speeding up tests

## Summary
- Fixed auto-frame timing in new builder - now calls `fitView` after
nodes are rendered instead of on mount
- Replaced manual viewport calculation in legacy builder with React
Flow's `fitView` for consistency
- Both builders now properly center and frame all blocks when opening an
agent

  ## Test plan
- [x] Open an existing agent with multiple blocks in the new builder -
verify all blocks are visible and centered
- [x] Open an existing agent in the legacy builder - verify all blocks
are visible and centered
  - [x] Verify the manual "Frame" button still works correctly

.github/copilot-instructions.md

@@ -12,6 +12,7 @@ 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
@@ -23,15 +24,17 @@ 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
@@ -48,6 +51,7 @@ 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
 ```
@@ -58,6 +62,7 @@ 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)
@@ -68,6 +73,7 @@ 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
@@ -81,23 +87,27 @@ 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:
@@ -108,6 +118,7 @@ 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
@@ -121,6 +132,7 @@ 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
@@ -136,6 +148,7 @@ pnpm storybook                      # Start component development server
 ### 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
@@ -146,11 +159,13 @@ 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/supabase/` - Authentication and database client
@@ -160,6 +175,7 @@ pnpm storybook                      # Start component development server
 ### 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
@@ -167,6 +183,7 @@ 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
@@ -174,13 +191,15 @@ 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)  
+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
@@ -189,6 +208,7 @@ 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
@@ -198,6 +218,7 @@ 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
@@ -205,21 +226,76 @@ 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
-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
+
+**📖 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
 
 ### 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
@@ -229,6 +305,7 @@ 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
@@ -237,8 +314,9 @@ 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.

.github/workflows/claude-dependabot.yml

@@ -80,7 +80,7 @@ jobs:
       - name: Set up Node.js
         uses: actions/setup-node@v4
         with:
-          node-version: "21"
+          node-version: "22"
 
       - name: Enable corepack
         run: corepack enable

.github/workflows/claude.yml

@@ -44,6 +44,12 @@ 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
@@ -90,7 +96,7 @@ jobs:
       - name: Set up Node.js
         uses: actions/setup-node@v4
         with:
-          node-version: "21"
+          node-version: "22"
 
       - name: Enable corepack
         run: corepack enable

.github/workflows/copilot-setup-steps.yml

@@ -78,7 +78,7 @@ jobs:
       - name: Set up Node.js
         uses: actions/setup-node@v4
         with:
-          node-version: "21"
+          node-version: "22"
 
       - name: Enable corepack
         run: corepack enable
@@ -299,4 +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)"

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

@@ -12,6 +12,10 @@ on:
       - "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
@@ -30,7 +34,7 @@ jobs:
       - name: Set up Node.js
         uses: actions/setup-node@v4
         with:
-          node-version: "21"
+          node-version: "22.18.0"
 
       - name: Enable corepack
         run: corepack enable
@@ -62,7 +66,7 @@ jobs:
       - name: Set up Node.js
         uses: actions/setup-node@v4
         with:
-          node-version: "21"
+          node-version: "22.18.0"
 
       - name: Enable corepack
         run: corepack enable
@@ -97,7 +101,7 @@ jobs:
       - name: Set up Node.js
         uses: actions/setup-node@v4
         with:
-          node-version: "21"
+          node-version: "22.18.0"
 
       - name: Enable corepack
         run: corepack enable
@@ -138,7 +142,7 @@ jobs:
       - name: Set up Node.js
         uses: actions/setup-node@v4
         with:
-          node-version: "21"
+          node-version: "22.18.0"
 
       - name: Enable corepack
         run: corepack enable

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

@@ -12,6 +12,10 @@ on:
       - "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
@@ -30,7 +34,7 @@ jobs:
       - name: Set up Node.js
         uses: actions/setup-node@v4
         with:
-          node-version: "21"
+          node-version: "22.18.0"
 
       - name: Enable corepack
         run: corepack enable
@@ -66,7 +70,7 @@ jobs:
       - name: Set up Node.js
         uses: actions/setup-node@v4
         with:
-          node-version: "21"
+          node-version: "22.18.0"
 
       - name: Enable corepack
         run: corepack enable

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

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

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

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

.gitignore

@@ -178,3 +178,4 @@ autogpt_platform/backend/settings.py
 *.ign.*
 .test-contents
 .claude/settings.local.json
+/autogpt_platform/backend/logs

.pr_agent.toml

@@ -1,6 +1,3 @@
-[pr_reviewer]
-num_code_suggestions=0
-
 [pr_code_suggestions]
 commitable_code_suggestions=false
 num_code_suggestions=0

autogpt_platform/CLAUDE.md

@@ -63,6 +63,9 @@ 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
 
@@ -75,12 +78,23 @@ pnpm storybook
 # Build production
 pnpm build
 
+# Format and lint
+pnpm format
+
 # Type checking
 pnpm types
 ```
 
-We have a components library in autogpt_platform/frontend/src/components/atoms that should be used when adding new pages and components. 
+**📖 Complete Guide**: See `/frontend/CONTRIBUTING.md` and `/frontend/.cursorrules` for comprehensive frontend patterns.
 
+**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
 
@@ -95,11 +109,16 @@ We have a components library in autogpt_platform/frontend/src/components/atoms t
 
 ### Frontend Architecture
 
-- **Framework**: Next.js App Router with React Server Components
-- **State Management**: React hooks + Supabase client for real-time updates
+- **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)
 - **Workflow Builder**: Visual graph editor using @xyflow/react
-- **UI Components**: Radix UI primitives with Tailwind CSS styling
+- **UI Components**: shadcn/ui (Radix UI primitives) with Tailwind CSS styling
+- **Icons**: Phosphor Icons only
 - **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
 
@@ -153,6 +172,7 @@ 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)
@@ -160,6 +180,7 @@ 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
@@ -171,6 +192,8 @@ 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/`
@@ -180,10 +203,20 @@ ex: do the inputs and outputs tie well together?
 
 **Frontend feature development:**
 
-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
+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
 
 ### Security Implementation
 

autogpt_platform/Makefile

@@ -0,0 +1,61 @@
+.PHONY: start-core stop-core logs-core format lint migrate run-backend run-frontend load-store-agents
+
+# Run just Supabase + Redis + RabbitMQ
+start-core:
+	docker compose up -d deps
+
+# Stop core services
+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
+
+# Run formatting and linting for backend and frontend
+format:
+	cd backend && poetry run format
+	cd frontend && pnpm format
+	cd frontend && pnpm lint
+
+init-env:
+	cp -n .env.default .env || true
+	cd backend && cp -n .env.default .env || true
+	cd frontend && cp -n .env.default .env || true
+
+
+# Run migrations for backend
+migrate:
+	cd backend && poetry run prisma migrate deploy
+	cd backend && poetry run prisma generate
+
+run-backend:
+	cd backend && poetry run app
+
+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 (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"

autogpt_platform/README.md

@@ -38,6 +38,37 @@ To run the AutoGPT Platform, follow these steps:
 
 4. After all the services are in ready state, open your browser and navigate to `http://localhost:3000` to access the AutoGPT Platform frontend.
 
+### Running Just Core services
+
+You can now run the following to enable just the core services.
+
+```
+# For help
+make help
+
+# Run just Supabase + Redis + RabbitMQ
+make start-core
+
+# Stop core services
+make stop-core
+
+# View logs from core services 
+make logs-core
+
+# Run formatting and linting for backend and frontend
+make format
+
+# Run migrations for backend database
+make migrate
+
+# Run backend server
+make run-backend
+
+# Run frontend development server
+make run-frontend
+
+```
+
 ### Docker Compose Commands
 
 Here are some useful Docker Compose commands for managing your AutoGPT Platform:

autogpt_platform/autogpt_libs/autogpt_libs/auth/__init__.py

@@ -1,5 +1,10 @@
 from .config import verify_settings
-from .dependencies import get_user_id, requires_admin_user, requires_user
+from .dependencies import (
+    get_optional_user_id,
+    get_user_id,
+    requires_admin_user,
+    requires_user,
+)
 from .helpers import add_auth_responses_to_openapi
 from .models import User
 
@@ -8,6 +13,7 @@ __all__ = [
     "get_user_id",
     "requires_admin_user",
     "requires_user",
+    "get_optional_user_id",
     "add_auth_responses_to_openapi",
     "User",
 ]

autogpt_platform/autogpt_libs/autogpt_libs/auth/dependencies.py

@@ -4,13 +4,55 @@ 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)
 
-def requires_user(jwt_payload: dict = fastapi.Security(get_jwt_payload)) -> User:
+# 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:
     """
     FastAPI dependency that requires a valid authenticated user.
 
@@ -20,7 +62,9 @@ def requires_user(jwt_payload: dict = fastapi.Security(get_jwt_payload)) -> User
     return verify_user(jwt_payload, admin_only=False)
 
 
-def requires_admin_user(jwt_payload: dict = fastapi.Security(get_jwt_payload)) -> User:
+async def requires_admin_user(
+    jwt_payload: dict = fastapi.Security(get_jwt_payload),
+) -> User:
     """
     FastAPI dependency that requires a valid admin user.
 
@@ -30,16 +74,44 @@ def requires_admin_user(jwt_payload: dict = fastapi.Security(get_jwt_payload)) -
     return verify_user(jwt_payload, admin_only=True)
 
 
-def get_user_id(jwt_payload: dict = fastapi.Security(get_jwt_payload)) -> str:
+async def get_user_id(
+    request: fastapi.Request, 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

autogpt_platform/autogpt_libs/autogpt_libs/auth/dependencies_test.py

@@ -4,9 +4,10 @@ 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, Security
+from fastapi import FastAPI, HTTPException, Request, Security
 from fastapi.testclient import TestClient
 from pytest_mock import MockerFixture
 
@@ -45,7 +46,8 @@ class TestAuthDependencies:
         """Create a test client."""
         return TestClient(app)
 
-    def test_requires_user_with_valid_jwt_payload(self, mocker: MockerFixture):
+    @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"}
 
@@ -53,12 +55,13 @@ class TestAuthDependencies:
         mocker.patch(
             "autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
         )
-        user = requires_user(jwt_payload)
+        user = await requires_user(jwt_payload)
         assert isinstance(user, User)
         assert user.user_id == "user-123"
         assert user.role == "user"
 
-    def test_requires_user_with_admin_jwt_payload(self, mocker: MockerFixture):
+    @pytest.mark.asyncio
+    async def test_requires_user_with_admin_jwt_payload(self, mocker: MockerFixture):
         """Test requires_user accepts admin users."""
         jwt_payload = {
             "sub": "admin-456",
@@ -69,28 +72,31 @@ class TestAuthDependencies:
         mocker.patch(
             "autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
         )
-        user = requires_user(jwt_payload)
+        user = await requires_user(jwt_payload)
         assert user.user_id == "admin-456"
         assert user.role == "admin"
 
-    def test_requires_user_missing_sub(self):
+    @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"}
 
         with pytest.raises(HTTPException) as exc_info:
-            requires_user(jwt_payload)
+            await requires_user(jwt_payload)
         assert exc_info.value.status_code == 401
         assert "User ID not found" in exc_info.value.detail
 
-    def test_requires_user_empty_sub(self):
+    @pytest.mark.asyncio
+    async def test_requires_user_empty_sub(self):
         """Test requires_user with empty user ID."""
         jwt_payload = {"sub": "", "role": "user"}
 
         with pytest.raises(HTTPException) as exc_info:
-            requires_user(jwt_payload)
+            await requires_user(jwt_payload)
         assert exc_info.value.status_code == 401
 
-    def test_requires_admin_user_with_admin(self, mocker: MockerFixture):
+    @pytest.mark.asyncio
+    async def test_requires_admin_user_with_admin(self, mocker: MockerFixture):
         """Test requires_admin_user with admin role."""
         jwt_payload = {
             "sub": "admin-789",
@@ -101,51 +107,62 @@ class TestAuthDependencies:
         mocker.patch(
             "autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
         )
-        user = requires_admin_user(jwt_payload)
+        user = await requires_admin_user(jwt_payload)
         assert user.user_id == "admin-789"
         assert user.role == "admin"
 
-    def test_requires_admin_user_with_regular_user(self):
+    @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"}
 
         with pytest.raises(HTTPException) as exc_info:
-            requires_admin_user(jwt_payload)
+            await requires_admin_user(jwt_payload)
         assert exc_info.value.status_code == 403
         assert "Admin access required" in exc_info.value.detail
 
-    def test_requires_admin_user_missing_role(self):
+    @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"}
 
         with pytest.raises(KeyError):
-            requires_admin_user(jwt_payload)
+            await requires_admin_user(jwt_payload)
 
-    def test_get_user_id_with_valid_payload(self, mocker: MockerFixture):
+    @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 = get_user_id(jwt_payload)
+        user_id = await get_user_id(request, jwt_payload)
         assert user_id == "user-id-xyz"
 
-    def test_get_user_id_missing_sub(self):
+    @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:
-            get_user_id(jwt_payload)
+            await get_user_id(request, jwt_payload)
         assert exc_info.value.status_code == 401
         assert "User ID not found" in exc_info.value.detail
 
-    def test_get_user_id_none_sub(self):
+    @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:
-            get_user_id(jwt_payload)
+            await get_user_id(request, jwt_payload)
         assert exc_info.value.status_code == 401
 
 
@@ -170,7 +187,8 @@ class TestAuthDependenciesIntegration:
 
         return _create_token
 
-    def test_endpoint_auth_enabled_no_token(self):
+    @pytest.mark.asyncio
+    async def test_endpoint_auth_enabled_no_token(self):
         """Test endpoints require token when auth is enabled."""
         app = FastAPI()
 
@@ -184,7 +202,8 @@ class TestAuthDependenciesIntegration:
         response = client.get("/test")
         assert response.status_code == 401
 
-    def test_endpoint_with_valid_token(self, create_token):
+    @pytest.mark.asyncio
+    async def test_endpoint_with_valid_token(self, create_token):
         """Test endpoint with valid JWT token."""
         app = FastAPI()
 
@@ -203,7 +222,8 @@ class TestAuthDependenciesIntegration:
         assert response.status_code == 200
         assert response.json()["user_id"] == "test-user"
 
-    def test_admin_endpoint_requires_admin_role(self, create_token):
+    @pytest.mark.asyncio
+    async def test_admin_endpoint_requires_admin_role(self, create_token):
         """Test admin endpoint rejects non-admin users."""
         app = FastAPI()
 
@@ -240,7 +260,8 @@ class TestAuthDependenciesIntegration:
 class TestAuthDependenciesEdgeCases:
     """Edge case tests for authentication dependencies."""
 
-    def test_dependency_with_complex_payload(self):
+    @pytest.mark.asyncio
+    async def test_dependency_with_complex_payload(self):
         """Test dependencies handle complex JWT payloads."""
         complex_payload = {
             "sub": "user-123",
@@ -256,14 +277,15 @@ class TestAuthDependenciesEdgeCases:
             "exp": 9999999999,
         }
 
-        user = requires_user(complex_payload)
+        user = await requires_user(complex_payload)
         assert user.user_id == "user-123"
         assert user.email == "test@example.com"
 
-        admin = requires_admin_user(complex_payload)
+        admin = await requires_admin_user(complex_payload)
         assert admin.role == "admin"
 
-    def test_dependency_with_unicode_in_payload(self):
+    @pytest.mark.asyncio
+    async def test_dependency_with_unicode_in_payload(self):
         """Test dependencies handle unicode in JWT payloads."""
         unicode_payload = {
             "sub": "user-😀-123",
@@ -272,11 +294,12 @@ class TestAuthDependenciesEdgeCases:
             "name": "日本語",
         }
 
-        user = requires_user(unicode_payload)
+        user = await requires_user(unicode_payload)
         assert "😀" in user.user_id
         assert user.email == "测试@example.com"
 
-    def test_dependency_with_null_values(self):
+    @pytest.mark.asyncio
+    async def test_dependency_with_null_values(self):
         """Test dependencies handle null values in payload."""
         null_payload = {
             "sub": "user-123",
@@ -286,18 +309,19 @@ class TestAuthDependenciesEdgeCases:
             "metadata": None,
         }
 
-        user = requires_user(null_payload)
+        user = await requires_user(null_payload)
         assert user.user_id == "user-123"
         assert user.email is None
 
-    def test_concurrent_requests_isolation(self):
+    @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"}
         payload2 = {"sub": "user-2", "role": "admin"}
 
         # Simulate concurrent processing
-        user1 = requires_user(payload1)
-        user2 = requires_admin_user(payload2)
+        user1 = await requires_user(payload1)
+        user2 = await requires_admin_user(payload2)
 
         assert user1.user_id == "user-1"
         assert user2.user_id == "user-2"
@@ -314,7 +338,8 @@ class TestAuthDependenciesEdgeCases:
             ({"sub": "user", "role": "user"}, "Admin access required", True),
         ],
     )
-    def test_dependency_error_cases(
+    @pytest.mark.asyncio
+    async def test_dependency_error_cases(
         self, payload, expected_error: str, admin_only: bool
     ):
         """Test that errors propagate correctly through dependencies."""
@@ -325,7 +350,8 @@ class TestAuthDependenciesEdgeCases:
             verify_user(payload, admin_only=admin_only)
         assert expected_error in exc_info.value.detail
 
-    def test_dependency_valid_user(self):
+    @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
         from autogpt_libs.auth.jwt_utils import verify_user
@@ -333,3 +359,196 @@ 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()

autogpt_platform/autogpt_libs/autogpt_libs/auth/jwt_utils.py

@@ -16,7 +16,7 @@ bearer_jwt_auth = HTTPBearer(
 )
 
 
-def get_jwt_payload(
+async def get_jwt_payload(
     credentials: HTTPAuthorizationCredentials | None = Security(bearer_jwt_auth),
 ) -> dict[str, Any]:
     """

autogpt_platform/autogpt_libs/autogpt_libs/auth/jwt_utils_test.py

@@ -116,32 +116,32 @@ def test_parse_jwt_token_missing_audience():
     assert "Invalid token" in str(exc_info.value)
 
 
-def test_get_jwt_payload_with_valid_token():
+async def test_get_jwt_payload_with_valid_token():
     """Test extracting JWT payload with valid bearer token."""
     token = create_token(TEST_USER_PAYLOAD)
     credentials = HTTPAuthorizationCredentials(scheme="Bearer", credentials=token)
 
-    result = jwt_utils.get_jwt_payload(credentials)
+    result = await jwt_utils.get_jwt_payload(credentials)
     assert result["sub"] == "test-user-id"
     assert result["role"] == "user"
 
 
-def test_get_jwt_payload_no_credentials():
+async def test_get_jwt_payload_no_credentials():
     """Test JWT payload when no credentials provided."""
     with pytest.raises(HTTPException) as exc_info:
-        jwt_utils.get_jwt_payload(None)
+        await jwt_utils.get_jwt_payload(None)
     assert exc_info.value.status_code == 401
     assert "Authorization header is missing" in exc_info.value.detail
 
 
-def test_get_jwt_payload_invalid_token():
+async def test_get_jwt_payload_invalid_token():
     """Test JWT payload extraction with invalid token."""
     credentials = HTTPAuthorizationCredentials(
         scheme="Bearer", credentials="invalid.token.here"
     )
 
     with pytest.raises(HTTPException) as exc_info:
-        jwt_utils.get_jwt_payload(credentials)
+        await jwt_utils.get_jwt_payload(credentials)
     assert exc_info.value.status_code == 401
     assert "Invalid token" in exc_info.value.detail
 

autogpt_platform/autogpt_libs/autogpt_libs/logging/config.py

@@ -4,6 +4,7 @@ import logging
 import os
 import socket
 import sys
+from logging.handlers import RotatingFileHandler
 from pathlib import Path
 
 from pydantic import Field, field_validator
@@ -93,42 +94,36 @@ 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
-    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))
+    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))
 
-    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
-    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,
-        )
+    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
 
-        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)
+        structured_log_handler = StructuredLogHandler(stream=sys.stdout)
+        structured_log_handler.setLevel(config.level)
+        log_handlers.append(structured_log_handler)
 
     # File logging setup
     if config.enable_file_logging:
@@ -139,8 +134,13 @@ def configure_logging(force_cloud_logging: bool = False) -> None:
         print(f"Log directory: {config.log_dir}")
 
         # Activity log handler (INFO and above)
-        activity_log_handler = logging.FileHandler(
-            config.log_dir / LOG_FILE, "a", "utf-8"
+        # Security fix: Use RotatingFileHandler with size limits to prevent disk exhaustion
+        activity_log_handler = RotatingFileHandler(
+            config.log_dir / LOG_FILE,
+            mode="a",
+            encoding="utf-8",
+            maxBytes=10 * 1024 * 1024,  # 10MB per file
+            backupCount=3,  # Keep 3 backup files (40MB total)
         )
         activity_log_handler.setLevel(config.level)
         activity_log_handler.setFormatter(
@@ -150,8 +150,13 @@ def configure_logging(force_cloud_logging: bool = False) -> None:
 
         if config.level == logging.DEBUG:
             # Debug log handler (all levels)
-            debug_log_handler = logging.FileHandler(
-                config.log_dir / DEBUG_LOG_FILE, "a", "utf-8"
+            # Security fix: Use RotatingFileHandler with size limits
+            debug_log_handler = RotatingFileHandler(
+                config.log_dir / DEBUG_LOG_FILE,
+                mode="a",
+                encoding="utf-8",
+                maxBytes=10 * 1024 * 1024,  # 10MB per file
+                backupCount=3,  # Keep 3 backup files (40MB total)
             )
             debug_log_handler.setLevel(logging.DEBUG)
             debug_log_handler.setFormatter(
@@ -160,8 +165,13 @@ def configure_logging(force_cloud_logging: bool = False) -> None:
             log_handlers.append(debug_log_handler)
 
