-          "x": 872.8268131538296,
-          "y": 614.9436919065381
-        }
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-      "input_links": [],
-      "output_links": [],
-      "graph_id": "ed2091cf-5b27-45a9-b3ea-42396f95b256",
-      "graph_version": 12,
-      "webhook_id": null,
-      "webhook": null
-    },
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-      "block_id": "363ae599-353e-4804-937e-b2ee3cef3da4",
-      "input_default": {
-        "name": "Generated Image"
-      },
-      "metadata": {
-        "position": {
-          "x": 1453.6844137728922,
-          "y": 963.2466395125115
-        }
-      },
-      "input_links": [
-        {
-          "id": "06665d23-2f3d-4445-8f22-573446fcff5b",
-          "source_id": "50bc23e9-f2b7-4959-8710-99679ed9eeea",
-          "sink_id": "0d1dec1a-e4ee-4349-9673-449a01bbf14e",
-          "source_name": "result",
-          "sink_name": "value",
-          "is_static": false
-        }
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-      "output_links": [],
-      "graph_id": "ed2091cf-5b27-45a9-b3ea-42396f95b256",
-      "graph_version": 12,
-      "webhook_id": null,
-      "webhook": null
-    },
-    {
-      "id": "6f24c45f-1548-4eda-9784-da06ce0abef8",
-      "block_id": "c0a8e994-ebf1-4a9c-a4d8-89d09c86741b",
-      "input_default": {
-        "name": "Image Subject",
-        "value": "Otto the friendly, purple \"Chief Automation Octopus\" helping people automate their tedious tasks.",
-        "description": "The subject of the image"
-      },
-      "metadata": {
-        "position": {
-          "x": -314.43009631839783,
-          "y": 962.935949165938
-        }
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-      "input_links": [],
-      "output_links": [
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-          "source_id": "6f24c45f-1548-4eda-9784-da06ce0abef8",
-          "sink_id": "0d1bca9a-d9b8-4bfd-a19c-fe50b54f4b12",
-          "source_name": "result",
-          "sink_name": "prompt_values_#_TOPIC",
-          "is_static": true
-        }
-      ],
-      "graph_id": "ed2091cf-5b27-45a9-b3ea-42396f95b256",
-      "graph_version": 12,
-      "webhook_id": null,
-      "webhook": null
-    },
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-      "id": "50bc23e9-f2b7-4959-8710-99679ed9eeea",
-      "block_id": "90f8c45e-e983-4644-aa0b-b4ebe2f531bc",
-      "input_default": {
-        "prompt": "dog",
-        "output_format": "png",
-        "replicate_model_name": "Flux Pro 1.1"
-      },
-      "metadata": {
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-          "sink_id": "50bc23e9-f2b7-4959-8710-99679ed9eeea",
-          "source_name": "response",
-          "sink_name": "prompt",
-          "is_static": false
-        }
-      ],
-      "output_links": [
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-          "id": "06665d23-2f3d-4445-8f22-573446fcff5b",
-          "source_id": "50bc23e9-f2b7-4959-8710-99679ed9eeea",
-          "sink_id": "0d1dec1a-e4ee-4349-9673-449a01bbf14e",
-          "source_name": "result",
-          "sink_name": "value",
-          "is_static": false
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-      "graph_id": "ed2091cf-5b27-45a9-b3ea-42396f95b256",
-      "graph_version": 12,
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-      "webhook": null
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-      "block_id": "1f292d4a-41a4-4977-9684-7c8d560b9f91",
-      "input_default": {
-        "model": "gpt-4o-mini",
-        "prompt": "Generate an incredibly detailed, photorealistic image prompt about {{TOPIC}}, describing the camera it's taken with and prompting the diffusion model to use all the best quality techniques.\n\nOutput only the prompt with no additional commentary.",
-        "prompt_values": {}
-      },
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-          "sink_id": "0d1bca9a-d9b8-4bfd-a19c-fe50b54f4b12",
-          "source_name": "result",
-          "sink_name": "prompt_values_#_TOPIC",
-          "is_static": true
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-          "sink_id": "50bc23e9-f2b7-4959-8710-99679ed9eeea",
-          "source_name": "response",
-          "sink_name": "prompt",
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-      "graph_id": "ed2091cf-5b27-45a9-b3ea-42396f95b256",
-      "graph_version": 12,
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-      "webhook": null
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-  "links": [
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-      "id": "1077c61a-a32a-4ed7-becf-11bcf835b914",
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-      "sink_id": "0d1bca9a-d9b8-4bfd-a19c-fe50b54f4b12",
-      "source_name": "result",
-      "sink_name": "prompt_values_#_TOPIC",
-      "is_static": true
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-      "id": "06665d23-2f3d-4445-8f22-573446fcff5b",
-      "source_id": "50bc23e9-f2b7-4959-8710-99679ed9eeea",
-      "sink_id": "0d1dec1a-e4ee-4349-9673-449a01bbf14e",
-      "source_name": "result",
-      "sink_name": "value",
-      "is_static": false
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-    {
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-      "source_id": "0d1bca9a-d9b8-4bfd-a19c-fe50b54f4b12",
-      "sink_id": "50bc23e9-f2b7-4959-8710-99679ed9eeea",
-      "source_name": "response",
-      "sink_name": "prompt",
-      "is_static": false
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-  ],
-  "forked_from_id": null,
-  "forked_from_version": null,
-  "sub_graphs": [],
-  "user_id": "",
-  "created_at": "2024-12-20T18:46:11.492Z",
-  "input_schema": {
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-    "properties": {
-      "Image Subject": {
-        "advanced": false,
-        "secret": false,
-        "title": "Image Subject",
-        "description": "The subject of the image",
-        "default": "Otto the friendly, purple \"Chief Automation Octopus\" helping people automate their tedious tasks."
-      }
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-    "required": []
-  },
-  "output_schema": {
-    "type": "object",
-    "properties": {
-      "Generated Image": {
-        "advanced": false,
-        "secret": false,
-        "title": "Generated Image"
-      }
-    },
-    "required": [
-      "Generated Image"
-    ]
-  },
-  "has_external_trigger": false,
-  "has_human_in_the_loop": false,
-  "trigger_setup_info": null,
-  "credentials_input_schema": {
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-      "replicate_api_key_credentials": {
-        "credentials_provider": [
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-        ],
-        "credentials_types": [
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-            "title": "Title"
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-            "title": "Provider",
-            "type": "string"
-          },
-          "type": {
-            "const": "api_key",
-            "title": "Type",
-            "type": "string"
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-        "required": [
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-          "provider",
-          "type"
-        ],
-        "title": "CredentialsMetaInput[Literal[<ProviderName.REPLICATE: 'replicate'>], Literal['api_key']]",
-        "type": "object",
-        "discriminator_values": []
-      },
-      "openai_api_key_credentials": {
-        "credentials_provider": [
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-        "credentials_types": [
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-        "properties": {
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-            "title": "Provider",
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-            "const": "api_key",
-            "title": "Type",
-            "type": "string"
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-          "type"
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-        "title": "CredentialsMetaInput[Literal[<ProviderName.OPENAI: 'openai'>], Literal['api_key']]",
-        "type": "object",
-        "discriminator": "model",
-        "discriminator_mapping": {
-          "Llama-3.3-70B-Instruct": "llama_api",
-          "Llama-3.3-8B-Instruct": "llama_api",
-          "Llama-4-Maverick-17B-128E-Instruct-FP8": "llama_api",
-          "Llama-4-Scout-17B-16E-Instruct-FP8": "llama_api",
-          "Qwen/Qwen2.5-72B-Instruct-Turbo": "aiml_api",
-          "amazon/nova-lite-v1": "open_router",
-          "amazon/nova-micro-v1": "open_router",
-          "amazon/nova-pro-v1": "open_router",
-          "claude-3-7-sonnet-20250219": "anthropic",
-          "claude-3-haiku-20240307": "anthropic",
-          "claude-haiku-4-5-20251001": "anthropic",
-          "claude-opus-4-1-20250805": "anthropic",
-          "claude-opus-4-20250514": "anthropic",
-          "claude-opus-4-5-20251101": "anthropic",
-          "claude-sonnet-4-20250514": "anthropic",
-          "claude-sonnet-4-5-20250929": "anthropic",
-          "cohere/command-r-08-2024": "open_router",
-          "cohere/command-r-plus-08-2024": "open_router",
-          "deepseek/deepseek-chat": "open_router",
-          "deepseek/deepseek-r1-0528": "open_router",
-          "dolphin-mistral:latest": "ollama",
-          "google/gemini-2.0-flash-001": "open_router",
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-          "google/gemini-2.5-flash-lite-preview-06-17": "open_router",
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-          "google/gemini-3-pro-preview": "open_router",
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-          "gpt-4o": "openai",
-          "gpt-4o-mini": "openai",
-          "gpt-5-2025-08-07": "openai",
-          "gpt-5-chat-latest": "openai",
-          "gpt-5-mini-2025-08-07": "openai",
-          "gpt-5-nano-2025-08-07": "openai",
-          "gpt-5.1-2025-11-13": "openai",
-          "gryphe/mythomax-l2-13b": "open_router",
-          "llama-3.1-8b-instant": "groq",
-          "llama-3.3-70b-versatile": "groq",
-          "llama3": "ollama",
-          "llama3.1:405b": "ollama",
-          "llama3.2": "ollama",
-          "llama3.3": "ollama",
-          "meta-llama/Llama-3.2-3B-Instruct-Turbo": "aiml_api",
-          "meta-llama/Llama-3.3-70B-Instruct-Turbo": "aiml_api",
-          "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo": "aiml_api",
-          "meta-llama/llama-4-maverick": "open_router",
-          "meta-llama/llama-4-scout": "open_router",
-          "microsoft/wizardlm-2-8x22b": "open_router",
-          "mistralai/mistral-nemo": "open_router",
-          "moonshotai/kimi-k2": "open_router",
-          "nousresearch/hermes-3-llama-3.1-405b": "open_router",
-          "nousresearch/hermes-3-llama-3.1-70b": "open_router",
-          "nvidia/llama-3.1-nemotron-70b-instruct": "aiml_api",
-          "o1": "openai",
-          "o1-mini": "openai",
-          "o3-2025-04-16": "openai",
-          "o3-mini": "openai",
-          "openai/gpt-oss-120b": "open_router",
-          "openai/gpt-oss-20b": "open_router",
-          "perplexity/sonar": "open_router",
-          "perplexity/sonar-deep-research": "open_router",
-          "perplexity/sonar-pro": "open_router",
-          "qwen/qwen3-235b-a22b-thinking-2507": "open_router",
-          "qwen/qwen3-coder": "open_router",
-          "v0-1.0-md": "v0",
-          "v0-1.5-lg": "v0",
-          "v0-1.5-md": "v0",
-          "x-ai/grok-4": "open_router",
-          "x-ai/grok-4-fast": "open_router",
-          "x-ai/grok-4.1-fast": "open_router",
-          "x-ai/grok-code-fast-1": "open_router"
-        },
-        "discriminator_values": [
-          "gpt-4o-mini"
-        ]
-      }
-    },
-    "required": [
-      "replicate_api_key_credentials",
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--- a/autogpt_platform/backend/agents/agent_e437cc95-e671-489d-b915-76561fba8c7f.json
+++ b/autogpt_platform/backend/agents/agent_e437cc95-e671-489d-b915-76561fba8c7f.json
--- a/autogpt_platform/backend/agents/agent_e7bb29a1-23c7-4fee-aa3b-5426174b8c52.json
+++ b/autogpt_platform/backend/agents/agent_e7bb29a1-23c7-4fee-aa3b-5426174b8c52.json
--- a/autogpt_platform/backend/agents/agent_eafa21d3-bf14-4f63-a97f-a5ee41df83b3.json
+++ b/autogpt_platform/backend/agents/agent_eafa21d3-bf14-4f63-a97f-a5ee41df83b3.json
--- a/autogpt_platform/backend/agents/agent_f2cc74bb-f43f-4395-9c35-ecb30b5b4fc9.json
+++ b/autogpt_platform/backend/agents/agent_f2cc74bb-f43f-4395-9c35-ecb30b5b4fc9.json
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-      }
-    },
-    "required": [
-      "jina_api_key_credentials",
-      "openai_api_key_credentials"
-    ],
-    "title": "AIWebpageCopyImproverCredentialsInputSchema",
-    "type": "object"
-  }
-}
--- a/autogpt_platform/backend/agents/agent_fc2c9976-0962-4625-a27b-d316573a9e7f.json
+++ b/autogpt_platform/backend/agents/agent_fc2c9976-0962-4625-a27b-d316573a9e7f.json
@@ -1,615 +0,0 @@
-{
-  "id": "4c6b68cb-bb75-4044-b1cb-2cee3fd39b26",
-  "version": 29,
-  "is_active": true,
-  "name": "Email Address Finder",
-  "description": "Input information of a business and find their email address",
-  "instructions": null,
-  "recommended_schedule_cron": null,
-  "nodes": [
-    {
-      "id": "04cad535-9f1a-4876-8b07-af5897d8c282",
-      "block_id": "c0a8e994-ebf1-4a9c-a4d8-89d09c86741b",
-      "input_default": {
-        "name": "Address",
-        "value": "USA"
-      },
-      "metadata": {
-        "position": {
-          "x": 1047.9357219838776,
-          "y": 1067.9123910370954
-        }
-      },
-      "input_links": [],
-      "output_links": [
-        {
-          "id": "aac29f7b-3cd1-4c91-9a2a-72a8301c0957",
-          "source_id": "04cad535-9f1a-4876-8b07-af5897d8c282",
-          "sink_id": "28b5ddcc-dc20-41cc-ad21-c54ff459f694",
-          "source_name": "result",
-          "sink_name": "values_#_ADDRESS",
-          "is_static": true
-        }
-      ],
-      "graph_id": "4c6b68cb-bb75-4044-b1cb-2cee3fd39b26",
-      "graph_version": 29,
-      "webhook_id": null,
-      "webhook": null
-    },
-    {
-      "id": "a6e7355e-5bf8-4b09-b11c-a5e140389981",
-      "block_id": "3146e4fe-2cdd-4f29-bd12-0c9d5bb4deb0",
-      "input_default": {
-        "group": 1,
-        "pattern": "<email>(.*?)<\\/email>"
-      },
-      "metadata": {
-        "position": {
-          "x": 3381.2821481740634,
-          "y": 246.091098184158
-        }
-      },
-      "input_links": [
-        {
-          "id": "9f8188ce-1f3d-46fb-acda-b2a57c0e5da6",
-          "source_id": "510937b3-0134-4e45-b2ba-05a447bbaf50",
-          "sink_id": "a6e7355e-5bf8-4b09-b11c-a5e140389981",
-          "source_name": "response",
-          "sink_name": "text",
-          "is_static": false
-        }
-      ],
-      "output_links": [
-        {
-          "id": "b15b5143-27b7-486e-a166-4095e72e5235",
-          "source_id": "a6e7355e-5bf8-4b09-b11c-a5e140389981",
-          "sink_id": "266b7255-11c4-4b88-99e2-85db31a2e865",
-          "source_name": "negative",
-          "sink_name": "values_#_Result",
-          "is_static": false
-        },
-        {
-          "id": "23591872-3c6b-4562-87d3-5b6ade698e48",
-          "source_id": "a6e7355e-5bf8-4b09-b11c-a5e140389981",
-          "sink_id": "310c8fab-2ae6-4158-bd48-01dbdc434130",
-          "source_name": "positive",
-          "sink_name": "value",
-          "is_static": false
-        }
-      ],
-      "graph_id": "4c6b68cb-bb75-4044-b1cb-2cee3fd39b26",
-      "graph_version": 29,
-      "webhook_id": null,
-      "webhook": null
-    },
-    {
-      "id": "310c8fab-2ae6-4158-bd48-01dbdc434130",
-      "block_id": "363ae599-353e-4804-937e-b2ee3cef3da4",
-      "input_default": {
-        "name": "Email"
-      },
-      "metadata": {
-        "position": {
-          "x": 4525.4246310882,
-          "y": 246.36913665010354
-        }
-      },
-      "input_links": [
-        {
-          "id": "d87b07ea-dcec-4d38-a644-2c1d741ea3cb",
-          "source_id": "266b7255-11c4-4b88-99e2-85db31a2e865",
-          "sink_id": "310c8fab-2ae6-4158-bd48-01dbdc434130",
-          "source_name": "output",
-          "sink_name": "value",
-          "is_static": false
-        },
-        {
-          "id": "23591872-3c6b-4562-87d3-5b6ade698e48",
-          "source_id": "a6e7355e-5bf8-4b09-b11c-a5e140389981",
-          "sink_id": "310c8fab-2ae6-4158-bd48-01dbdc434130",
-          "source_name": "positive",
-          "sink_name": "value",
-          "is_static": false
-        }
-      ],
-      "output_links": [],
-      "graph_id": "4c6b68cb-bb75-4044-b1cb-2cee3fd39b26",
-      "graph_version": 29,
-      "webhook_id": null,
-      "webhook": null
-    },
-    {
-      "id": "4a41df99-ffe2-4c12-b528-632979c9c030",
-      "block_id": "87840993-2053-44b7-8da4-187ad4ee518c",
-      "input_default": {},
-      "metadata": {
-        "position": {
-          "x": 2182.7499999999995,
-          "y": 242.00001144409185
-        }
-      },
-      "input_links": [
-        {
-          "id": "2e411d3d-79ba-4958-9c1c-b76a45a2e649",
-          "source_id": "28b5ddcc-dc20-41cc-ad21-c54ff459f694",
-          "sink_id": "4a41df99-ffe2-4c12-b528-632979c9c030",
-          "source_name": "output",
-          "sink_name": "query",
-          "is_static": false
-        }
-      ],
-      "output_links": [
-        {
-          "id": "899cc7d8-a96b-4107-b3c6-4c78edcf0c6b",
-          "source_id": "4a41df99-ffe2-4c12-b528-632979c9c030",
-          "sink_id": "510937b3-0134-4e45-b2ba-05a447bbaf50",
-          "source_name": "results",
-          "sink_name": "prompt_values_#_WEBSITE_CONTENT",
-          "is_static": false
-        }
-      ],
-      "graph_id": "4c6b68cb-bb75-4044-b1cb-2cee3fd39b26",
-      "graph_version": 29,
-      "webhook_id": null,
-      "webhook": null
-    },
-    {
-      "id": "9708a10a-8be0-4c44-abb3-bd0f7c594794",
-      "block_id": "c0a8e994-ebf1-4a9c-a4d8-89d09c86741b",
-      "input_default": {
-        "name": "Business Name",
-        "value": "Tim Cook"
-      },
-      "metadata": {
-        "position": {
-          "x": 1049.9704155272595,
-          "y": 244.49931152418344
-        }
-      },
-      "input_links": [],
-      "output_links": [
-        {
-          "id": "946b522c-365f-4ee0-96f9-28863d9882ea",
-          "source_id": "9708a10a-8be0-4c44-abb3-bd0f7c594794",
-          "sink_id": "28b5ddcc-dc20-41cc-ad21-c54ff459f694",
-          "source_name": "result",
-          "sink_name": "values_#_NAME",
-          "is_static": true
-        },
-        {
-          "id": "43e920a7-0bb4-4fae-9a22-91df95c7342a",
-          "source_id": "9708a10a-8be0-4c44-abb3-bd0f7c594794",
-          "sink_id": "510937b3-0134-4e45-b2ba-05a447bbaf50",
-          "source_name": "result",
-          "sink_name": "prompt_values_#_BUSINESS_NAME",
-          "is_static": true
-        }
-      ],
-      "graph_id": "4c6b68cb-bb75-4044-b1cb-2cee3fd39b26",
-      "graph_version": 29,
-      "webhook_id": null,
-      "webhook": null
-    },
-    {
-      "id": "28b5ddcc-dc20-41cc-ad21-c54ff459f694",
-      "block_id": "db7d8f02-2f44-4c55-ab7a-eae0941f0c30",
-      "input_default": {
-        "format": "Email Address of {{NAME}}, {{ADDRESS}}",
-        "values": {}
-      },
-      "metadata": {
-        "position": {
-          "x": 1625.25,
-          "y": 243.25001144409185
-        }
-      },
-      "input_links": [
-        {
-          "id": "946b522c-365f-4ee0-96f9-28863d9882ea",
-          "source_id": "9708a10a-8be0-4c44-abb3-bd0f7c594794",
-          "sink_id": "28b5ddcc-dc20-41cc-ad21-c54ff459f694",
-          "source_name": "result",
-          "sink_name": "values_#_NAME",
-          "is_static": true
-        },
-        {
-          "id": "aac29f7b-3cd1-4c91-9a2a-72a8301c0957",
-          "source_id": "04cad535-9f1a-4876-8b07-af5897d8c282",
-          "sink_id": "28b5ddcc-dc20-41cc-ad21-c54ff459f694",
-          "source_name": "result",
-          "sink_name": "values_#_ADDRESS",
-          "is_static": true
-        }
-      ],
-      "output_links": [
-        {
-          "id": "2e411d3d-79ba-4958-9c1c-b76a45a2e649",
-          "source_id": "28b5ddcc-dc20-41cc-ad21-c54ff459f694",
-          "sink_id": "4a41df99-ffe2-4c12-b528-632979c9c030",
-          "source_name": "output",
-          "sink_name": "query",
-          "is_static": false
-        }
-      ],
-      "graph_id": "4c6b68cb-bb75-4044-b1cb-2cee3fd39b26",
-      "graph_version": 29,
-      "webhook_id": null,
-      "webhook": null
-    },
-    {
-      "id": "266b7255-11c4-4b88-99e2-85db31a2e865",
-      "block_id": "db7d8f02-2f44-4c55-ab7a-eae0941f0c30",
-      "input_default": {
-        "format": "Failed to find email. \nResult:\n{{RESULT}}",
-        "values": {}
-      },
-      "metadata": {
-        "position": {
-          "x": 3949.7493830805934,
-          "y": 705.209819698647
-        }
-      },
-      "input_links": [
-        {
-          "id": "b15b5143-27b7-486e-a166-4095e72e5235",
-          "source_id": "a6e7355e-5bf8-4b09-b11c-a5e140389981",
-          "sink_id": "266b7255-11c4-4b88-99e2-85db31a2e865",
-          "source_name": "negative",
-          "sink_name": "values_#_Result",
-          "is_static": false
-        }
-      ],
-      "output_links": [
-        {
-          "id": "d87b07ea-dcec-4d38-a644-2c1d741ea3cb",
-          "source_id": "266b7255-11c4-4b88-99e2-85db31a2e865",
-          "sink_id": "310c8fab-2ae6-4158-bd48-01dbdc434130",
-          "source_name": "output",
-          "sink_name": "value",
-          "is_static": false
-        }
-      ],
-      "graph_id": "4c6b68cb-bb75-4044-b1cb-2cee3fd39b26",
-      "graph_version": 29,
-      "webhook_id": null,
-      "webhook": null
-    },
-    {
-      "id": "510937b3-0134-4e45-b2ba-05a447bbaf50",
-      "block_id": "1f292d4a-41a4-4977-9684-7c8d560b9f91",
-      "input_default": {
-        "model": "claude-sonnet-4-5-20250929",
-        "prompt": "<business_website>\n{{WEBSITE_CONTENT}}\n</business_website>\n\nExtract the Contact Email of {{BUSINESS_NAME}}.\n\nIf no email that can be used to contact {{BUSINESS_NAME}} is present, output `N/A`.\nDo not share any emails other than the email for this specific entity.\n\nIf multiple present pick the likely best one.\n\nRespond with the email (or N/A) inside <email></email> tags.\n\nExample Response:\n\n<thoughts_or_comments>\nThere were many emails present, but luckily one was for {{BUSINESS_NAME}} which I have included below.\n</thoughts_or_comments>\n<email>\nexample@email.com\n</email>",
-        "prompt_values": {}
-      },
-      "metadata": {
-        "position": {
-          "x": 2774.879259081777,
-          "y": 243.3102035752969
-        }
-      },
-      "input_links": [
-        {
-          "id": "43e920a7-0bb4-4fae-9a22-91df95c7342a",
-          "source_id": "9708a10a-8be0-4c44-abb3-bd0f7c594794",
-          "sink_id": "510937b3-0134-4e45-b2ba-05a447bbaf50",
-          "source_name": "result",
-          "sink_name": "prompt_values_#_BUSINESS_NAME",
-          "is_static": true
-        },
-        {
-          "id": "899cc7d8-a96b-4107-b3c6-4c78edcf0c6b",
-          "source_id": "4a41df99-ffe2-4c12-b528-632979c9c030",
-          "sink_id": "510937b3-0134-4e45-b2ba-05a447bbaf50",
-          "source_name": "results",
-          "sink_name": "prompt_values_#_WEBSITE_CONTENT",
-          "is_static": false
-        }
-      ],
-      "output_links": [
-        {
-          "id": "9f8188ce-1f3d-46fb-acda-b2a57c0e5da6",
-          "source_id": "510937b3-0134-4e45-b2ba-05a447bbaf50",
-          "sink_id": "a6e7355e-5bf8-4b09-b11c-a5e140389981",
-          "source_name": "response",
-          "sink_name": "text",
-          "is_static": false
-        }
-      ],
-      "graph_id": "4c6b68cb-bb75-4044-b1cb-2cee3fd39b26",
-      "graph_version": 29,
-      "webhook_id": null,
-      "webhook": null
-    }
-  ],
-  "links": [
-    {
-      "id": "9f8188ce-1f3d-46fb-acda-b2a57c0e5da6",
-      "source_id": "510937b3-0134-4e45-b2ba-05a447bbaf50",
-      "sink_id": "a6e7355e-5bf8-4b09-b11c-a5e140389981",
-      "source_name": "response",
-      "sink_name": "text",
-      "is_static": false
-    },
-    {
-      "id": "b15b5143-27b7-486e-a166-4095e72e5235",
-      "source_id": "a6e7355e-5bf8-4b09-b11c-a5e140389981",
-      "sink_id": "266b7255-11c4-4b88-99e2-85db31a2e865",
-      "source_name": "negative",
-      "sink_name": "values_#_Result",
-      "is_static": false
-    },
-    {
-      "id": "d87b07ea-dcec-4d38-a644-2c1d741ea3cb",
-      "source_id": "266b7255-11c4-4b88-99e2-85db31a2e865",
-      "sink_id": "310c8fab-2ae6-4158-bd48-01dbdc434130",
-      "source_name": "output",
-      "sink_name": "value",
-      "is_static": false
-    },
-    {
-      "id": "946b522c-365f-4ee0-96f9-28863d9882ea",
-      "source_id": "9708a10a-8be0-4c44-abb3-bd0f7c594794",
-      "sink_id": "28b5ddcc-dc20-41cc-ad21-c54ff459f694",
-      "source_name": "result",
-      "sink_name": "values_#_NAME",
-      "is_static": true
-    },
-    {
-      "id": "23591872-3c6b-4562-87d3-5b6ade698e48",
-      "source_id": "a6e7355e-5bf8-4b09-b11c-a5e140389981",
-      "sink_id": "310c8fab-2ae6-4158-bd48-01dbdc434130",
-      "source_name": "positive",
-      "sink_name": "value",
-      "is_static": false
-    },
-    {
-      "id": "43e920a7-0bb4-4fae-9a22-91df95c7342a",
-      "source_id": "9708a10a-8be0-4c44-abb3-bd0f7c594794",
-      "sink_id": "510937b3-0134-4e45-b2ba-05a447bbaf50",
-      "source_name": "result",
-      "sink_name": "prompt_values_#_BUSINESS_NAME",
-      "is_static": true
-    },
-    {
-      "id": "2e411d3d-79ba-4958-9c1c-b76a45a2e649",
-      "source_id": "28b5ddcc-dc20-41cc-ad21-c54ff459f694",
-      "sink_id": "4a41df99-ffe2-4c12-b528-632979c9c030",
-      "source_name": "output",
-      "sink_name": "query",
-      "is_static": false
-    },
-    {
-      "id": "aac29f7b-3cd1-4c91-9a2a-72a8301c0957",
-      "source_id": "04cad535-9f1a-4876-8b07-af5897d8c282",
-      "sink_id": "28b5ddcc-dc20-41cc-ad21-c54ff459f694",
-      "source_name": "result",
-      "sink_name": "values_#_ADDRESS",
-      "is_static": true
-    },
-    {
-      "id": "899cc7d8-a96b-4107-b3c6-4c78edcf0c6b",
-      "source_id": "4a41df99-ffe2-4c12-b528-632979c9c030",
-      "sink_id": "510937b3-0134-4e45-b2ba-05a447bbaf50",
-      "source_name": "results",
-      "sink_name": "prompt_values_#_WEBSITE_CONTENT",
-      "is_static": false
-    }
-  ],
-  "forked_from_id": null,
-  "forked_from_version": null,
-  "sub_graphs": [],
-  "user_id": "",
-  "created_at": "2025-01-03T00:46:30.244Z",
-  "input_schema": {
-    "type": "object",
-    "properties": {
-      "Address": {
-        "advanced": false,
-        "secret": false,
-        "title": "Address",
-        "default": "USA"
-      },
-      "Business Name": {
-        "advanced": false,
-        "secret": false,
-        "title": "Business Name",
-        "default": "Tim Cook"
-      }
-    },
-    "required": []
-  },
-  "output_schema": {
-    "type": "object",
-    "properties": {
-      "Email": {
-        "advanced": false,
-        "secret": false,
-        "title": "Email"
-      }
-    },
-    "required": [
-      "Email"
-    ]
-  },
-  "has_external_trigger": false,
-  "has_human_in_the_loop": false,
-  "trigger_setup_info": null,
-  "credentials_input_schema": {
-    "properties": {
-      "jina_api_key_credentials": {
-        "credentials_provider": [
-          "jina"
-        ],
-        "credentials_types": [
-          "api_key"
-        ],
-        "properties": {
-          "id": {
-            "title": "Id",
-            "type": "string"
-          },
-          "title": {
-            "anyOf": [
-              {
-                "type": "string"
-              },
-              {
-                "type": "null"
-              }
-            ],
-            "default": null,
-            "title": "Title"
-          },
-          "provider": {
-            "const": "jina",
-            "title": "Provider",
-            "type": "string"
-          },
-          "type": {
-            "const": "api_key",
-            "title": "Type",
-            "type": "string"
-          }
-        },
-        "required": [
-          "id",
-          "provider",
-          "type"
-        ],
-        "title": "CredentialsMetaInput[Literal[<ProviderName.JINA: 'jina'>], Literal['api_key']]",
-        "type": "object",
-        "discriminator_values": []
-      },
-      "anthropic_api_key_credentials": {
-        "credentials_provider": [
-          "anthropic"
-        ],
-        "credentials_types": [
-          "api_key"
-        ],
-        "properties": {
-          "id": {
-            "title": "Id",
-            "type": "string"
-          },
-          "title": {
-            "anyOf": [
-              {
-                "type": "string"
-              },
-              {
-                "type": "null"
-              }
-            ],
-            "default": null,
-            "title": "Title"
-          },
-          "provider": {
-            "const": "anthropic",
-            "title": "Provider",
-            "type": "string"
-          },
-          "type": {
-            "const": "api_key",
-            "title": "Type",
-            "type": "string"
-          }
-        },
-        "required": [
-          "id",
-          "provider",
-          "type"
-        ],
-        "title": "CredentialsMetaInput[Literal[<ProviderName.ANTHROPIC: 'anthropic'>], Literal['api_key']]",
-        "type": "object",
-        "discriminator": "model",
-        "discriminator_mapping": {
-          "Llama-3.3-70B-Instruct": "llama_api",
-          "Llama-3.3-8B-Instruct": "llama_api",
-          "Llama-4-Maverick-17B-128E-Instruct-FP8": "llama_api",
-          "Llama-4-Scout-17B-16E-Instruct-FP8": "llama_api",
-          "Qwen/Qwen2.5-72B-Instruct-Turbo": "aiml_api",
-          "amazon/nova-lite-v1": "open_router",
-          "amazon/nova-micro-v1": "open_router",
-          "amazon/nova-pro-v1": "open_router",
-          "claude-3-7-sonnet-20250219": "anthropic",
-          "claude-3-haiku-20240307": "anthropic",
-          "claude-haiku-4-5-20251001": "anthropic",
-          "claude-opus-4-1-20250805": "anthropic",
-          "claude-opus-4-20250514": "anthropic",
-          "claude-opus-4-5-20251101": "anthropic",
-          "claude-sonnet-4-20250514": "anthropic",
-          "claude-sonnet-4-5-20250929": "anthropic",
-          "cohere/command-r-08-2024": "open_router",
-          "cohere/command-r-plus-08-2024": "open_router",
-          "deepseek/deepseek-chat": "open_router",
-          "deepseek/deepseek-r1-0528": "open_router",
-          "dolphin-mistral:latest": "ollama",
-          "google/gemini-2.0-flash-001": "open_router",
-          "google/gemini-2.0-flash-lite-001": "open_router",
-          "google/gemini-2.5-flash": "open_router",
-          "google/gemini-2.5-flash-lite-preview-06-17": "open_router",
-          "google/gemini-2.5-pro-preview-03-25": "open_router",
-          "google/gemini-3-pro-preview": "open_router",
-          "gpt-3.5-turbo": "openai",
-          "gpt-4-turbo": "openai",
-          "gpt-4.1-2025-04-14": "openai",
-          "gpt-4.1-mini-2025-04-14": "openai",
-          "gpt-4o": "openai",
-          "gpt-4o-mini": "openai",
-          "gpt-5-2025-08-07": "openai",
-          "gpt-5-chat-latest": "openai",
-          "gpt-5-mini-2025-08-07": "openai",
-          "gpt-5-nano-2025-08-07": "openai",
-          "gpt-5.1-2025-11-13": "openai",
-          "gryphe/mythomax-l2-13b": "open_router",
-          "llama-3.1-8b-instant": "groq",
-          "llama-3.3-70b-versatile": "groq",
-          "llama3": "ollama",
-          "llama3.1:405b": "ollama",
-          "llama3.2": "ollama",
-          "llama3.3": "ollama",
-          "meta-llama/Llama-3.2-3B-Instruct-Turbo": "aiml_api",
-          "meta-llama/Llama-3.3-70B-Instruct-Turbo": "aiml_api",
-          "meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo": "aiml_api",
-          "meta-llama/llama-4-maverick": "open_router",
-          "meta-llama/llama-4-scout": "open_router",
-          "microsoft/wizardlm-2-8x22b": "open_router",
-          "mistralai/mistral-nemo": "open_router",
-          "moonshotai/kimi-k2": "open_router",
-          "nousresearch/hermes-3-llama-3.1-405b": "open_router",
-          "nousresearch/hermes-3-llama-3.1-70b": "open_router",
-          "nvidia/llama-3.1-nemotron-70b-instruct": "aiml_api",
-          "o1": "openai",
-          "o1-mini": "openai",
-          "o3-2025-04-16": "openai",
-          "o3-mini": "openai",
-          "openai/gpt-oss-120b": "open_router",
-          "openai/gpt-oss-20b": "open_router",
-          "perplexity/sonar": "open_router",
-          "perplexity/sonar-deep-research": "open_router",
-          "perplexity/sonar-pro": "open_router",
-          "qwen/qwen3-235b-a22b-thinking-2507": "open_router",
-          "qwen/qwen3-coder": "open_router",
-          "v0-1.0-md": "v0",
-          "v0-1.5-lg": "v0",
-          "v0-1.5-md": "v0",
-          "x-ai/grok-4": "open_router",
-          "x-ai/grok-4-fast": "open_router",
-          "x-ai/grok-4.1-fast": "open_router",
-          "x-ai/grok-code-fast-1": "open_router"
-        },
-        "discriminator_values": [
-          "claude-sonnet-4-5-20250929"
-        ]
-      }
-    },
-    "required": [
-      "jina_api_key_credentials",
-      "anthropic_api_key_credentials"
-    ],
-    "title": "EmailAddressFinderCredentialsInputSchema",
-    "type": "object"
-  }
-}
--- a/autogpt_platform/backend/backend/blocks/init.py
+++ b/autogpt_platform/backend/backend/blocks/init.py
@@ -5,7 +5,7 @@ import re
 from pathlib import Path
 from typing import TYPE_CHECKING, TypeVar