         # Error log handler (ERROR and above)
-        error_log_handler = logging.FileHandler(
-            config.log_dir / ERROR_LOG_FILE, "a", "utf-8"
+        # Security fix: Use RotatingFileHandler with size limits
+        error_log_handler = RotatingFileHandler(
+            config.log_dir / ERROR_LOG_FILE,
+            mode="a",
+            encoding="utf-8",
+            maxBytes=10 * 1024 * 1024,  # 10MB per file
+            backupCount=3,  # Keep 3 backup files (40MB total)
         )
         error_log_handler.setLevel(logging.ERROR)
         error_log_handler.setFormatter(AGPTFormatter(DEBUG_LOG_FORMAT, no_color=True))
@@ -169,7 +179,13 @@ def configure_logging(force_cloud_logging: bool = False) -> None:
 
     # Configure the root logger
     logging.basicConfig(
-        format=DEBUG_LOG_FORMAT if config.level == logging.DEBUG else SIMPLE_LOG_FORMAT,
+        format=(
+            "%(levelname)s  %(message)s"
+            if structured_logging
+            else (
+                DEBUG_LOG_FORMAT if config.level == logging.DEBUG else SIMPLE_LOG_FORMAT
+            )
+        ),
         level=config.level,
         handlers=log_handlers,
     )

autogpt_platform/autogpt_libs/autogpt_libs/utils/cache.py

@@ -1,328 +0,0 @@
-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 locks
-        cache_storage = {}
-
-        if inspect.iscoroutinefunction(target_func):
-            # Async function with asyncio.Lock
-            cache_lock = asyncio.Lock()
-
-            @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 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()

autogpt_platform/backend/.env.default

@@ -134,13 +134,6 @@ 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=
 

autogpt_platform/backend/Dockerfile

@@ -47,6 +47,7 @@ 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
@@ -92,6 +93,7 @@ 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

autogpt_platform/backend/agents/StoreAgent_rows.csv

@@ -0,0 +1,242 @@
+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:
+Finding a domain name that fits your brand shouldn’t take hours of searching and failed checks. The Domain Name Finder Agent turns your pitch into hundreds of creative, brand-ready domain ideas—filtered by live availability so every result is actionable.
+
+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.
+
+
+Key Use Cases
+• Startup Founders: Quickly find brand-ready domains for MVP launches or rebrands.
+• 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

autogpt_platform/backend/agents/agent_31daf49d-31d3-476b-aa4c-099abc59b458.json

@@ -0,0 +1,590 @@
+{
+  "id": "7b2e2095-782a-4f8d-adda-e62b661bccf5",
+  "version": 29,
+  "is_active": false,
+  "name": "Unspirational Poster Maker",
+  "description": "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 clich\u00e9s 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.",
+  "instructions": null,
+  "recommended_schedule_cron": null,
+  "nodes": [
+    {
+      "id": "5ac3727a-1ea7-436b-a902-ef1bfd883a30",
+      "block_id": "363ae599-353e-4804-937e-b2ee3cef3da4",
+      "input_default": {
+        "name": "Generated Image",
+        "description": "The resulting generated image ready for you to review and post."
+      },
+      "metadata": {
+        "position": {
+          "x": 2329.937006807125,
+          "y": 80.49068076698347
+        }
+      },
+      "input_links": [
+        {
+          "id": "c6c511e8-e6a4-4969-9bc8-f67d60c1e229",
+          "source_id": "86665e90-ffbf-48fb-ad3f-e5d31fd50c51",
+          "sink_id": "5ac3727a-1ea7-436b-a902-ef1bfd883a30",
+          "source_name": "result",
+          "sink_name": "value",
+          "is_static": false
+        },
+        {
+          "id": "20845dda-91de-4508-8077-0504b1a5ae03",
+          "source_id": "28bda769-b88b-44c9-be5c-52c2667f137e",
+          "sink_id": "5ac3727a-1ea7-436b-a902-ef1bfd883a30",
+          "source_name": "result",
+          "sink_name": "value",
+          "is_static": false
+        },
+        {
+          "id": "6524c611-774b-45e9-899d-9a6aa80c549c",
+          "source_id": "e7cdc1a2-4427-4a8a-a31b-63c8e74842f8",
+          "sink_id": "5ac3727a-1ea7-436b-a902-ef1bfd883a30",
+          "source_name": "result",
+          "sink_name": "value",
+          "is_static": false
+        },
+        {
+          "id": "714a0821-e5ba-4af7-9432-50491adda7b1",
+          "source_id": "576c5677-9050-4d1c-aad4-36b820c04fef",
+          "sink_id": "5ac3727a-1ea7-436b-a902-ef1bfd883a30",
+          "source_name": "result",
+          "sink_name": "value",
+          "is_static": false
+        }
+      ],
+      "output_links": [],
+      "graph_id": "7b2e2095-782a-4f8d-adda-e62b661bccf5",
+      "graph_version": 29,
+      "webhook_id": null,
+      "webhook": null
+    },
+    {
+      "id": "7e026d19-f9a6-412f-8082-610f9ba0c410",
+      "block_id": "c0a8e994-ebf1-4a9c-a4d8-89d09c86741b",
+      "input_default": {
+        "name": "Theme",
+        "value": "Cooking"
+      },
+      "metadata": {
+        "position": {
+          "x": -1219.5966324967521,
+          "y": 80.50339731789956
+        }
+      },
+      "input_links": [],
+      "output_links": [
+        {
+          "id": "8c2bd1f7-b17b-4835-81b6-bb336097aa7a",
+          "source_id": "7e026d19-f9a6-412f-8082-610f9ba0c410",
+          "sink_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
+          "source_name": "result",
+          "sink_name": "prompt_values_#_THEME",
+          "is_static": true
+        }
+      ],
+      "graph_id": "7b2e2095-782a-4f8d-adda-e62b661bccf5",
+      "graph_version": 29,
+      "webhook_id": null,
+      "webhook": null
+    },
+    {
+      "id": "28bda769-b88b-44c9-be5c-52c2667f137e",
+      "block_id": "6ab085e2-20b3-4055-bc3e-08036e01eca6",
+      "input_default": {
+        "upscale": "No Upscale"
+      },
+      "metadata": {
+        "position": {
+          "x": 1132.373897280427,
+          "y": 88.44610377514573
+        }
+      },
+      "input_links": [
+        {
+          "id": "54588c74-e090-4e49-89e4-844b9952a585",
+          "source_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
+          "sink_id": "28bda769-b88b-44c9-be5c-52c2667f137e",
+          "source_name": "response",
+          "sink_name": "prompt",
+          "is_static": false
+        }
+      ],
+      "output_links": [
+        {
+          "id": "20845dda-91de-4508-8077-0504b1a5ae03",
+          "source_id": "28bda769-b88b-44c9-be5c-52c2667f137e",
+          "sink_id": "5ac3727a-1ea7-436b-a902-ef1bfd883a30",
+          "source_name": "result",
+          "sink_name": "value",
+          "is_static": false
+        }
+      ],
+      "graph_id": "7b2e2095-782a-4f8d-adda-e62b661bccf5",
+      "graph_version": 29,
+      "webhook_id": null,
+      "webhook": null
+    },
+    {
+      "id": "e7cdc1a2-4427-4a8a-a31b-63c8e74842f8",
+      "block_id": "6ab085e2-20b3-4055-bc3e-08036e01eca6",
+      "input_default": {
+        "upscale": "No Upscale"
+      },
+      "metadata": {
+        "position": {
+          "x": 590.7543882245375,
+          "y": 85.69546832466654
+        }
+      },
+      "input_links": [
+        {
+          "id": "66646786-3006-4417-a6b7-0158f2603d1d",
+          "source_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
+          "sink_id": "e7cdc1a2-4427-4a8a-a31b-63c8e74842f8",
+          "source_name": "response",
+          "sink_name": "prompt",
+          "is_static": false
+        }
+      ],
+      "output_links": [
+        {
+          "id": "6524c611-774b-45e9-899d-9a6aa80c549c",
+          "source_id": "e7cdc1a2-4427-4a8a-a31b-63c8e74842f8",
+          "sink_id": "5ac3727a-1ea7-436b-a902-ef1bfd883a30",
+          "source_name": "result",
+          "sink_name": "value",
+          "is_static": false
+        }
+      ],
+      "graph_id": "7b2e2095-782a-4f8d-adda-e62b661bccf5",
+      "graph_version": 29,
+      "webhook_id": null,
+      "webhook": null
+    },
+    {
+      "id": "576c5677-9050-4d1c-aad4-36b820c04fef",
+      "block_id": "6ab085e2-20b3-4055-bc3e-08036e01eca6",
+      "input_default": {
+        "upscale": "No Upscale"
+      },
+      "metadata": {
+        "position": {
+          "x": 60.48904654237981,
+          "y": 86.06183359510214
+        }
+      },
+      "input_links": [
+        {
+          "id": "201d3e03-bc06-4cee-846d-4c3c804d8857",
+          "source_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
+          "sink_id": "576c5677-9050-4d1c-aad4-36b820c04fef",
+          "source_name": "response",
+          "sink_name": "prompt",
+          "is_static": false
+        }
+      ],
+      "output_links": [
+        {
+          "id": "714a0821-e5ba-4af7-9432-50491adda7b1",
+          "source_id": "576c5677-9050-4d1c-aad4-36b820c04fef",
+          "sink_id": "5ac3727a-1ea7-436b-a902-ef1bfd883a30",
+          "source_name": "result",
+          "sink_name": "value",
+          "is_static": false
+        }
+      ],
+      "graph_id": "7b2e2095-782a-4f8d-adda-e62b661bccf5",
+      "graph_version": 29,
+      "webhook_id": null,
+      "webhook": null
+    },
+    {
+      "id": "86665e90-ffbf-48fb-ad3f-e5d31fd50c51",
+      "block_id": "6ab085e2-20b3-4055-bc3e-08036e01eca6",
+      "input_default": {
+        "prompt": "A cat sprawled dramatically across an important-looking document during a work-from-home meeting, making direct eye contact with the camera while knocking over a coffee mug in slow motion. Text Overlay: \"Chaos is a career path. Be the obstacle everyone has to work around.\"",
+        "upscale": "No Upscale"
+      },
+      "metadata": {
+        "position": {
+          "x": 1668.3572666956795,
+          "y": 89.69665262457966
+        }
+      },
+      "input_links": [
+        {
+          "id": "509b7587-1940-4a06-808d-edde9a74f400",
+          "source_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
+          "sink_id": "86665e90-ffbf-48fb-ad3f-e5d31fd50c51",
+          "source_name": "response",
+          "sink_name": "prompt",
+          "is_static": false
+        }
+      ],
+      "output_links": [
+        {
+          "id": "c6c511e8-e6a4-4969-9bc8-f67d60c1e229",
+          "source_id": "86665e90-ffbf-48fb-ad3f-e5d31fd50c51",
+          "sink_id": "5ac3727a-1ea7-436b-a902-ef1bfd883a30",
+          "source_name": "result",
+          "sink_name": "value",
+          "is_static": false
+        }
+      ],
+      "graph_id": "7b2e2095-782a-4f8d-adda-e62b661bccf5",
+      "graph_version": 29,
+      "webhook_id": null,
+      "webhook": null
+    },
+    {
+      "id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
+      "block_id": "1f292d4a-41a4-4977-9684-7c8d560b9f91",
+      "input_default": {
+        "model": "gpt-4o",
+        "prompt": "<example_output>\nA photo of a sloth lounging on a desk, with its head resting on a keyboard. The keyboard is on top of a laptop with a blank spreadsheet open. A to-do list is placed beside the laptop, with the top item written as \"Do literally anything\". There is a text overlay that says \"If you can't outwork them, outnap them.\".\n</example_output>\n\nCreate a relatable satirical, snarky, user-deprecating motivational style image based on the theme: \"{{THEME}}\".\n\nOutput only the image description and caption, without any additional commentary or formatting.",
+        "prompt_values": {}
+      },
+      "metadata": {
+        "position": {
+          "x": -561.1139207164056,
+          "y": 78.60434452403524
+        }
+      },
+      "input_links": [
+        {
+          "id": "8c2bd1f7-b17b-4835-81b6-bb336097aa7a",
+          "source_id": "7e026d19-f9a6-412f-8082-610f9ba0c410",
+          "sink_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
+          "source_name": "result",
+          "sink_name": "prompt_values_#_THEME",
+          "is_static": true
+        }
+      ],
+      "output_links": [
+        {
+          "id": "54588c74-e090-4e49-89e4-844b9952a585",
+          "source_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
+          "sink_id": "28bda769-b88b-44c9-be5c-52c2667f137e",
+          "source_name": "response",
+          "sink_name": "prompt",
+          "is_static": false
+        },
+        {
+          "id": "201d3e03-bc06-4cee-846d-4c3c804d8857",
+          "source_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
+          "sink_id": "576c5677-9050-4d1c-aad4-36b820c04fef",
+          "source_name": "response",
+          "sink_name": "prompt",
+          "is_static": false
+        },
+        {
+          "id": "509b7587-1940-4a06-808d-edde9a74f400",
+          "source_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
+          "sink_id": "86665e90-ffbf-48fb-ad3f-e5d31fd50c51",
+          "source_name": "response",
+          "sink_name": "prompt",
+          "is_static": false
+        },
+        {
+          "id": "66646786-3006-4417-a6b7-0158f2603d1d",
+          "source_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
+          "sink_id": "e7cdc1a2-4427-4a8a-a31b-63c8e74842f8",
+          "source_name": "response",
+          "sink_name": "prompt",
+          "is_static": false
+        }
+      ],
+      "graph_id": "7b2e2095-782a-4f8d-adda-e62b661bccf5",
+      "graph_version": 29,
+      "webhook_id": null,
+      "webhook": null
+    }
+  ],
+  "links": [
+    {
+      "id": "66646786-3006-4417-a6b7-0158f2603d1d",
+      "source_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
+      "sink_id": "e7cdc1a2-4427-4a8a-a31b-63c8e74842f8",
+      "source_name": "response",
+      "sink_name": "prompt",
+      "is_static": false
+    },
+    {
+      "id": "c6c511e8-e6a4-4969-9bc8-f67d60c1e229",
+      "source_id": "86665e90-ffbf-48fb-ad3f-e5d31fd50c51",
+      "sink_id": "5ac3727a-1ea7-436b-a902-ef1bfd883a30",
+      "source_name": "result",
+      "sink_name": "value",
+      "is_static": false
+    },
+    {
+      "id": "6524c611-774b-45e9-899d-9a6aa80c549c",
+      "source_id": "e7cdc1a2-4427-4a8a-a31b-63c8e74842f8",
+      "sink_id": "5ac3727a-1ea7-436b-a902-ef1bfd883a30",
+      "source_name": "result",
+      "sink_name": "value",
+      "is_static": false
+    },
+    {
+      "id": "20845dda-91de-4508-8077-0504b1a5ae03",
+      "source_id": "28bda769-b88b-44c9-be5c-52c2667f137e",
+      "sink_id": "5ac3727a-1ea7-436b-a902-ef1bfd883a30",
+      "source_name": "result",
+      "sink_name": "value",
+      "is_static": false
+    },
+    {
+      "id": "8c2bd1f7-b17b-4835-81b6-bb336097aa7a",
+      "source_id": "7e026d19-f9a6-412f-8082-610f9ba0c410",
+      "sink_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
+      "source_name": "result",
+      "sink_name": "prompt_values_#_THEME",
+      "is_static": true
+    },
+    {
+      "id": "201d3e03-bc06-4cee-846d-4c3c804d8857",
+      "source_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
+      "sink_id": "576c5677-9050-4d1c-aad4-36b820c04fef",
+      "source_name": "response",
+      "sink_name": "prompt",
+      "is_static": false
+    },
+    {
+      "id": "714a0821-e5ba-4af7-9432-50491adda7b1",
+      "source_id": "576c5677-9050-4d1c-aad4-36b820c04fef",
+      "sink_id": "5ac3727a-1ea7-436b-a902-ef1bfd883a30",
+      "source_name": "result",
+      "sink_name": "value",
+      "is_static": false
+    },
+    {
+      "id": "54588c74-e090-4e49-89e4-844b9952a585",
+      "source_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
+      "sink_id": "28bda769-b88b-44c9-be5c-52c2667f137e",
+      "source_name": "response",
+      "sink_name": "prompt",
+      "is_static": false
+    },
+    {
+      "id": "509b7587-1940-4a06-808d-edde9a74f400",
+      "source_id": "7543b9b0-0409-4cf8-bc4e-e0336273e2c4",
+      "sink_id": "86665e90-ffbf-48fb-ad3f-e5d31fd50c51",
+      "source_name": "response",
+      "sink_name": "prompt",
+      "is_static": false
+    }
+  ],
+  "forked_from_id": null,
+  "forked_from_version": null,
+  "sub_graphs": [],
+  "user_id": "",
+  "created_at": "2024-12-20T19:58:34.390Z",
+  "input_schema": {
+    "type": "object",
+    "properties": {
+      "Theme": {
+        "advanced": false,
+        "secret": false,
+        "title": "Theme",
+        "default": "Cooking"
+      }
+    },
+    "required": []
+  },
+  "output_schema": {
+    "type": "object",
+    "properties": {
+      "Generated Image": {
+        "advanced": false,
+        "secret": false,
+        "title": "Generated Image",
+        "description": "The resulting generated image ready for you to review and post."
+      }
+    },
+    "required": [
+      "Generated Image"
+    ]
+  },
+  "has_external_trigger": false,
+  "has_human_in_the_loop": false,
+  "trigger_setup_info": null,
+  "credentials_input_schema": {
+    "properties": {
+      "ideogram_api_key_credentials": {
+        "credentials_provider": [
+          "ideogram"
+        ],
+        "credentials_types": [
+          "api_key"
+        ],
+        "properties": {
+          "id": {
+            "title": "Id",
+            "type": "string"
+          },
+          "title": {
+            "anyOf": [
+              {
+                "type": "string"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "title": "Title"
+          },
+          "provider": {
+            "const": "ideogram",
+            "title": "Provider",
+            "type": "string"
+          },
+          "type": {
+            "const": "api_key",
+            "title": "Type",
+            "type": "string"
+          }
+        },
+        "required": [
+          "id",
+          "provider",
+          "type"
+        ],
+        "title": "CredentialsMetaInput[Literal[<ProviderName.IDEOGRAM: 'ideogram'>], Literal['api_key']]",
+        "type": "object",
+        "discriminator_values": []
+      },
+      "openai_api_key_credentials": {
+        "credentials_provider": [
+          "openai"
+        ],
+        "credentials_types": [
+          "api_key"
+        ],
+        "properties": {
+          "id": {
+            "title": "Id",
+            "type": "string"
+          },
+          "title": {
+            "anyOf": [
+              {
+                "type": "string"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "title": "Title"
+          },
+          "provider": {
+            "const": "openai",
+            "title": "Provider",
+            "type": "string"
+          },
+          "type": {
+            "const": "api_key",
+            "title": "Type",
+            "type": "string"
+          }
+        },
+        "required": [
+          "id",
+          "provider",
+          "type"
+        ],
+        "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",
+          "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": [
+          "gpt-4o"
+        ]
+      }
+    },
+    "required": [
+      "ideogram_api_key_credentials",
+      "openai_api_key_credentials"
+    ],
+    "title": "UnspirationalPosterMakerCredentialsInputSchema",
+    "type": "object"
+  }
+}