-from backend.util.cache import cached
+from autogpt_libs.utils.cache import cached

 logger = logging.getLogger(__name__)

@@ -16,7 +16,7 @@ if TYPE_CHECKING:
 T = TypeVar("T")


-@cached(ttl_seconds=3600)
+@cached()
 def load_all_blocks() -> dict[str, type["Block"]]:
    from backend.data.block import Block
    from backend.util.settings import Config
--- a/autogpt_platform/backend/backend/blocks/agent.py
+++ b/autogpt_platform/backend/backend/blocks/agent.py
@@ -7,11 +7,10 @@ from backend.data.block import (
    BlockInput,
    BlockOutput,
    BlockSchema,
-    BlockSchemaInput,
    BlockType,
    get_block,
 )
-from backend.data.execution import ExecutionContext, ExecutionStatus, NodesInputMasks
+from backend.data.execution import ExecutionStatus, NodesInputMasks
 from backend.data.model import NodeExecutionStats, SchemaField
 from backend.util.json import validate_with_jsonschema
 from backend.util.retry import func_retry
@@ -20,7 +19,7 @@ _logger = logging.getLogger(__name__)


 class AgentExecutorBlock(Block):
-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        user_id: str = SchemaField(description="User ID")
        graph_id: str = SchemaField(description="Graph ID")
        graph_version: int = SchemaField(description="Graph Version")
@@ -54,7 +53,6 @@ class AgentExecutorBlock(Block):
            return validate_with_jsonschema(cls.get_input_schema(data), data)

    class Output(BlockSchema):
-        # Use BlockSchema to avoid automatic error field that could clash with graph outputs
        pass

    def __init__(self):
@@ -67,14 +65,8 @@ class AgentExecutorBlock(Block):
            categories={BlockCategory.AGENT},
        )

-    async def run(
-        self,
-        input_data: Input,
-        *,
-        graph_exec_id: str,
-        execution_context: ExecutionContext,
-        **kwargs,
-    ) -> BlockOutput:
+    async def run(self, input_data: Input, **kwargs) -> BlockOutput:
+
        from backend.executor import utils as execution_utils

        graph_exec = await execution_utils.add_graph_execution(
@@ -83,9 +75,6 @@ class AgentExecutorBlock(Block):
            user_id=input_data.user_id,
            inputs=input_data.inputs,
            nodes_input_masks=input_data.nodes_input_masks,
-            execution_context=execution_context.model_copy(
-                update={"parent_execution_id": graph_exec_id},
-            ),
        )

        logger = execution_utils.LogMetadata(
--- a/autogpt_platform/backend/backend/blocks/ai_condition.py
+++ b/autogpt_platform/backend/backend/blocks/ai_condition.py
@@ -10,12 +10,7 @@ from backend.blocks.llm import (
    LLMResponse,
    llm_call,
 )
-from backend.data.block import (
-    BlockCategory,
-    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
-)
+from backend.data.block import BlockCategory, BlockOutput, BlockSchema
 from backend.data.model import APIKeyCredentials, NodeExecutionStats, SchemaField


@@ -28,7 +23,7 @@ class AIConditionBlock(AIBlockBase):
    It provides the same yes/no data pass-through functionality as the standard ConditionBlock.
    """

-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        input_value: Any = SchemaField(
            description="The input value to evaluate with the AI condition",
            placeholder="Enter the value to be evaluated (text, number, or any data)",
@@ -55,7 +50,7 @@ class AIConditionBlock(AIBlockBase):
        )
        credentials: AICredentials = AICredentialsField()

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        result: bool = SchemaField(
            description="The result of the AI condition evaluation (True or False)"
        )
--- a/autogpt_platform/backend/backend/blocks/ai_image_customizer.py
+++ b/autogpt_platform/backend/backend/blocks/ai_image_customizer.py
@@ -1,4 +1,3 @@
-import asyncio
 from enum import Enum
 from typing import Literal

@@ -6,13 +5,7 @@ from pydantic import SecretStr
 from replicate.client import Client as ReplicateClient
 from replicate.helpers import FileOutput