autogpt_platform/backend/agents/agent_516d813b-d1bc-470f-add7-c63a4b2c2bad.json

@@ -0,0 +1,447 @@
+{
+  "id": "622849a7-5848-4838-894d-01f8f07e3fad",
+  "version": 18,
+  "is_active": true,
+  "name": "AI Function",
+  "description": "## AI-Powered Function Magic: Never code again!\nProvide a description of a python function and your inputs and AI will provide the results.",
+  "instructions": null,
+  "recommended_schedule_cron": null,
+  "nodes": [
+    {
+      "id": "26ff2973-3f9a-451d-b902-d45e5da0a7fe",
+      "block_id": "363ae599-353e-4804-937e-b2ee3cef3da4",
+      "input_default": {
+        "name": "return",
+        "title": null,
+        "value": null,
+        "format": "",
+        "secret": false,
+        "advanced": false,
+        "description": "The value returned by the function"
+      },
+      "metadata": {
+        "position": {
+          "x": 1598.8622921127233,
+          "y": 291.59140862204725
+        }
+      },
+      "input_links": [
+        {
+          "id": "caecc1de-fdbc-4fd9-9570-074057bb15f9",
+          "source_id": "c5d16ee4-de9e-4d93-bf32-ac2d15760d5b",
+          "sink_id": "26ff2973-3f9a-451d-b902-d45e5da0a7fe",
+          "source_name": "response",
+          "sink_name": "value",
+          "is_static": false
+        }
+      ],
+      "output_links": [],
+      "graph_id": "622849a7-5848-4838-894d-01f8f07e3fad",
+      "graph_version": 18,
+      "webhook_id": null,
+      "webhook": null
+    },
+    {
+      "id": "c5d16ee4-de9e-4d93-bf32-ac2d15760d5b",
+      "block_id": "1f292d4a-41a4-4977-9684-7c8d560b9f91",
+      "input_default": {
+        "model": "o3-mini",
+        "retry": 3,
+        "prompt": "{{ARGS}}",
+        "sys_prompt": "You are now the following python function:\n\n```\n# {{DESCRIPTION}}\n{{FUNCTION}}\n```\n\nThe user will provide your input arguments.\nOnly respond with your `return` value.\nDo not include any commentary or additional text in your response. \nDo not include ``` backticks or any other decorators.",
+        "ollama_host": "localhost:11434",
+        "prompt_values": {}
+      },
+      "metadata": {
+        "position": {
+          "x": 995,
+          "y": 290.50000000000006
+        }
+      },
+      "input_links": [
+        {
+          "id": "dc7cb15f-76cc-4533-b96c-dd9e3f7f75ed",
+          "source_id": "4eab3a55-20f2-4c1d-804c-7377ba8202d2",
+          "sink_id": "c5d16ee4-de9e-4d93-bf32-ac2d15760d5b",
+          "source_name": "result",
+          "sink_name": "prompt_values_#_FUNCTION",
+          "is_static": true
+        },
+        {
+          "id": "093bdca5-9f44-42f9-8e1c-276dd2971675",
+          "source_id": "844530de-2354-46d8-b748-67306b7bbca1",
+          "sink_id": "c5d16ee4-de9e-4d93-bf32-ac2d15760d5b",
+          "source_name": "result",
+          "sink_name": "prompt_values_#_ARGS",
+          "is_static": true
+        },
+        {
+          "id": "6c63d8ee-b63d-4ff6-bae0-7db8f99bb7af",
+          "source_id": "0fd6ef54-c1cd-478d-b764-17e40f882b99",
+          "sink_id": "c5d16ee4-de9e-4d93-bf32-ac2d15760d5b",
+          "source_name": "result",
+          "sink_name": "prompt_values_#_DESCRIPTION",
+          "is_static": true
+        }
+      ],
+      "output_links": [
+        {
+          "id": "caecc1de-fdbc-4fd9-9570-074057bb15f9",
+          "source_id": "c5d16ee4-de9e-4d93-bf32-ac2d15760d5b",
+          "sink_id": "26ff2973-3f9a-451d-b902-d45e5da0a7fe",
+          "source_name": "response",
+          "sink_name": "value",
+          "is_static": false
+        }
+      ],
+      "graph_id": "622849a7-5848-4838-894d-01f8f07e3fad",
+      "graph_version": 18,
+      "webhook_id": null,
+      "webhook": null
+    },
+    {
+      "id": "4eab3a55-20f2-4c1d-804c-7377ba8202d2",
+      "block_id": "7fcd3bcb-8e1b-4e69-903d-32d3d4a92158",
+      "input_default": {
+        "name": "Function Definition",
+        "title": null,
+        "value": "def fake_people(n: int) -> list[dict]:",
+        "secret": false,
+        "advanced": false,
+        "description": "The function definition (text). This is what you would type on the first line of the function when programming.\n\ne.g \"def fake_people(n: int) -> list[dict]:\"",
+        "placeholder_values": []
+      },
+      "metadata": {
+        "position": {
+          "x": -672.6908629664215,
+          "y": 302.42044359789116
+        }
+      },
+      "input_links": [],
+      "output_links": [
+        {
+          "id": "dc7cb15f-76cc-4533-b96c-dd9e3f7f75ed",
+          "source_id": "4eab3a55-20f2-4c1d-804c-7377ba8202d2",
+          "sink_id": "c5d16ee4-de9e-4d93-bf32-ac2d15760d5b",
+          "source_name": "result",
+          "sink_name": "prompt_values_#_FUNCTION",
+          "is_static": true
+        }
+      ],
+      "graph_id": "622849a7-5848-4838-894d-01f8f07e3fad",
+      "graph_version": 18,
+      "webhook_id": null,
+      "webhook": null
+    },
+    {
+      "id": "844530de-2354-46d8-b748-67306b7bbca1",
+      "block_id": "7fcd3bcb-8e1b-4e69-903d-32d3d4a92158",
+      "input_default": {
+        "name": "Arguments",
+        "title": null,
+        "value": "20",
+        "secret": false,
+        "advanced": false,
+        "description": "The function's inputs\n\ne.g \"20\"",
+        "placeholder_values": []
+      },
+      "metadata": {
+        "position": {
+          "x": -158.1623599617334,
+          "y": 295.410856928333
+        }
+      },
+      "input_links": [],
+      "output_links": [
+        {
+          "id": "093bdca5-9f44-42f9-8e1c-276dd2971675",
+          "source_id": "844530de-2354-46d8-b748-67306b7bbca1",
+          "sink_id": "c5d16ee4-de9e-4d93-bf32-ac2d15760d5b",
+          "source_name": "result",
+          "sink_name": "prompt_values_#_ARGS",
+          "is_static": true
+        }
+      ],
+      "graph_id": "622849a7-5848-4838-894d-01f8f07e3fad",
+      "graph_version": 18,
+      "webhook_id": null,
+      "webhook": null
+    },
+    {
+      "id": "0fd6ef54-c1cd-478d-b764-17e40f882b99",
+      "block_id": "90a56ffb-7024-4b2b-ab50-e26c5e5ab8ba",
+      "input_default": {
+        "name": "Description",
+        "title": null,
+        "value": "Generates n examples of fake data representing people, each with a name, DoB, Job title, and an age.",
+        "secret": false,
+        "advanced": false,
+        "description": "Describe what the function does.\n\ne.g \"Generates n examples of fake data representing people, each with a name, DoB, Job title, and an age.\"",
+        "placeholder_values": []
+      },
+      "metadata": {
+        "position": {
+          "x": 374.4548658057796,
+          "y": 290.3779121974126
+        }
+      },
+      "input_links": [],
+      "output_links": [
+        {
+          "id": "6c63d8ee-b63d-4ff6-bae0-7db8f99bb7af",
+          "source_id": "0fd6ef54-c1cd-478d-b764-17e40f882b99",
+          "sink_id": "c5d16ee4-de9e-4d93-bf32-ac2d15760d5b",
+          "source_name": "result",
+          "sink_name": "prompt_values_#_DESCRIPTION",
+          "is_static": true
+        }
+      ],
+      "graph_id": "622849a7-5848-4838-894d-01f8f07e3fad",
+      "graph_version": 18,
+      "webhook_id": null,
+      "webhook": null
+    }
+  ],
+  "links": [
+    {
+      "id": "caecc1de-fdbc-4fd9-9570-074057bb15f9",
+      "source_id": "c5d16ee4-de9e-4d93-bf32-ac2d15760d5b",
+      "sink_id": "26ff2973-3f9a-451d-b902-d45e5da0a7fe",
+      "source_name": "response",
+      "sink_name": "value",
+      "is_static": false
+    },
+    {
+      "id": "6c63d8ee-b63d-4ff6-bae0-7db8f99bb7af",
+      "source_id": "0fd6ef54-c1cd-478d-b764-17e40f882b99",
+      "sink_id": "c5d16ee4-de9e-4d93-bf32-ac2d15760d5b",
+      "source_name": "result",
+      "sink_name": "prompt_values_#_DESCRIPTION",
+      "is_static": true
+    },
+    {
+      "id": "093bdca5-9f44-42f9-8e1c-276dd2971675",
+      "source_id": "844530de-2354-46d8-b748-67306b7bbca1",
+      "sink_id": "c5d16ee4-de9e-4d93-bf32-ac2d15760d5b",
+      "source_name": "result",
+      "sink_name": "prompt_values_#_ARGS",
+      "is_static": true
+    },
+    {
+      "id": "dc7cb15f-76cc-4533-b96c-dd9e3f7f75ed",
+      "source_id": "4eab3a55-20f2-4c1d-804c-7377ba8202d2",
+      "sink_id": "c5d16ee4-de9e-4d93-bf32-ac2d15760d5b",
+      "source_name": "result",
+      "sink_name": "prompt_values_#_FUNCTION",
+      "is_static": true
+    }
+  ],
+  "forked_from_id": null,
+  "forked_from_version": null,
+  "sub_graphs": [],
+  "user_id": "",
+  "created_at": "2025-04-19T17:10:48.857Z",
+  "input_schema": {
+    "type": "object",
+    "properties": {
+      "Function Definition": {
+        "advanced": false,
+        "anyOf": [
+          {
+            "format": "short-text",
+            "type": "string"
+          },
+          {
+            "type": "null"
+          }
+        ],
+        "secret": false,
+        "title": "Function Definition",
+        "description": "The function definition (text). This is what you would type on the first line of the function when programming.\n\ne.g \"def fake_people(n: int) -> list[dict]:\"",
+        "default": "def fake_people(n: int) -> list[dict]:"
+      },
+      "Arguments": {
+        "advanced": false,
+        "anyOf": [
+          {
+            "format": "short-text",
+            "type": "string"
+          },
+          {
+            "type": "null"
+          }
+        ],
+        "secret": false,
+        "title": "Arguments",
+        "description": "The function's inputs\n\ne.g \"20\"",
+        "default": "20"
+      },
+      "Description": {
+        "advanced": false,
+        "anyOf": [
+          {
+            "format": "long-text",
+            "type": "string"
+          },
+          {
+            "type": "null"
+          }
+        ],
+        "secret": false,
+        "title": "Description",
+        "description": "Describe what the function does.\n\ne.g \"Generates n examples of fake data representing people, each with a name, DoB, Job title, and an age.\"",
+        "default": "Generates n examples of fake data representing people, each with a name, DoB, Job title, and an age."
+      }
+    },
+    "required": []
+  },
+  "output_schema": {
+    "type": "object",
+    "properties": {
+      "return": {
+        "advanced": false,
+        "secret": false,
+        "title": "return",
+        "description": "The value returned by the function"
+      }
+    },
+    "required": [
+      "return"
+    ]
+  },
+  "has_external_trigger": false,
+  "has_human_in_the_loop": false,
+  "trigger_setup_info": null,
+  "credentials_input_schema": {
+    "properties": {
+      "openai_api_key_credentials": {
+        "credentials_provider": [
+          "openai"
+        ],
+        "credentials_types": [
+          "api_key"
+        ],
+        "properties": {
+          "id": {
+            "title": "Id",
+            "type": "string"
+          },
+          "title": {
+            "anyOf": [
+              {
+                "type": "string"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "title": "Title"
+          },
+          "provider": {
+            "const": "openai",
+            "title": "Provider",
+            "type": "string"
+          },
+          "type": {
+            "const": "api_key",
+            "title": "Type",
+            "type": "string"
+          }
+        },
+        "required": [
+          "id",
+          "provider",
+          "type"
+        ],
+        "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",
+          "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"
+  }
+}

autogpt_platform/backend/agents/agent_d85882b8-633f-44ce-a315-c20a8c123d19.json

@@ -0,0 +1,403 @@
+{
+  "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
+        }
+      },
+      "input_links": [],
+      "output_links": [],
+      "graph_id": "ed2091cf-5b27-45a9-b3ea-42396f95b256",
+      "graph_version": 12,
+      "webhook_id": null,
+      "webhook": null
+    },
+    {
+      "id": "0d1dec1a-e4ee-4349-9673-449a01bbf14e",
+      "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
+        }
+      ],
+      "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
+        }
+      },
+      "input_links": [],
+      "output_links": [
+        {
+          "id": "1077c61a-a32a-4ed7-becf-11bcf835b914",
+          "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
+    },
+    {
+      "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": {
+        "position": {
+          "x": 873.0119949791526,
+          "y": 966.1604399052493
+        }
+      },
+      "input_links": [
+        {
+          "id": "a17ec505-9377-4700-8fe0-124ca81d43a9",
+          "source_id": "0d1bca9a-d9b8-4bfd-a19c-fe50b54f4b12",
+          "sink_id": "50bc23e9-f2b7-4959-8710-99679ed9eeea",
+          "source_name": "response",
+          "sink_name": "prompt",
+          "is_static": false
+        }
+      ],
+      "output_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
+        }
+      ],
+      "graph_id": "ed2091cf-5b27-45a9-b3ea-42396f95b256",
+      "graph_version": 12,
+      "webhook_id": null,
+      "webhook": null
+    },
+    {
+      "id": "0d1bca9a-d9b8-4bfd-a19c-fe50b54f4b12",
+      "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": {}
+      },
+      "metadata": {
+        "position": {
+          "x": 277.3057034159709,
+          "y": 962.8382498113764
+        }
+      },
+      "input_links": [
+        {
+          "id": "1077c61a-a32a-4ed7-becf-11bcf835b914",
+          "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
+        }
+      ],
+      "output_links": [
+        {
+          "id": "a17ec505-9377-4700-8fe0-124ca81d43a9",
+          "source_id": "0d1bca9a-d9b8-4bfd-a19c-fe50b54f4b12",
+          "sink_id": "50bc23e9-f2b7-4959-8710-99679ed9eeea",
+          "source_name": "response",
+          "sink_name": "prompt",
+          "is_static": false
+        }
+      ],
+      "graph_id": "ed2091cf-5b27-45a9-b3ea-42396f95b256",
+      "graph_version": 12,
+      "webhook_id": null,
+      "webhook": null
+    }
+  ],
+  "links": [
+    {
+      "id": "1077c61a-a32a-4ed7-becf-11bcf835b914",
+      "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
+    },
+    {
+      "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
+    },
+    {
+      "id": "a17ec505-9377-4700-8fe0-124ca81d43a9",
+      "source_id": "0d1bca9a-d9b8-4bfd-a19c-fe50b54f4b12",
+      "sink_id": "50bc23e9-f2b7-4959-8710-99679ed9eeea",
+      "source_name": "response",
+      "sink_name": "prompt",
+      "is_static": false
+    }
+  ],
+  "forked_from_id": null,
+  "forked_from_version": null,
+  "sub_graphs": [],
+  "user_id": "",
+  "created_at": "2024-12-20T18:46:11.492Z",
+  "input_schema": {
+    "type": "object",
+    "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."
+      }
+    },
+    "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": {
+    "properties": {
+      "replicate_api_key_credentials": {
+        "credentials_provider": [
+          "replicate"
+        ],
+        "credentials_types": [
+          "api_key"
+        ],
+        "properties": {
+          "id": {
+            "title": "Id",
+            "type": "string"
+          },
+          "title": {
+            "anyOf": [
+              {
+                "type": "string"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "title": "Title"
+          },
+          "provider": {
+            "const": "replicate",
+            "title": "Provider",
+            "type": "string"
+          },
+          "type": {
+            "const": "api_key",
+            "title": "Type",
+            "type": "string"
+          }
+        },
+        "required": [
+          "id",
+          "provider",
+          "type"
+        ],
+        "title": "CredentialsMetaInput[Literal[<ProviderName.REPLICATE: 'replicate'>], Literal['api_key']]",
+        "type": "object",
+        "discriminator_values": []
+      },
+      "openai_api_key_credentials": {
+        "credentials_provider": [
+          "openai"
+        ],
+        "credentials_types": [
+          "api_key"
+        ],
+        "properties": {
+          "id": {
+            "title": "Id",
+            "type": "string"
+          },
+          "title": {
+            "anyOf": [
+              {
+                "type": "string"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "title": "Title"
+          },
+          "provider": {
+            "const": "openai",
+            "title": "Provider",
+            "type": "string"
+          },
+          "type": {
+            "const": "api_key",
+            "title": "Type",
+            "type": "string"
+          }
+        },
+        "required": [
+          "id",
+          "provider",
+          "type"
+        ],
+        "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",
+          "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": [
+          "gpt-4o-mini"
+        ]
+      }
+    },
+    "required": [
+      "replicate_api_key_credentials",
+      "openai_api_key_credentials"
+    ],
+    "title": "FluxAIImageGeneratorCredentialsInputSchema",
+    "type": "object"
+  }
+}