-from backend.data.block import (
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
-)
+from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
 from backend.data.model import (
    APIKeyCredentials,
    CredentialsField,
@@ -20,26 +13,11 @@ from backend.data.model import (
    SchemaField,
 )
 from backend.integrations.providers import ProviderName
-from backend.util.file import MediaFileType, store_media_file
+from backend.util.file import MediaFileType


 class GeminiImageModel(str, Enum):
    NANO_BANANA = "google/nano-banana"
-    NANO_BANANA_PRO = "google/nano-banana-pro"
-
-
-class AspectRatio(str, Enum):
-    MATCH_INPUT_IMAGE = "match_input_image"
-    ASPECT_1_1 = "1:1"
-    ASPECT_2_3 = "2:3"
-    ASPECT_3_2 = "3:2"
-    ASPECT_3_4 = "3:4"
-    ASPECT_4_3 = "4:3"
-    ASPECT_4_5 = "4:5"
-    ASPECT_5_4 = "5:4"
-    ASPECT_9_16 = "9:16"
-    ASPECT_16_9 = "16:9"
-    ASPECT_21_9 = "21:9"


 class OutputFormat(str, Enum):
@@ -64,7 +42,7 @@ TEST_CREDENTIALS_INPUT = {


 class AIImageCustomizerBlock(Block):
-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        credentials: CredentialsMetaInput[
            Literal[ProviderName.REPLICATE], Literal["api_key"]
        ] = CredentialsField(
@@ -84,19 +62,15 @@ class AIImageCustomizerBlock(Block):
            default=[],
            title="Input Images",
        )
-        aspect_ratio: AspectRatio = SchemaField(
-            description="Aspect ratio of the generated image",
-            default=AspectRatio.MATCH_INPUT_IMAGE,
-            title="Aspect Ratio",
-        )
        output_format: OutputFormat = SchemaField(
            description="Format of the output image",
            default=OutputFormat.PNG,
            title="Output Format",
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        image_url: MediaFileType = SchemaField(description="URL of the generated image")
+        error: str = SchemaField(description="Error message if generation failed")

    def __init__(self):
        super().__init__(
@@ -112,7 +86,6 @@ class AIImageCustomizerBlock(Block):
                "prompt": "Make the scene more vibrant and colorful",
                "model": GeminiImageModel.NANO_BANANA,
                "images": [],
-                "aspect_ratio": AspectRatio.MATCH_INPUT_IMAGE,
                "output_format": OutputFormat.JPG,
                "credentials": TEST_CREDENTIALS_INPUT,
            },
@@ -137,25 +110,11 @@ class AIImageCustomizerBlock(Block):
        **kwargs,
    ) -> BlockOutput:
        try:
-            # Convert local file paths to Data URIs (base64) so Replicate can access them
-            processed_images = await asyncio.gather(
-                *(
-                    store_media_file(
-                        graph_exec_id=graph_exec_id,
-                        file=img,
-                        user_id=user_id,
-                        return_content=True,
-                    )
-                    for img in input_data.images
-                )
-            )
-
            result = await self.run_model(
                api_key=credentials.api_key,
                model_name=input_data.model.value,
                prompt=input_data.prompt,
-                images=processed_images,
-                aspect_ratio=input_data.aspect_ratio.value,
+                images=input_data.images,
                output_format=input_data.output_format.value,
            )
            yield "image_url", result
@@ -168,14 +127,12 @@ class AIImageCustomizerBlock(Block):
        model_name: str,
        prompt: str,
        images: list[MediaFileType],
-        aspect_ratio: str,
        output_format: str,
    ) -> MediaFileType:
        client = ReplicateClient(api_token=api_key.get_secret_value())

        input_params: dict = {
            "prompt": prompt,
-            "aspect_ratio": aspect_ratio,
            "output_format": output_format,
        }

--- a/autogpt_platform/backend/backend/blocks/ai_image_generator_block.py
+++ b/autogpt_platform/backend/backend/blocks/ai_image_generator_block.py
@@ -5,7 +5,7 @@ from pydantic import SecretStr
 from replicate.client import Client as ReplicateClient
 from replicate.helpers import FileOutput

-from backend.data.block import Block, BlockCategory, BlockSchemaInput, BlockSchemaOutput
+from backend.data.block import Block, BlockCategory, BlockSchema
 from backend.data.model import (
    APIKeyCredentials,
    CredentialsField,
@@ -60,14 +60,6 @@ SIZE_TO_RECRAFT_DIMENSIONS = {
    ImageSize.TALL: "1024x1536",
 }

-SIZE_TO_NANO_BANANA_RATIO = {
-    ImageSize.SQUARE: "1:1",
-    ImageSize.LANDSCAPE: "4:3",
-    ImageSize.PORTRAIT: "3:4",
-    ImageSize.WIDE: "16:9",
-    ImageSize.TALL: "9:16",
-}
-

 class ImageStyle(str, Enum):
    """
@@ -106,11 +98,10 @@ class ImageGenModel(str, Enum):
    FLUX_ULTRA = "Flux 1.1 Pro Ultra"
    RECRAFT = "Recraft v3"
    SD3_5 = "Stable Diffusion 3.5 Medium"
-    NANO_BANANA_PRO = "Nano Banana Pro"


 class AIImageGeneratorBlock(Block):
-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        credentials: CredentialsMetaInput[
            Literal[ProviderName.REPLICATE], Literal["api_key"]
        ] = CredentialsField(
@@ -144,8 +135,9 @@ class AIImageGeneratorBlock(Block):
            title="Image Style",
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        image_url: str = SchemaField(description="URL of the generated image")
+        error: str = SchemaField(description="Error message if generation failed")

    def __init__(self):
        super().__init__(
@@ -270,20 +262,6 @@ class AIImageGeneratorBlock(Block):
                )
                return output

-            elif input_data.model == ImageGenModel.NANO_BANANA_PRO:
-                # Use Nano Banana Pro (Google Gemini 3 Pro Image)
-                input_params = {
-                    "prompt": modified_prompt,
-                    "aspect_ratio": SIZE_TO_NANO_BANANA_RATIO[input_data.size],
-                    "resolution": "2K",  # Default to 2K for good quality/cost balance
-                    "output_format": "jpg",
-                    "safety_filter_level": "block_only_high",  # Most permissive
-                }
-                output = await self._run_client(
-                    credentials, "google/nano-banana-pro", input_params
-                )
-                return output
-
        except Exception as e:
            raise RuntimeError(f"Failed to generate image: {str(e)}")

--- a/autogpt_platform/backend/backend/blocks/ai_music_generator.py
+++ b/autogpt_platform/backend/backend/blocks/ai_music_generator.py
@@ -6,13 +6,7 @@ from typing import Literal
 from pydantic import SecretStr
 from replicate.client import Client as ReplicateClient

-from backend.data.block import (
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
-)
+from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
 from backend.data.model import (
    APIKeyCredentials,
    CredentialsField,
@@ -60,7 +54,7 @@ class NormalizationStrategy(str, Enum):


 class AIMusicGeneratorBlock(Block):
-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        credentials: CredentialsMetaInput[
            Literal[ProviderName.REPLICATE], Literal["api_key"]
        ] = CredentialsField(
@@ -113,8 +107,9 @@ class AIMusicGeneratorBlock(Block):
            title="Normalization Strategy",
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        result: str = SchemaField(description="URL of the generated audio file")
+        error: str = SchemaField(description="Error message if the model run failed")

    def __init__(self):
        super().__init__(
--- a/autogpt_platform/backend/backend/blocks/ai_shortform_video_block.py
+++ b/autogpt_platform/backend/backend/blocks/ai_shortform_video_block.py
@@ -6,13 +6,7 @@ from typing import Literal

 from pydantic import SecretStr

-from backend.data.block import (
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
-)
+from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
 from backend.data.model import (
    APIKeyCredentials,
    CredentialsField,
@@ -20,7 +14,6 @@ from backend.data.model import (
    SchemaField,
 )
 from backend.integrations.providers import ProviderName
-from backend.util.exceptions import BlockExecutionError
 from backend.util.request import Requests

 TEST_CREDENTIALS = APIKeyCredentials(
@@ -155,7 +148,7 @@ logger = logging.getLogger(__name__)
 class AIShortformVideoCreatorBlock(Block):
    """Creates a short‑form text‑to‑video clip using stock or AI imagery."""

-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        credentials: CredentialsMetaInput[
            Literal[ProviderName.REVID], Literal["api_key"]
        ] = CredentialsField(
@@ -194,8 +187,9 @@ class AIShortformVideoCreatorBlock(Block):
            placeholder=VisualMediaType.STOCK_VIDEOS,
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        video_url: str = SchemaField(description="The URL of the created video")
+        error: str = SchemaField(description="Error message if the request failed")

    async def create_webhook(self) -> tuple[str, str]:
        """Create a new webhook URL for receiving notifications."""
@@ -247,11 +241,7 @@ class AIShortformVideoCreatorBlock(Block):
            await asyncio.sleep(10)

        logger.error("Video creation timed out")
-        raise BlockExecutionError(
-            message="Video creation timed out",
-            block_name=self.name,
-            block_id=self.id,
-        )
+        raise TimeoutError("Video creation timed out")

    def __init__(self):
        super().__init__(
@@ -346,7 +336,7 @@ class AIShortformVideoCreatorBlock(Block):
 class AIAdMakerVideoCreatorBlock(Block):
    """Generates a 30‑second vertical AI advert using optional user‑supplied imagery."""

-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        credentials: CredentialsMetaInput[
            Literal[ProviderName.REVID], Literal["api_key"]
        ] = CredentialsField(
@@ -374,8 +364,9 @@ class AIAdMakerVideoCreatorBlock(Block):
            description="Restrict visuals to supplied images only.", default=True
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        video_url: str = SchemaField(description="URL of the finished advert")
+        error: str = SchemaField(description="Error message on failure")

    async def create_webhook(self) -> tuple[str, str]:
        """Create a new webhook URL for receiving notifications."""
@@ -427,11 +418,7 @@ class AIAdMakerVideoCreatorBlock(Block):
            await asyncio.sleep(10)

        logger.error("Video creation timed out")
-        raise BlockExecutionError(
-            message="Video creation timed out",
-            block_name=self.name,
-            block_id=self.id,
-        )
+        raise TimeoutError("Video creation timed out")

    def __init__(self):
        super().__init__(
@@ -537,7 +524,7 @@ class AIAdMakerVideoCreatorBlock(Block):
 class AIScreenshotToVideoAdBlock(Block):
    """Creates an advert where the supplied screenshot is narrated by an AI avatar."""

-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        credentials: CredentialsMetaInput[
            Literal[ProviderName.REVID], Literal["api_key"]
        ] = CredentialsField(description="Revid.ai API key")
@@ -555,8 +542,9 @@ class AIScreenshotToVideoAdBlock(Block):
            default=AudioTrack.DONT_STOP_ME_ABSTRACT_FUTURE_BASS
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        video_url: str = SchemaField(description="Rendered video URL")
+        error: str = SchemaField(description="Error, if encountered")

    async def create_webhook(self) -> tuple[str, str]:
        """Create a new webhook URL for receiving notifications."""
@@ -608,11 +596,7 @@ class AIScreenshotToVideoAdBlock(Block):
            await asyncio.sleep(10)

        logger.error("Video creation timed out")
-        raise BlockExecutionError(
-            message="Video creation timed out",
-            block_name=self.name,
-            block_id=self.id,
-        )
+        raise TimeoutError("Video creation timed out")

    def __init__(self):
        super().__init__(
--- a/autogpt_platform/backend/backend/blocks/airtable/_api.py
+++ b/autogpt_platform/backend/backend/blocks/airtable/_api.py
@@ -1371,7 +1371,7 @@ async def create_base(
    if tables:
        params["tables"] = tables

-    logger.debug(f"Creating Airtable base with params: {params}")
+    print(params)

    response = await Requests().post(
        "https://api.airtable.com/v0/meta/bases",
--- a/autogpt_platform/backend/backend/blocks/airtable/bases.py
+++ b/autogpt_platform/backend/backend/blocks/airtable/bases.py
@@ -9,8 +9,7 @@ from backend.sdk import (
    Block,
    BlockCategory,
    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
+    BlockSchema,
    CredentialsMetaInput,
    SchemaField,
 )
@@ -24,7 +23,7 @@ class AirtableCreateBaseBlock(Block):
    Creates a new base in an Airtable workspace, or returns existing base if one with the same name exists.
    """

-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        credentials: CredentialsMetaInput = airtable.credentials_field(
            description="Airtable API credentials"
        )
@@ -54,7 +53,7 @@ class AirtableCreateBaseBlock(Block):
            ],
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        base_id: str = SchemaField(description="The ID of the created or found base")
        tables: list[dict] = SchemaField(description="Array of table objects")
        table: dict = SchemaField(description="A single table object")
@@ -119,7 +118,7 @@ class AirtableListBasesBlock(Block):
    Lists all bases in an Airtable workspace that the user has access to.
    """

-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        credentials: CredentialsMetaInput = airtable.credentials_field(
            description="Airtable API credentials"
        )
@@ -130,7 +129,7 @@ class AirtableListBasesBlock(Block):
            description="Pagination offset from previous request", default=""
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        bases: list[dict] = SchemaField(description="Array of base objects")
        offset: Optional[str] = SchemaField(
            description="Offset for next page (null if no more bases)", default=None
--- a/autogpt_platform/backend/backend/blocks/airtable/records.py
+++ b/autogpt_platform/backend/backend/blocks/airtable/records.py
@@ -9,8 +9,7 @@ from backend.sdk import (
    Block,
    BlockCategory,
    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
+    BlockSchema,
    CredentialsMetaInput,
    SchemaField,
 )
@@ -32,7 +31,7 @@ class AirtableListRecordsBlock(Block):
    Lists records from an Airtable table with optional filtering, sorting, and pagination.
    """

-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        credentials: CredentialsMetaInput = airtable.credentials_field(
            description="Airtable API credentials"
        )
@@ -66,7 +65,7 @@ class AirtableListRecordsBlock(Block):
            default=False,
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        records: list[dict] = SchemaField(description="Array of record objects")
        offset: Optional[str] = SchemaField(
            description="Offset for next page (null if no more records)", default=None
@@ -138,7 +137,7 @@ class AirtableGetRecordBlock(Block):
    Retrieves a single record from an Airtable table by its ID.
    """

-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        credentials: CredentialsMetaInput = airtable.credentials_field(
            description="Airtable API credentials"
        )
@@ -154,7 +153,7 @@ class AirtableGetRecordBlock(Block):
            default=False,
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        id: str = SchemaField(description="The record ID")
        fields: dict = SchemaField(description="The record fields")
        created_time: str = SchemaField(description="The record created time")
@@ -218,7 +217,7 @@ class AirtableCreateRecordsBlock(Block):
    Creates one or more records in an Airtable table.
    """

-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        credentials: CredentialsMetaInput = airtable.credentials_field(
            description="Airtable API credentials"
        )
@@ -240,7 +239,7 @@ class AirtableCreateRecordsBlock(Block):
            default=None,
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        records: list[dict] = SchemaField(description="Array of created record objects")
        details: dict = SchemaField(description="Details of the created records")

@@ -291,7 +290,7 @@ class AirtableUpdateRecordsBlock(Block):
    Updates one or more existing records in an Airtable table.
    """

-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        credentials: CredentialsMetaInput = airtable.credentials_field(
            description="Airtable API credentials"
        )
@@ -307,7 +306,7 @@ class AirtableUpdateRecordsBlock(Block):
            default=None,
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        records: list[dict] = SchemaField(description="Array of updated record objects")

    def __init__(self):
@@ -340,7 +339,7 @@ class AirtableDeleteRecordsBlock(Block):
    Deletes one or more records from an Airtable table.
    """

-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        credentials: CredentialsMetaInput = airtable.credentials_field(
            description="Airtable API credentials"
        )
@@ -352,7 +351,7 @@ class AirtableDeleteRecordsBlock(Block):
            description="Array of upto 10 record IDs to delete"
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        records: list[dict] = SchemaField(description="Array of deletion results")

    def __init__(self):
--- a/autogpt_platform/backend/backend/blocks/airtable/schema.py
+++ b/autogpt_platform/backend/backend/blocks/airtable/schema.py
@@ -7,8 +7,7 @@ from backend.sdk import (
    Block,
    BlockCategory,
    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
+    BlockSchema,
    CredentialsMetaInput,
    Requests,
    SchemaField,
@@ -24,13 +23,13 @@ class AirtableListSchemaBlock(Block):
    fields, and views.
    """

-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        credentials: CredentialsMetaInput = airtable.credentials_field(
            description="Airtable API credentials"
        )
        base_id: str = SchemaField(description="The Airtable base ID")

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        base_schema: dict = SchemaField(
            description="Complete base schema with tables, fields, and views"
        )
@@ -67,7 +66,7 @@ class AirtableCreateTableBlock(Block):
    Creates a new table in an Airtable base with specified fields and views.
    """

-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        credentials: CredentialsMetaInput = airtable.credentials_field(
            description="Airtable API credentials"
        )
@@ -78,7 +77,7 @@ class AirtableCreateTableBlock(Block):
            default=[{"name": "Name", "type": "singleLineText"}],
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        table: dict = SchemaField(description="Created table object")
        table_id: str = SchemaField(description="ID of the created table")

@@ -110,7 +109,7 @@ class AirtableUpdateTableBlock(Block):
    Updates an existing table's properties such as name or description.
    """

-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        credentials: CredentialsMetaInput = airtable.credentials_field(
            description="Airtable API credentials"
        )
@@ -126,7 +125,7 @@ class AirtableUpdateTableBlock(Block):
            description="The date dependency of the table to update", default=None
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        table: dict = SchemaField(description="Updated table object")

    def __init__(self):
@@ -158,7 +157,7 @@ class AirtableCreateFieldBlock(Block):
    Adds a new field (column) to an existing Airtable table.
    """

-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        credentials: CredentialsMetaInput = airtable.credentials_field(
            description="Airtable API credentials"
        )
@@ -177,7 +176,7 @@ class AirtableCreateFieldBlock(Block):
            description="The options of the field to create", default=None
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        field: dict = SchemaField(description="Created field object")
        field_id: str = SchemaField(description="ID of the created field")

@@ -210,7 +209,7 @@ class AirtableUpdateFieldBlock(Block):
    Updates an existing field's properties in an Airtable table.
    """

-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        credentials: CredentialsMetaInput = airtable.credentials_field(
            description="Airtable API credentials"
        )
@@ -226,7 +225,7 @@ class AirtableUpdateFieldBlock(Block):
            advanced=False,
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        field: dict = SchemaField(description="Updated field object")

    def __init__(self):
--- a/autogpt_platform/backend/backend/blocks/airtable/triggers.py
+++ b/autogpt_platform/backend/backend/blocks/airtable/triggers.py
@@ -3,8 +3,7 @@ from backend.sdk import (
    Block,
    BlockCategory,
    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
+    BlockSchema,
    BlockType,
    BlockWebhookConfig,
    CredentialsMetaInput,
@@ -33,7 +32,7 @@ class AirtableWebhookTriggerBlock(Block):
    Thin wrapper just forwards the payloads one at a time to the next block.
    """

-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        credentials: CredentialsMetaInput = airtable.credentials_field(
            description="Airtable API credentials"
        )
@@ -44,7 +43,7 @@ class AirtableWebhookTriggerBlock(Block):
            description="Airtable webhook event filter"
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        payload: WebhookPayload = SchemaField(description="Airtable webhook payload")

    def __init__(self):
--- a/autogpt_platform/backend/backend/blocks/apollo/organization.py
+++ b/autogpt_platform/backend/backend/blocks/apollo/organization.py
@@ -10,20 +10,14 @@ from backend.blocks.apollo.models import (
    PrimaryPhone,
    SearchOrganizationsRequest,
 )
-from backend.data.block import (
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
-)
+from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
 from backend.data.model import CredentialsField, SchemaField


 class SearchOrganizationsBlock(Block):
    """Search for organizations in Apollo"""

-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        organization_num_employees_range: list[int] = SchemaField(
            description="""The number range of employees working for the company. This enables you to find companies based on headcount. You can add multiple ranges to expand your search results.

@@ -75,7 +69,7 @@ To find IDs, identify the values for organization_id when you call this endpoint
            description="Apollo credentials",
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        organizations: list[Organization] = SchemaField(
            description="List of organizations found",
            default_factory=list,
--- a/autogpt_platform/backend/backend/blocks/apollo/people.py
+++ b/autogpt_platform/backend/backend/blocks/apollo/people.py
@@ -14,20 +14,14 @@ from backend.blocks.apollo.models import (
    SearchPeopleRequest,
    SenorityLevels,
 )
-from backend.data.block import (
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
-)
+from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
 from backend.data.model import CredentialsField, SchemaField


 class SearchPeopleBlock(Block):
    """Search for people in Apollo"""

-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        person_titles: list[str] = SchemaField(
            description="""Job titles held by the people you want to find. For a person to be included in search results, they only need to match 1 of the job titles you add. Adding more job titles expands your search results.

@@ -115,7 +109,7 @@ class SearchPeopleBlock(Block):
            description="Apollo credentials",
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        people: list[Contact] = SchemaField(
            description="List of people found",
            default_factory=list,
--- a/autogpt_platform/backend/backend/blocks/apollo/person.py
+++ b/autogpt_platform/backend/backend/blocks/apollo/person.py
@@ -6,20 +6,14 @@ from backend.blocks.apollo._auth import (
    ApolloCredentialsInput,
 )
 from backend.blocks.apollo.models import Contact, EnrichPersonRequest
-from backend.data.block import (
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
-)
+from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
 from backend.data.model import CredentialsField, SchemaField


 class GetPersonDetailBlock(Block):
    """Get detailed person data with Apollo API, including email reveal"""

-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        person_id: str = SchemaField(
            description="Apollo person ID to enrich (most accurate method)",
            default="",
@@ -74,7 +68,7 @@ class GetPersonDetailBlock(Block):
            description="Apollo credentials",
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        contact: Contact = SchemaField(
            description="Enriched contact information",
        )
--- a/autogpt_platform/backend/backend/blocks/ayrshare/_util.py
+++ b/autogpt_platform/backend/backend/blocks/ayrshare/_util.py
@@ -3,7 +3,7 @@ from typing import Optional

 from pydantic import BaseModel, Field

-from backend.data.block import BlockSchemaInput
+from backend.data.block import BlockSchema
 from backend.data.model import SchemaField, UserIntegrations
 from backend.integrations.ayrshare import AyrshareClient
 from backend.util.clients import get_database_manager_async_client
@@ -17,7 +17,7 @@ async def get_profile_key(user_id: str):
    return user_integrations.managed_credentials.ayrshare_profile_key


-class BaseAyrshareInput(BlockSchemaInput):
+class BaseAyrshareInput(BlockSchema):
    """Base input model for Ayrshare social media posts with common fields."""

    post: str = SchemaField(
--- a/autogpt_platform/backend/backend/blocks/ayrshare/post_to_bluesky.py
+++ b/autogpt_platform/backend/backend/blocks/ayrshare/post_to_bluesky.py
@@ -3,7 +3,7 @@ from backend.sdk import (
    Block,
    BlockCategory,
    BlockOutput,
-    BlockSchemaOutput,
+    BlockSchema,
    BlockType,
    SchemaField,
 )
@@ -38,7 +38,7 @@ class PostToBlueskyBlock(Block):
            advanced=True,
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        post_result: PostResponse = SchemaField(description="The result of the post")
        post: PostIds = SchemaField(description="The result of the post")

--- a/autogpt_platform/backend/backend/blocks/ayrshare/post_to_facebook.py
+++ b/autogpt_platform/backend/backend/blocks/ayrshare/post_to_facebook.py
@@ -3,7 +3,7 @@ from backend.sdk import (
    Block,
    BlockCategory,
    BlockOutput,
-    BlockSchemaOutput,
+    BlockSchema,
    BlockType,
    SchemaField,
 )
@@ -101,7 +101,7 @@ class PostToFacebookBlock(Block):
            description="URL for custom link preview", default="", advanced=True
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        post_result: PostResponse = SchemaField(description="The result of the post")
        post: PostIds = SchemaField(description="The result of the post")

--- a/autogpt_platform/backend/backend/blocks/ayrshare/post_to_gmb.py
+++ b/autogpt_platform/backend/backend/blocks/ayrshare/post_to_gmb.py
@@ -3,7 +3,7 @@ from backend.sdk import (
    Block,
    BlockCategory,
    BlockOutput,
-    BlockSchemaOutput,
+    BlockSchema,
    BlockType,
    SchemaField,
 )
@@ -94,7 +94,7 @@ class PostToGMBBlock(Block):
            advanced=True,
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        post_result: PostResponse = SchemaField(description="The result of the post")
        post: PostIds = SchemaField(description="The result of the post")

--- a/autogpt_platform/backend/backend/blocks/ayrshare/post_to_instagram.py
+++ b/autogpt_platform/backend/backend/blocks/ayrshare/post_to_instagram.py
@@ -5,7 +5,7 @@ from backend.sdk import (
    Block,
    BlockCategory,
    BlockOutput,
-    BlockSchemaOutput,
+    BlockSchema,
    BlockType,
    SchemaField,
 )
@@ -94,7 +94,7 @@ class PostToInstagramBlock(Block):
            advanced=True,
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        post_result: PostResponse = SchemaField(description="The result of the post")
        post: PostIds = SchemaField(description="The result of the post")

--- a/autogpt_platform/backend/backend/blocks/ayrshare/post_to_linkedin.py
+++ b/autogpt_platform/backend/backend/blocks/ayrshare/post_to_linkedin.py
@@ -3,7 +3,7 @@ from backend.sdk import (
    Block,
    BlockCategory,
    BlockOutput,
-    BlockSchemaOutput,
+    BlockSchema,
    BlockType,
    SchemaField,
 )
@@ -94,7 +94,7 @@ class PostToLinkedInBlock(Block):
            advanced=True,
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        post_result: PostResponse = SchemaField(description="The result of the post")
        post: PostIds = SchemaField(description="The result of the post")

--- a/autogpt_platform/backend/backend/blocks/ayrshare/post_to_pinterest.py
+++ b/autogpt_platform/backend/backend/blocks/ayrshare/post_to_pinterest.py
@@ -3,7 +3,7 @@ from backend.sdk import (
    Block,
    BlockCategory,
    BlockOutput,
-    BlockSchemaOutput,
+    BlockSchema,
    BlockType,
    SchemaField,
 )
@@ -73,7 +73,7 @@ class PostToPinterestBlock(Block):
            advanced=True,
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        post_result: PostResponse = SchemaField(description="The result of the post")
        post: PostIds = SchemaField(description="The result of the post")

--- a/autogpt_platform/backend/backend/blocks/ayrshare/post_to_reddit.py
+++ b/autogpt_platform/backend/backend/blocks/ayrshare/post_to_reddit.py
@@ -3,7 +3,7 @@ from backend.sdk import (
    Block,
    BlockCategory,
    BlockOutput,
-    BlockSchemaOutput,
+    BlockSchema,
    BlockType,
    SchemaField,
 )
@@ -19,7 +19,7 @@ class PostToRedditBlock(Block):

        pass  # Uses all base fields

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        post_result: PostResponse = SchemaField(description="The result of the post")
        post: PostIds = SchemaField(description="The result of the post")

--- a/autogpt_platform/backend/backend/blocks/ayrshare/post_to_snapchat.py
+++ b/autogpt_platform/backend/backend/blocks/ayrshare/post_to_snapchat.py
@@ -3,7 +3,7 @@ from backend.sdk import (
    Block,
    BlockCategory,
    BlockOutput,
-    BlockSchemaOutput,
+    BlockSchema,
    BlockType,
    SchemaField,
 )
@@ -43,7 +43,7 @@ class PostToSnapchatBlock(Block):
            advanced=True,
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        post_result: PostResponse = SchemaField(description="The result of the post")
        post: PostIds = SchemaField(description="The result of the post")

--- a/autogpt_platform/backend/backend/blocks/ayrshare/post_to_telegram.py
+++ b/autogpt_platform/backend/backend/blocks/ayrshare/post_to_telegram.py
@@ -3,7 +3,7 @@ from backend.sdk import (
    Block,
    BlockCategory,
    BlockOutput,
-    BlockSchemaOutput,
+    BlockSchema,
    BlockType,
    SchemaField,
 )
@@ -38,7 +38,7 @@ class PostToTelegramBlock(Block):
            advanced=True,
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        post_result: PostResponse = SchemaField(description="The result of the post")
        post: PostIds = SchemaField(description="The result of the post")

--- a/autogpt_platform/backend/backend/blocks/ayrshare/post_to_threads.py
+++ b/autogpt_platform/backend/backend/blocks/ayrshare/post_to_threads.py
@@ -3,7 +3,7 @@ from backend.sdk import (
    Block,
    BlockCategory,
    BlockOutput,
-    BlockSchemaOutput,
+    BlockSchema,
    BlockType,
    SchemaField,
 )
@@ -31,7 +31,7 @@ class PostToThreadsBlock(Block):
            advanced=False,
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        post_result: PostResponse = SchemaField(description="The result of the post")
        post: PostIds = SchemaField(description="The result of the post")

--- a/autogpt_platform/backend/backend/blocks/ayrshare/post_to_tiktok.py
+++ b/autogpt_platform/backend/backend/blocks/ayrshare/post_to_tiktok.py
@@ -5,7 +5,7 @@ from backend.sdk import (
    Block,
    BlockCategory,
    BlockOutput,
-    BlockSchemaOutput,
+    BlockSchema,
    BlockType,
    SchemaField,
 )
@@ -98,7 +98,7 @@ class PostToTikTokBlock(Block):
            advanced=True,
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        post_result: PostResponse = SchemaField(description="The result of the post")
        post: PostIds = SchemaField(description="The result of the post")

--- a/autogpt_platform/backend/backend/blocks/ayrshare/post_to_x.py
+++ b/autogpt_platform/backend/backend/blocks/ayrshare/post_to_x.py
@@ -3,7 +3,7 @@ from backend.sdk import (
    Block,
    BlockCategory,
    BlockOutput,
-    BlockSchemaOutput,
+    BlockSchema,
    BlockType,
    SchemaField,
 )
@@ -97,7 +97,7 @@ class PostToXBlock(Block):
            advanced=True,
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        post_result: PostResponse = SchemaField(description="The result of the post")
        post: PostIds = SchemaField(description="The result of the post")

--- a/autogpt_platform/backend/backend/blocks/ayrshare/post_to_youtube.py
+++ b/autogpt_platform/backend/backend/blocks/ayrshare/post_to_youtube.py
@@ -6,7 +6,7 @@ from backend.sdk import (
    Block,
    BlockCategory,
    BlockOutput,
-    BlockSchemaOutput,
+    BlockSchema,
    BlockType,
    SchemaField,
 )
@@ -119,7 +119,7 @@ class PostToYouTubeBlock(Block):
            advanced=True,
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        post_result: PostResponse = SchemaField(description="The result of the post")
        post: PostIds = SchemaField(description="The result of the post")

--- a/autogpt_platform/backend/backend/blocks/baas/bots.py
+++ b/autogpt_platform/backend/backend/blocks/baas/bots.py
@@ -9,8 +9,7 @@ from backend.sdk import (
    Block,
    BlockCategory,
    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
+    BlockSchema,
    CredentialsMetaInput,
    SchemaField,
 )
@@ -24,7 +23,7 @@ class BaasBotJoinMeetingBlock(Block):
    Deploy a bot immediately or at a scheduled start_time to join and record a meeting.
    """

-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        credentials: CredentialsMetaInput = baas.credentials_field(
            description="Meeting BaaS API credentials"
        )
@@ -58,7 +57,7 @@ class BaasBotJoinMeetingBlock(Block):
            description="Custom metadata to attach to the bot", default={}
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        bot_id: str = SchemaField(description="UUID of the deployed bot")
        join_response: dict = SchemaField(
            description="Full response from join operation"
@@ -104,13 +103,13 @@ class BaasBotLeaveMeetingBlock(Block):
    Force the bot to exit the call.
    """

-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        credentials: CredentialsMetaInput = baas.credentials_field(
            description="Meeting BaaS API credentials"
        )
        bot_id: str = SchemaField(description="UUID of the bot to remove from meeting")

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        left: bool = SchemaField(description="Whether the bot successfully left")

    def __init__(self):
@@ -139,7 +138,7 @@ class BaasBotFetchMeetingDataBlock(Block):
    Pull MP4 URL, transcript & metadata for a completed meeting.
    """

-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        credentials: CredentialsMetaInput = baas.credentials_field(
            description="Meeting BaaS API credentials"
        )
@@ -148,7 +147,7 @@ class BaasBotFetchMeetingDataBlock(Block):
            description="Include transcript data in response", default=True
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        mp4_url: str = SchemaField(
            description="URL to download the meeting recording (time-limited)"
        )
@@ -186,13 +185,13 @@ class BaasBotDeleteRecordingBlock(Block):
    Purge MP4 + transcript data for privacy or storage management.
    """

-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        credentials: CredentialsMetaInput = baas.credentials_field(
            description="Meeting BaaS API credentials"
        )
        bot_id: str = SchemaField(description="UUID of the bot whose data to delete")

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        deleted: bool = SchemaField(
            description="Whether the data was successfully deleted"
        )
--- a/autogpt_platform/backend/backend/blocks/bannerbear/text_overlay.py
+++ b/autogpt_platform/backend/backend/blocks/bannerbear/text_overlay.py
@@ -11,8 +11,7 @@ from backend.sdk import (
    Block,
    BlockCategory,
    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
+    BlockSchema,
    CredentialsMetaInput,
    Requests,
    SchemaField,
@@ -28,7 +27,7 @@ TEST_CREDENTIALS = APIKeyCredentials(
 )


-class TextModification(BlockSchemaInput):
+class TextModification(BlockSchema):
    name: str = SchemaField(
        description="The name of the layer to modify in the template"
    )
@@ -61,7 +60,7 @@ class TextModification(BlockSchemaInput):


 class BannerbearTextOverlayBlock(Block):
-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        credentials: CredentialsMetaInput = bannerbear.credentials_field(
            description="API credentials for Bannerbear"
        )
@@ -97,7 +96,7 @@ class BannerbearTextOverlayBlock(Block):
            advanced=True,
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        success: bool = SchemaField(
            description="Whether the image generation was successfully initiated"
        )
@@ -106,6 +105,7 @@ class BannerbearTextOverlayBlock(Block):
        )
        uid: str = SchemaField(description="Unique identifier for the generated image")
        status: str = SchemaField(description="Status of the image generation")
+        error: str = SchemaField(description="Error message if the operation failed")

    def __init__(self):
        super().__init__(
--- a/autogpt_platform/backend/backend/blocks/basic.py
+++ b/autogpt_platform/backend/backend/blocks/basic.py
@@ -1,21 +1,14 @@
 import enum
 from typing import Any

-from backend.data.block import (
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
-    BlockType,
-)
+from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema, BlockType
 from backend.data.model import SchemaField
 from backend.util.file import store_media_file
 from backend.util.type import MediaFileType, convert


 class FileStoreBlock(Block):
-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        file_in: MediaFileType = SchemaField(
            description="The file to store in the temporary directory, it can be a URL, data URI, or local path."
        )
@@ -26,7 +19,7 @@ class FileStoreBlock(Block):
            title="Produce Base64 Output",
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        file_out: MediaFileType = SchemaField(
            description="The relative path to the stored file in the temporary directory."
        )
@@ -64,7 +57,7 @@ class StoreValueBlock(Block):
    The block output will be static, the output can be consumed multiple times.
    """

-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        input: Any = SchemaField(
            description="Trigger the block to produce the output. "
            "The value is only used when `data` is None."
@@ -75,7 +68,7 @@ class StoreValueBlock(Block):
            default=None,
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        output: Any = SchemaField(description="The stored data retained in the block.")

    def __init__(self):
@@ -101,10 +94,10 @@ class StoreValueBlock(Block):


 class PrintToConsoleBlock(Block):
-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        text: Any = SchemaField(description="The data to print to the console.")

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        output: Any = SchemaField(description="The data printed to the console.")
        status: str = SchemaField(description="The status of the print operation.")

@@ -128,10 +121,10 @@ class PrintToConsoleBlock(Block):


 class NoteBlock(Block):
-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        text: str = SchemaField(description="The text to display in the sticky note.")

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        output: str = SchemaField(description="The text to display in the sticky note.")

    def __init__(self):
@@ -161,14 +154,15 @@ class TypeOptions(enum.Enum):


 class UniversalTypeConverterBlock(Block):
-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        value: Any = SchemaField(
            description="The value to convert to a universal type."
        )
        type: TypeOptions = SchemaField(description="The type to convert the value to.")

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        value: Any = SchemaField(description="The converted value.")
+        error: str = SchemaField(description="Error message if conversion failed.")

    def __init__(self):
        super().__init__(
@@ -201,10 +195,10 @@ class ReverseListOrderBlock(Block):
    A block which takes in a list and returns it in the opposite order.
    """

-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        input_list: list[Any] = SchemaField(description="The list to reverse")

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        reversed_list: list[Any] = SchemaField(description="The list in reversed order")

    def __init__(self):
--- a/autogpt_platform/backend/backend/blocks/block.py
+++ b/autogpt_platform/backend/backend/blocks/block.py
@@ -2,13 +2,7 @@ import os
 import re
 from typing import Type

-from backend.data.block import (
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
-)
+from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
 from backend.data.model import SchemaField


@@ -21,12 +15,12 @@ class BlockInstallationBlock(Block):
        for development purposes only.
    """

-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        code: str = SchemaField(
            description="Python code of the block to be installed",
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        success: str = SchemaField(
            description="Success message if the block is installed successfully",
        )
--- a/autogpt_platform/backend/backend/blocks/branching.py
+++ b/autogpt_platform/backend/backend/blocks/branching.py
@@ -1,13 +1,7 @@
 from enum import Enum
 from typing import Any

-from backend.data.block import (
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
-)
+from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
 from backend.data.model import SchemaField
 from backend.util.type import convert

@@ -22,7 +16,7 @@ class ComparisonOperator(Enum):


 class ConditionBlock(Block):
-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        value1: Any = SchemaField(
            description="Enter the first value for comparison",
            placeholder="For example: 10 or 'hello' or True",
@@ -46,7 +40,7 @@ class ConditionBlock(Block):
            default=None,
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        result: bool = SchemaField(
            description="The result of the condition evaluation (True or False)"
        )
@@ -106,10 +100,7 @@ class ConditionBlock(Block):
            ComparisonOperator.LESS_THAN_OR_EQUAL: lambda a, b: a <= b,
        }

-        try:
-            result = comparison_funcs[operator](value1, value2)
-        except Exception as e:
-            raise ValueError(f"Comparison failed: {e}") from e
+        result = comparison_funcs[operator](value1, value2)

        yield "result", result

@@ -120,7 +111,7 @@ class ConditionBlock(Block):


 class IfInputMatchesBlock(Block):
-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        input: Any = SchemaField(
            description="The input to match against",
            placeholder="For example: 10 or 'hello' or True",
@@ -140,7 +131,7 @@ class IfInputMatchesBlock(Block):
            default=None,
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        result: bool = SchemaField(
            description="The result of the condition evaluation (True or False)"
        )
--- a/autogpt_platform/backend/backend/blocks/code_executor.py
+++ b/autogpt_platform/backend/backend/blocks/code_executor.py
@@ -4,15 +4,9 @@ from typing import Any, Literal, Optional
 from e2b_code_interpreter import AsyncSandbox
 from e2b_code_interpreter import Result as E2BExecutionResult
 from e2b_code_interpreter.charts import Chart as E2BExecutionResultChart
-from pydantic import BaseModel, Field, JsonValue, SecretStr
+from pydantic import BaseModel, JsonValue, SecretStr