autogpt_platform/backend/agents/agent_f2cc74bb-f43f-4395-9c35-ecb30b5b4fc9.json

@@ -0,0 +1,505 @@
+{
+  "id": "0d440799-44ba-4d6c-85b3-b3739f1e1287",
+  "version": 12,
+  "is_active": true,
+  "name": "AI Webpage Copy Improver",
+  "description": "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.",
+  "instructions": null,
+  "recommended_schedule_cron": null,
+  "nodes": [
+    {
+      "id": "130ec496-f75d-4fe2-9cd6-8c00d08ea4a7",
+      "block_id": "363ae599-353e-4804-937e-b2ee3cef3da4",
+      "input_default": {
+        "name": "Improved Webpage Copy"
+      },
+      "metadata": {
+        "position": {
+          "x": 1039.5884372540172,
+          "y": -0.8359099621230968
+        }
+      },
+      "input_links": [
+        {
+          "id": "d4334477-3616-454f-a430-614ca27f5b36",
+          "source_id": "c9924577-70d8-4ccb-9106-6f796df09ef9",
+          "sink_id": "130ec496-f75d-4fe2-9cd6-8c00d08ea4a7",
+          "source_name": "response",
+          "sink_name": "value",
+          "is_static": false
+        }
+      ],
+      "output_links": [],
+      "graph_id": "0d440799-44ba-4d6c-85b3-b3739f1e1287",
+      "graph_version": 12,
+      "webhook_id": null,
+      "webhook": null
+    },
+    {
+      "id": "cefccd07-fe70-4feb-bf76-46b20aaa5d35",
+      "block_id": "363ae599-353e-4804-937e-b2ee3cef3da4",
+      "input_default": {
+        "name": "Original Page Analysis",
+        "description": "Analysis of the webpage as it currently stands."
+      },
+      "metadata": {
+        "position": {
+          "x": 1037.7724103954706,
+          "y": -606.5934325506903
+        }
+      },
+      "input_links": [
+        {
+          "id": "f979ab78-0903-4f19-a7c2-a419d5d81aef",
+          "source_id": "08612ce2-625b-4c17-accd-3acace7b6477",
+          "sink_id": "cefccd07-fe70-4feb-bf76-46b20aaa5d35",
+          "source_name": "response",
+          "sink_name": "value",
+          "is_static": false
+        }
+      ],
+      "output_links": [],
+      "graph_id": "0d440799-44ba-4d6c-85b3-b3739f1e1287",
+      "graph_version": 12,
+      "webhook_id": null,
+      "webhook": null
+    },
+    {
+      "id": "375f8bc3-afd9-4025-ad8e-9aeb329af7ce",
+      "block_id": "c0a8e994-ebf1-4a9c-a4d8-89d09c86741b",
+      "input_default": {
+        "name": "Homepage URL",
+        "value": "https://agpt.co",
+        "description": "Enter the URL of the homepage you want to improve"
+      },
+      "metadata": {
+        "position": {
+          "x": -1195.1455674454749,
+          "y": 0
+        }
+      },
+      "input_links": [],
+      "output_links": [
+        {
+          "id": "cbb12335-fefd-4560-9fff-98675130fbad",
+          "source_id": "375f8bc3-afd9-4025-ad8e-9aeb329af7ce",
+          "sink_id": "b40595c6-dba3-4779-a129-cd4f01fff103",
+          "source_name": "result",
+          "sink_name": "url",
+          "is_static": true
+        }
+      ],
+      "graph_id": "0d440799-44ba-4d6c-85b3-b3739f1e1287",
+      "graph_version": 12,
+      "webhook_id": null,
+      "webhook": null
+    },
+    {
+      "id": "b40595c6-dba3-4779-a129-cd4f01fff103",
+      "block_id": "436c3984-57fd-4b85-8e9a-459b356883bd",
+      "input_default": {
+        "raw_content": false
+      },
+      "metadata": {
+        "position": {
+          "x": -631.7330786555249,
+          "y": 1.9638396496230826
+        }
+      },
+      "input_links": [
+        {
+          "id": "cbb12335-fefd-4560-9fff-98675130fbad",
+          "source_id": "375f8bc3-afd9-4025-ad8e-9aeb329af7ce",
+          "sink_id": "b40595c6-dba3-4779-a129-cd4f01fff103",
+          "source_name": "result",
+          "sink_name": "url",
+          "is_static": true
+        }
+      ],
+      "output_links": [
+        {
+          "id": "adfa6113-77b3-4e32-b136-3e694b87553e",
+          "source_id": "b40595c6-dba3-4779-a129-cd4f01fff103",
+          "sink_id": "c9924577-70d8-4ccb-9106-6f796df09ef9",
+          "source_name": "content",
+          "sink_name": "prompt_values_#_CONTENT",
+          "is_static": false
+        },
+        {
+          "id": "5d5656fd-4208-4296-bc70-e39cc31caada",
+          "source_id": "b40595c6-dba3-4779-a129-cd4f01fff103",
+          "sink_id": "08612ce2-625b-4c17-accd-3acace7b6477",
+          "source_name": "content",
+          "sink_name": "prompt_values_#_CONTENT",
+          "is_static": false
+        }
+      ],
+      "graph_id": "0d440799-44ba-4d6c-85b3-b3739f1e1287",
+      "graph_version": 12,
+      "webhook_id": null,
+      "webhook": null
+    },
+    {
+      "id": "c9924577-70d8-4ccb-9106-6f796df09ef9",
+      "block_id": "1f292d4a-41a4-4977-9684-7c8d560b9f91",
+      "input_default": {
+        "model": "gpt-4o",
+        "prompt": "Current Webpage Content:\n```\n{{CONTENT}}\n```\n\nBased on the following analysis of the webpage content:\n\n```\n{{ANALYSIS}}\n```\n\nRewrite and improve the content to address the identified issues. Focus on:\n1. Enhancing clarity and readability\n2. Optimizing for SEO (suggest and incorporate relevant keywords)\n3. Improving calls-to-action for better conversion rates\n4. Refining the structure and organization\n5. Maintaining brand consistency while improving the overall tone\n\nProvide the improved content in HTML format inside a code-block with \"```\" backticks, preserving the original structure where appropriate. Also, include a brief summary of the changes made and their potential impact.",
+        "prompt_values": {}
+      },
+      "metadata": {
+        "position": {
+          "x": 488.37278423303917,
+          "y": 0
+        }
+      },
+      "input_links": [
+        {
+          "id": "adfa6113-77b3-4e32-b136-3e694b87553e",
+          "source_id": "b40595c6-dba3-4779-a129-cd4f01fff103",
+          "sink_id": "c9924577-70d8-4ccb-9106-6f796df09ef9",
+          "source_name": "content",
+          "sink_name": "prompt_values_#_CONTENT",
+          "is_static": false
+        },
+        {
+          "id": "6bcca45d-c9d5-439e-ac43-e4a1264d8f57",
+          "source_id": "08612ce2-625b-4c17-accd-3acace7b6477",
+          "sink_id": "c9924577-70d8-4ccb-9106-6f796df09ef9",
+          "source_name": "response",
+          "sink_name": "prompt_values_#_ANALYSIS",
+          "is_static": false
+        }
+      ],
+      "output_links": [
+        {
+          "id": "d4334477-3616-454f-a430-614ca27f5b36",
+          "source_id": "c9924577-70d8-4ccb-9106-6f796df09ef9",
+          "sink_id": "130ec496-f75d-4fe2-9cd6-8c00d08ea4a7",
+          "source_name": "response",
+          "sink_name": "value",
+          "is_static": false
+        }
+      ],
+      "graph_id": "0d440799-44ba-4d6c-85b3-b3739f1e1287",
+      "graph_version": 12,
+      "webhook_id": null,
+      "webhook": null
+    },
+    {
+      "id": "08612ce2-625b-4c17-accd-3acace7b6477",
+      "block_id": "1f292d4a-41a4-4977-9684-7c8d560b9f91",
+      "input_default": {
+        "model": "gpt-4o",
+        "prompt": "Analyze the following webpage content and provide a detailed report on its current state, including strengths and weaknesses in terms of clarity, SEO optimization, and potential for conversion:\n\n{{CONTENT}}\n\nInclude observations on:\n1. Overall readability and clarity\n2. Use of keywords and SEO-friendly language\n3. Effectiveness of calls-to-action\n4. Structure and organization of content\n5. Tone and brand consistency",
+        "prompt_values": {}
+      },
+      "metadata": {
+        "position": {
+          "x": -72.66206703605442,
+          "y": -0.58403945075381
+        }
+      },
+      "input_links": [
+        {
+          "id": "5d5656fd-4208-4296-bc70-e39cc31caada",
+          "source_id": "b40595c6-dba3-4779-a129-cd4f01fff103",
+          "sink_id": "08612ce2-625b-4c17-accd-3acace7b6477",
+          "source_name": "content",
+          "sink_name": "prompt_values_#_CONTENT",
+          "is_static": false
+        }
+      ],
+      "output_links": [
+        {
+          "id": "f979ab78-0903-4f19-a7c2-a419d5d81aef",
+          "source_id": "08612ce2-625b-4c17-accd-3acace7b6477",
+          "sink_id": "cefccd07-fe70-4feb-bf76-46b20aaa5d35",
+          "source_name": "response",
+          "sink_name": "value",
+          "is_static": false
+        },
+        {
+          "id": "6bcca45d-c9d5-439e-ac43-e4a1264d8f57",
+          "source_id": "08612ce2-625b-4c17-accd-3acace7b6477",
+          "sink_id": "c9924577-70d8-4ccb-9106-6f796df09ef9",
+          "source_name": "response",
+          "sink_name": "prompt_values_#_ANALYSIS",
+          "is_static": false
+        }
+      ],
+      "graph_id": "0d440799-44ba-4d6c-85b3-b3739f1e1287",
+      "graph_version": 12,
+      "webhook_id": null,
+      "webhook": null
+    }
+  ],
+  "links": [
+    {
+      "id": "adfa6113-77b3-4e32-b136-3e694b87553e",
+      "source_id": "b40595c6-dba3-4779-a129-cd4f01fff103",
+      "sink_id": "c9924577-70d8-4ccb-9106-6f796df09ef9",
+      "source_name": "content",
+      "sink_name": "prompt_values_#_CONTENT",
+      "is_static": false
+    },
+    {
+      "id": "d4334477-3616-454f-a430-614ca27f5b36",
+      "source_id": "c9924577-70d8-4ccb-9106-6f796df09ef9",
+      "sink_id": "130ec496-f75d-4fe2-9cd6-8c00d08ea4a7",
+      "source_name": "response",
+      "sink_name": "value",
+      "is_static": false
+    },
+    {
+      "id": "5d5656fd-4208-4296-bc70-e39cc31caada",
+      "source_id": "b40595c6-dba3-4779-a129-cd4f01fff103",
+      "sink_id": "08612ce2-625b-4c17-accd-3acace7b6477",
+      "source_name": "content",
+      "sink_name": "prompt_values_#_CONTENT",
+      "is_static": false
+    },
+    {
+      "id": "f979ab78-0903-4f19-a7c2-a419d5d81aef",
+      "source_id": "08612ce2-625b-4c17-accd-3acace7b6477",
+      "sink_id": "cefccd07-fe70-4feb-bf76-46b20aaa5d35",
+      "source_name": "response",
+      "sink_name": "value",
+      "is_static": false
+    },
+    {
+      "id": "6bcca45d-c9d5-439e-ac43-e4a1264d8f57",
+      "source_id": "08612ce2-625b-4c17-accd-3acace7b6477",
+      "sink_id": "c9924577-70d8-4ccb-9106-6f796df09ef9",
+      "source_name": "response",
+      "sink_name": "prompt_values_#_ANALYSIS",
+      "is_static": false
+    },
+    {
+      "id": "cbb12335-fefd-4560-9fff-98675130fbad",
+      "source_id": "375f8bc3-afd9-4025-ad8e-9aeb329af7ce",
+      "sink_id": "b40595c6-dba3-4779-a129-cd4f01fff103",
+      "source_name": "result",
+      "sink_name": "url",
+      "is_static": true
+    }
+  ],
+  "forked_from_id": null,
+  "forked_from_version": null,
+  "sub_graphs": [],
+  "user_id": "",
+  "created_at": "2024-12-20T19:47:22.036Z",
+  "input_schema": {
+    "type": "object",
+    "properties": {
+      "Homepage URL": {
+        "advanced": false,
+        "secret": false,
+        "title": "Homepage URL",
+        "description": "Enter the URL of the homepage you want to improve",
+        "default": "https://agpt.co"
+      }
+    },
+    "required": []
+  },
+  "output_schema": {
+    "type": "object",
+    "properties": {
+      "Improved Webpage Copy": {
+        "advanced": false,
+        "secret": false,
+        "title": "Improved Webpage Copy"
+      },
+      "Original Page Analysis": {
+        "advanced": false,
+        "secret": false,
+        "title": "Original Page Analysis",
+        "description": "Analysis of the webpage as it currently stands."
+      }
+    },
+    "required": [
+      "Improved Webpage Copy",
+      "Original Page Analysis"
+    ]
+  },
+  "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": []
+      },
+      "openai_api_key_credentials": {
+        "credentials_provider": [
+          "openai"
+        ],
+        "credentials_types": [
+          "api_key"
+        ],
+        "properties": {
+          "id": {
+            "title": "Id",
+            "type": "string"
+          },
+          "title": {
+            "anyOf": [
+              {
+                "type": "string"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "default": null,
+            "title": "Title"
+          },
+          "provider": {
+            "const": "openai",
+            "title": "Provider",
+            "type": "string"
+          },
+          "type": {
+            "const": "api_key",
+            "title": "Type",
+            "type": "string"
+          }
+        },
+        "required": [
+          "id",
+          "provider",
+          "type"
+        ],
+        "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",
+          "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": [
+          "gpt-4o"
+        ]
+      }
+    },
+    "required": [
+      "jina_api_key_credentials",
+      "openai_api_key_credentials"
+    ],
+    "title": "AIWebpageCopyImproverCredentialsInputSchema",
+    "type": "object"
+  }
+}

autogpt_platform/backend/agents/agent_fc2c9976-0962-4625-a27b-d316573a9e7f.json

@@ -0,0 +1,615 @@
+{
+  "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"
+  }
+}

autogpt_platform/backend/backend/blocks/__init__.py

@@ -5,7 +5,7 @@ import re
 from pathlib import Path
 from typing import TYPE_CHECKING, TypeVar
 
-from autogpt_libs.utils.cache import cached
+from backend.util.cache import cached
 
 logger = logging.getLogger(__name__)
 
@@ -16,7 +16,7 @@ if TYPE_CHECKING:
 T = TypeVar("T")
 
 
-@cached()
+@cached(ttl_seconds=3600)
 def load_all_blocks() -> dict[str, type["Block"]]:
     from backend.data.block import Block
     from backend.util.settings import Config

autogpt_platform/backend/backend/blocks/agent.py

@@ -7,10 +7,11 @@ from backend.data.block import (
     BlockInput,
     BlockOutput,
     BlockSchema,
+    BlockSchemaInput,
     BlockType,
     get_block,
 )
-from backend.data.execution import ExecutionStatus, NodesInputMasks
+from backend.data.execution import ExecutionContext, ExecutionStatus, NodesInputMasks
 from backend.data.model import NodeExecutionStats, SchemaField
 from backend.util.json import validate_with_jsonschema
 from backend.util.retry import func_retry
@@ -19,7 +20,7 @@ _logger = logging.getLogger(__name__)
 
 
 class AgentExecutorBlock(Block):
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         user_id: str = SchemaField(description="User ID")
         graph_id: str = SchemaField(description="Graph ID")
         graph_version: int = SchemaField(description="Graph Version")
@@ -53,6 +54,7 @@ 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):
@@ -65,8 +67,14 @@ class AgentExecutorBlock(Block):
             categories={BlockCategory.AGENT},
         )
 
-    async def run(self, input_data: Input, **kwargs) -> BlockOutput:
-
+    async def run(
+        self,
+        input_data: Input,
+        *,
+        graph_exec_id: str,
+        execution_context: ExecutionContext,
+        **kwargs,
+    ) -> BlockOutput:
         from backend.executor import utils as execution_utils
 
         graph_exec = await execution_utils.add_graph_execution(
@@ -75,6 +83,9 @@ 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(

autogpt_platform/backend/backend/blocks/ai_condition.py

@@ -0,0 +1,219 @@
+from typing import Any
+
+from backend.blocks.llm import (
+    TEST_CREDENTIALS,
+    TEST_CREDENTIALS_INPUT,
+    AIBlockBase,
+    AICredentials,
+    AICredentialsField,
+    LlmModel,
+    LLMResponse,
+    llm_call,
+)
+from backend.data.block import (
+    BlockCategory,
+    BlockOutput,
+    BlockSchemaInput,
+    BlockSchemaOutput,
+)
+from backend.data.model import APIKeyCredentials, NodeExecutionStats, SchemaField
+
+
+class AIConditionBlock(AIBlockBase):
+    """
+    An AI-powered condition block that uses natural language to evaluate conditions.
+
+    This block allows users to define conditions in plain English (e.g., "the input is an email address",
+    "the input is a city in the USA") and uses AI to determine if the input satisfies the condition.
+    It provides the same yes/no data pass-through functionality as the standard ConditionBlock.
+    """
+
+    class Input(BlockSchemaInput):
+        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)",
+        )
+        condition: str = SchemaField(
+            description="A plaintext English description of the condition to evaluate",
+            placeholder="E.g., 'the input is the body of an email', 'the input is a City in the USA', 'the input is an error or a refusal'",
+        )
+        yes_value: Any = SchemaField(
+            description="(Optional) Value to output if the condition is true. If not provided, input_value will be used.",
+            placeholder="Leave empty to use input_value, or enter a specific value",
+            default=None,
+        )
+        no_value: Any = SchemaField(
+            description="(Optional) Value to output if the condition is false. If not provided, input_value will be used.",
+            placeholder="Leave empty to use input_value, or enter a specific value",
+            default=None,
+        )
+        model: LlmModel = SchemaField(
+            title="LLM Model",
+            default=LlmModel.GPT4O,
+            description="The language model to use for evaluating the condition.",
+            advanced=False,
+        )
+        credentials: AICredentials = AICredentialsField()
+
+    class Output(BlockSchemaOutput):
+        result: bool = SchemaField(
+            description="The result of the AI condition evaluation (True or False)"
+        )
+        yes_output: Any = SchemaField(
+            description="The output value if the condition is true"
+        )
+        no_output: Any = SchemaField(
+            description="The output value if the condition is false"
+        )
+        error: str = SchemaField(
+            description="Error message if the AI evaluation is uncertain or fails"
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="553ec5b8-6c45-4299-8d75-b394d05f72ff",
+            input_schema=AIConditionBlock.Input,
+            output_schema=AIConditionBlock.Output,
+            description="Uses AI to evaluate natural language conditions and provide conditional outputs",
+            categories={BlockCategory.AI, BlockCategory.LOGIC},
+            test_input={
+                "input_value": "john@example.com",
+                "condition": "the input is an email address",
+                "yes_value": "Valid email",
+                "no_value": "Not an email",
+                "model": LlmModel.GPT4O,
+                "credentials": TEST_CREDENTIALS_INPUT,
+            },
+            test_credentials=TEST_CREDENTIALS,
+            test_output=[
+                ("result", True),
+                ("yes_output", "Valid email"),
+            ],
+            test_mock={
+                "llm_call": lambda *args, **kwargs: LLMResponse(
+                    raw_response="",
+                    prompt=[],
+                    response="true",
+                    tool_calls=None,
+                    prompt_tokens=50,
+                    completion_tokens=10,
+                    reasoning=None,
+                )
+            },
+        )
+
+    async def llm_call(
+        self,
+        credentials: APIKeyCredentials,
+        llm_model: LlmModel,
+        prompt: list,
+        max_tokens: int,
+    ) -> LLMResponse:
+        """Wrapper method for llm_call to enable mocking in tests."""
+        return await llm_call(
+            credentials=credentials,
+            llm_model=llm_model,
+            prompt=prompt,
+            force_json_output=False,
+            max_tokens=max_tokens,
+        )
+
+    async def run(
+        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
+    ) -> BlockOutput:
+        """
+        Evaluate the AI condition and return appropriate outputs.
+        """
+        # Prepare the yes and no values, using input_value as default
+        yes_value = (
+            input_data.yes_value
+            if input_data.yes_value is not None
+            else input_data.input_value
+        )
+        no_value = (
+            input_data.no_value
+            if input_data.no_value is not None
+            else input_data.input_value
+        )
+
+        # Convert input_value to string for AI evaluation
+        input_str = str(input_data.input_value)
+
+        # Create the prompt for AI evaluation
+        prompt = [
+            {
+                "role": "system",
+                "content": (
+                    "You are an AI assistant that evaluates conditions based on input data. "
+                    "You must respond with only 'true' or 'false' (lowercase) to indicate whether "
+                    "the given condition is met by the input value. Be accurate and consider the "
+                    "context and meaning of both the input and the condition."
+                ),
+            },
+            {
+                "role": "user",
+                "content": (
+                    f"Input value: {input_str}\n"
+                    f"Condition to evaluate: {input_data.condition}\n\n"
+                    f"Does the input value satisfy the condition? Respond with only 'true' or 'false'."
+                ),
+            },
+        ]
+
+        # Call the LLM
+        try:
+            response = await self.llm_call(
+                credentials=credentials,
+                llm_model=input_data.model,
+                prompt=prompt,
+                max_tokens=10,  # We only expect a true/false response
+            )
+
+            # Extract the boolean result from the response
+            response_text = response.response.strip().lower()
+            if response_text == "true":
+                result = True
+            elif response_text == "false":
+                result = False
+            else:
+                # If the response is not clear, try to interpret it using word boundaries
+                import re
+
+                # Use word boundaries to avoid false positives like 'untrue' or '10'
+                tokens = set(re.findall(r"\b(true|false|yes|no|1|0)\b", response_text))
+
+                if tokens == {"true"} or tokens == {"yes"} or tokens == {"1"}:
+                    result = True
+                elif tokens == {"false"} or tokens == {"no"} or tokens == {"0"}:
+                    result = False
+                else:
+                    # Unclear or conflicting response - default to False and yield error
+                    result = False
+                    yield "error", f"Unclear AI response: '{response.response}'"
+
+            # Update internal stats
+            self.merge_stats(
+                NodeExecutionStats(
+                    input_token_count=response.prompt_tokens,
+                    output_token_count=response.completion_tokens,
+                )
+            )
+            self.prompt = response.prompt
+
+        except Exception as e:
+            # In case of any error, default to False to be safe
+            result = False
+            # Log the error but don't fail the block execution
+            import logging
+
+            logger = logging.getLogger(__name__)
+            logger.error(f"AI condition evaluation failed: {str(e)}")
+            yield "error", f"AI evaluation failed: {str(e)}"
+
+        # Yield results
+        yield "result", result
+
+        if result:
+            yield "yes_output", yes_value
+        else:
+            yield "no_output", no_value

autogpt_platform/backend/backend/blocks/ai_image_customizer.py

@@ -1,3 +1,4 @@
+import asyncio
 from enum import Enum
 from typing import Literal
 
@@ -5,7 +6,13 @@ from pydantic import SecretStr
 from replicate.client import Client as ReplicateClient
 from replicate.helpers import FileOutput
 
-from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
+from backend.data.block import (
+    Block,
+    BlockCategory,
+    BlockOutput,
+    BlockSchemaInput,
+    BlockSchemaOutput,
+)
 from backend.data.model import (
     APIKeyCredentials,
     CredentialsField,
@@ -13,11 +20,26 @@ from backend.data.model import (
     SchemaField,
 )
 from backend.integrations.providers import ProviderName
-from backend.util.file import MediaFileType
+from backend.util.file import MediaFileType, store_media_file
 
 
 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):
@@ -42,7 +64,7 @@ TEST_CREDENTIALS_INPUT = {
 
 
 class AIImageCustomizerBlock(Block):
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         credentials: CredentialsMetaInput[
             Literal[ProviderName.REPLICATE], Literal["api_key"]
         ] = CredentialsField(
@@ -62,15 +84,19 @@ 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(BlockSchema):
+    class Output(BlockSchemaOutput):
         image_url: MediaFileType = SchemaField(description="URL of the generated image")
-        error: str = SchemaField(description="Error message if generation failed")
 
     def __init__(self):
         super().__init__(
@@ -86,6 +112,7 @@ 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,
             },
@@ -110,11 +137,25 @@ 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=input_data.images,
+                images=processed_images,
+                aspect_ratio=input_data.aspect_ratio.value,
                 output_format=input_data.output_format.value,
             )
             yield "image_url", result
@@ -127,12 +168,14 @@ 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,
         }
 

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, BlockSchema
+from backend.data.block import Block, BlockCategory, BlockSchemaInput, BlockSchemaOutput
 from backend.data.model import (
     APIKeyCredentials,
     CredentialsField,
@@ -60,6 +60,14 @@ 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):
     """
@@ -98,10 +106,11 @@ 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(BlockSchema):
+    class Input(BlockSchemaInput):
         credentials: CredentialsMetaInput[
             Literal[ProviderName.REPLICATE], Literal["api_key"]
         ] = CredentialsField(
@@ -135,9 +144,8 @@ class AIImageGeneratorBlock(Block):
             title="Image Style",
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         image_url: str = SchemaField(description="URL of the generated image")
-        error: str = SchemaField(description="Error message if generation failed")
 
     def __init__(self):
         super().__init__(
@@ -262,6 +270,20 @@ 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)}")
 

autogpt_platform/backend/backend/blocks/ai_music_generator.py

@@ -6,7 +6,13 @@ from typing import Literal
 from pydantic import SecretStr
 from replicate.client import Client as ReplicateClient
 
-from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
+from backend.data.block import (
+    Block,
+    BlockCategory,
+    BlockOutput,
+    BlockSchemaInput,
+    BlockSchemaOutput,
+)
 from backend.data.model import (
     APIKeyCredentials,
     CredentialsField,
@@ -54,7 +60,7 @@ class NormalizationStrategy(str, Enum):
 
 
 class AIMusicGeneratorBlock(Block):
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         credentials: CredentialsMetaInput[
             Literal[ProviderName.REPLICATE], Literal["api_key"]
         ] = CredentialsField(
@@ -107,9 +113,8 @@ class AIMusicGeneratorBlock(Block):
             title="Normalization Strategy",
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         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__(

autogpt_platform/backend/backend/blocks/ai_shortform_video_block.py

@@ -6,7 +6,13 @@ from typing import Literal
 
 from pydantic import SecretStr
 
-from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
+from backend.data.block import (
+    Block,
+    BlockCategory,
+    BlockOutput,
+    BlockSchemaInput,
+    BlockSchemaOutput,
+)
 from backend.data.model import (
     APIKeyCredentials,
     CredentialsField,
@@ -148,7 +154,7 @@ logger = logging.getLogger(__name__)
 class AIShortformVideoCreatorBlock(Block):
     """Creates a short‑form text‑to‑video clip using stock or AI imagery."""
 
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         credentials: CredentialsMetaInput[
             Literal[ProviderName.REVID], Literal["api_key"]
         ] = CredentialsField(
@@ -187,9 +193,8 @@ class AIShortformVideoCreatorBlock(Block):
             placeholder=VisualMediaType.STOCK_VIDEOS,
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         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."""
@@ -336,7 +341,7 @@ class AIShortformVideoCreatorBlock(Block):
 class AIAdMakerVideoCreatorBlock(Block):
     """Generates a 30‑second vertical AI advert using optional user‑supplied imagery."""
 
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         credentials: CredentialsMetaInput[
             Literal[ProviderName.REVID], Literal["api_key"]
         ] = CredentialsField(
@@ -364,9 +369,8 @@ class AIAdMakerVideoCreatorBlock(Block):
             description="Restrict visuals to supplied images only.", default=True
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         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."""
@@ -524,7 +528,7 @@ class AIAdMakerVideoCreatorBlock(Block):
 class AIScreenshotToVideoAdBlock(Block):
     """Creates an advert where the supplied screenshot is narrated by an AI avatar."""
 