-from backend.data.block import (
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
-)
+from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
 from backend.data.model import (
    APIKeyCredentials,
    CredentialsField,
@@ -67,7 +61,7 @@ class MainCodeExecutionResult(BaseModel):
    jpeg: Optional[str] = None
    pdf: Optional[str] = None
    latex: Optional[str] = None
-    json_data: Optional[JsonValue] = Field(None, alias="json")
+    json: Optional[JsonValue] = None  # type: ignore (reportIncompatibleMethodOverride)
    javascript: Optional[str] = None
    data: Optional[dict] = None
    chart: Optional[Chart] = None
@@ -165,7 +159,7 @@ class ExecuteCodeBlock(Block, BaseE2BExecutorMixin):
    # TODO : Add support to upload and download files
    # NOTE: Currently, you can only customize the CPU and Memory
    #       by creating a pre customized sandbox template
-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        credentials: CredentialsMetaInput[
            Literal[ProviderName.E2B], Literal["api_key"]
        ] = CredentialsField(
@@ -223,7 +217,7 @@ class ExecuteCodeBlock(Block, BaseE2BExecutorMixin):
            advanced=True,
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        main_result: MainCodeExecutionResult = SchemaField(
            title="Main Result", description="The main result from the code execution"
        )
@@ -238,6 +232,7 @@ class ExecuteCodeBlock(Block, BaseE2BExecutorMixin):
            description="Standard output logs from execution"
        )
        stderr_logs: str = SchemaField(description="Standard error logs from execution")
+        error: str = SchemaField(description="Error message if execution failed")

    def __init__(self):
        super().__init__(
@@ -301,7 +296,7 @@ class ExecuteCodeBlock(Block, BaseE2BExecutorMixin):


 class InstantiateCodeSandboxBlock(Block, BaseE2BExecutorMixin):
-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        credentials: CredentialsMetaInput[
            Literal[ProviderName.E2B], Literal["api_key"]
        ] = CredentialsField(
@@ -351,7 +346,7 @@ class InstantiateCodeSandboxBlock(Block, BaseE2BExecutorMixin):
            advanced=True,
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        sandbox_id: str = SchemaField(description="ID of the sandbox instance")
        response: str = SchemaField(
            title="Text Result",
@@ -361,6 +356,7 @@ class InstantiateCodeSandboxBlock(Block, BaseE2BExecutorMixin):
            description="Standard output logs from execution"
        )
        stderr_logs: str = SchemaField(description="Standard error logs from execution")
+        error: str = SchemaField(description="Error message if execution failed")

    def __init__(self):
        super().__init__(
@@ -425,7 +421,7 @@ class InstantiateCodeSandboxBlock(Block, BaseE2BExecutorMixin):


 class ExecuteCodeStepBlock(Block, BaseE2BExecutorMixin):
-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        credentials: CredentialsMetaInput[
            Literal[ProviderName.E2B], Literal["api_key"]
        ] = CredentialsField(
@@ -458,7 +454,7 @@ class ExecuteCodeStepBlock(Block, BaseE2BExecutorMixin):
            default=False,
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        main_result: MainCodeExecutionResult = SchemaField(
            title="Main Result", description="The main result from the code execution"
        )
@@ -473,6 +469,7 @@ class ExecuteCodeStepBlock(Block, BaseE2BExecutorMixin):
            description="Standard output logs from execution"
        )
        stderr_logs: str = SchemaField(description="Standard error logs from execution")
+        error: str = SchemaField(description="Error message if execution failed")

    def __init__(self):
        super().__init__(
--- a/autogpt_platform/backend/backend/blocks/code_extraction_block.py
+++ b/autogpt_platform/backend/backend/blocks/code_extraction_block.py
@@ -1,23 +1,17 @@
 import re

-from backend.data.block import (
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
-)
+from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
 from backend.data.model import SchemaField


 class CodeExtractionBlock(Block):
-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        text: str = SchemaField(
            description="Text containing code blocks to extract (e.g., AI response)",
            placeholder="Enter text containing code blocks",
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        html: str = SchemaField(description="Extracted HTML code")
        css: str = SchemaField(description="Extracted CSS code")
        javascript: str = SchemaField(description="Extracted JavaScript code")
--- a/autogpt_platform/backend/backend/blocks/codex.py
+++ b/autogpt_platform/backend/backend/blocks/codex.py
@@ -1,224 +0,0 @@
-from dataclasses import dataclass
-from enum import Enum
-from typing import Any, Literal
-
-from openai import AsyncOpenAI
-from openai.types.responses import Response as OpenAIResponse
-from pydantic import SecretStr
-
-from backend.data.block import (
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
-)
-from backend.data.model import (
-    APIKeyCredentials,
-    CredentialsField,
-    CredentialsMetaInput,
-    NodeExecutionStats,
-    SchemaField,
-)
-from backend.integrations.providers import ProviderName
-
-
-@dataclass
-class CodexCallResult:
-    """Structured response returned by Codex invocations."""
-
-    response: str
-    reasoning: str
-    response_id: str
-
-
-class CodexModel(str, Enum):
-    """Codex-capable OpenAI models."""
-
-    GPT5_1_CODEX = "gpt-5.1-codex"
-
-
-class CodexReasoningEffort(str, Enum):
-    """Configuration for the Responses API reasoning effort."""
-
-    NONE = "none"
-    LOW = "low"
-    MEDIUM = "medium"
-    HIGH = "high"
-
-
-CodexCredentials = CredentialsMetaInput[
-    Literal[ProviderName.OPENAI], Literal["api_key"]
-]
-
-TEST_CREDENTIALS = APIKeyCredentials(
-    id="e2fcb203-3f2d-4ad4-a344-8df3bc7db36b",
-    provider="openai",
-    api_key=SecretStr("mock-openai-api-key"),
-    title="Mock OpenAI API key",
-    expires_at=None,
-)
-TEST_CREDENTIALS_INPUT = {
-    "provider": TEST_CREDENTIALS.provider,
-    "id": TEST_CREDENTIALS.id,
-    "type": TEST_CREDENTIALS.type,
-    "title": TEST_CREDENTIALS.title,
-}
-
-
-def CodexCredentialsField() -> CodexCredentials:
-    return CredentialsField(
-        description="OpenAI API key with access to Codex models (Responses API).",
-    )
-
-
-class CodeGenerationBlock(Block):
-    """Block that talks to Codex models via the OpenAI Responses API."""
-
-    class Input(BlockSchemaInput):
-        prompt: str = SchemaField(
-            description="Primary coding request passed to the Codex model.",
-            placeholder="Generate a Python function that reverses a list.",
-        )
-        system_prompt: str = SchemaField(
-            title="System Prompt",
-            default=(
-                "You are Codex, an elite software engineer. "
-                "Favor concise, working code and highlight important caveats."
-            ),
-            description="Optional instructions injected via the Responses API instructions field.",
-            advanced=True,
-        )
-        model: CodexModel = SchemaField(
-            title="Codex Model",
-            default=CodexModel.GPT5_1_CODEX,
-            description="Codex-optimized model served via the Responses API.",
-            advanced=False,
-        )
-        reasoning_effort: CodexReasoningEffort = SchemaField(
-            title="Reasoning Effort",
-            default=CodexReasoningEffort.MEDIUM,
-            description="Controls the Responses API reasoning budget. Select 'none' to skip reasoning configs.",
-            advanced=True,
-        )
-        max_output_tokens: int | None = SchemaField(
-            title="Max Output Tokens",
-            default=2048,
-            description="Upper bound for generated tokens (hard limit 128,000). Leave blank to let OpenAI decide.",
-            advanced=True,
-        )
-        credentials: CodexCredentials = CodexCredentialsField()
-
-    class Output(BlockSchemaOutput):
-        response: str = SchemaField(
-            description="Code-focused response returned by the Codex model."
-        )
-        reasoning: str = SchemaField(
-            description="Reasoning summary returned by the model, if available.",
-            default="",
-        )
-        response_id: str = SchemaField(
-            description="ID of the Responses API call for auditing/debugging.",
-            default="",
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="86a2a099-30df-47b4-b7e4-34ae5f83e0d5",
-            description="Generate or refactor code using OpenAI's Codex (Responses API).",
-            categories={BlockCategory.AI, BlockCategory.DEVELOPER_TOOLS},
-            input_schema=CodeGenerationBlock.Input,
-            output_schema=CodeGenerationBlock.Output,
-            test_input=[
-                {
-                    "prompt": "Write a TypeScript function that deduplicates an array.",
-                    "credentials": TEST_CREDENTIALS_INPUT,
-                }
-            ],
-            test_output=[
-                ("response", str),
-                ("reasoning", str),
-                ("response_id", str),
-            ],
-            test_mock={
-                "call_codex": lambda *_args, **_kwargs: CodexCallResult(
-                    response="function dedupe<T>(items: T[]): T[] { return [...new Set(items)]; }",
-                    reasoning="Used Set to remove duplicates in O(n).",
-                    response_id="resp_test",
-                )
-            },
-            test_credentials=TEST_CREDENTIALS,
-        )
-        self.execution_stats = NodeExecutionStats()
-
-    async def call_codex(
-        self,
-        *,
-        credentials: APIKeyCredentials,
-        model: CodexModel,
-        prompt: str,
-        system_prompt: str,
-        max_output_tokens: int | None,
-        reasoning_effort: CodexReasoningEffort,
-    ) -> CodexCallResult:
-        """Invoke the OpenAI Responses API."""
-        client = AsyncOpenAI(api_key=credentials.api_key.get_secret_value())
-
-        request_payload: dict[str, Any] = {
-            "model": model.value,
-            "input": prompt,
-        }
-        if system_prompt:
-            request_payload["instructions"] = system_prompt
-        if max_output_tokens is not None:
-            request_payload["max_output_tokens"] = max_output_tokens
-        if reasoning_effort != CodexReasoningEffort.NONE:
-            request_payload["reasoning"] = {"effort": reasoning_effort.value}
-
-        response = await client.responses.create(**request_payload)
-        if not isinstance(response, OpenAIResponse):
-            raise TypeError(f"Expected OpenAIResponse, got {type(response).__name__}")
-
-        # Extract data directly from typed response
-        text_output = response.output_text or ""
-        reasoning_summary = (
-            str(response.reasoning.summary)
-            if response.reasoning and response.reasoning.summary
-            else ""
-        )
-        response_id = response.id or ""
-
-        # Update usage stats
-        self.execution_stats.input_token_count = (
-            response.usage.input_tokens if response.usage else 0
-        )
-        self.execution_stats.output_token_count = (
-            response.usage.output_tokens if response.usage else 0
-        )
-        self.execution_stats.llm_call_count += 1
-
-        return CodexCallResult(
-            response=text_output,
-            reasoning=reasoning_summary,
-            response_id=response_id,
-        )
-
-    async def run(
-        self,
-        input_data: Input,
-        *,
-        credentials: APIKeyCredentials,
-        **_kwargs,
-    ) -> BlockOutput:
-        result = await self.call_codex(
-            credentials=credentials,
-            model=input_data.model,
-            prompt=input_data.prompt,
-            system_prompt=input_data.system_prompt,
-            max_output_tokens=input_data.max_output_tokens,
-            reasoning_effort=input_data.reasoning_effort,
-        )
-
-        yield "response", result.response
-        yield "reasoning", result.reasoning
-        yield "response_id", result.response_id
--- a/autogpt_platform/backend/backend/blocks/compass/triggers.py
+++ b/autogpt_platform/backend/backend/blocks/compass/triggers.py
@@ -5,8 +5,7 @@ from backend.data.block import (
    BlockCategory,
    BlockManualWebhookConfig,
    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
+    BlockSchema,
 )
 from backend.data.model import SchemaField
 from backend.integrations.providers import ProviderName
@@ -28,10 +27,10 @@ class TranscriptionDataModel(BaseModel):


 class CompassAITriggerBlock(Block):
-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        payload: TranscriptionDataModel = SchemaField(hidden=True)

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        transcription: str = SchemaField(
            description="The contents of the compass transcription."
        )
--- a/autogpt_platform/backend/backend/blocks/count_words_and_char_block.py
+++ b/autogpt_platform/backend/backend/blocks/count_words_and_char_block.py
@@ -1,22 +1,16 @@
-from backend.data.block import (
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
-)
+from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
 from backend.data.model import SchemaField


 class WordCharacterCountBlock(Block):
-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        text: str = SchemaField(
            description="Input text to count words and characters",
            placeholder="Enter your text here",
            advanced=False,
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        word_count: int = SchemaField(description="Number of words in the input text")
        character_count: int = SchemaField(
            description="Number of characters in the input text"
--- a/autogpt_platform/backend/backend/blocks/data_manipulation.py
+++ b/autogpt_platform/backend/backend/blocks/data_manipulation.py
@@ -1,12 +1,6 @@
 from typing import Any, List

-from backend.data.block import (
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
-)
+from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
 from backend.data.model import SchemaField
 from backend.util.json import loads
 from backend.util.mock import MockObject
@@ -18,13 +12,13 @@ from backend.util.prompt import estimate_token_count_str


 class CreateDictionaryBlock(Block):
-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        values: dict[str, Any] = SchemaField(
            description="Key-value pairs to create the dictionary with",
            placeholder="e.g., {'name': 'Alice', 'age': 25}",
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        dictionary: dict[str, Any] = SchemaField(
            description="The created dictionary containing the specified key-value pairs"
        )
@@ -68,7 +62,7 @@ class CreateDictionaryBlock(Block):


 class AddToDictionaryBlock(Block):
-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        dictionary: dict[Any, Any] = SchemaField(
            default_factory=dict,
            description="The dictionary to add the entry to. If not provided, a new dictionary will be created.",
@@ -92,10 +86,11 @@ class AddToDictionaryBlock(Block):
            advanced=True,
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        updated_dictionary: dict = SchemaField(
            description="The dictionary with the new entry added."
        )
+        error: str = SchemaField(description="Error message if the operation failed.")

    def __init__(self):
        super().__init__(
@@ -146,11 +141,11 @@ class AddToDictionaryBlock(Block):


 class FindInDictionaryBlock(Block):
-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        input: Any = SchemaField(description="Dictionary to lookup from")
        key: str | int = SchemaField(description="Key to lookup in the dictionary")

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        output: Any = SchemaField(description="Value found for the given key")
        missing: Any = SchemaField(
            description="Value of the input that missing the key"
@@ -206,7 +201,7 @@ class FindInDictionaryBlock(Block):


 class RemoveFromDictionaryBlock(Block):
-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        dictionary: dict[Any, Any] = SchemaField(
            description="The dictionary to modify."
        )
@@ -215,11 +210,12 @@ class RemoveFromDictionaryBlock(Block):
            default=False, description="Whether to return the removed value."
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        updated_dictionary: dict[Any, Any] = SchemaField(
            description="The dictionary after removal."
        )
        removed_value: Any = SchemaField(description="The removed value if requested.")
+        error: str = SchemaField(description="Error message if the operation failed.")

    def __init__(self):
        super().__init__(
@@ -255,18 +251,19 @@ class RemoveFromDictionaryBlock(Block):


 class ReplaceDictionaryValueBlock(Block):
-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        dictionary: dict[Any, Any] = SchemaField(
            description="The dictionary to modify."
        )
        key: str | int = SchemaField(description="Key to replace the value for.")
        value: Any = SchemaField(description="The new value for the given key.")

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        updated_dictionary: dict[Any, Any] = SchemaField(
            description="The dictionary after replacement."
        )
        old_value: Any = SchemaField(description="The value that was replaced.")
+        error: str = SchemaField(description="Error message if the operation failed.")

    def __init__(self):
        super().__init__(
@@ -303,10 +300,10 @@ class ReplaceDictionaryValueBlock(Block):


 class DictionaryIsEmptyBlock(Block):
-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        dictionary: dict[Any, Any] = SchemaField(description="The dictionary to check.")

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        is_empty: bool = SchemaField(description="True if the dictionary is empty.")

    def __init__(self):
@@ -330,7 +327,7 @@ class DictionaryIsEmptyBlock(Block):


 class CreateListBlock(Block):
-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        values: List[Any] = SchemaField(
            description="A list of values to be combined into a new list.",
            placeholder="e.g., ['Alice', 25, True]",
@@ -346,10 +343,11 @@ class CreateListBlock(Block):
            advanced=True,
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        list: List[Any] = SchemaField(
            description="The created list containing the specified values."
        )
+        error: str = SchemaField(description="Error message if list creation failed.")

    def __init__(self):
        super().__init__(
@@ -406,7 +404,7 @@ class CreateListBlock(Block):


 class AddToListBlock(Block):
-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        list: List[Any] = SchemaField(
            default_factory=list,
            advanced=False,
@@ -427,10 +425,11 @@ class AddToListBlock(Block):
            description="The position to insert the new entry. If not provided, the entry will be appended to the end of the list.",
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        updated_list: List[Any] = SchemaField(
            description="The list with the new entry added."
        )
+        error: str = SchemaField(description="Error message if the operation failed.")

    def __init__(self):
        super().__init__(
@@ -485,11 +484,11 @@ class AddToListBlock(Block):


 class FindInListBlock(Block):
-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        list: List[Any] = SchemaField(description="The list to search in.")
        value: Any = SchemaField(description="The value to search for.")

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        index: int = SchemaField(description="The index of the value in the list.")
        found: bool = SchemaField(
            description="Whether the value was found in the list."
@@ -527,14 +526,15 @@ class FindInListBlock(Block):


 class GetListItemBlock(Block):
-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        list: List[Any] = SchemaField(description="The list to get the item from.")
        index: int = SchemaField(
            description="The 0-based index of the item (supports negative indices)."
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        item: Any = SchemaField(description="The item at the specified index.")
+        error: str = SchemaField(description="Error message if the operation failed.")

    def __init__(self):
        super().__init__(
@@ -561,7 +561,7 @@ class GetListItemBlock(Block):


 class RemoveFromListBlock(Block):
-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        list: List[Any] = SchemaField(description="The list to modify.")
        value: Any = SchemaField(
            default=None, description="Value to remove from the list."
@@ -574,9 +574,10 @@ class RemoveFromListBlock(Block):
            default=False, description="Whether to return the removed item."
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        updated_list: List[Any] = SchemaField(description="The list after removal.")
        removed_item: Any = SchemaField(description="The removed item if requested.")
+        error: str = SchemaField(description="Error message if the operation failed.")

    def __init__(self):
        super().__init__(
@@ -617,16 +618,17 @@ class RemoveFromListBlock(Block):


 class ReplaceListItemBlock(Block):
-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        list: List[Any] = SchemaField(description="The list to modify.")
        index: int = SchemaField(
            description="Index of the item to replace (supports negative indices)."
        )
        value: Any = SchemaField(description="The new value for the given index.")

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        updated_list: List[Any] = SchemaField(description="The list after replacement.")
        old_item: Any = SchemaField(description="The item that was replaced.")
+        error: str = SchemaField(description="Error message if the operation failed.")

    def __init__(self):
        super().__init__(
@@ -661,10 +663,10 @@ class ReplaceListItemBlock(Block):


 class ListIsEmptyBlock(Block):
-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        list: List[Any] = SchemaField(description="The list to check.")

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        is_empty: bool = SchemaField(description="True if the list is empty.")

    def __init__(self):
--- a/autogpt_platform/backend/backend/blocks/dataforseo/keyword_suggestions.py
+++ b/autogpt_platform/backend/backend/blocks/dataforseo/keyword_suggestions.py
@@ -8,8 +8,7 @@ from backend.sdk import (
    Block,
    BlockCategory,
    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
+    BlockSchema,
    CredentialsMetaInput,
    SchemaField,
    UserPasswordCredentials,
@@ -19,7 +18,7 @@ from ._api import DataForSeoClient
 from ._config import dataforseo


-class KeywordSuggestion(BlockSchemaInput):
+class KeywordSuggestion(BlockSchema):
    """Schema for a keyword suggestion result."""

    keyword: str = SchemaField(description="The keyword suggestion")
@@ -46,7 +45,7 @@ class KeywordSuggestion(BlockSchemaInput):
 class DataForSeoKeywordSuggestionsBlock(Block):
    """Block for getting keyword suggestions from DataForSEO Labs."""

-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        credentials: CredentialsMetaInput = dataforseo.credentials_field(
            description="DataForSEO credentials (username and password)"
        )
@@ -78,7 +77,7 @@ class DataForSeoKeywordSuggestionsBlock(Block):
            le=3000,
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        suggestions: List[KeywordSuggestion] = SchemaField(
            description="List of keyword suggestions with metrics"
        )
@@ -91,6 +90,7 @@ class DataForSeoKeywordSuggestionsBlock(Block):
        seed_keyword: str = SchemaField(
            description="The seed keyword used for the query"
        )
+        error: str = SchemaField(description="Error message if the API call failed")

    def __init__(self):
        super().__init__(
@@ -213,12 +213,12 @@ class DataForSeoKeywordSuggestionsBlock(Block):
 class KeywordSuggestionExtractorBlock(Block):
    """Extracts individual fields from a KeywordSuggestion object."""

-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        suggestion: KeywordSuggestion = SchemaField(
            description="The keyword suggestion object to extract fields from"
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        keyword: str = SchemaField(description="The keyword suggestion")
        search_volume: Optional[int] = SchemaField(
            description="Monthly search volume", default=None
--- a/autogpt_platform/backend/backend/blocks/dataforseo/related_keywords.py
+++ b/autogpt_platform/backend/backend/blocks/dataforseo/related_keywords.py
@@ -8,8 +8,7 @@ from backend.sdk import (
    Block,
    BlockCategory,
    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
+    BlockSchema,
    CredentialsMetaInput,
    SchemaField,
    UserPasswordCredentials,
@@ -19,7 +18,7 @@ from ._api import DataForSeoClient
 from ._config import dataforseo


-class RelatedKeyword(BlockSchemaInput):
+class RelatedKeyword(BlockSchema):
    """Schema for a related keyword result."""

    keyword: str = SchemaField(description="The related keyword")
@@ -46,7 +45,7 @@ class RelatedKeyword(BlockSchemaInput):
 class DataForSeoRelatedKeywordsBlock(Block):
    """Block for getting related keywords from DataForSEO Labs."""