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         credentials: CredentialsMetaInput[
             Literal[ProviderName.REVID], Literal["api_key"]
         ] = CredentialsField(description="Revid.ai API key")
@@ -542,9 +546,8 @@ class AIScreenshotToVideoAdBlock(Block):
             default=AudioTrack.DONT_STOP_ME_ABSTRACT_FUTURE_BASS
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         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."""

autogpt_platform/backend/backend/blocks/airtable/_api.py

@@ -1371,7 +1371,7 @@ async def create_base(
     if tables:
         params["tables"] = tables
 
-    print(params)
+    logger.debug(f"Creating Airtable base with params: {params}")
 
     response = await Requests().post(
         "https://api.airtable.com/v0/meta/bases",

autogpt_platform/backend/backend/blocks/airtable/bases.py

@@ -9,7 +9,8 @@ from backend.sdk import (
     Block,
     BlockCategory,
     BlockOutput,
-    BlockSchema,
+    BlockSchemaInput,
+    BlockSchemaOutput,
     CredentialsMetaInput,
     SchemaField,
 )
@@ -23,7 +24,7 @@ class AirtableCreateBaseBlock(Block):
     Creates a new base in an Airtable workspace, or returns existing base if one with the same name exists.
     """
 
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         credentials: CredentialsMetaInput = airtable.credentials_field(
             description="Airtable API credentials"
         )
@@ -53,7 +54,7 @@ class AirtableCreateBaseBlock(Block):
             ],
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         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")
@@ -118,7 +119,7 @@ class AirtableListBasesBlock(Block):
     Lists all bases in an Airtable workspace that the user has access to.
     """
 
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         credentials: CredentialsMetaInput = airtable.credentials_field(
             description="Airtable API credentials"
         )
@@ -129,7 +130,7 @@ class AirtableListBasesBlock(Block):
             description="Pagination offset from previous request", default=""
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         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

autogpt_platform/backend/backend/blocks/airtable/records.py

@@ -9,7 +9,8 @@ from backend.sdk import (
     Block,
     BlockCategory,
     BlockOutput,
-    BlockSchema,
+    BlockSchemaInput,
+    BlockSchemaOutput,
     CredentialsMetaInput,
     SchemaField,
 )
@@ -31,7 +32,7 @@ class AirtableListRecordsBlock(Block):
     Lists records from an Airtable table with optional filtering, sorting, and pagination.
     """
 
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         credentials: CredentialsMetaInput = airtable.credentials_field(
             description="Airtable API credentials"
         )
@@ -65,7 +66,7 @@ class AirtableListRecordsBlock(Block):
             default=False,
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         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
@@ -137,7 +138,7 @@ class AirtableGetRecordBlock(Block):
     Retrieves a single record from an Airtable table by its ID.
     """
 
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         credentials: CredentialsMetaInput = airtable.credentials_field(
             description="Airtable API credentials"
         )
@@ -153,7 +154,7 @@ class AirtableGetRecordBlock(Block):
             default=False,
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         id: str = SchemaField(description="The record ID")
         fields: dict = SchemaField(description="The record fields")
         created_time: str = SchemaField(description="The record created time")
@@ -217,7 +218,7 @@ class AirtableCreateRecordsBlock(Block):
     Creates one or more records in an Airtable table.
     """
 
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         credentials: CredentialsMetaInput = airtable.credentials_field(
             description="Airtable API credentials"
         )
@@ -239,7 +240,7 @@ class AirtableCreateRecordsBlock(Block):
             default=None,
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         records: list[dict] = SchemaField(description="Array of created record objects")
         details: dict = SchemaField(description="Details of the created records")
 
@@ -290,7 +291,7 @@ class AirtableUpdateRecordsBlock(Block):
     Updates one or more existing records in an Airtable table.
     """
 
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         credentials: CredentialsMetaInput = airtable.credentials_field(
             description="Airtable API credentials"
         )
@@ -306,7 +307,7 @@ class AirtableUpdateRecordsBlock(Block):
             default=None,
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         records: list[dict] = SchemaField(description="Array of updated record objects")
 
     def __init__(self):
@@ -339,7 +340,7 @@ class AirtableDeleteRecordsBlock(Block):
     Deletes one or more records from an Airtable table.
     """
 
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         credentials: CredentialsMetaInput = airtable.credentials_field(
             description="Airtable API credentials"
         )
@@ -351,7 +352,7 @@ class AirtableDeleteRecordsBlock(Block):
             description="Array of upto 10 record IDs to delete"
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         records: list[dict] = SchemaField(description="Array of deletion results")
 
     def __init__(self):

autogpt_platform/backend/backend/blocks/airtable/schema.py

@@ -7,7 +7,8 @@ from backend.sdk import (
     Block,
     BlockCategory,
     BlockOutput,
-    BlockSchema,
+    BlockSchemaInput,
+    BlockSchemaOutput,
     CredentialsMetaInput,
     Requests,
     SchemaField,
@@ -23,13 +24,13 @@ class AirtableListSchemaBlock(Block):
     fields, and views.
     """
 
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         credentials: CredentialsMetaInput = airtable.credentials_field(
             description="Airtable API credentials"
         )
         base_id: str = SchemaField(description="The Airtable base ID")
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         base_schema: dict = SchemaField(
             description="Complete base schema with tables, fields, and views"
         )
@@ -66,7 +67,7 @@ class AirtableCreateTableBlock(Block):
     Creates a new table in an Airtable base with specified fields and views.
     """
 
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         credentials: CredentialsMetaInput = airtable.credentials_field(
             description="Airtable API credentials"
         )
@@ -77,7 +78,7 @@ class AirtableCreateTableBlock(Block):
             default=[{"name": "Name", "type": "singleLineText"}],
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         table: dict = SchemaField(description="Created table object")
         table_id: str = SchemaField(description="ID of the created table")
 
@@ -109,7 +110,7 @@ class AirtableUpdateTableBlock(Block):
     Updates an existing table's properties such as name or description.
     """
 
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         credentials: CredentialsMetaInput = airtable.credentials_field(
             description="Airtable API credentials"
         )
@@ -125,7 +126,7 @@ class AirtableUpdateTableBlock(Block):
             description="The date dependency of the table to update", default=None
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         table: dict = SchemaField(description="Updated table object")
 
     def __init__(self):
@@ -157,7 +158,7 @@ class AirtableCreateFieldBlock(Block):
     Adds a new field (column) to an existing Airtable table.
     """
 
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         credentials: CredentialsMetaInput = airtable.credentials_field(
             description="Airtable API credentials"
         )
@@ -176,7 +177,7 @@ class AirtableCreateFieldBlock(Block):
             description="The options of the field to create", default=None
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         field: dict = SchemaField(description="Created field object")
         field_id: str = SchemaField(description="ID of the created field")
 
@@ -209,7 +210,7 @@ class AirtableUpdateFieldBlock(Block):
     Updates an existing field's properties in an Airtable table.
     """
 
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         credentials: CredentialsMetaInput = airtable.credentials_field(
             description="Airtable API credentials"
         )
@@ -225,7 +226,7 @@ class AirtableUpdateFieldBlock(Block):
             advanced=False,
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         field: dict = SchemaField(description="Updated field object")
 
     def __init__(self):

autogpt_platform/backend/backend/blocks/airtable/triggers.py

@@ -3,7 +3,8 @@ from backend.sdk import (
     Block,
     BlockCategory,
     BlockOutput,
-    BlockSchema,
+    BlockSchemaInput,
+    BlockSchemaOutput,
     BlockType,
     BlockWebhookConfig,
     CredentialsMetaInput,
@@ -32,7 +33,7 @@ class AirtableWebhookTriggerBlock(Block):
     Thin wrapper just forwards the payloads one at a time to the next block.
     """
 
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         credentials: CredentialsMetaInput = airtable.credentials_field(
             description="Airtable API credentials"
         )
@@ -43,7 +44,7 @@ class AirtableWebhookTriggerBlock(Block):
             description="Airtable webhook event filter"
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         payload: WebhookPayload = SchemaField(description="Airtable webhook payload")
 
     def __init__(self):

autogpt_platform/backend/backend/blocks/apollo/organization.py

@@ -10,14 +10,20 @@ from backend.blocks.apollo.models import (
     PrimaryPhone,
     SearchOrganizationsRequest,
 )
-from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
+from backend.data.block import (
+    Block,
+    BlockCategory,
+    BlockOutput,
+    BlockSchemaInput,
+    BlockSchemaOutput,
+)
 from backend.data.model import CredentialsField, SchemaField
 
 
 class SearchOrganizationsBlock(Block):
     """Search for organizations in Apollo"""
 
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         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.
 
@@ -69,7 +75,7 @@ To find IDs, identify the values for organization_id when you call this endpoint
             description="Apollo credentials",
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         organizations: list[Organization] = SchemaField(
             description="List of organizations found",
             default_factory=list,

autogpt_platform/backend/backend/blocks/apollo/people.py

@@ -14,14 +14,20 @@ from backend.blocks.apollo.models import (
     SearchPeopleRequest,
     SenorityLevels,
 )
-from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
+from backend.data.block import (
+    Block,
+    BlockCategory,
+    BlockOutput,
+    BlockSchemaInput,
+    BlockSchemaOutput,
+)
 from backend.data.model import CredentialsField, SchemaField
 
 
 class SearchPeopleBlock(Block):
     """Search for people in Apollo"""
 
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         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.
 
@@ -109,7 +115,7 @@ class SearchPeopleBlock(Block):
             description="Apollo credentials",
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         people: list[Contact] = SchemaField(
             description="List of people found",
             default_factory=list,

autogpt_platform/backend/backend/blocks/apollo/person.py

@@ -6,14 +6,20 @@ from backend.blocks.apollo._auth import (
     ApolloCredentialsInput,
 )
 from backend.blocks.apollo.models import Contact, EnrichPersonRequest
-from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
+from backend.data.block import (
+    Block,
+    BlockCategory,
+    BlockOutput,
+    BlockSchemaInput,
+    BlockSchemaOutput,
+)
 from backend.data.model import CredentialsField, SchemaField
 
 
 class GetPersonDetailBlock(Block):
     """Get detailed person data with Apollo API, including email reveal"""
 
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         person_id: str = SchemaField(
             description="Apollo person ID to enrich (most accurate method)",
             default="",
@@ -68,7 +74,7 @@ class GetPersonDetailBlock(Block):
             description="Apollo credentials",
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         contact: Contact = SchemaField(
             description="Enriched contact information",
         )

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 BlockSchema
+from backend.data.block import BlockSchemaInput
 from backend.data.model import SchemaField, UserIntegrations
 from backend.integrations.ayrshare import AyrshareClient
 from backend.util.clients import get_database_manager_async_client
@@ -17,7 +17,7 @@ async def get_profile_key(user_id: str):
     return user_integrations.managed_credentials.ayrshare_profile_key
 
 
-class BaseAyrshareInput(BlockSchema):
+class BaseAyrshareInput(BlockSchemaInput):
     """Base input model for Ayrshare social media posts with common fields."""
 
     post: str = SchemaField(

autogpt_platform/backend/backend/blocks/ayrshare/post_to_bluesky.py

@@ -3,7 +3,7 @@ from backend.sdk import (
     Block,
     BlockCategory,
     BlockOutput,
-    BlockSchema,
+    BlockSchemaOutput,
     BlockType,
     SchemaField,
 )
@@ -38,7 +38,7 @@ class PostToBlueskyBlock(Block):
             advanced=True,
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         post_result: PostResponse = SchemaField(description="The result of the post")
         post: PostIds = SchemaField(description="The result of the post")
 

autogpt_platform/backend/backend/blocks/ayrshare/post_to_facebook.py

@@ -3,7 +3,7 @@ from backend.sdk import (
     Block,
     BlockCategory,
     BlockOutput,
-    BlockSchema,
+    BlockSchemaOutput,
     BlockType,
     SchemaField,
 )
@@ -101,7 +101,7 @@ class PostToFacebookBlock(Block):
             description="URL for custom link preview", default="", advanced=True
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         post_result: PostResponse = SchemaField(description="The result of the post")
         post: PostIds = SchemaField(description="The result of the post")
 

autogpt_platform/backend/backend/blocks/ayrshare/post_to_gmb.py

@@ -3,7 +3,7 @@ from backend.sdk import (
     Block,
     BlockCategory,
     BlockOutput,
-    BlockSchema,
+    BlockSchemaOutput,
     BlockType,
     SchemaField,
 )
@@ -94,7 +94,7 @@ class PostToGMBBlock(Block):
             advanced=True,
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         post_result: PostResponse = SchemaField(description="The result of the post")
         post: PostIds = SchemaField(description="The result of the post")
 

autogpt_platform/backend/backend/blocks/ayrshare/post_to_instagram.py

@@ -5,7 +5,7 @@ from backend.sdk import (
     Block,
     BlockCategory,
     BlockOutput,
-    BlockSchema,
+    BlockSchemaOutput,
     BlockType,
     SchemaField,
 )
@@ -94,7 +94,7 @@ class PostToInstagramBlock(Block):
             advanced=True,
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         post_result: PostResponse = SchemaField(description="The result of the post")
         post: PostIds = SchemaField(description="The result of the post")
 

autogpt_platform/backend/backend/blocks/ayrshare/post_to_linkedin.py

@@ -3,7 +3,7 @@ from backend.sdk import (
     Block,
     BlockCategory,
     BlockOutput,
-    BlockSchema,
+    BlockSchemaOutput,
     BlockType,
     SchemaField,
 )
@@ -94,7 +94,7 @@ class PostToLinkedInBlock(Block):
             advanced=True,
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         post_result: PostResponse = SchemaField(description="The result of the post")
         post: PostIds = SchemaField(description="The result of the post")
 

autogpt_platform/backend/backend/blocks/ayrshare/post_to_pinterest.py

@@ -3,7 +3,7 @@ from backend.sdk import (
     Block,
     BlockCategory,
     BlockOutput,
-    BlockSchema,
+    BlockSchemaOutput,
     BlockType,
     SchemaField,
 )
@@ -73,7 +73,7 @@ class PostToPinterestBlock(Block):
             advanced=True,
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         post_result: PostResponse = SchemaField(description="The result of the post")
         post: PostIds = SchemaField(description="The result of the post")
 

autogpt_platform/backend/backend/blocks/ayrshare/post_to_reddit.py

@@ -3,7 +3,7 @@ from backend.sdk import (
     Block,
     BlockCategory,
     BlockOutput,
-    BlockSchema,
+    BlockSchemaOutput,
     BlockType,
     SchemaField,
 )
@@ -19,7 +19,7 @@ class PostToRedditBlock(Block):
 
         pass  # Uses all base fields
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         post_result: PostResponse = SchemaField(description="The result of the post")
         post: PostIds = SchemaField(description="The result of the post")
 

autogpt_platform/backend/backend/blocks/ayrshare/post_to_snapchat.py

@@ -3,7 +3,7 @@ from backend.sdk import (
     Block,
     BlockCategory,
     BlockOutput,
-    BlockSchema,
+    BlockSchemaOutput,
     BlockType,
     SchemaField,
 )
@@ -43,7 +43,7 @@ class PostToSnapchatBlock(Block):
             advanced=True,
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         post_result: PostResponse = SchemaField(description="The result of the post")
         post: PostIds = SchemaField(description="The result of the post")
 

autogpt_platform/backend/backend/blocks/ayrshare/post_to_telegram.py

@@ -3,7 +3,7 @@ from backend.sdk import (
     Block,
     BlockCategory,
     BlockOutput,
-    BlockSchema,
+    BlockSchemaOutput,
     BlockType,
     SchemaField,
 )
@@ -38,7 +38,7 @@ class PostToTelegramBlock(Block):
             advanced=True,
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         post_result: PostResponse = SchemaField(description="The result of the post")
         post: PostIds = SchemaField(description="The result of the post")
 

autogpt_platform/backend/backend/blocks/ayrshare/post_to_threads.py

@@ -3,7 +3,7 @@ from backend.sdk import (
     Block,
     BlockCategory,
     BlockOutput,
-    BlockSchema,
+    BlockSchemaOutput,
     BlockType,
     SchemaField,
 )
@@ -31,7 +31,7 @@ class PostToThreadsBlock(Block):
             advanced=False,
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         post_result: PostResponse = SchemaField(description="The result of the post")
         post: PostIds = SchemaField(description="The result of the post")
 

autogpt_platform/backend/backend/blocks/ayrshare/post_to_tiktok.py

@@ -5,7 +5,7 @@ from backend.sdk import (
     Block,
     BlockCategory,
     BlockOutput,
-    BlockSchema,
+    BlockSchemaOutput,
     BlockType,
     SchemaField,
 )
@@ -98,7 +98,7 @@ class PostToTikTokBlock(Block):
             advanced=True,
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         post_result: PostResponse = SchemaField(description="The result of the post")
         post: PostIds = SchemaField(description="The result of the post")
 

autogpt_platform/backend/backend/blocks/ayrshare/post_to_x.py

@@ -3,7 +3,7 @@ from backend.sdk import (
     Block,
     BlockCategory,
     BlockOutput,
-    BlockSchema,
+    BlockSchemaOutput,
     BlockType,
     SchemaField,
 )
@@ -97,7 +97,7 @@ class PostToXBlock(Block):
             advanced=True,
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         post_result: PostResponse = SchemaField(description="The result of the post")
         post: PostIds = SchemaField(description="The result of the post")
 

autogpt_platform/backend/backend/blocks/ayrshare/post_to_youtube.py

@@ -6,7 +6,7 @@ from backend.sdk import (
     Block,
     BlockCategory,
     BlockOutput,
-    BlockSchema,
+    BlockSchemaOutput,
     BlockType,
     SchemaField,
 )
@@ -119,7 +119,7 @@ class PostToYouTubeBlock(Block):
             advanced=True,
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         post_result: PostResponse = SchemaField(description="The result of the post")
         post: PostIds = SchemaField(description="The result of the post")
 

autogpt_platform/backend/backend/blocks/baas/bots.py

@@ -9,7 +9,8 @@ from backend.sdk import (
     Block,
     BlockCategory,
     BlockOutput,
-    BlockSchema,
+    BlockSchemaInput,
+    BlockSchemaOutput,
     CredentialsMetaInput,
     SchemaField,
 )
@@ -23,7 +24,7 @@ class BaasBotJoinMeetingBlock(Block):
     Deploy a bot immediately or at a scheduled start_time to join and record a meeting.
     """
 
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         credentials: CredentialsMetaInput = baas.credentials_field(
             description="Meeting BaaS API credentials"
         )
@@ -57,7 +58,7 @@ class BaasBotJoinMeetingBlock(Block):
             description="Custom metadata to attach to the bot", default={}
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         bot_id: str = SchemaField(description="UUID of the deployed bot")
         join_response: dict = SchemaField(
             description="Full response from join operation"
@@ -103,13 +104,13 @@ class BaasBotLeaveMeetingBlock(Block):
     Force the bot to exit the call.
     """
 
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         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(BlockSchema):
+    class Output(BlockSchemaOutput):
         left: bool = SchemaField(description="Whether the bot successfully left")
 
     def __init__(self):
@@ -138,7 +139,7 @@ class BaasBotFetchMeetingDataBlock(Block):
     Pull MP4 URL, transcript & metadata for a completed meeting.
     """
 
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         credentials: CredentialsMetaInput = baas.credentials_field(
             description="Meeting BaaS API credentials"
         )
@@ -147,7 +148,7 @@ class BaasBotFetchMeetingDataBlock(Block):
             description="Include transcript data in response", default=True
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         mp4_url: str = SchemaField(
             description="URL to download the meeting recording (time-limited)"
         )
@@ -185,13 +186,13 @@ class BaasBotDeleteRecordingBlock(Block):
     Purge MP4 + transcript data for privacy or storage management.
     """
 
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         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(BlockSchema):
+    class Output(BlockSchemaOutput):
         deleted: bool = SchemaField(
             description="Whether the data was successfully deleted"
         )

autogpt_platform/backend/backend/blocks/bannerbear/text_overlay.py

@@ -11,7 +11,8 @@ from backend.sdk import (
     Block,
     BlockCategory,
     BlockOutput,
-    BlockSchema,
+    BlockSchemaInput,
+    BlockSchemaOutput,
     CredentialsMetaInput,
     Requests,
     SchemaField,
@@ -27,7 +28,7 @@ TEST_CREDENTIALS = APIKeyCredentials(
 )
 
 
-class TextModification(BlockSchema):
+class TextModification(BlockSchemaInput):
     name: str = SchemaField(
         description="The name of the layer to modify in the template"
     )
@@ -60,7 +61,7 @@ class TextModification(BlockSchema):
 
 
 class BannerbearTextOverlayBlock(Block):
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         credentials: CredentialsMetaInput = bannerbear.credentials_field(
             description="API credentials for Bannerbear"
         )
@@ -96,7 +97,7 @@ class BannerbearTextOverlayBlock(Block):
             advanced=True,
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         success: bool = SchemaField(
             description="Whether the image generation was successfully initiated"
         )
@@ -105,7 +106,6 @@ 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__(

autogpt_platform/backend/backend/blocks/basic.py

@@ -1,14 +1,21 @@
 import enum
 from typing import Any
 
-from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema, BlockType
+from backend.data.block import (
+    Block,
+    BlockCategory,
+    BlockOutput,
+    BlockSchemaInput,
+    BlockSchemaOutput,
+    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(BlockSchema):
+    class Input(BlockSchemaInput):
         file_in: MediaFileType = SchemaField(
             description="The file to store in the temporary directory, it can be a URL, data URI, or local path."
         )
@@ -19,7 +26,7 @@ class FileStoreBlock(Block):
             title="Produce Base64 Output",
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         file_out: MediaFileType = SchemaField(
             description="The relative path to the stored file in the temporary directory."
         )
@@ -57,7 +64,7 @@ class StoreValueBlock(Block):
     The block output will be static, the output can be consumed multiple times.
     """
 
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         input: Any = SchemaField(
             description="Trigger the block to produce the output. "
             "The value is only used when `data` is None."
@@ -68,7 +75,7 @@ class StoreValueBlock(Block):
             default=None,
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         output: Any = SchemaField(description="The stored data retained in the block.")
 
     def __init__(self):
@@ -94,10 +101,10 @@ class StoreValueBlock(Block):
 
 
 class PrintToConsoleBlock(Block):
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         text: Any = SchemaField(description="The data to print to the console.")
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         output: Any = SchemaField(description="The data printed to the console.")
         status: str = SchemaField(description="The status of the print operation.")
 
@@ -121,10 +128,10 @@ class PrintToConsoleBlock(Block):
 
 
 class NoteBlock(Block):
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         text: str = SchemaField(description="The text to display in the sticky note.")
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         output: str = SchemaField(description="The text to display in the sticky note.")
 
     def __init__(self):
@@ -154,15 +161,14 @@ class TypeOptions(enum.Enum):
 
 
 class UniversalTypeConverterBlock(Block):
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         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(BlockSchema):
+    class Output(BlockSchemaOutput):
         value: Any = SchemaField(description="The converted value.")
-        error: str = SchemaField(description="Error message if conversion failed.")
 
     def __init__(self):
         super().__init__(
@@ -195,10 +201,10 @@ class ReverseListOrderBlock(Block):
     A block which takes in a list and returns it in the opposite order.
     """
 
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         input_list: list[Any] = SchemaField(description="The list to reverse")
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         reversed_list: list[Any] = SchemaField(description="The list in reversed order")
 
     def __init__(self):

autogpt_platform/backend/backend/blocks/block.py

@@ -2,7 +2,13 @@ import os
 import re
 from typing import Type
 
-from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
+from backend.data.block import (
+    Block,
+    BlockCategory,
+    BlockOutput,
+    BlockSchemaInput,
+    BlockSchemaOutput,
+)
 from backend.data.model import SchemaField
 
 
@@ -15,12 +21,12 @@ class BlockInstallationBlock(Block):
         for development purposes only.
     """
 
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         code: str = SchemaField(
             description="Python code of the block to be installed",
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         success: str = SchemaField(
             description="Success message if the block is installed successfully",
         )

autogpt_platform/backend/backend/blocks/branching.py

@@ -1,7 +1,13 @@
 from enum import Enum
 from typing import Any
 
-from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
+from backend.data.block import (
+    Block,
+    BlockCategory,
+    BlockOutput,
+    BlockSchemaInput,
+    BlockSchemaOutput,
+)
 from backend.data.model import SchemaField
 from backend.util.type import convert
 
@@ -16,7 +22,7 @@ class ComparisonOperator(Enum):
 
 
 class ConditionBlock(Block):
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         value1: Any = SchemaField(
             description="Enter the first value for comparison",
             placeholder="For example: 10 or 'hello' or True",
@@ -40,7 +46,7 @@ class ConditionBlock(Block):
             default=None,
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         result: bool = SchemaField(
             description="The result of the condition evaluation (True or False)"
         )
@@ -111,7 +117,7 @@ class ConditionBlock(Block):
 
 
 class IfInputMatchesBlock(Block):
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         input: Any = SchemaField(
             description="The input to match against",
             placeholder="For example: 10 or 'hello' or True",
@@ -131,7 +137,7 @@ class IfInputMatchesBlock(Block):
             default=None,
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         result: bool = SchemaField(
             description="The result of the condition evaluation (True or False)"
         )

autogpt_platform/backend/backend/blocks/code_executor.py

@@ -1,10 +1,18 @@
 from enum import Enum
-from typing import Literal
+from typing import Any, Literal, Optional
 
 from e2b_code_interpreter import AsyncSandbox
-from pydantic import SecretStr
+from e2b_code_interpreter import Result as E2BExecutionResult
+from e2b_code_interpreter.charts import Chart as E2BExecutionResultChart
+from pydantic import BaseModel, Field, JsonValue, SecretStr
 
-from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
+from backend.data.block import (
+    Block,
+    BlockCategory,
+    BlockOutput,
+    BlockSchemaInput,
+    BlockSchemaOutput,
+)
 from backend.data.model import (
     APIKeyCredentials,
     CredentialsField,
@@ -36,14 +44,135 @@ class ProgrammingLanguage(Enum):
     JAVA = "java"
 
 
-class CodeExecutionBlock(Block):
+class MainCodeExecutionResult(BaseModel):
+    """
+    *Pydantic model mirroring `e2b_code_interpreter.Result`*
+
+    Represents the data to be displayed as a result of executing a cell in a Jupyter notebook.
+    The result is similar to the structure returned by ipython kernel: https://ipython.readthedocs.io/en/stable/development/execution.html#execution-semantics
+
+    The result can contain multiple types of data, such as text, images, plots, etc. Each type of data is represented
+    as a string, and the result can contain multiple types of data. The display calls don't have to have text representation,
+    for the actual result the representation is always present for the result, the other representations are always optional.
+    """  # noqa
+
+    class Chart(BaseModel, E2BExecutionResultChart):
+        pass
+
+    text: Optional[str] = None
+    html: Optional[str] = None
+    markdown: Optional[str] = None
+    svg: Optional[str] = None
+    png: Optional[str] = None
+    jpeg: Optional[str] = None
+    pdf: Optional[str] = None
+    latex: Optional[str] = None
+    json_data: Optional[JsonValue] = Field(None, alias="json")
+    javascript: Optional[str] = None
+    data: Optional[dict] = None
+    chart: Optional[Chart] = None
+    extra: Optional[dict] = None
+    """Extra data that can be included. Not part of the standard types."""
+
+
+class CodeExecutionResult(MainCodeExecutionResult):
+    __doc__ = MainCodeExecutionResult.__doc__
+
+    is_main_result: bool = False
+    """Whether this data is the main result of the cell. Data can be produced by display calls of which can be multiple in a cell."""  # noqa
+
+
+class BaseE2BExecutorMixin:
+    """Shared implementation methods for E2B executor blocks."""
+
+    async def execute_code(
+        self,
+        api_key: str,
+        code: str,
+        language: ProgrammingLanguage,
+        template_id: str = "",
+        setup_commands: Optional[list[str]] = None,
+        timeout: Optional[int] = None,
+        sandbox_id: Optional[str] = None,
+        dispose_sandbox: bool = False,
+    ):
+        """
+        Unified code execution method that handles all three use cases:
+        1. Create new sandbox and execute (ExecuteCodeBlock)
+        2. Create new sandbox, execute, and return sandbox_id (InstantiateCodeSandboxBlock)
+        3. Connect to existing sandbox and execute (ExecuteCodeStepBlock)
+        """  # noqa
+        sandbox = None
+        try:
+            if sandbox_id:
+                # Connect to existing sandbox (ExecuteCodeStepBlock case)
+                sandbox = await AsyncSandbox.connect(
+                    sandbox_id=sandbox_id, api_key=api_key
+                )
+            else:
+                # Create new sandbox (ExecuteCodeBlock/InstantiateCodeSandboxBlock case)
+                sandbox = await AsyncSandbox.create(
+                    api_key=api_key, template=template_id, timeout=timeout
+                )
+                if setup_commands:
+                    for cmd in setup_commands:
+                        await sandbox.commands.run(cmd)
+
+            # Execute the code
+            execution = await sandbox.run_code(
+                code,
+                language=language.value,
+                on_error=lambda e: sandbox.kill(),  # Kill the sandbox on error
+            )
+
+            if execution.error:
+                raise Exception(execution.error)
+
+            results = execution.results
+            text_output = execution.text
+            stdout_logs = "".join(execution.logs.stdout)
+            stderr_logs = "".join(execution.logs.stderr)
+
+            return results, text_output, stdout_logs, stderr_logs, sandbox.sandbox_id
+        finally:
+            # Dispose of sandbox if requested to reduce usage costs
+            if dispose_sandbox and sandbox:
+                await sandbox.kill()
+
+    def process_execution_results(
+        self, results: list[E2BExecutionResult]
+    ) -> tuple[dict[str, Any] | None, list[dict[str, Any]]]:
+        """Process and filter execution results."""
+        # Filter out empty formats and convert to dicts
+        processed_results = [
+            {
+                f: value
+                for f in [*r.formats(), "extra", "is_main_result"]
+                if (value := getattr(r, f, None)) is not None
+            }
+            for r in results
+        ]
+        if main_result := next(
+            (r for r in processed_results if r.get("is_main_result")), None
+        ):
+            # Make main_result a copy we can modify & remove is_main_result
+            (main_result := {**main_result}).pop("is_main_result")
+
+        return main_result, processed_results
+
+
+class ExecuteCodeBlock(Block, BaseE2BExecutorMixin):
     # TODO : Add support to upload and download files
-    # Currently, You can customized the CPU and Memory, only by creating a pre customized sandbox template
-    class Input(BlockSchema):
+    # NOTE: Currently, you can only customize the CPU and Memory
+    #       by creating a pre customized sandbox template
+    class Input(BlockSchemaInput):
         credentials: CredentialsMetaInput[
             Literal[ProviderName.E2B], Literal["api_key"]
         ] = CredentialsField(
-            description="Enter your api key for the E2B Sandbox. You can get it in here - https://e2b.dev/docs",
+            description=(
+                "Enter your API key for the E2B platform. "
+                "You can get it in here - https://e2b.dev/docs"
+            ),
         )
 
         # Todo : Option to run commond in background
@@ -76,6 +205,14 @@ class CodeExecutionBlock(Block):
             description="Execution timeout in seconds", default=300
         )
 
+        dispose_sandbox: bool = SchemaField(
+            description=(
+                "Whether to dispose of the sandbox immediately after execution. "
+                "If disabled, the sandbox will run until its timeout expires."
+            ),
+            default=True,
+        )
+
         template_id: str = SchemaField(
             description=(
                 "You can use an E2B sandbox template by entering its ID here. "
@@ -86,21 +223,29 @@ class CodeExecutionBlock(Block):
             advanced=True,
         )
 
-    class Output(BlockSchema):
-        response: str = SchemaField(description="Response from code execution")
+    class Output(BlockSchemaOutput):
+        main_result: MainCodeExecutionResult = SchemaField(
+            title="Main Result", description="The main result from the code execution"
+        )
+        results: list[CodeExecutionResult] = SchemaField(
+            description="List of results from the code execution"
+        )
+        response: str = SchemaField(
+            title="Main Text Output",
+            description="Text output (if any) of the main execution result",
+        )
         stdout_logs: str = SchemaField(
             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__(
             id="0b02b072-abe7-11ef-8372-fb5d162dd712",
-            description="Executes code in an isolated sandbox environment with internet access.",
+            description="Executes code in a sandbox environment with internet access.",
             categories={BlockCategory.DEVELOPER_TOOLS},
-            input_schema=CodeExecutionBlock.Input,
-            output_schema=CodeExecutionBlock.Output,
+            input_schema=ExecuteCodeBlock.Input,
+            output_schema=ExecuteCodeBlock.Output,
             test_credentials=TEST_CREDENTIALS,
             test_input={
                 "credentials": TEST_CREDENTIALS_INPUT,
@@ -111,91 +256,59 @@ class CodeExecutionBlock(Block):
                 "template_id": "",
             },
             test_output=[
+                ("results", []),
                 ("response", "Hello World"),
                 ("stdout_logs", "Hello World\n"),
             ],
             test_mock={
-                "execute_code": lambda code, language, setup_commands, timeout, api_key, template_id: (
-                    "Hello World",
-                    "Hello World\n",
-                    "",
+                "execute_code": lambda api_key, code, language, template_id, setup_commands, timeout, dispose_sandbox: (  # noqa
+                    [],  # results
+                    "Hello World",  # text_output
+                    "Hello World\n",  # stdout_logs
+                    "",  # stderr_logs
+                    "sandbox_id",  # sandbox_id
                 ),
             },
         )
 
-    async def execute_code(
-        self,
-        code: str,
-        language: ProgrammingLanguage,
-        setup_commands: list[str],
-        timeout: int,
-        api_key: str,
-        template_id: str,
-    ):
-        try:
-            sandbox = None
-            if template_id:
-                sandbox = await AsyncSandbox.create(
-                    template=template_id, api_key=api_key, timeout=timeout
-                )
-            else:
-                sandbox = await AsyncSandbox.create(api_key=api_key, timeout=timeout)
-
-            if not sandbox:
-                raise Exception("Sandbox not created")
-
-            # Running setup commands
-            for cmd in setup_commands:
-                await sandbox.commands.run(cmd)
-
-            # Executing the code
-            execution = await sandbox.run_code(
-                code,
-                language=language.value,
-                on_error=lambda e: sandbox.kill(),  # Kill the sandbox if there is an error
-            )
-
-            if execution.error:
-                raise Exception(execution.error)
-
-            response = execution.text
-            stdout_logs = "".join(execution.logs.stdout)
-            stderr_logs = "".join(execution.logs.stderr)
-
-            return response, stdout_logs, stderr_logs
-
-        except Exception as e:
-            raise e
-
     async def run(
         self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
     ) -> BlockOutput:
         try:
-            response, stdout_logs, stderr_logs = await self.execute_code(
-                input_data.code,
-                input_data.language,
-                input_data.setup_commands,
-                input_data.timeout,
-                credentials.api_key.get_secret_value(),
-                input_data.template_id,
+            results, text_output, stdout, stderr, _ = await self.execute_code(
+                api_key=credentials.api_key.get_secret_value(),
+                code=input_data.code,
+                language=input_data.language,
+                template_id=input_data.template_id,
+                setup_commands=input_data.setup_commands,
+                timeout=input_data.timeout,
+                dispose_sandbox=input_data.dispose_sandbox,
             )
 
-            if response:
-                yield "response", response
-            if stdout_logs:
-                yield "stdout_logs", stdout_logs
-            if stderr_logs:
-                yield "stderr_logs", stderr_logs
+            # Determine result object shape & filter out empty formats
+            main_result, results = self.process_execution_results(results)
+            if main_result:
+                yield "main_result", main_result
+            yield "results", results
+            if text_output:
+                yield "response", text_output
+            if stdout:
+                yield "stdout_logs", stdout
+            if stderr:
+                yield "stderr_logs", stderr
         except Exception as e:
             yield "error", str(e)
 
 
-class InstantiationBlock(Block):
-    class Input(BlockSchema):
+class InstantiateCodeSandboxBlock(Block, BaseE2BExecutorMixin):
+    class Input(BlockSchemaInput):
         credentials: CredentialsMetaInput[
             Literal[ProviderName.E2B], Literal["api_key"]
         ] = CredentialsField(
-            description="Enter your api key for the E2B Sandbox. You can get it in here - https://e2b.dev/docs",
+            description=(
+                "Enter your API key for the E2B platform. "
+                "You can get it in here - https://e2b.dev/docs"
+            )
         )
 
         # Todo : Option to run commond in background
@@ -238,22 +351,27 @@ class InstantiationBlock(Block):
             advanced=True,
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         sandbox_id: str = SchemaField(description="ID of the sandbox instance")
-        response: str = SchemaField(description="Response from code execution")
+        response: str = SchemaField(
+            title="Text Result",
+            description="Text result (if any) of the setup code execution",
+        )
         stdout_logs: str = SchemaField(
             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__(
             id="ff0861c9-1726-4aec-9e5b-bf53f3622112",
-            description="Instantiate an isolated sandbox environment with internet access where to execute code in.",
+            description=(
+                "Instantiate a sandbox environment with internet access "
+                "in which you can execute code with the Execute Code Step block."
+            ),
             categories={BlockCategory.DEVELOPER_TOOLS},
-            input_schema=InstantiationBlock.Input,
-            output_schema=InstantiationBlock.Output,
+            input_schema=InstantiateCodeSandboxBlock.Input,
+            output_schema=InstantiateCodeSandboxBlock.Output,
             test_credentials=TEST_CREDENTIALS,
             test_input={
                 "credentials": TEST_CREDENTIALS_INPUT,
@@ -269,11 +387,12 @@ class InstantiationBlock(Block):
                 ("stdout_logs", "Hello World\n"),
             ],
             test_mock={
-                "execute_code": lambda setup_code, language, setup_commands, timeout, api_key, template_id: (
-                    "sandbox_id",
-                    "Hello World",
-                    "Hello World\n",
-                    "",
+                "execute_code": lambda api_key, code, language, template_id, setup_commands, timeout: (  # noqa
+                    [],  # results
+                    "Hello World",  # text_output
+                    "Hello World\n",  # stdout_logs
+                    "",  # stderr_logs
+                    "sandbox_id",  # sandbox_id
                 ),
             },
         )
@@ -282,78 +401,38 @@ class InstantiationBlock(Block):
         self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
     ) -> BlockOutput:
         try:
-            sandbox_id, response, stdout_logs, stderr_logs = await self.execute_code(
-                input_data.setup_code,
-                input_data.language,
-                input_data.setup_commands,
-                input_data.timeout,
-                credentials.api_key.get_secret_value(),
-                input_data.template_id,
+            _, text_output, stdout, stderr, sandbox_id = await self.execute_code(
+                api_key=credentials.api_key.get_secret_value(),
+                code=input_data.setup_code,
+                language=input_data.language,
+                template_id=input_data.template_id,
+                setup_commands=input_data.setup_commands,
+                timeout=input_data.timeout,
             )
             if sandbox_id:
                 yield "sandbox_id", sandbox_id
             else:
                 yield "error", "Sandbox ID not found"
-            if response:
-                yield "response", response
-            if stdout_logs:
-                yield "stdout_logs", stdout_logs
-            if stderr_logs:
-                yield "stderr_logs", stderr_logs
+
+            if text_output:
+                yield "response", text_output
+            if stdout:
+                yield "stdout_logs", stdout
+            if stderr:
+                yield "stderr_logs", stderr
         except Exception as e:
             yield "error", str(e)
 
-    async def execute_code(
-        self,
-        code: str,
-        language: ProgrammingLanguage,
-        setup_commands: list[str],
-        timeout: int,
-        api_key: str,
-        template_id: str,
-    ):
-        try:
-            sandbox = None
-            if template_id:
-                sandbox = await AsyncSandbox.create(
-                    template=template_id, api_key=api_key, timeout=timeout
-                )
-            else:
-                sandbox = await AsyncSandbox.create(api_key=api_key, timeout=timeout)
 
-            if not sandbox:
-                raise Exception("Sandbox not created")
-
-            # Running setup commands
-            for cmd in setup_commands:
-                await sandbox.commands.run(cmd)
-
-            # Executing the code
-            execution = await sandbox.run_code(
-                code,
-                language=language.value,
-                on_error=lambda e: sandbox.kill(),  # Kill the sandbox if there is an error
-            )
-
-            if execution.error:
-                raise Exception(execution.error)
-
-            response = execution.text
-            stdout_logs = "".join(execution.logs.stdout)
-            stderr_logs = "".join(execution.logs.stderr)
-
-            return sandbox.sandbox_id, response, stdout_logs, stderr_logs
-
-        except Exception as e:
-            raise e
-
-
-class StepExecutionBlock(Block):
-    class Input(BlockSchema):
+class ExecuteCodeStepBlock(Block, BaseE2BExecutorMixin):
+    class Input(BlockSchemaInput):
         credentials: CredentialsMetaInput[
             Literal[ProviderName.E2B], Literal["api_key"]
         ] = CredentialsField(
-            description="Enter your api key for the E2B Sandbox. You can get it in here - https://e2b.dev/docs",
+            description=(
+                "Enter your API key for the E2B platform. "
+                "You can get it in here - https://e2b.dev/docs"
+            ),
         )
 
         sandbox_id: str = SchemaField(
@@ -374,21 +453,34 @@ class StepExecutionBlock(Block):
             advanced=False,
         )
 
-    class Output(BlockSchema):
-        response: str = SchemaField(description="Response from code execution")
+        dispose_sandbox: bool = SchemaField(
+            description="Whether to dispose of the sandbox after executing this code.",
+            default=False,
+        )
+
+    class Output(BlockSchemaOutput):
+        main_result: MainCodeExecutionResult = SchemaField(
+            title="Main Result", description="The main result from the code execution"
+        )
+        results: list[CodeExecutionResult] = SchemaField(
+            description="List of results from the code execution"
+        )
+        response: str = SchemaField(
+            title="Main Text Output",
+            description="Text output (if any) of the main execution result",
+        )
         stdout_logs: str = SchemaField(
             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__(
             id="82b59b8e-ea10-4d57-9161-8b169b0adba6",
-            description="Execute code in a previously instantiated sandbox environment.",
+            description="Execute code in a previously instantiated sandbox.",
             categories={BlockCategory.DEVELOPER_TOOLS},
-            input_schema=StepExecutionBlock.Input,
-            output_schema=StepExecutionBlock.Output,
+            input_schema=ExecuteCodeStepBlock.Input,
+            output_schema=ExecuteCodeStepBlock.Output,
             test_credentials=TEST_CREDENTIALS,
             test_input={
                 "credentials": TEST_CREDENTIALS_INPUT,
@@ -397,61 +489,43 @@ class StepExecutionBlock(Block):
                 "language": ProgrammingLanguage.PYTHON.value,
             },
             test_output=[
+                ("results", []),
                 ("response", "Hello World"),
                 ("stdout_logs", "Hello World\n"),
             ],
             test_mock={
-                "execute_step_code": lambda sandbox_id, step_code, language, api_key: (
-                    "Hello World",
-                    "Hello World\n",
-                    "",
+                "execute_code": lambda api_key, code, language, sandbox_id, dispose_sandbox: (  # noqa
+                    [],  # results
+                    "Hello World",  # text_output
+                    "Hello World\n",  # stdout_logs
+                    "",  # stderr_logs
+                    sandbox_id,  # sandbox_id
                 ),
             },
         )
 