-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        credentials: CredentialsMetaInput = dataforseo.credentials_field(
            description="DataForSEO credentials (username and password)"
        )
@@ -86,7 +85,7 @@ class DataForSeoRelatedKeywordsBlock(Block):
            le=4,
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        related_keywords: List[RelatedKeyword] = SchemaField(
            description="List of related keywords with metrics"
        )
@@ -99,6 +98,7 @@ class DataForSeoRelatedKeywordsBlock(Block):
        seed_keyword: str = SchemaField(
            description="The seed keyword used for the query"
        )
+        error: str = SchemaField(description="Error message if the API call failed")

    def __init__(self):
        super().__init__(
@@ -231,12 +231,12 @@ class DataForSeoRelatedKeywordsBlock(Block):
 class RelatedKeywordExtractorBlock(Block):
    """Extracts individual fields from a RelatedKeyword object."""

-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        related_keyword: RelatedKeyword = SchemaField(
            description="The related keyword object to extract fields from"
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        keyword: str = SchemaField(description="The related keyword")
        search_volume: Optional[int] = SchemaField(
            description="Monthly search volume", default=None
--- a/autogpt_platform/backend/backend/blocks/decoder_block.py
+++ b/autogpt_platform/backend/backend/blocks/decoder_block.py
@@ -1,23 +1,17 @@
 import codecs

-from backend.data.block import (
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
-)
+from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
 from backend.data.model import SchemaField


 class TextDecoderBlock(Block):
-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        text: str = SchemaField(
            description="A string containing escaped characters to be decoded",
            placeholder='Your entire text block with \\n and \\" escaped characters',
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        decoded_text: str = SchemaField(
            description="The decoded text with escape sequences processed"
        )
--- a/autogpt_platform/backend/backend/blocks/discord/bot_blocks.py
+++ b/autogpt_platform/backend/backend/blocks/discord/bot_blocks.py
@@ -1,23 +1,16 @@
 import base64
 import io
 import mimetypes
-from enum import Enum
 from pathlib import Path
-from typing import Any, Literal, cast
+from typing import Any

+import aiohttp
 import discord
 from pydantic import SecretStr

-from backend.data.block import (
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
-)
+from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
 from backend.data.model import APIKeyCredentials, SchemaField
 from backend.util.file import store_media_file
-from backend.util.request import Requests
 from backend.util.type import MediaFileType

 from ._auth import (
@@ -34,24 +27,11 @@ TEST_CREDENTIALS = TEST_BOT_CREDENTIALS
 TEST_CREDENTIALS_INPUT = TEST_BOT_CREDENTIALS_INPUT


-class ThreadArchiveDuration(str, Enum):
-    """Discord thread auto-archive duration options"""
-
-    ONE_HOUR = "60"
-    ONE_DAY = "1440"
-    THREE_DAYS = "4320"
-    ONE_WEEK = "10080"
-
-    def to_minutes(self) -> int:
-        """Convert the duration string to minutes for Discord API"""
-        return int(self.value)
-
-
 class ReadDiscordMessagesBlock(Block):
-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        credentials: DiscordCredentials = DiscordCredentialsField()

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        message_content: str = SchemaField(
            description="The content of the message received"
        )
@@ -134,9 +114,10 @@ class ReadDiscordMessagesBlock(Block):
            if message.attachments:
                attachment = message.attachments[0]  # Process the first attachment
                if attachment.filename.endswith((".txt", ".py")):
-                    response = await Requests().get(attachment.url)
-                    file_content = response.text()
-                    self.output_data += f"\n\nFile from user: {attachment.filename}\nContent: {file_content}"
+                    async with aiohttp.ClientSession() as session:
+                        async with session.get(attachment.url) as response:
+                            file_content = response.text()
+                            self.output_data += f"\n\nFile from user: {attachment.filename}\nContent: {file_content}"

            await client.close()

@@ -184,7 +165,7 @@ class ReadDiscordMessagesBlock(Block):


 class SendDiscordMessageBlock(Block):
-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        credentials: DiscordCredentials = DiscordCredentialsField()
        message_content: str = SchemaField(
            description="The content of the message to send"
@@ -198,7 +179,7 @@ class SendDiscordMessageBlock(Block):
            default="",
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        status: str = SchemaField(
            description="The status of the operation (e.g., 'Message sent', 'Error')"
        )
@@ -330,7 +311,7 @@ class SendDiscordMessageBlock(Block):


 class SendDiscordDMBlock(Block):
-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        credentials: DiscordCredentials = DiscordCredentialsField()
        user_id: str = SchemaField(
            description="The Discord user ID to send the DM to (e.g., '123456789012345678')"
@@ -339,7 +320,7 @@ class SendDiscordDMBlock(Block):
            description="The content of the direct message to send"
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        status: str = SchemaField(description="The status of the operation")
        message_id: str = SchemaField(description="The ID of the sent message")

@@ -419,7 +400,7 @@ class SendDiscordDMBlock(Block):


 class SendDiscordEmbedBlock(Block):
-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        credentials: DiscordCredentials = DiscordCredentialsField()
        channel_identifier: str = SchemaField(
            description="Channel ID or channel name to send the embed to"
@@ -456,7 +437,7 @@ class SendDiscordEmbedBlock(Block):
            default=[],
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        status: str = SchemaField(description="Operation status")
        message_id: str = SchemaField(description="ID of the sent embed message")

@@ -606,7 +587,7 @@ class SendDiscordEmbedBlock(Block):


 class SendDiscordFileBlock(Block):
-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        credentials: DiscordCredentials = DiscordCredentialsField()
        channel_identifier: str = SchemaField(
            description="Channel ID or channel name to send the file to"
@@ -627,7 +608,7 @@ class SendDiscordFileBlock(Block):
            description="Optional message to send with the file", default=""
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        status: str = SchemaField(description="Operation status")
        message_id: str = SchemaField(description="ID of the sent message")

@@ -718,15 +699,16 @@ class SendDiscordFileBlock(Block):

                elif file.startswith(("http://", "https://")):
                    # URL - download the file
-                    response = await Requests().get(file)
-                    file_bytes = response.content
+                    async with aiohttp.ClientSession() as session:
+                        async with session.get(file) as response:
+                            file_bytes = await response.read()

-                    # Try to get filename from URL if not provided
-                    if not filename:
-                        from urllib.parse import urlparse
+                            # Try to get filename from URL if not provided
+                            if not filename:
+                                from urllib.parse import urlparse

-                        path = urlparse(file).path
-                        detected_filename = Path(path).name or "download"
+                                path = urlparse(file).path
+                                detected_filename = Path(path).name or "download"
                else:
                    # Local file path - read from stored media file
                    # This would be a path from a previous block's output
@@ -808,7 +790,7 @@ class SendDiscordFileBlock(Block):


 class ReplyToDiscordMessageBlock(Block):
-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        credentials: DiscordCredentials = DiscordCredentialsField()
        channel_id: str = SchemaField(
            description="The channel ID where the message to reply to is located"
@@ -819,7 +801,7 @@ class ReplyToDiscordMessageBlock(Block):
            description="Whether to mention the original message author", default=True
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        status: str = SchemaField(description="Operation status")
        reply_id: str = SchemaField(description="ID of the reply message")

@@ -933,13 +915,13 @@ class ReplyToDiscordMessageBlock(Block):


 class DiscordUserInfoBlock(Block):
-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        credentials: DiscordCredentials = DiscordCredentialsField()
        user_id: str = SchemaField(
            description="The Discord user ID to get information about"
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        user_id: str = SchemaField(
            description="The user's ID (passed through for chaining)"
        )
@@ -1050,7 +1032,7 @@ class DiscordUserInfoBlock(Block):


 class DiscordChannelInfoBlock(Block):
-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        credentials: DiscordCredentials = DiscordCredentialsField()
        channel_identifier: str = SchemaField(
            description="Channel name or channel ID to look up"
@@ -1061,7 +1043,7 @@ class DiscordChannelInfoBlock(Block):
            default="",
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        channel_id: str = SchemaField(description="The channel's ID")
        channel_name: str = SchemaField(description="The channel's name")
        server_id: str = SchemaField(description="The server's ID")
@@ -1180,211 +1162,3 @@ class DiscordChannelInfoBlock(Block):
            raise ValueError(f"Login error occurred: {login_err}")
        except Exception as e:
            raise ValueError(f"An error occurred: {e}")
-
-
-class CreateDiscordThreadBlock(Block):
-    class Input(BlockSchemaInput):
-        credentials: DiscordCredentials = DiscordCredentialsField()
-        channel_name: str = SchemaField(
-            description="Channel ID or channel name to create the thread in"
-        )
-        server_name: str = SchemaField(
-            description="Server name (only needed if using channel name)",
-            advanced=True,
-            default="",
-        )
-        thread_name: str = SchemaField(description="The name of the thread to create")
-        is_private: bool = SchemaField(
-            description="Whether to create a private thread (requires Boost Level 2+) or public thread",
-            default=False,
-        )
-        auto_archive_duration: ThreadArchiveDuration = SchemaField(
-            description="Duration before the thread is automatically archived",
-            advanced=True,
-            default=ThreadArchiveDuration.ONE_WEEK,
-        )
-        message_content: str = SchemaField(
-            description="Optional initial message to send in the thread",
-            advanced=True,
-            default="",
-        )
-
-    class Output(BlockSchemaOutput):
-        status: str = SchemaField(description="Operation status")
-        thread_id: str = SchemaField(description="ID of the created thread")
-        thread_name: str = SchemaField(description="Name of the created thread")
-
-    def __init__(self):
-        super().__init__(
-            id="e8f3c9a2-7b5d-4f1e-9c6a-3d8e2b4f7a1c",
-            input_schema=CreateDiscordThreadBlock.Input,
-            output_schema=CreateDiscordThreadBlock.Output,
-            description="Creates a new thread in a Discord channel.",
-            categories={BlockCategory.SOCIAL},
-            test_input={
-                "channel_name": "general",
-                "thread_name": "Test Thread",
-                "is_private": False,
-                "auto_archive_duration": ThreadArchiveDuration.ONE_HOUR,
-                "credentials": TEST_CREDENTIALS_INPUT,
-            },
-            test_output=[
-                ("status", "Thread created successfully"),
-                ("thread_id", "123456789012345678"),
-                ("thread_name", "Test Thread"),
-            ],
-            test_mock={
-                "create_thread": lambda *args, **kwargs: {
-                    "status": "Thread created successfully",
-                    "thread_id": "123456789012345678",
-                    "thread_name": "Test Thread",
-                }
-            },
-            test_credentials=TEST_CREDENTIALS,
-        )
-
-    async def create_thread(
-        self,
-        token: str,
-        channel_name: str,
-        server_name: str | None,
-        thread_name: str,
-        is_private: bool,
-        auto_archive_duration: ThreadArchiveDuration,
-        message_content: str,
-    ) -> dict:
-        intents = discord.Intents.default()
-        intents.guilds = True
-        intents.message_content = True  # Required for sending messages in threads
-        client = discord.Client(intents=intents)
-
-        result = {}
-
-        @client.event
-        async def on_ready():
-            channel = None
-
-            # Try to parse as channel ID first
-            try:
-                channel_id = int(channel_name)
-                try:
-                    channel = await client.fetch_channel(channel_id)
-                except discord.errors.NotFound:
-                    result["status"] = f"Channel with ID {channel_id} not found"
-                    await client.close()
-                    return
-                except discord.errors.Forbidden:
-                    result["status"] = (
-                        f"Bot does not have permission to view channel {channel_id}"
-                    )
-                    await client.close()
-                    return
-            except ValueError:
-                # Not an ID, treat as channel name
-                # Collect all matching channels to detect duplicates
-                matching_channels = []
-                for guild in client.guilds:
-                    # Skip guilds if server_name is provided and doesn't match
-                    if (
-                        server_name
-                        and server_name.strip()
-                        and guild.name != server_name
-                    ):
-                        continue
-                    for ch in guild.text_channels:
-                        if ch.name == channel_name:
-                            matching_channels.append(ch)
-
-                if not matching_channels:
-                    result["status"] = f"Channel not found: {channel_name}"
-                    await client.close()
-                    return
-                elif len(matching_channels) > 1:
-                    result["status"] = (
-                        f"Multiple channels named '{channel_name}' found. "
-                        "Please specify server_name to disambiguate."
-                    )
-                    await client.close()
-                    return
-                else:
-                    channel = matching_channels[0]
-
-            if not channel:
-                result["status"] = "Failed to resolve channel"
-                await client.close()
-                return
-
-            # Type check - ensure it's a text channel that can create threads
-            if not hasattr(channel, "create_thread"):
-                result["status"] = (
-                    f"Channel {channel_name} cannot create threads (not a text channel)"
-                )
-                await client.close()
-                return
-
-            # After the hasattr check, we know channel is a TextChannel
-            channel = cast(discord.TextChannel, channel)
-
-            try:
-                # Create the thread using discord.py 2.0+ API
-                thread_type = (
-                    discord.ChannelType.private_thread
-                    if is_private
-                    else discord.ChannelType.public_thread
-                )
-
-                # Cast to the specific Literal type that discord.py expects
-                duration_minutes = cast(
-                    Literal[60, 1440, 4320, 10080], auto_archive_duration.to_minutes()
-                )
-
-                # The 'type' parameter exists in discord.py 2.0+ but isn't in type stubs yet
-                # pyright: ignore[reportCallIssue]
-                thread = await channel.create_thread(
-                    name=thread_name,
-                    type=thread_type,
-                    auto_archive_duration=duration_minutes,
-                )
-
-                # Send initial message if provided
-                if message_content:
-                    await thread.send(message_content)
-
-                result["status"] = "Thread created successfully"
-                result["thread_id"] = str(thread.id)
-                result["thread_name"] = thread.name
-
-            except discord.errors.Forbidden as e:
-                result["status"] = (
-                    f"Bot does not have permission to create threads in this channel. {str(e)}"
-                )
-            except Exception as e:
-                result["status"] = f"Error creating thread: {str(e)}"
-            finally:
-                await client.close()
-
-        await client.start(token)
-        return result
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        try:
-            result = await self.create_thread(
-                token=credentials.api_key.get_secret_value(),
-                channel_name=input_data.channel_name,
-                server_name=input_data.server_name or None,
-                thread_name=input_data.thread_name,
-                is_private=input_data.is_private,
-                auto_archive_duration=input_data.auto_archive_duration,
-                message_content=input_data.message_content,
-            )
-
-            yield "status", result.get("status", "Unknown error")
-            if "thread_id" in result:
-                yield "thread_id", result["thread_id"]
-            if "thread_name" in result:
-                yield "thread_name", result["thread_name"]
-
-        except discord.errors.LoginFailure as login_err:
-            raise ValueError(f"Login error occurred: {login_err}")
--- a/autogpt_platform/backend/backend/blocks/discord/oauth_blocks.py
+++ b/autogpt_platform/backend/backend/blocks/discord/oauth_blocks.py
@@ -2,13 +2,7 @@
 Discord OAuth-based blocks.
 """

-from backend.data.block import (
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
-)
+from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
 from backend.data.model import OAuth2Credentials, SchemaField

 from ._api import DiscordOAuthUser, get_current_user
@@ -27,12 +21,12 @@ class DiscordGetCurrentUserBlock(Block):
    This block requires Discord OAuth2 credentials (not bot tokens).
    """

-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        credentials: DiscordOAuthCredentialsInput = DiscordOAuthCredentialsField(
            ["identify"]
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        user_id: str = SchemaField(description="The authenticated user's Discord ID")
        username: str = SchemaField(description="The user's username")
        avatar_url: str = SchemaField(description="URL to the user's avatar image")
--- a/autogpt_platform/backend/backend/blocks/email_block.py
+++ b/autogpt_platform/backend/backend/blocks/email_block.py
@@ -1,19 +1,11 @@
 import smtplib
-import socket
-import ssl
 from email.mime.multipart import MIMEMultipart
 from email.mime.text import MIMEText
 from typing import Literal

 from pydantic import BaseModel, ConfigDict, SecretStr

-from backend.data.block import (
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
-)
+from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
 from backend.data.model import (
    CredentialsField,
    CredentialsMetaInput,
@@ -50,14 +42,16 @@ def SMTPCredentialsField() -> SMTPCredentialsInput:


 class SMTPConfig(BaseModel):
-    smtp_server: str = SchemaField(description="SMTP server address")
+    smtp_server: str = SchemaField(
+        default="smtp.example.com", description="SMTP server address"
+    )
    smtp_port: int = SchemaField(default=25, description="SMTP port number")

    model_config = ConfigDict(title="SMTP Config")


 class SendEmailBlock(Block):
-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        to_email: str = SchemaField(
            description="Recipient email address", placeholder="recipient@example.com"
        )
@@ -67,10 +61,13 @@ class SendEmailBlock(Block):
        body: str = SchemaField(
            description="Body of the email", placeholder="Enter the email body"
        )
-        config: SMTPConfig = SchemaField(description="SMTP Config")
+        config: SMTPConfig = SchemaField(
+            description="SMTP Config",
+            default=SMTPConfig(),
+        )
        credentials: SMTPCredentialsInput = SMTPCredentialsField()

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        status: str = SchemaField(description="Status of the email sending operation")
        error: str = SchemaField(
            description="Error message if the email sending failed"
@@ -117,7 +114,7 @@ class SendEmailBlock(Block):
        msg["Subject"] = subject
        msg.attach(MIMEText(body, "plain"))

-        with smtplib.SMTP(smtp_server, smtp_port, timeout=30) as server:
+        with smtplib.SMTP(smtp_server, smtp_port) as server:
            server.starttls()
            server.login(smtp_username, smtp_password)
            server.sendmail(smtp_username, to_email, msg.as_string())
@@ -127,59 +124,10 @@ class SendEmailBlock(Block):
    async def run(
        self, input_data: Input, *, credentials: SMTPCredentials, **kwargs
    ) -> BlockOutput:
-        try:
-            status = self.send_email(
-                config=input_data.config,
-                to_email=input_data.to_email,
-                subject=input_data.subject,
-                body=input_data.body,
-                credentials=credentials,
-            )
-            yield "status", status
-        except socket.gaierror:
-            yield "error", (
-                f"Cannot connect to SMTP server '{input_data.config.smtp_server}'. "
-                "Please verify the server address is correct."
-            )
-        except socket.timeout:
-            yield "error", (
-                f"Connection timeout to '{input_data.config.smtp_server}' "
-                f"on port {input_data.config.smtp_port}. "
-                "The server may be down or unreachable."
-            )
-        except ConnectionRefusedError:
-            yield "error", (
-                f"Connection refused to '{input_data.config.smtp_server}' "
-                f"on port {input_data.config.smtp_port}. "
-                "Common SMTP ports are: 587 (TLS), 465 (SSL), 25 (plain). "
-                "Please verify the port is correct."
-            )
-        except smtplib.SMTPNotSupportedError:
-            yield "error", (
-                f"STARTTLS not supported by server '{input_data.config.smtp_server}'. "
-                "Try using port 465 for SSL or port 25 for unencrypted connection."
-            )
-        except ssl.SSLError as e:
-            yield "error", (
-                f"SSL/TLS error when connecting to '{input_data.config.smtp_server}': {str(e)}. "
-                "The server may require a different security protocol."
-            )
-        except smtplib.SMTPAuthenticationError:
-            yield "error", (
-                "Authentication failed. Please verify your username and password are correct."
-            )
-        except smtplib.SMTPRecipientsRefused:
-            yield "error", (
-                f"Recipient email address '{input_data.to_email}' was rejected by the server. "
-                "Please verify the email address is valid."
-            )
-        except smtplib.SMTPSenderRefused:
-            yield "error", (
-                "Sender email address defined in the credentials that where used"
-                "was rejected by the server. "
-                "Please verify your account is authorized to send emails."
-            )
-        except smtplib.SMTPDataError as e:
-            yield "error", f"Email data rejected by server: {str(e)}"
-        except Exception as e:
-            raise e
+        yield "status", self.send_email(
+            config=input_data.config,
+            to_email=input_data.to_email,
+            subject=input_data.subject,
+            body=input_data.body,
+            credentials=credentials,
+        )
--- a/autogpt_platform/backend/backend/blocks/enrichlayer/linkedin.py
+++ b/autogpt_platform/backend/backend/blocks/enrichlayer/linkedin.py
@@ -8,13 +8,7 @@ which provides access to LinkedIn profile data and related information.
 import logging
 from typing import Optional

-from backend.data.block import (
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
-)
+from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
 from backend.data.model import APIKeyCredentials, CredentialsField, SchemaField
 from backend.util.type import MediaFileType

@@ -35,7 +29,7 @@ logger = logging.getLogger(__name__)
 class GetLinkedinProfileBlock(Block):
    """Block to fetch LinkedIn profile data using Enrichlayer API."""

-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        """Input schema for GetLinkedinProfileBlock."""

        linkedin_url: str = SchemaField(
@@ -86,12 +80,13 @@ class GetLinkedinProfileBlock(Block):
            description="Enrichlayer API credentials"
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        """Output schema for GetLinkedinProfileBlock."""

        profile: PersonProfileResponse = SchemaField(
            description="LinkedIn profile data"
        )
+        error: str = SchemaField(description="Error message if the request failed")

    def __init__(self):
        """Initialize GetLinkedinProfileBlock."""
@@ -204,7 +199,7 @@ class GetLinkedinProfileBlock(Block):
 class LinkedinPersonLookupBlock(Block):
    """Block to look up LinkedIn profiles by person's information using Enrichlayer API."""

-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        """Input schema for LinkedinPersonLookupBlock."""

        first_name: str = SchemaField(
@@ -247,12 +242,13 @@ class LinkedinPersonLookupBlock(Block):
            description="Enrichlayer API credentials"
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        """Output schema for LinkedinPersonLookupBlock."""

        lookup_result: PersonLookupResponse = SchemaField(
            description="LinkedIn profile lookup result"
        )
+        error: str = SchemaField(description="Error message if the request failed")

    def __init__(self):
        """Initialize LinkedinPersonLookupBlock."""
@@ -350,7 +346,7 @@ class LinkedinPersonLookupBlock(Block):
 class LinkedinRoleLookupBlock(Block):
    """Block to look up LinkedIn profiles by role in a company using Enrichlayer API."""

-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        """Input schema for LinkedinRoleLookupBlock."""

        role: str = SchemaField(
@@ -370,12 +366,13 @@ class LinkedinRoleLookupBlock(Block):
            description="Enrichlayer API credentials"
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        """Output schema for LinkedinRoleLookupBlock."""

        role_lookup_result: RoleLookupResponse = SchemaField(
            description="LinkedIn role lookup result"
        )
+        error: str = SchemaField(description="Error message if the request failed")

    def __init__(self):
        """Initialize LinkedinRoleLookupBlock."""
@@ -452,7 +449,7 @@ class LinkedinRoleLookupBlock(Block):
 class GetLinkedinProfilePictureBlock(Block):
    """Block to get LinkedIn profile pictures using Enrichlayer API."""

-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        """Input schema for GetLinkedinProfilePictureBlock."""

        linkedin_profile_url: str = SchemaField(
@@ -463,12 +460,13 @@ class GetLinkedinProfilePictureBlock(Block):
            description="Enrichlayer API credentials"
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        """Output schema for GetLinkedinProfilePictureBlock."""

        profile_picture_url: MediaFileType = SchemaField(
            description="LinkedIn profile picture URL"
        )
+        error: str = SchemaField(description="Error message if the request failed")

    def __init__(self):
        """Initialize GetLinkedinProfilePictureBlock."""
--- a/autogpt_platform/backend/backend/blocks/exa/_test.py
+++ b/autogpt_platform/backend/backend/blocks/exa/_test.py
@@ -1,22 +0,0 @@
-"""
-Test credentials and helpers for Exa blocks.
-"""
-
-from pydantic import SecretStr
-
-from backend.data.model import APIKeyCredentials
-
-TEST_CREDENTIALS = APIKeyCredentials(
-    id="01234567-89ab-cdef-0123-456789abcdef",
-    provider="exa",
-    api_key=SecretStr("mock-exa-api-key"),
-    title="Mock Exa API key",
-    expires_at=None,
-)
-
-TEST_CREDENTIALS_INPUT = {
-    "provider": TEST_CREDENTIALS.provider,
-    "id": TEST_CREDENTIALS.id,
-    "type": TEST_CREDENTIALS.type,
-    "title": TEST_CREDENTIALS.title,
-}
--- a/autogpt_platform/backend/backend/blocks/exa/answers.py
+++ b/autogpt_platform/backend/backend/blocks/exa/answers.py
@@ -1,59 +1,55 @@
-from typing import Optional
-
-from exa_py import AsyncExa
-from exa_py.api import AnswerResponse
-from pydantic import BaseModel
-
 from backend.sdk import (
    APIKeyCredentials,
+    BaseModel,
    Block,
    BlockCategory,
    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
+    BlockSchema,
    CredentialsMetaInput,
-    MediaFileType,
+    Requests,
    SchemaField,
 )

 from ._config import exa


-class AnswerCitation(BaseModel):
-    """Citation model for answer endpoint."""
+class CostBreakdown(BaseModel):
+    keywordSearch: float
+    neuralSearch: float
+    contentText: float
+    contentHighlight: float
+    contentSummary: float

-    id: str = SchemaField(description="The temporary ID for the document")
-    url: str = SchemaField(description="The URL of the search result")
-    title: Optional[str] = SchemaField(description="The title of the search result")
-    author: Optional[str] = SchemaField(description="The author of the content")
-    publishedDate: Optional[str] = SchemaField(
-        description="An estimate of the creation date"
-    )
-    text: Optional[str] = SchemaField(description="The full text content of the source")
-    image: Optional[MediaFileType] = SchemaField(
-        description="The URL of the image associated with the result"
-    )
-    favicon: Optional[MediaFileType] = SchemaField(
-        description="The URL of the favicon for the domain"
-    )

-    @classmethod
-    def from_sdk(cls, sdk_citation) -> "AnswerCitation":
-        """Convert SDK AnswerResult (dataclass) to our Pydantic model."""
-        return cls(
-            id=getattr(sdk_citation, "id", ""),
-            url=getattr(sdk_citation, "url", ""),
-            title=getattr(sdk_citation, "title", None),
-            author=getattr(sdk_citation, "author", None),
-            publishedDate=getattr(sdk_citation, "published_date", None),
-            text=getattr(sdk_citation, "text", None),
-            image=getattr(sdk_citation, "image", None),
-            favicon=getattr(sdk_citation, "favicon", None),
-        )
+class SearchBreakdown(BaseModel):
+    search: float
+    contents: float
+    breakdown: CostBreakdown
+
+
+class PerRequestPrices(BaseModel):
+    neuralSearch_1_25_results: float
+    neuralSearch_26_100_results: float
+    neuralSearch_100_plus_results: float
+    keywordSearch_1_100_results: float
+    keywordSearch_100_plus_results: float
+
+
+class PerPagePrices(BaseModel):
+    contentText: float
+    contentHighlight: float
+    contentSummary: float
+
+
+class CostDollars(BaseModel):
+    total: float
+    breakDown: list[SearchBreakdown]
+    perRequestPrices: PerRequestPrices
+    perPagePrices: PerPagePrices


 class ExaAnswerBlock(Block):
-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        credentials: CredentialsMetaInput = exa.credentials_field(
            description="The Exa integration requires an API Key."
        )
@@ -62,21 +58,31 @@ class ExaAnswerBlock(Block):
            placeholder="What is the latest valuation of SpaceX?",
        )
        text: bool = SchemaField(
-            description="Include full text content in the search results used for the answer",
-            default=True,
+            default=False,
+            description="If true, the response includes full text content in the search results",
+            advanced=True,
+        )
+        model: str = SchemaField(
+            default="exa",
+            description="The search model to use (exa or exa-pro)",
+            placeholder="exa",
+            advanced=True,
        )

-    class Output(BlockSchemaOutput):
+    class Output(BlockSchema):
        answer: str = SchemaField(
            description="The generated answer based on search results"
        )
-        citations: list[AnswerCitation] = SchemaField(
-            description="Search results used to generate the answer"
+        citations: list[dict] = SchemaField(
+            description="Search results used to generate the answer",
+            default_factory=list,
        )
-        citation: AnswerCitation = SchemaField(
-            description="Individual citation from the answer"
+        cost_dollars: CostDollars = SchemaField(
+            description="Cost breakdown of the request"
+        )
+        error: str = SchemaField(
+            description="Error message if the request failed", default=""
        )
-        error: str = SchemaField(description="Error message if the request failed")

    def __init__(self):
        super().__init__(
@@ -90,24 +96,26 @@ class ExaAnswerBlock(Block):
    async def run(
        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
    ) -> BlockOutput:
-        aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
+        url = "https://api.exa.ai/answer"
+        headers = {
+            "Content-Type": "application/json",
+            "x-api-key": credentials.api_key.get_secret_value(),
+        }

-        # Get answer using SDK (stream=False for blocks) - this IS async, needs await
-        response = await aexa.answer(
-            query=input_data.query, text=input_data.text, stream=False
-        )
+        # Build the payload
+        payload = {
+            "query": input_data.query,
+            "text": input_data.text,
+            "model": input_data.model,
+        }

-        # this should remain true as long as they don't start defaulting to streaming only.
-        # provides a bit of safety for sdk updates.
-        assert type(response) is AnswerResponse
+        try:
+            response = await Requests().post(url, headers=headers, json=payload)
+            data = response.json()

-        yield "answer", response.answer
+            yield "answer", data.get("answer", "")
+            yield "citations", data.get("citations", [])
+            yield "cost_dollars", data.get("costDollars", {})

-        citations = [
-            AnswerCitation.from_sdk(sdk_citation)
-            for sdk_citation in response.citations or []
-        ]
-
-        yield "citations", citations
-        for citation in citations:
-            yield "citation", citation
+        except Exception as e:
+            yield "error", str(e)
--- a/autogpt_platform/backend/backend/blocks/exa/code_context.py
+++ b/autogpt_platform/backend/backend/blocks/exa/code_context.py
@@ -1,118 +0,0 @@
-"""
-Exa Code Context Block
-
-Provides code search capabilities to find relevant code snippets and examples
-from open source repositories, documentation, and Stack Overflow.
-"""
-
-from typing import Union
-
-from pydantic import BaseModel
-
-from backend.sdk import (
-    APIKeyCredentials,
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
-    CredentialsMetaInput,
-    Requests,
-    SchemaField,
-)
-
-from ._config import exa
-
-
-class CodeContextResponse(BaseModel):
-    """Stable output model for code context responses."""
-
-    request_id: str
-    query: str
-    response: str
-    results_count: int
-    cost_dollars: str
-    search_time: float
-    output_tokens: int
-
-    @classmethod
-    def from_api(cls, data: dict) -> "CodeContextResponse":
-        """Convert API response to our stable model."""
-        return cls(
-            request_id=data.get("requestId", ""),
-            query=data.get("query", ""),
-            response=data.get("response", ""),
-            results_count=data.get("resultsCount", 0),
-            cost_dollars=data.get("costDollars", ""),
-            search_time=data.get("searchTime", 0.0),
-            output_tokens=data.get("outputTokens", 0),
-        )
-
-
-class ExaCodeContextBlock(Block):
-    """Get relevant code snippets and examples from open source repositories."""
-
-    class Input(BlockSchemaInput):
-        credentials: CredentialsMetaInput = exa.credentials_field(
-            description="The Exa integration requires an API Key."
-        )
-        query: str = SchemaField(
-            description="Search query to find relevant code snippets. Describe what you're trying to do or what code you're looking for.",
-            placeholder="how to use React hooks for state management",
-        )
-        tokens_num: Union[str, int] = SchemaField(
-            default="dynamic",
-            description="Token limit for response. Use 'dynamic' for automatic sizing, 5000 for standard queries, or 10000 for comprehensive examples.",
-            placeholder="dynamic",
-        )
-
-    class Output(BlockSchemaOutput):
-        request_id: str = SchemaField(description="Unique identifier for this request")
-        query: str = SchemaField(description="The search query used")
-        response: str = SchemaField(
-            description="Formatted code snippets and contextual examples with sources"
-        )
-        results_count: int = SchemaField(
-            description="Number of code sources found and included"
-        )
-        cost_dollars: str = SchemaField(description="Cost of this request in dollars")
-        search_time: float = SchemaField(
-            description="Time taken to search in milliseconds"
-        )
-        output_tokens: int = SchemaField(description="Number of tokens in the response")
-
-    def __init__(self):
-        super().__init__(
-            id="8f9e0d1c-2b3a-4567-8901-23456789abcd",
-            description="Search billions of GitHub repos, docs, and Stack Overflow for relevant code examples",
-            categories={BlockCategory.SEARCH, BlockCategory.DEVELOPER_TOOLS},
-            input_schema=ExaCodeContextBlock.Input,
-            output_schema=ExaCodeContextBlock.Output,
-        )
-
-    async def run(
-        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
-    ) -> BlockOutput:
-        url = "https://api.exa.ai/context"
-        headers = {
-            "Content-Type": "application/json",
-            "x-api-key": credentials.api_key.get_secret_value(),
-        }
-
-        payload = {
-            "query": input_data.query,
-            "tokensNum": input_data.tokens_num,
-        }
-
-        response = await Requests().post(url, headers=headers, json=payload)
-        data = response.json()
-
-        context = CodeContextResponse.from_api(data)
-
-        yield "request_id", context.request_id
-        yield "query", context.query
-        yield "response", context.response
-        yield "results_count", context.results_count
-        yield "cost_dollars", context.cost_dollars
-        yield "search_time", context.search_time
-        yield "output_tokens", context.output_tokens
--- a/autogpt_platform/backend/backend/blocks/exa/contents.py
+++ b/autogpt_platform/backend/backend/blocks/exa/contents.py
@@ -1,127 +1,39 @@
-from enum import Enum
-from typing import Optional
-
-from exa_py import AsyncExa
-from pydantic import BaseModel
-
 from backend.sdk import (
    APIKeyCredentials,
    Block,
    BlockCategory,
    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
+    BlockSchema,
    CredentialsMetaInput,
+    Requests,
    SchemaField,
 )

 from ._config import exa
-from .helpers import (
-    CostDollars,
-    ExaSearchResults,
-    ExtrasSettings,
-    HighlightSettings,
-    LivecrawlTypes,
-    SummarySettings,
-)
-
-
-class ContentStatusTag(str, Enum):
-    CRAWL_NOT_FOUND = "CRAWL_NOT_FOUND"
-    CRAWL_TIMEOUT = "CRAWL_TIMEOUT"
-    CRAWL_LIVECRAWL_TIMEOUT = "CRAWL_LIVECRAWL_TIMEOUT"
-    SOURCE_NOT_AVAILABLE = "SOURCE_NOT_AVAILABLE"
-    CRAWL_UNKNOWN_ERROR = "CRAWL_UNKNOWN_ERROR"
-
-
-class ContentError(BaseModel):
-    tag: Optional[ContentStatusTag] = SchemaField(
-        default=None, description="Specific error type"
-    )
-    httpStatusCode: Optional[int] = SchemaField(
-        default=None, description="The corresponding HTTP status code"
-    )
-
-
-class ContentStatus(BaseModel):
-    id: str = SchemaField(description="The URL that was requested")
-    status: str = SchemaField(
-        description="Status of the content fetch operation (success or error)"
-    )
-    error: Optional[ContentError] = SchemaField(
-        default=None, description="Error details, only present when status is 'error'"
-    )
+from .helpers import ContentSettings


 class ExaContentsBlock(Block):
-    class Input(BlockSchemaInput):
+    class Input(BlockSchema):
        credentials: CredentialsMetaInput = exa.credentials_field(
            description="The Exa integration requires an API Key."
        )
-        urls: list[str] = SchemaField(
-            description="Array of URLs to crawl (preferred over 'ids')",
-            default_factory=list,
-            advanced=False,
-        )
        ids: list[str] = SchemaField(
-            description="[DEPRECATED - use 'urls' instead] Array of document IDs obtained from searches",
-            default_factory=list,
-            advanced=True,
+            description="Array of document IDs obtained from searches"
        )
-        text: bool = SchemaField(
-            description="Retrieve text content from pages",
-            default=True,
-        )
-        highlights: HighlightSettings = SchemaField(
-            description="Text snippets most relevant from each page",
-            default=HighlightSettings(),
-        )
-        summary: SummarySettings = SchemaField(
-            description="LLM-generated summary of the webpage",
-            default=SummarySettings(),
-        )
-        livecrawl: Optional[LivecrawlTypes] = SchemaField(
-            description="Livecrawling options: never, fallback (default), always, preferred",
-            default=LivecrawlTypes.FALLBACK,
-            advanced=True,
-        )
-        livecrawl_timeout: Optional[int] = SchemaField(
-            description="Timeout for livecrawling in milliseconds",
-            default=10000,
-            advanced=True,
-        )
-        subpages: Optional[int] = SchemaField(
-            description="Number of subpages to crawl", default=0, ge=0, advanced=True
-        )
-        subpage_target: Optional[str | list[str]] = SchemaField(
-            description="Keyword(s) to find specific subpages of search results",
-            default=None,
-            advanced=True,
-        )
-        extras: ExtrasSettings = SchemaField(
-            description="Extra parameters for additional content",
-            default=ExtrasSettings(),
+        contents: ContentSettings = SchemaField(
+            description="Content retrieval settings",
+            default=ContentSettings(),
            advanced=True,
        )

-    class Output(BlockSchemaOutput):
-        results: list[ExaSearchResults] = SchemaField(
-            description="List of document contents with metadata"
+    class Output(BlockSchema):
+        results: list = SchemaField(
+            description="List of document contents", default_factory=list
        )
-        result: ExaSearchResults = SchemaField(
-            description="Single document content result"
+        error: str = SchemaField(
+            description="Error message if the request failed", default=""
        )
-        context: str = SchemaField(
-            description="A formatted string of the results ready for LLMs"
-        )
-        request_id: str = SchemaField(description="Unique identifier for the request")
-        statuses: list[ContentStatus] = SchemaField(
-            description="Status information for each requested URL"
-        )
-        cost_dollars: Optional[CostDollars] = SchemaField(
-            description="Cost breakdown for the request"
-        )
-        error: str = SchemaField(description="Error message if the request failed")

    def __init__(self):
        super().__init__(
@@ -135,91 +47,23 @@ class ExaContentsBlock(Block):
    async def run(
        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
    ) -> BlockOutput:
-        if not input_data.urls and not input_data.ids:
-            raise ValueError("Either 'urls' or 'ids' must be provided")
+        url = "https://api.exa.ai/contents"
+        headers = {
+            "Content-Type": "application/json",
+            "x-api-key": credentials.api_key.get_secret_value(),
+        }

-        sdk_kwargs = {}
+        # Convert ContentSettings to API format
+        payload = {
+            "ids": input_data.ids,
+            "text": input_data.contents.text,
+            "highlights": input_data.contents.highlights,
+            "summary": input_data.contents.summary,
+        }

-        # Prefer urls over ids
-        if input_data.urls:
-            sdk_kwargs["urls"] = input_data.urls
-        elif input_data.ids:
-            sdk_kwargs["ids"] = input_data.ids
-
-        if input_data.text:
-            sdk_kwargs["text"] = {"includeHtmlTags": True}
-
-        # Handle highlights - only include if modified from defaults
-        if input_data.highlights and (
-            input_data.highlights.num_sentences != 1
-            or input_data.highlights.highlights_per_url != 1
-            or input_data.highlights.query is not None
-        ):
-            highlights_dict = {}
-            highlights_dict["numSentences"] = input_data.highlights.num_sentences
-            highlights_dict["highlightsPerUrl"] = (
-                input_data.highlights.highlights_per_url
-            )
-            if input_data.highlights.query:
-                highlights_dict["query"] = input_data.highlights.query
-            sdk_kwargs["highlights"] = highlights_dict
-
-        # Handle summary - only include if modified from defaults
-        if input_data.summary and (
-            input_data.summary.query is not None
-            or input_data.summary.schema is not None
-        ):
-            summary_dict = {}
-            if input_data.summary.query:
-                summary_dict["query"] = input_data.summary.query
-            if input_data.summary.schema:
-                summary_dict["schema"] = input_data.summary.schema
-            sdk_kwargs["summary"] = summary_dict
-
-        if input_data.livecrawl:
-            sdk_kwargs["livecrawl"] = input_data.livecrawl.value
-
-        if input_data.livecrawl_timeout is not None:
-            sdk_kwargs["livecrawl_timeout"] = input_data.livecrawl_timeout
-
-        if input_data.subpages is not None:
-            sdk_kwargs["subpages"] = input_data.subpages
-
-        if input_data.subpage_target:
-            sdk_kwargs["subpage_target"] = input_data.subpage_target
-
-        # Handle extras - only include if modified from defaults
-        if input_data.extras and (
-            input_data.extras.links > 0 or input_data.extras.image_links > 0
-        ):
-            extras_dict = {}
-            if input_data.extras.links:
-                extras_dict["links"] = input_data.extras.links
-            if input_data.extras.image_links:
-                extras_dict["image_links"] = input_data.extras.image_links
-            sdk_kwargs["extras"] = extras_dict
-
-        # Always enable context for LLM-ready output
-        sdk_kwargs["context"] = True
-
-        aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
-        response = await aexa.get_contents(**sdk_kwargs)
-
-        converted_results = [
-            ExaSearchResults.from_sdk(sdk_result)
-            for sdk_result in response.results or []
-        ]
-
-        yield "results", converted_results
-
-        for result in converted_results:
-            yield "result", result
-
-        if response.context:
-            yield "context", response.context
-
-        if response.statuses:
-            yield "statuses", response.statuses
-
-        if response.cost_dollars:
-            yield "cost_dollars", response.cost_dollars
+        try:
+            response = await Requests().post(url, headers=headers, json=payload)
+            data = response.json()
+            yield "results", data.get("results", [])
+        except Exception as e:
+            yield "error", str(e)
--- a/autogpt_platform/backend/backend/blocks/exa/helpers.py
+++ b/autogpt_platform/backend/backend/blocks/exa/helpers.py
@@ -1,150 +1,51 @@
-from enum import Enum
-from typing import Any, Dict, Literal, Optional, Union
+from typing import Optional