-    async def execute_step_code(
-        self,
-        sandbox_id: str,
-        code: str,
-        language: ProgrammingLanguage,
-        api_key: str,
-    ):
-        try:
-            sandbox = await AsyncSandbox.connect(sandbox_id=sandbox_id, api_key=api_key)
-            if not sandbox:
-                raise Exception("Sandbox not found")
-
-            # Executing the code
-            execution = await sandbox.run_code(code, language=language.value)
-
-            if execution.error:
-                raise Exception(execution.error)
-
-            response = execution.text
-            stdout_logs = "".join(execution.logs.stdout)
-            stderr_logs = "".join(execution.logs.stderr)
-
-            return response, stdout_logs, stderr_logs
-
-        except Exception as e:
-            raise e
-
     async def run(
         self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
     ) -> BlockOutput:
         try:
-            response, stdout_logs, stderr_logs = await self.execute_step_code(
-                input_data.sandbox_id,
-                input_data.step_code,
-                input_data.language,
-                credentials.api_key.get_secret_value(),
+            results, text_output, stdout, stderr, _ = await self.execute_code(
+                api_key=credentials.api_key.get_secret_value(),
+                code=input_data.step_code,
+                language=input_data.language,
+                sandbox_id=input_data.sandbox_id,
+                dispose_sandbox=input_data.dispose_sandbox,
             )
 
-            if response:
-                yield "response", response
-            if stdout_logs:
-                yield "stdout_logs", stdout_logs
-            if stderr_logs:
-                yield "stderr_logs", stderr_logs
+            # Determine result object shape & filter out empty formats
+            main_result, results = self.process_execution_results(results)
+            if main_result:
+                yield "main_result", main_result
+            yield "results", results
+            if text_output:
+                yield "response", text_output
+            if stdout:
+                yield "stdout_logs", stdout
+            if stderr:
+                yield "stderr_logs", stderr
         except Exception as e:
             yield "error", str(e)

autogpt_platform/backend/backend/blocks/code_extraction_block.py

@@ -1,17 +1,23 @@
 import re
 
-from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
+from backend.data.block import (
+    Block,
+    BlockCategory,
+    BlockOutput,
+    BlockSchemaInput,
+    BlockSchemaOutput,
+)
 from backend.data.model import SchemaField
 
 
 class CodeExtractionBlock(Block):
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         text: str = SchemaField(
             description="Text containing code blocks to extract (e.g., AI response)",
             placeholder="Enter text containing code blocks",
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         html: str = SchemaField(description="Extracted HTML code")
         css: str = SchemaField(description="Extracted CSS code")
         javascript: str = SchemaField(description="Extracted JavaScript code")
@@ -90,7 +96,7 @@ class CodeExtractionBlock(Block):
                 for aliases in language_aliases.values()
                 for alias in aliases
             )
-            + r")\s+[\s\S]*?```"
+            + r")[ \t]*\n[\s\S]*?```"
         )
 
         remaining_text = re.sub(pattern, "", input_data.text).strip()
@@ -103,7 +109,9 @@ class CodeExtractionBlock(Block):
         # Escape special regex characters in the language string
         language = re.escape(language)
         # Extract all code blocks enclosed in ```language``` blocks
-        pattern = re.compile(rf"```{language}\s+(.*?)```", re.DOTALL | re.IGNORECASE)
+        pattern = re.compile(
+            rf"```{language}[ \t]*\n(.*?)\n```", re.DOTALL | re.IGNORECASE
+        )
         matches = pattern.finditer(text)
         # Combine all code blocks for this language with newlines between them
         code_blocks = [match.group(1).strip() for match in matches]

autogpt_platform/backend/backend/blocks/codex.py

@@ -0,0 +1,224 @@
+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

autogpt_platform/backend/backend/blocks/compass/triggers.py

@@ -5,7 +5,8 @@ from backend.data.block import (
     BlockCategory,
     BlockManualWebhookConfig,
     BlockOutput,
-    BlockSchema,
+    BlockSchemaInput,
+    BlockSchemaOutput,
 )
 from backend.data.model import SchemaField
 from backend.integrations.providers import ProviderName
@@ -27,10 +28,10 @@ class TranscriptionDataModel(BaseModel):
 
 
 class CompassAITriggerBlock(Block):
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         payload: TranscriptionDataModel = SchemaField(hidden=True)
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         transcription: str = SchemaField(
             description="The contents of the compass transcription."
         )

autogpt_platform/backend/backend/blocks/count_words_and_char_block.py

@@ -1,16 +1,22 @@
-from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
+from backend.data.block import (
+    Block,
+    BlockCategory,
+    BlockOutput,
+    BlockSchemaInput,
+    BlockSchemaOutput,
+)
 from backend.data.model import SchemaField
 
 
 class WordCharacterCountBlock(Block):
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         text: str = SchemaField(
             description="Input text to count words and characters",
             placeholder="Enter your text here",
             advanced=False,
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         word_count: int = SchemaField(description="Number of words in the input text")
         character_count: int = SchemaField(
             description="Number of characters in the input text"

autogpt_platform/backend/backend/blocks/data_manipulation.py

@@ -1,6 +1,12 @@
 from typing import Any, List
 
-from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
+from backend.data.block import (
+    Block,
+    BlockCategory,
+    BlockOutput,
+    BlockSchemaInput,
+    BlockSchemaOutput,
+)
 from backend.data.model import SchemaField
 from backend.util.json import loads
 from backend.util.mock import MockObject
@@ -12,13 +18,13 @@ from backend.util.prompt import estimate_token_count_str
 
 
 class CreateDictionaryBlock(Block):
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         values: dict[str, Any] = SchemaField(
             description="Key-value pairs to create the dictionary with",
             placeholder="e.g., {'name': 'Alice', 'age': 25}",
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         dictionary: dict[str, Any] = SchemaField(
             description="The created dictionary containing the specified key-value pairs"
         )
@@ -62,10 +68,11 @@ class CreateDictionaryBlock(Block):
 
 
 class AddToDictionaryBlock(Block):
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         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.",
+            advanced=False,
         )
         key: str = SchemaField(
             default="",
@@ -85,11 +92,10 @@ class AddToDictionaryBlock(Block):
             advanced=True,
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         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__(
@@ -140,11 +146,11 @@ class AddToDictionaryBlock(Block):
 
 
 class FindInDictionaryBlock(Block):
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         input: Any = SchemaField(description="Dictionary to lookup from")
         key: str | int = SchemaField(description="Key to lookup in the dictionary")
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         output: Any = SchemaField(description="Value found for the given key")
         missing: Any = SchemaField(
             description="Value of the input that missing the key"
@@ -200,7 +206,7 @@ class FindInDictionaryBlock(Block):
 
 
 class RemoveFromDictionaryBlock(Block):
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         dictionary: dict[Any, Any] = SchemaField(
             description="The dictionary to modify."
         )
@@ -209,12 +215,11 @@ class RemoveFromDictionaryBlock(Block):
             default=False, description="Whether to return the removed value."
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         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__(
@@ -250,19 +255,18 @@ class RemoveFromDictionaryBlock(Block):
 
 
 class ReplaceDictionaryValueBlock(Block):
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         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(BlockSchema):
+    class Output(BlockSchemaOutput):
         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__(
@@ -299,10 +303,10 @@ class ReplaceDictionaryValueBlock(Block):
 
 
 class DictionaryIsEmptyBlock(Block):
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         dictionary: dict[Any, Any] = SchemaField(description="The dictionary to check.")
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         is_empty: bool = SchemaField(description="True if the dictionary is empty.")
 
     def __init__(self):
@@ -326,7 +330,7 @@ class DictionaryIsEmptyBlock(Block):
 
 
 class CreateListBlock(Block):
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         values: List[Any] = SchemaField(
             description="A list of values to be combined into a new list.",
             placeholder="e.g., ['Alice', 25, True]",
@@ -342,11 +346,10 @@ class CreateListBlock(Block):
             advanced=True,
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         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__(
@@ -403,7 +406,7 @@ class CreateListBlock(Block):
 
 
 class AddToListBlock(Block):
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         list: List[Any] = SchemaField(
             default_factory=list,
             advanced=False,
@@ -424,11 +427,10 @@ 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(BlockSchema):
+    class Output(BlockSchemaOutput):
         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__(
@@ -483,11 +485,11 @@ class AddToListBlock(Block):
 
 
 class FindInListBlock(Block):
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         list: List[Any] = SchemaField(description="The list to search in.")
         value: Any = SchemaField(description="The value to search for.")
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         index: int = SchemaField(description="The index of the value in the list.")
         found: bool = SchemaField(
             description="Whether the value was found in the list."
@@ -525,15 +527,14 @@ class FindInListBlock(Block):
 
 
 class GetListItemBlock(Block):
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         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(BlockSchema):
+    class Output(BlockSchemaOutput):
         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__(
@@ -560,7 +561,7 @@ class GetListItemBlock(Block):
 
 
 class RemoveFromListBlock(Block):
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         list: List[Any] = SchemaField(description="The list to modify.")
         value: Any = SchemaField(
             default=None, description="Value to remove from the list."
@@ -573,10 +574,9 @@ class RemoveFromListBlock(Block):
             default=False, description="Whether to return the removed item."
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         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__(
@@ -617,17 +617,16 @@ class RemoveFromListBlock(Block):
 
 
 class ReplaceListItemBlock(Block):
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         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(BlockSchema):
+    class Output(BlockSchemaOutput):
         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__(
@@ -662,10 +661,10 @@ class ReplaceListItemBlock(Block):
 
 
 class ListIsEmptyBlock(Block):
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         list: List[Any] = SchemaField(description="The list to check.")
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         is_empty: bool = SchemaField(description="True if the list is empty.")
 
     def __init__(self):

autogpt_platform/backend/backend/blocks/dataforseo/keyword_suggestions.py

@@ -8,7 +8,8 @@ from backend.sdk import (
     Block,
     BlockCategory,
     BlockOutput,
-    BlockSchema,
+    BlockSchemaInput,
+    BlockSchemaOutput,
     CredentialsMetaInput,
     SchemaField,
     UserPasswordCredentials,
@@ -18,7 +19,7 @@ from ._api import DataForSeoClient
 from ._config import dataforseo
 
 
-class KeywordSuggestion(BlockSchema):
+class KeywordSuggestion(BlockSchemaInput):
     """Schema for a keyword suggestion result."""
 
     keyword: str = SchemaField(description="The keyword suggestion")
@@ -45,7 +46,7 @@ class KeywordSuggestion(BlockSchema):
 class DataForSeoKeywordSuggestionsBlock(Block):
     """Block for getting keyword suggestions from DataForSEO Labs."""
 
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         credentials: CredentialsMetaInput = dataforseo.credentials_field(
             description="DataForSEO credentials (username and password)"
         )
@@ -77,7 +78,7 @@ class DataForSeoKeywordSuggestionsBlock(Block):
             le=3000,
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         suggestions: List[KeywordSuggestion] = SchemaField(
             description="List of keyword suggestions with metrics"
         )
@@ -161,54 +162,63 @@ class DataForSeoKeywordSuggestionsBlock(Block):
         **kwargs,
     ) -> BlockOutput:
         """Execute the keyword suggestions query."""
-        client = DataForSeoClient(credentials)
+        try:
+            client = DataForSeoClient(credentials)
 
-        results = await self._fetch_keyword_suggestions(client, input_data)
+            results = await self._fetch_keyword_suggestions(client, input_data)
 
-        # Process and format the results
-        suggestions = []
-        if results and len(results) > 0:
-            # results is a list, get the first element
-            first_result = results[0] if isinstance(results, list) else results
-            items = (
-                first_result.get("items", []) if isinstance(first_result, dict) else []
-            )
-            for item in items:
-                # Create the KeywordSuggestion object
-                suggestion = KeywordSuggestion(
-                    keyword=item.get("keyword", ""),
-                    search_volume=item.get("keyword_info", {}).get("search_volume"),
-                    competition=item.get("keyword_info", {}).get("competition"),
-                    cpc=item.get("keyword_info", {}).get("cpc"),
-                    keyword_difficulty=item.get("keyword_properties", {}).get(
-                        "keyword_difficulty"
-                    ),
-                    serp_info=(
-                        item.get("serp_info") if input_data.include_serp_info else None
-                    ),
-                    clickstream_data=(
-                        item.get("clickstream_keyword_info")
-                        if input_data.include_clickstream_data
-                        else None
-                    ),
+            # Process and format the results
+            suggestions = []
+            if results and len(results) > 0:
+                # results is a list, get the first element
+                first_result = results[0] if isinstance(results, list) else results
+                items = (
+                    first_result.get("items", [])
+                    if isinstance(first_result, dict)
+                    else []
                 )
-                yield "suggestion", suggestion
-                suggestions.append(suggestion)
+                if items is None:
+                    items = []
+                for item in items:
+                    # Create the KeywordSuggestion object
+                    suggestion = KeywordSuggestion(
+                        keyword=item.get("keyword", ""),
+                        search_volume=item.get("keyword_info", {}).get("search_volume"),
+                        competition=item.get("keyword_info", {}).get("competition"),
+                        cpc=item.get("keyword_info", {}).get("cpc"),
+                        keyword_difficulty=item.get("keyword_properties", {}).get(
+                            "keyword_difficulty"
+                        ),
+                        serp_info=(
+                            item.get("serp_info")
+                            if input_data.include_serp_info
+                            else None
+                        ),
+                        clickstream_data=(
+                            item.get("clickstream_keyword_info")
+                            if input_data.include_clickstream_data
+                            else None
+                        ),
+                    )
+                    yield "suggestion", suggestion
+                    suggestions.append(suggestion)
 
-        yield "suggestions", suggestions
-        yield "total_count", len(suggestions)
-        yield "seed_keyword", input_data.keyword
+            yield "suggestions", suggestions
+            yield "total_count", len(suggestions)
+            yield "seed_keyword", input_data.keyword
+        except Exception as e:
+            yield "error", f"Failed to fetch keyword suggestions: {str(e)}"
 
 
 class KeywordSuggestionExtractorBlock(Block):
     """Extracts individual fields from a KeywordSuggestion object."""
 
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         suggestion: KeywordSuggestion = SchemaField(
             description="The keyword suggestion object to extract fields from"
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         keyword: str = SchemaField(description="The keyword suggestion")
         search_volume: Optional[int] = SchemaField(
             description="Monthly search volume", default=None

autogpt_platform/backend/backend/blocks/dataforseo/related_keywords.py

@@ -8,7 +8,8 @@ from backend.sdk import (
     Block,
     BlockCategory,
     BlockOutput,
-    BlockSchema,
+    BlockSchemaInput,
+    BlockSchemaOutput,
     CredentialsMetaInput,
     SchemaField,
     UserPasswordCredentials,
@@ -18,7 +19,7 @@ from ._api import DataForSeoClient
 from ._config import dataforseo
 
 
-class RelatedKeyword(BlockSchema):
+class RelatedKeyword(BlockSchemaInput):
     """Schema for a related keyword result."""
 
     keyword: str = SchemaField(description="The related keyword")
@@ -45,7 +46,7 @@ class RelatedKeyword(BlockSchema):
 class DataForSeoRelatedKeywordsBlock(Block):
     """Block for getting related keywords from DataForSEO Labs."""
 
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         credentials: CredentialsMetaInput = dataforseo.credentials_field(
             description="DataForSEO credentials (username and password)"
         )
@@ -85,7 +86,7 @@ class DataForSeoRelatedKeywordsBlock(Block):
             le=4,
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         related_keywords: List[RelatedKeyword] = SchemaField(
             description="List of related keywords with metrics"
         )
@@ -171,61 +172,71 @@ class DataForSeoRelatedKeywordsBlock(Block):
         **kwargs,
     ) -> BlockOutput:
         """Execute the related keywords query."""
-        client = DataForSeoClient(credentials)
+        try:
+            client = DataForSeoClient(credentials)
 
-        results = await self._fetch_related_keywords(client, input_data)
+            results = await self._fetch_related_keywords(client, input_data)
 
-        # Process and format the results
-        related_keywords = []
-        if results and len(results) > 0:
-            # results is a list, get the first element
-            first_result = results[0] if isinstance(results, list) else results
-            items = (
-                first_result.get("items", []) if isinstance(first_result, dict) else []
-            )
-            for item in items:
-                # Extract keyword_data from the item
-                keyword_data = item.get("keyword_data", {})
-
-                # Create the RelatedKeyword object
-                keyword = RelatedKeyword(
-                    keyword=keyword_data.get("keyword", ""),
-                    search_volume=keyword_data.get("keyword_info", {}).get(
-                        "search_volume"
-                    ),
-                    competition=keyword_data.get("keyword_info", {}).get("competition"),
-                    cpc=keyword_data.get("keyword_info", {}).get("cpc"),
-                    keyword_difficulty=keyword_data.get("keyword_properties", {}).get(
-                        "keyword_difficulty"
-                    ),
-                    serp_info=(
-                        keyword_data.get("serp_info")
-                        if input_data.include_serp_info
-                        else None
-                    ),
-                    clickstream_data=(
-                        keyword_data.get("clickstream_keyword_info")
-                        if input_data.include_clickstream_data
-                        else None
-                    ),
+            # Process and format the results
+            related_keywords = []
+            if results and len(results) > 0:
+                # results is a list, get the first element
+                first_result = results[0] if isinstance(results, list) else results
+                items = (
+                    first_result.get("items", [])
+                    if isinstance(first_result, dict)
+                    else []
                 )
-                yield "related_keyword", keyword
-                related_keywords.append(keyword)
+                # Ensure items is never None
+                if items is None:
+                    items = []
+                for item in items:
+                    # Extract keyword_data from the item
+                    keyword_data = item.get("keyword_data", {})
 
-        yield "related_keywords", related_keywords
-        yield "total_count", len(related_keywords)
-        yield "seed_keyword", input_data.keyword
+                    # Create the RelatedKeyword object
+                    keyword = RelatedKeyword(
+                        keyword=keyword_data.get("keyword", ""),
+                        search_volume=keyword_data.get("keyword_info", {}).get(
+                            "search_volume"
+                        ),
+                        competition=keyword_data.get("keyword_info", {}).get(
+                            "competition"
+                        ),
+                        cpc=keyword_data.get("keyword_info", {}).get("cpc"),
+                        keyword_difficulty=keyword_data.get(
+                            "keyword_properties", {}
+                        ).get("keyword_difficulty"),
+                        serp_info=(
+                            keyword_data.get("serp_info")
+                            if input_data.include_serp_info
+                            else None
+                        ),
+                        clickstream_data=(
+                            keyword_data.get("clickstream_keyword_info")
+                            if input_data.include_clickstream_data
+                            else None
+                        ),
+                    )
+                    yield "related_keyword", keyword
+                    related_keywords.append(keyword)
+
+            yield "related_keywords", related_keywords
+            yield "total_count", len(related_keywords)
+            yield "seed_keyword", input_data.keyword
+        except Exception as e:
+            yield "error", f"Failed to fetch related keywords: {str(e)}"
 
 
 class RelatedKeywordExtractorBlock(Block):
     """Extracts individual fields from a RelatedKeyword object."""
 