-from backend.sdk import BaseModel, MediaFileType, SchemaField
+from backend.sdk import BaseModel, SchemaField


-class LivecrawlTypes(str, Enum):
-    NEVER = "never"
-    FALLBACK = "fallback"
-    ALWAYS = "always"
-    PREFERRED = "preferred"
-
-
-class TextEnabled(BaseModel):
-    discriminator: Literal["enabled"] = "enabled"
-
-
-class TextDisabled(BaseModel):
-    discriminator: Literal["disabled"] = "disabled"
-
-
-class TextAdvanced(BaseModel):
-    discriminator: Literal["advanced"] = "advanced"
-    max_characters: Optional[int] = SchemaField(
-        default=None,
+class TextSettings(BaseModel):
+    max_characters: int = SchemaField(
+        default=1000,
        description="Maximum number of characters to return",
        placeholder="1000",
    )
    include_html_tags: bool = SchemaField(
        default=False,
-        description="Include HTML tags in the response, helps LLMs understand text structure",
+        description="Whether to include HTML tags in the text",
        placeholder="False",
    )


 class HighlightSettings(BaseModel):
    num_sentences: int = SchemaField(
-        default=1,
+        default=3,
        description="Number of sentences per highlight",
-        placeholder="1",
-        ge=1,
+        placeholder="3",
    )
    highlights_per_url: int = SchemaField(
-        default=1,
+        default=3,
        description="Number of highlights per URL",
-        placeholder="1",
-        ge=1,
-    )
-    query: Optional[str] = SchemaField(
-        default=None,
-        description="Custom query to direct the LLM's selection of highlights",
-        placeholder="Key advancements",
+        placeholder="3",
    )


 class SummarySettings(BaseModel):
    query: Optional[str] = SchemaField(
-        default=None,
-        description="Custom query for the LLM-generated summary",
-        placeholder="Main developments",
-    )
-    schema: Optional[dict] = SchemaField(  # type: ignore
-        default=None,
-        description="JSON schema for structured output from summary",
-        advanced=True,
-    )
-
-
-class ExtrasSettings(BaseModel):
-    links: int = SchemaField(
-        default=0,
-        description="Number of URLs to return from each webpage",
-        placeholder="1",
-        ge=0,
-    )
-    image_links: int = SchemaField(
-        default=0,
-        description="Number of images to return for each result",
-        placeholder="1",
-        ge=0,
-    )
-
-
-class ContextEnabled(BaseModel):
-    discriminator: Literal["enabled"] = "enabled"
-
-
-class ContextDisabled(BaseModel):
-    discriminator: Literal["disabled"] = "disabled"
-
-
-class ContextAdvanced(BaseModel):
-    discriminator: Literal["advanced"] = "advanced"
-    max_characters: Optional[int] = SchemaField(
-        default=None,
-        description="Maximum character limit for context string",
-        placeholder="10000",
+        default="",
+        description="Query string for summarization",
+        placeholder="Enter query",
    )


 class ContentSettings(BaseModel):
-    text: Optional[Union[bool, TextEnabled, TextDisabled, TextAdvanced]] = SchemaField(
-        default=None,
-        description="Text content retrieval. Boolean for simple enable/disable or object for advanced settings",
+    text: TextSettings = SchemaField(
+        default=TextSettings(),
    )
-    highlights: Optional[HighlightSettings] = SchemaField(
-        default=None,
-        description="Text snippets most relevant from each page",
+    highlights: HighlightSettings = SchemaField(
+        default=HighlightSettings(),
    )
-    summary: Optional[SummarySettings] = SchemaField(
-        default=None,
-        description="LLM-generated summary of the webpage",
-    )
-    livecrawl: Optional[LivecrawlTypes] = SchemaField(
-        default=None,
-        description="Livecrawling options: never, fallback, always, preferred",
-        advanced=True,
-    )
-    livecrawl_timeout: Optional[int] = SchemaField(
-        default=None,
-        description="Timeout for livecrawling in milliseconds",
-        placeholder="10000",
-        advanced=True,
-    )
-    subpages: Optional[int] = SchemaField(
-        default=None,
-        description="Number of subpages to crawl",
-        placeholder="0",
-        ge=0,
-        advanced=True,
-    )
-    subpage_target: Optional[Union[str, list[str]]] = SchemaField(
-        default=None,
-        description="Keyword(s) to find specific subpages of search results",
-        advanced=True,
-    )
-    extras: Optional[ExtrasSettings] = SchemaField(
-        default=None,
-        description="Extra parameters for additional content",
-        advanced=True,
-    )
-    context: Optional[Union[bool, ContextEnabled, ContextDisabled, ContextAdvanced]] = (
-        SchemaField(
-            default=None,
-            description="Format search results into a context string for LLMs",
-            advanced=True,
-        )
+    summary: SummarySettings = SchemaField(
+        default=SummarySettings(),
    )


@@ -226,225 +127,3 @@ class WebsetEnrichmentConfig(BaseModel):
        default=None,
        description="Options for the enrichment",
    )
-
-
-# Shared result models
-class ExaSearchExtras(BaseModel):
-    links: list[str] = SchemaField(
-        default_factory=list, description="Array of links from the search result"
-    )
-    imageLinks: list[str] = SchemaField(
-        default_factory=list, description="Array of image links from the search result"
-    )
-
-
-class ExaSearchResults(BaseModel):
-    title: str | None = None
-    url: str | None = None
-    publishedDate: str | None = None
-    author: str | None = None
-    id: str
-    image: MediaFileType | None = None
-    favicon: MediaFileType | None = None
-    text: str | None = None
-    highlights: list[str] = SchemaField(default_factory=list)
-    highlightScores: list[float] = SchemaField(default_factory=list)
-    summary: str | None = None
-    subpages: list[dict] = SchemaField(default_factory=list)
-    extras: ExaSearchExtras | None = None
-
-    @classmethod
-    def from_sdk(cls, sdk_result) -> "ExaSearchResults":
-        """Convert SDK Result (dataclass) to our Pydantic model."""
-        return cls(
-            id=getattr(sdk_result, "id", ""),
-            url=getattr(sdk_result, "url", None),
-            title=getattr(sdk_result, "title", None),
-            author=getattr(sdk_result, "author", None),
-            publishedDate=getattr(sdk_result, "published_date", None),
-            text=getattr(sdk_result, "text", None),
-            highlights=getattr(sdk_result, "highlights", None) or [],
-            highlightScores=getattr(sdk_result, "highlight_scores", None) or [],
-            summary=getattr(sdk_result, "summary", None),
-            subpages=getattr(sdk_result, "subpages", None) or [],
-            image=getattr(sdk_result, "image", None),
-            favicon=getattr(sdk_result, "favicon", None),
-            extras=getattr(sdk_result, "extras", None),
-        )
-
-
-# Cost tracking models
-class CostBreakdown(BaseModel):
-    keywordSearch: float = SchemaField(default=0.0)
-    neuralSearch: float = SchemaField(default=0.0)
-    contentText: float = SchemaField(default=0.0)
-    contentHighlight: float = SchemaField(default=0.0)
-    contentSummary: float = SchemaField(default=0.0)
-
-
-class CostBreakdownItem(BaseModel):
-    search: float = SchemaField(default=0.0)
-    contents: float = SchemaField(default=0.0)
-    breakdown: CostBreakdown = SchemaField(default_factory=CostBreakdown)
-
-
-class PerRequestPrices(BaseModel):
-    neuralSearch_1_25_results: float = SchemaField(default=0.005)
-    neuralSearch_26_100_results: float = SchemaField(default=0.025)
-    neuralSearch_100_plus_results: float = SchemaField(default=1.0)
-    keywordSearch_1_100_results: float = SchemaField(default=0.0025)
-    keywordSearch_100_plus_results: float = SchemaField(default=3.0)
-
-
-class PerPagePrices(BaseModel):
-    contentText: float = SchemaField(default=0.001)
-    contentHighlight: float = SchemaField(default=0.001)
-    contentSummary: float = SchemaField(default=0.001)
-
-
-class CostDollars(BaseModel):
-    total: float = SchemaField(description="Total dollar cost for your request")
-    breakDown: list[CostBreakdownItem] = SchemaField(
-        default_factory=list, description="Breakdown of costs by operation type"
-    )
-    perRequestPrices: PerRequestPrices = SchemaField(
-        default_factory=PerRequestPrices,
-        description="Standard price per request for different operations",
-    )
-    perPagePrices: PerPagePrices = SchemaField(
-        default_factory=PerPagePrices,
-        description="Standard price per page for different content operations",
-    )
-
-
-# Helper functions for payload processing
-def process_text_field(
-    text: Union[bool, TextEnabled, TextDisabled, TextAdvanced, None],
-) -> Optional[Union[bool, Dict[str, Any]]]:
-    """Process text field for API payload."""
-    if text is None:
-        return None
-
-    # Handle backward compatibility with boolean
-    if isinstance(text, bool):
-        return text
-    elif isinstance(text, TextDisabled):
-        return False
-    elif isinstance(text, TextEnabled):
-        return True
-    elif isinstance(text, TextAdvanced):
-        text_dict = {}
-        if text.max_characters:
-            text_dict["maxCharacters"] = text.max_characters
-        if text.include_html_tags:
-            text_dict["includeHtmlTags"] = text.include_html_tags
-        return text_dict if text_dict else True
-    return None
-
-
-def process_contents_settings(contents: Optional[ContentSettings]) -> Dict[str, Any]:
-    """Process ContentSettings into API payload format."""
-    if not contents:
-        return {}
-
-    content_settings = {}
-
-    # Handle text field (can be boolean or object)
-    text_value = process_text_field(contents.text)
-    if text_value is not None:
-        content_settings["text"] = text_value
-
-    # Handle highlights
-    if contents.highlights:
-        highlights_dict: Dict[str, Any] = {
-            "numSentences": contents.highlights.num_sentences,
-            "highlightsPerUrl": contents.highlights.highlights_per_url,
-        }
-        if contents.highlights.query:
-            highlights_dict["query"] = contents.highlights.query
-        content_settings["highlights"] = highlights_dict
-
-    if contents.summary:
-        summary_dict = {}
-        if contents.summary.query:
-            summary_dict["query"] = contents.summary.query
-        if contents.summary.schema:
-            summary_dict["schema"] = contents.summary.schema
-        content_settings["summary"] = summary_dict
-
-    if contents.livecrawl:
-        content_settings["livecrawl"] = contents.livecrawl.value
-
-    if contents.livecrawl_timeout is not None:
-        content_settings["livecrawlTimeout"] = contents.livecrawl_timeout
-
-    if contents.subpages is not None:
-        content_settings["subpages"] = contents.subpages
-
-    if contents.subpage_target:
-        content_settings["subpageTarget"] = contents.subpage_target
-
-    if contents.extras:
-        extras_dict = {}
-        if contents.extras.links:
-            extras_dict["links"] = contents.extras.links
-        if contents.extras.image_links:
-            extras_dict["imageLinks"] = contents.extras.image_links
-        content_settings["extras"] = extras_dict
-
-    context_value = process_context_field(contents.context)
-    if context_value is not None:
-        content_settings["context"] = context_value
-
-    return content_settings
-
-
-def process_context_field(
-    context: Union[bool, dict, ContextEnabled, ContextDisabled, ContextAdvanced, None],
-) -> Optional[Union[bool, Dict[str, int]]]:
-    """Process context field for API payload."""
-    if context is None:
-        return None
-
-    # Handle backward compatibility with boolean
-    if isinstance(context, bool):
-        return context if context else None
-    elif isinstance(context, dict) and "maxCharacters" in context:
-        return {"maxCharacters": context["maxCharacters"]}
-    elif isinstance(context, ContextDisabled):
-        return None  # Don't send context field at all when disabled
-    elif isinstance(context, ContextEnabled):
-        return True
-    elif isinstance(context, ContextAdvanced):
-        if context.max_characters:
-            return {"maxCharacters": context.max_characters}
-        return True
-    return None
-
-
-def format_date_fields(
-    input_data: Any, date_field_mapping: Dict[str, str]
-) -> Dict[str, str]:
-    """Format datetime fields for API payload."""
-    formatted_dates = {}
-    for input_field, api_field in date_field_mapping.items():
-        value = getattr(input_data, input_field, None)
-        if value:
-            formatted_dates[api_field] = value.strftime("%Y-%m-%dT%H:%M:%S.000Z")
-    return formatted_dates
-
-
-def add_optional_fields(
-    input_data: Any,
-    field_mapping: Dict[str, str],
-    payload: Dict[str, Any],
-    process_enums: bool = False,
-) -> None:
-    """Add optional fields to payload if they have values."""
-    for input_field, api_field in field_mapping.items():
-        value = getattr(input_data, input_field, None)
-        if value:  # Only add non-empty values
-            if process_enums and hasattr(value, "value"):
-                payload[api_field] = value.value
-            else:
-                payload[api_field] = value
--- a/autogpt_platform/backend/backend/blocks/exa/model.py
+++ b/autogpt_platform/backend/backend/blocks/exa/model.py
@@ -0,0 +1,247 @@
+from datetime import datetime
+from enum import Enum
+from typing import Any, Dict, List, Optional
+
+from pydantic import BaseModel, Field
+
+
+# Enum definitions based on available options
+class WebsetStatus(str, Enum):
+    IDLE = "idle"
+    PENDING = "pending"
+    RUNNING = "running"
+    PAUSED = "paused"
+
+
+class WebsetSearchStatus(str, Enum):
+    CREATED = "created"
+    # Add more if known, based on example it's "created"
+
+
+class ImportStatus(str, Enum):
+    PENDING = "pending"
+    # Add more if known
+
+
+class ImportFormat(str, Enum):
+    CSV = "csv"
+    # Add more if known
+
+
+class EnrichmentStatus(str, Enum):
+    PENDING = "pending"
+    # Add more if known
+
+
+class EnrichmentFormat(str, Enum):
+    TEXT = "text"
+    # Add more if known
+
+
+class MonitorStatus(str, Enum):
+    ENABLED = "enabled"
+    # Add more if known
+
+
+class MonitorBehaviorType(str, Enum):
+    SEARCH = "search"
+    # Add more if known
+
+
+class MonitorRunStatus(str, Enum):
+    CREATED = "created"
+    # Add more if known
+
+
+class CanceledReason(str, Enum):
+    WEBSET_DELETED = "webset_deleted"
+    # Add more if known
+
+
+class FailedReason(str, Enum):
+    INVALID_FORMAT = "invalid_format"
+    # Add more if known
+
+
+class Confidence(str, Enum):
+    HIGH = "high"
+    # Add more if known
+
+
+# Nested models
+
+
+class Entity(BaseModel):
+    type: str
+
+
+class Criterion(BaseModel):
+    description: str
+    successRate: Optional[int] = None
+
+
+class ExcludeItem(BaseModel):
+    source: str = Field(default="import")
+    id: str
+
+
+class Relationship(BaseModel):
+    definition: str
+    limit: Optional[float] = None
+
+
+class ScopeItem(BaseModel):
+    source: str = Field(default="import")
+    id: str
+    relationship: Optional[Relationship] = None
+
+
+class Progress(BaseModel):
+    found: int
+    analyzed: int
+    completion: int
+    timeLeft: int
+
+
+class Bounds(BaseModel):
+    min: int
+    max: int
+
+
+class Expected(BaseModel):
+    total: int
+    confidence: str = Field(default="high")  # Use str or Confidence enum
+    bounds: Bounds
+
+
+class Recall(BaseModel):
+    expected: Expected
+    reasoning: str
+
+
+class WebsetSearch(BaseModel):
+    id: str
+    object: str = Field(default="webset_search")
+    status: str = Field(default="created")  # Or use WebsetSearchStatus
+    websetId: str
+    query: str
+    entity: Entity
+    criteria: List[Criterion]
+    count: int
+    behavior: str = Field(default="override")
+    exclude: List[ExcludeItem]
+    scope: List[ScopeItem]
+    progress: Progress
+    recall: Recall
+    metadata: Dict[str, Any] = Field(default_factory=dict)
+    canceledAt: Optional[datetime] = None
+    canceledReason: Optional[str] = Field(default=None)  # Or use CanceledReason
+    createdAt: datetime
+    updatedAt: datetime
+
+
+class ImportEntity(BaseModel):
+    type: str
+
+
+class Import(BaseModel):
+    id: str
+    object: str = Field(default="import")
+    status: str = Field(default="pending")  # Or use ImportStatus
+    format: str = Field(default="csv")  # Or use ImportFormat
+    entity: ImportEntity
+    title: str
+    count: int
+    metadata: Dict[str, Any] = Field(default_factory=dict)
+    failedReason: Optional[str] = Field(default=None)  # Or use FailedReason
+    failedAt: Optional[datetime] = None
+    failedMessage: Optional[str] = None
+    createdAt: datetime
+    updatedAt: datetime
+
+
+class Option(BaseModel):
+    label: str
+
+
+class WebsetEnrichment(BaseModel):
+    id: str
+    object: str = Field(default="webset_enrichment")
+    status: str = Field(default="pending")  # Or use EnrichmentStatus
+    websetId: str
+    title: str
+    description: str
+    format: str = Field(default="text")  # Or use EnrichmentFormat
+    options: List[Option]
+    instructions: str
+    metadata: Dict[str, Any] = Field(default_factory=dict)
+    createdAt: datetime
+    updatedAt: datetime
+
+
+class Cadence(BaseModel):
+    cron: str
+    timezone: str = Field(default="Etc/UTC")
+
+
+class BehaviorConfig(BaseModel):
+    query: Optional[str] = None
+    criteria: Optional[List[Criterion]] = None
+    entity: Optional[Entity] = None
+    count: Optional[int] = None
+    behavior: Optional[str] = Field(default=None)
+
+
+class Behavior(BaseModel):
+    type: str = Field(default="search")  # Or use MonitorBehaviorType
+    config: BehaviorConfig
+
+
+class MonitorRun(BaseModel):
+    id: str
+    object: str = Field(default="monitor_run")
+    status: str = Field(default="created")  # Or use MonitorRunStatus
+    monitorId: str
+    type: str = Field(default="search")
+    completedAt: Optional[datetime] = None
+    failedAt: Optional[datetime] = None
+    failedReason: Optional[str] = None
+    canceledAt: Optional[datetime] = None
+    createdAt: datetime
+    updatedAt: datetime
+
+
+class Monitor(BaseModel):
+    id: str
+    object: str = Field(default="monitor")
+    status: str = Field(default="enabled")  # Or use MonitorStatus
+    websetId: str
+    cadence: Cadence
+    behavior: Behavior
+    lastRun: Optional[MonitorRun] = None
+    nextRunAt: Optional[datetime] = None
+    metadata: Dict[str, Any] = Field(default_factory=dict)
+    createdAt: datetime
+    updatedAt: datetime
+
+
+class Webset(BaseModel):
+    id: str
+    object: str = Field(default="webset")
+    status: WebsetStatus
+    externalId: Optional[str] = None
+    title: Optional[str] = None
+    searches: List[WebsetSearch]
+    imports: List[Import]
+    enrichments: List[WebsetEnrichment]
+    monitors: List[Monitor]
+    streams: List[Any]
+    createdAt: datetime
+    updatedAt: datetime
+    metadata: Dict[str, Any] = Field(default_factory=dict)
+
+
+class ListWebsets(BaseModel):
+    data: List[Webset]
+    hasMore: bool
+    nextCursor: Optional[str] = None
--- a/Show More
+++ b/Show More