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         related_keyword: RelatedKeyword = SchemaField(
             description="The related keyword object to extract fields from"
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         keyword: str = SchemaField(description="The related keyword")
         search_volume: Optional[int] = SchemaField(
             description="Monthly search volume", default=None

autogpt_platform/backend/backend/blocks/decoder_block.py

@@ -1,17 +1,23 @@
 import codecs
 
-from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
+from backend.data.block import (
+    Block,
+    BlockCategory,
+    BlockOutput,
+    BlockSchemaInput,
+    BlockSchemaOutput,
+)
 from backend.data.model import SchemaField
 
 
 class TextDecoderBlock(Block):
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         text: str = SchemaField(
             description="A string containing escaped characters to be decoded",
             placeholder='Your entire text block with \\n and \\" escaped characters',
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         decoded_text: str = SchemaField(
             description="The decoded text with escape sequences processed"
         )

autogpt_platform/backend/backend/blocks/discord/bot_blocks.py

@@ -1,16 +1,23 @@
 import base64
 import io
 import mimetypes
+from enum import Enum
 from pathlib import Path
-from typing import Any
+from typing import Any, Literal, cast
 
-import aiohttp
 import discord
 from pydantic import SecretStr
 
-from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
+from backend.data.block import (
+    Block,
+    BlockCategory,
+    BlockOutput,
+    BlockSchemaInput,
+    BlockSchemaOutput,
+)
 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 (
@@ -27,11 +34,24 @@ 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(BlockSchema):
+    class Input(BlockSchemaInput):
         credentials: DiscordCredentials = DiscordCredentialsField()
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         message_content: str = SchemaField(
             description="The content of the message received"
         )
@@ -114,10 +134,9 @@ class ReadDiscordMessagesBlock(Block):
             if message.attachments:
                 attachment = message.attachments[0]  # Process the first attachment
                 if attachment.filename.endswith((".txt", ".py")):
-                    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}"
+                    response = await Requests().get(attachment.url)
+                    file_content = response.text()
+                    self.output_data += f"\n\nFile from user: {attachment.filename}\nContent: {file_content}"
 
             await client.close()
 
@@ -165,21 +184,21 @@ class ReadDiscordMessagesBlock(Block):
 
 
 class SendDiscordMessageBlock(Block):
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         credentials: DiscordCredentials = DiscordCredentialsField()
         message_content: str = SchemaField(
             description="The content of the message to send"
         )
         channel_name: str = SchemaField(
-            description="The name of the channel the message will be sent to"
+            description="Channel ID or channel name to send the message to"
         )
         server_name: str = SchemaField(
-            description="The name of the server where the channel is located",
-            advanced=True,  # Optional field for server name
+            description="Server name (only needed if using channel name)",
+            advanced=True,
             default="",
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         status: str = SchemaField(
             description="The status of the operation (e.g., 'Message sent', 'Error')"
         )
@@ -231,25 +250,49 @@ class SendDiscordMessageBlock(Block):
         @client.event
         async def on_ready():
             print(f"Logged in as {client.user}")
-            for guild in client.guilds:
-                if server_name and guild.name != server_name:
-                    continue
-                for channel in guild.text_channels:
-                    if channel.name == channel_name:
-                        # Split message into chunks if it exceeds 2000 characters
-                        chunks = self.chunk_message(message_content)
-                        last_message = None
-                        for chunk in chunks:
-                            last_message = await channel.send(chunk)
-                        result["status"] = "Message sent"
-                        result["message_id"] = (
-                            str(last_message.id) if last_message else ""
-                        )
-                        result["channel_id"] = str(channel.id)
-                        await client.close()
-                        return
+            channel = None
 
-            result["status"] = "Channel not found"
+            # Try to parse as channel ID first
+            try:
+                channel_id = int(channel_name)
+                channel = client.get_channel(channel_id)
+            except ValueError:
+                # Not a valid ID, will try name lookup
+                pass
+
+            # If not found by ID (or not an ID), try name lookup
+            if not channel:
+                for guild in client.guilds:
+                    if server_name and guild.name != server_name:
+                        continue
+                    for ch in guild.text_channels:
+                        if ch.name == channel_name:
+                            channel = ch
+                            break
+                    if channel:
+                        break
+
+            if not channel:
+                result["status"] = f"Channel not found: {channel_name}"
+                await client.close()
+                return
+
+            # Type check - ensure it's a text channel that can send messages
+            if not hasattr(channel, "send"):
+                result["status"] = (
+                    f"Channel {channel_name} cannot receive messages (not a text channel)"
+                )
+                await client.close()
+                return
+
+            # Split message into chunks if it exceeds 2000 characters
+            chunks = self.chunk_message(message_content)
+            last_message = None
+            for chunk in chunks:
+                last_message = await channel.send(chunk)  # type: ignore
+            result["status"] = "Message sent"
+            result["message_id"] = str(last_message.id) if last_message else ""
+            result["channel_id"] = str(channel.id)
             await client.close()
 
         await client.start(token)
@@ -287,7 +330,7 @@ class SendDiscordMessageBlock(Block):
 
 
 class SendDiscordDMBlock(Block):
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         credentials: DiscordCredentials = DiscordCredentialsField()
         user_id: str = SchemaField(
             description="The Discord user ID to send the DM to (e.g., '123456789012345678')"
@@ -296,7 +339,7 @@ class SendDiscordDMBlock(Block):
             description="The content of the direct message to send"
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         status: str = SchemaField(description="The status of the operation")
         message_id: str = SchemaField(description="The ID of the sent message")
 
@@ -376,7 +419,7 @@ class SendDiscordDMBlock(Block):
 
 
 class SendDiscordEmbedBlock(Block):
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         credentials: DiscordCredentials = DiscordCredentialsField()
         channel_identifier: str = SchemaField(
             description="Channel ID or channel name to send the embed to"
@@ -413,7 +456,7 @@ class SendDiscordEmbedBlock(Block):
             default=[],
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         status: str = SchemaField(description="Operation status")
         message_id: str = SchemaField(description="ID of the sent embed message")
 
@@ -563,7 +606,7 @@ class SendDiscordEmbedBlock(Block):
 
 
 class SendDiscordFileBlock(Block):
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         credentials: DiscordCredentials = DiscordCredentialsField()
         channel_identifier: str = SchemaField(
             description="Channel ID or channel name to send the file to"
@@ -584,7 +627,7 @@ class SendDiscordFileBlock(Block):
             description="Optional message to send with the file", default=""
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         status: str = SchemaField(description="Operation status")
         message_id: str = SchemaField(description="ID of the sent message")
 
@@ -675,16 +718,15 @@ class SendDiscordFileBlock(Block):
 
                 elif file.startswith(("http://", "https://")):
                     # URL - download the file
-                    async with aiohttp.ClientSession() as session:
-                        async with session.get(file) as response:
-                            file_bytes = await response.read()
+                    response = await Requests().get(file)
+                    file_bytes = response.content
 
-                            # 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
@@ -766,7 +808,7 @@ class SendDiscordFileBlock(Block):
 
 
 class ReplyToDiscordMessageBlock(Block):
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         credentials: DiscordCredentials = DiscordCredentialsField()
         channel_id: str = SchemaField(
             description="The channel ID where the message to reply to is located"
@@ -777,7 +819,7 @@ class ReplyToDiscordMessageBlock(Block):
             description="Whether to mention the original message author", default=True
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         status: str = SchemaField(description="Operation status")
         reply_id: str = SchemaField(description="ID of the reply message")
 
@@ -891,13 +933,13 @@ class ReplyToDiscordMessageBlock(Block):
 
 
 class DiscordUserInfoBlock(Block):
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         credentials: DiscordCredentials = DiscordCredentialsField()
         user_id: str = SchemaField(
             description="The Discord user ID to get information about"
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         user_id: str = SchemaField(
             description="The user's ID (passed through for chaining)"
         )
@@ -1008,7 +1050,7 @@ class DiscordUserInfoBlock(Block):
 
 
 class DiscordChannelInfoBlock(Block):
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         credentials: DiscordCredentials = DiscordCredentialsField()
         channel_identifier: str = SchemaField(
             description="Channel name or channel ID to look up"
@@ -1019,7 +1061,7 @@ class DiscordChannelInfoBlock(Block):
             default="",
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         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")
@@ -1138,3 +1180,211 @@ 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}")

autogpt_platform/backend/backend/blocks/discord/oauth_blocks.py

@@ -2,7 +2,13 @@
 Discord OAuth-based blocks.
 """
 
-from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
+from backend.data.block import (
+    Block,
+    BlockCategory,
+    BlockOutput,
+    BlockSchemaInput,
+    BlockSchemaOutput,
+)
 from backend.data.model import OAuth2Credentials, SchemaField
 
 from ._api import DiscordOAuthUser, get_current_user
@@ -21,12 +27,12 @@ class DiscordGetCurrentUserBlock(Block):
     This block requires Discord OAuth2 credentials (not bot tokens).
     """
 
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         credentials: DiscordOAuthCredentialsInput = DiscordOAuthCredentialsField(
             ["identify"]
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         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")

autogpt_platform/backend/backend/blocks/email_block.py

@@ -1,11 +1,19 @@
 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, BlockSchema
+from backend.data.block import (
+    Block,
+    BlockCategory,
+    BlockOutput,
+    BlockSchemaInput,
+    BlockSchemaOutput,
+)
 from backend.data.model import (
     CredentialsField,
     CredentialsMetaInput,
@@ -42,16 +50,14 @@ def SMTPCredentialsField() -> SMTPCredentialsInput:
 
 
 class SMTPConfig(BaseModel):
-    smtp_server: str = SchemaField(
-        default="smtp.example.com", description="SMTP server address"
-    )
+    smtp_server: str = SchemaField(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(BlockSchema):
+    class Input(BlockSchemaInput):
         to_email: str = SchemaField(
             description="Recipient email address", placeholder="recipient@example.com"
         )
@@ -61,13 +67,10 @@ class SendEmailBlock(Block):
         body: str = SchemaField(
             description="Body of the email", placeholder="Enter the email body"
         )
-        config: SMTPConfig = SchemaField(
-            description="SMTP Config",
-            default=SMTPConfig(),
-        )
+        config: SMTPConfig = SchemaField(description="SMTP Config")
         credentials: SMTPCredentialsInput = SMTPCredentialsField()
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         status: str = SchemaField(description="Status of the email sending operation")
         error: str = SchemaField(
             description="Error message if the email sending failed"
@@ -114,7 +117,7 @@ class SendEmailBlock(Block):
         msg["Subject"] = subject
         msg.attach(MIMEText(body, "plain"))
 
-        with smtplib.SMTP(smtp_server, smtp_port) as server:
+        with smtplib.SMTP(smtp_server, smtp_port, timeout=30) as server:
             server.starttls()
             server.login(smtp_username, smtp_password)
             server.sendmail(smtp_username, to_email, msg.as_string())
@@ -124,10 +127,59 @@ class SendEmailBlock(Block):
     async def run(
         self, input_data: Input, *, credentials: SMTPCredentials, **kwargs
     ) -> BlockOutput:
-        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,
-        )
+        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

autogpt_platform/backend/backend/blocks/enrichlayer/linkedin.py

@@ -8,7 +8,13 @@ which provides access to LinkedIn profile data and related information.
 import logging
 from typing import Optional
 
-from backend.data.block import Block, BlockCategory, BlockOutput, BlockSchema
+from backend.data.block import (
+    Block,
+    BlockCategory,
+    BlockOutput,
+    BlockSchemaInput,
+    BlockSchemaOutput,
+)
 from backend.data.model import APIKeyCredentials, CredentialsField, SchemaField
 from backend.util.type import MediaFileType
 
@@ -29,7 +35,7 @@ logger = logging.getLogger(__name__)
 class GetLinkedinProfileBlock(Block):
     """Block to fetch LinkedIn profile data using Enrichlayer API."""
 
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         """Input schema for GetLinkedinProfileBlock."""
 
         linkedin_url: str = SchemaField(
@@ -80,13 +86,12 @@ class GetLinkedinProfileBlock(Block):
             description="Enrichlayer API credentials"
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         """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."""
@@ -199,7 +204,7 @@ class GetLinkedinProfileBlock(Block):
 class LinkedinPersonLookupBlock(Block):
     """Block to look up LinkedIn profiles by person's information using Enrichlayer API."""
 
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         """Input schema for LinkedinPersonLookupBlock."""
 
         first_name: str = SchemaField(
@@ -242,13 +247,12 @@ class LinkedinPersonLookupBlock(Block):
             description="Enrichlayer API credentials"
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         """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."""
@@ -346,7 +350,7 @@ class LinkedinPersonLookupBlock(Block):
 class LinkedinRoleLookupBlock(Block):
     """Block to look up LinkedIn profiles by role in a company using Enrichlayer API."""
 
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         """Input schema for LinkedinRoleLookupBlock."""
 
         role: str = SchemaField(
@@ -366,13 +370,12 @@ class LinkedinRoleLookupBlock(Block):
             description="Enrichlayer API credentials"
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         """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."""
@@ -449,7 +452,7 @@ class LinkedinRoleLookupBlock(Block):
 class GetLinkedinProfilePictureBlock(Block):
     """Block to get LinkedIn profile pictures using Enrichlayer API."""
 
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         """Input schema for GetLinkedinProfilePictureBlock."""
 
         linkedin_profile_url: str = SchemaField(
@@ -460,13 +463,12 @@ class GetLinkedinProfilePictureBlock(Block):
             description="Enrichlayer API credentials"
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         """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."""

autogpt_platform/backend/backend/blocks/exa/_test.py

@@ -0,0 +1,22 @@
+"""
+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,
+}

autogpt_platform/backend/backend/blocks/exa/answers.py

@@ -1,55 +1,59 @@
+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,
-    BlockSchema,
+    BlockSchemaInput,
+    BlockSchemaOutput,
     CredentialsMetaInput,
-    Requests,
+    MediaFileType,
     SchemaField,
 )
 
 from ._config import exa
 
 
-class CostBreakdown(BaseModel):
-    keywordSearch: float
-    neuralSearch: float
-    contentText: float
-    contentHighlight: float
-    contentSummary: float
+class AnswerCitation(BaseModel):
+    """Citation model for answer endpoint."""
 
+    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"
+    )
 
-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
+    @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 ExaAnswerBlock(Block):
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         credentials: CredentialsMetaInput = exa.credentials_field(
             description="The Exa integration requires an API Key."
         )
@@ -58,31 +62,21 @@ class ExaAnswerBlock(Block):
             placeholder="What is the latest valuation of SpaceX?",
         )
         text: bool = SchemaField(
-            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,
+            description="Include full text content in the search results used for the answer",
+            default=True,
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         answer: str = SchemaField(
             description="The generated answer based on search results"
         )
-        citations: list[dict] = SchemaField(
-            description="Search results used to generate the answer",
-            default_factory=list,
+        citations: list[AnswerCitation] = SchemaField(
+            description="Search results used to generate the answer"
         )
-        cost_dollars: CostDollars = SchemaField(
-            description="Cost breakdown of the request"
-        )
-        error: str = SchemaField(
-            description="Error message if the request failed", default=""
+        citation: AnswerCitation = SchemaField(
+            description="Individual citation from the answer"
         )
+        error: str = SchemaField(description="Error message if the request failed")
 
     def __init__(self):
         super().__init__(
@@ -96,26 +90,24 @@ class ExaAnswerBlock(Block):
     async def run(
         self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
     ) -> BlockOutput:
-        url = "https://api.exa.ai/answer"
-        headers = {
-            "Content-Type": "application/json",
-            "x-api-key": credentials.api_key.get_secret_value(),
-        }
+        aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
 
-        # Build the payload
-        payload = {
-            "query": input_data.query,
-            "text": input_data.text,
-            "model": input_data.model,
-        }
+        # 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
+        )
 
-        try:
-            response = await Requests().post(url, headers=headers, json=payload)
-            data = response.json()
+        # 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
 
-            yield "answer", data.get("answer", "")
-            yield "citations", data.get("citations", [])
-            yield "cost_dollars", data.get("costDollars", {})
+        yield "answer", response.answer
 
-        except Exception as e:
-            yield "error", str(e)
+        citations = [
+            AnswerCitation.from_sdk(sdk_citation)
+            for sdk_citation in response.citations or []
+        ]
+
+        yield "citations", citations
+        for citation in citations:
+            yield "citation", citation

autogpt_platform/backend/backend/blocks/exa/code_context.py

@@ -0,0 +1,118 @@
+"""
+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

autogpt_platform/backend/backend/blocks/exa/contents.py

@@ -1,39 +1,127 @@
+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,
-    BlockSchema,
+    BlockSchemaInput,
+    BlockSchemaOutput,
     CredentialsMetaInput,
-    Requests,
     SchemaField,
 )
 
 from ._config import exa
-from .helpers import ContentSettings
+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'"
+    )
 
 
 class ExaContentsBlock(Block):
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         credentials: CredentialsMetaInput = exa.credentials_field(
             description="The Exa integration requires an API Key."
         )
-        ids: list[str] = SchemaField(
-            description="Array of document IDs obtained from searches"
+        urls: list[str] = SchemaField(
+            description="Array of URLs to crawl (preferred over 'ids')",
+            default_factory=list,
+            advanced=False,
         )
-        contents: ContentSettings = SchemaField(
-            description="Content retrieval settings",
-            default=ContentSettings(),
+        ids: list[str] = SchemaField(
+            description="[DEPRECATED - use 'urls' instead] Array of document IDs obtained from searches",
+            default_factory=list,
+            advanced=True,
+        )
+        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(),
             advanced=True,
         )
 
-    class Output(BlockSchema):
-        results: list = SchemaField(
-            description="List of document contents", default_factory=list
+    class Output(BlockSchemaOutput):
+        results: list[ExaSearchResults] = SchemaField(
+            description="List of document contents with metadata"
         )
-        error: str = SchemaField(
-            description="Error message if the request failed", default=""
+        result: ExaSearchResults = SchemaField(
+            description="Single document content result"
         )
+        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__(
@@ -47,23 +135,91 @@ class ExaContentsBlock(Block):
     async def run(
         self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
     ) -> BlockOutput:
-        url = "https://api.exa.ai/contents"
-        headers = {
-            "Content-Type": "application/json",
-            "x-api-key": credentials.api_key.get_secret_value(),
-        }
+        if not input_data.urls and not input_data.ids:
+            raise ValueError("Either 'urls' or 'ids' must be provided")
 
-        # 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,
-        }
+        sdk_kwargs = {}
 
-        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)
+        # 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

autogpt_platform/backend/backend/blocks/exa/helpers.py

@@ -1,51 +1,150 @@
-from typing import Optional
+from enum import Enum
+from typing import Any, Dict, Literal, Optional, Union
 
-from backend.sdk import BaseModel, SchemaField
+from backend.sdk import BaseModel, MediaFileType, SchemaField
 
 
-class TextSettings(BaseModel):
-    max_characters: int = SchemaField(
-        default=1000,
+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,
         description="Maximum number of characters to return",
         placeholder="1000",
     )
     include_html_tags: bool = SchemaField(
         default=False,
-        description="Whether to include HTML tags in the text",
+        description="Include HTML tags in the response, helps LLMs understand text structure",
         placeholder="False",
     )
 
 
 class HighlightSettings(BaseModel):
     num_sentences: int = SchemaField(
-        default=3,
+        default=1,
         description="Number of sentences per highlight",
-        placeholder="3",
+        placeholder="1",
+        ge=1,
     )
     highlights_per_url: int = SchemaField(
-        default=3,
+        default=1,
         description="Number of highlights per URL",
-        placeholder="3",
+        placeholder="1",
+        ge=1,
+    )
+    query: Optional[str] = SchemaField(
+        default=None,
+        description="Custom query to direct the LLM's selection of highlights",
+        placeholder="Key advancements",
     )
 
 
 class SummarySettings(BaseModel):
     query: Optional[str] = SchemaField(
-        default="",
-        description="Query string for summarization",
-        placeholder="Enter query",
+        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",
     )
 
 
 class ContentSettings(BaseModel):
-    text: TextSettings = SchemaField(
-        default=TextSettings(),
+    text: Optional[Union[bool, TextEnabled, TextDisabled, TextAdvanced]] = SchemaField(
+        default=None,
+        description="Text content retrieval. Boolean for simple enable/disable or object for advanced settings",
     )
-    highlights: HighlightSettings = SchemaField(
-        default=HighlightSettings(),
+    highlights: Optional[HighlightSettings] = SchemaField(
+        default=None,
+        description="Text snippets most relevant from each page",
     )
-    summary: SummarySettings = SchemaField(
-        default=SummarySettings(),
+    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,
+        )
     )
 
 
@@ -127,3 +226,225 @@ 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

autogpt_platform/backend/backend/blocks/exa/model.py

@@ -1,247 +0,0 @@
-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

autogpt_platform/backend/backend/blocks/exa/research.py

@@ -0,0 +1,518 @@
+"""
+Exa Research Task Blocks
+
+Provides asynchronous research capabilities that explore the web, gather sources,
+synthesize findings, and return structured results with citations.
+"""
+
+import asyncio
+import time
+from enum import Enum
+from typing import Any, Dict, List, Optional
+
+from pydantic import BaseModel
+
+from backend.sdk import (
+    APIKeyCredentials,
+    Block,
+    BlockCategory,
+    BlockOutput,
+    BlockSchemaInput,
+    BlockSchemaOutput,
+    CredentialsMetaInput,
+    Requests,
+    SchemaField,
+)
+
+from ._config import exa
+
+
+class ResearchModel(str, Enum):
+    """Available research models."""
+
+    FAST = "exa-research-fast"
+    STANDARD = "exa-research"
+    PRO = "exa-research-pro"
+
+
+class ResearchStatus(str, Enum):
+    """Research task status."""
+
+    PENDING = "pending"
+    RUNNING = "running"
+    COMPLETED = "completed"
+    CANCELED = "canceled"
+    FAILED = "failed"
+
+
+class ResearchCostModel(BaseModel):
+    """Cost breakdown for a research request."""
+
+    total: float
+    num_searches: int
+    num_pages: int
+    reasoning_tokens: int
+
+    @classmethod
+    def from_api(cls, data: dict) -> "ResearchCostModel":
+        """Convert API response, rounding fractional counts to integers."""
+        return cls(
+            total=data.get("total", 0.0),
+            num_searches=int(round(data.get("numSearches", 0))),
+            num_pages=int(round(data.get("numPages", 0))),
+            reasoning_tokens=int(round(data.get("reasoningTokens", 0))),
+        )
+
+
+class ResearchOutputModel(BaseModel):
+    """Research output with content and optional structured data."""
+
+    content: str
+    parsed: Optional[Dict[str, Any]] = None
+
+
+class ResearchTaskModel(BaseModel):
+    """Stable output model for research tasks."""
+
+    research_id: str
+    created_at: int
+    model: str
+    instructions: str
+    status: str
+    output_schema: Optional[Dict[str, Any]] = None
+    output: Optional[ResearchOutputModel] = None
+    cost_dollars: Optional[ResearchCostModel] = None
+    finished_at: Optional[int] = None
+    error: Optional[str] = None
+
+    @classmethod
+    def from_api(cls, data: dict) -> "ResearchTaskModel":
+        """Convert API response to our stable model."""
+        output_data = data.get("output")
+        output = None
+        if output_data:
+            output = ResearchOutputModel(
+                content=output_data.get("content", ""),
+                parsed=output_data.get("parsed"),
+            )
+
+        cost_data = data.get("costDollars")
+        cost = None
+        if cost_data:
+            cost = ResearchCostModel.from_api(cost_data)
+
+        return cls(
+            research_id=data.get("researchId", ""),
+            created_at=data.get("createdAt", 0),
+            model=data.get("model", "exa-research"),
+            instructions=data.get("instructions", ""),
+            status=data.get("status", "pending"),
+            output_schema=data.get("outputSchema"),
+            output=output,
+            cost_dollars=cost,
+            finished_at=data.get("finishedAt"),
+            error=data.get("error"),
+        )
+
+
+class ExaCreateResearchBlock(Block):
+    """Create an asynchronous research task that explores the web and synthesizes findings."""
+
+    class Input(BlockSchemaInput):
+        credentials: CredentialsMetaInput = exa.credentials_field(
+            description="The Exa integration requires an API Key."
+        )
+        instructions: str = SchemaField(
+            description="Research instructions - clearly define what information to find, how to conduct research, and desired output format.",
+            placeholder="Research the top 5 AI coding assistants, their features, pricing, and user reviews",
+        )
+        model: ResearchModel = SchemaField(
+            default=ResearchModel.STANDARD,
+            description="Research model: 'fast' for quick results, 'standard' for balanced quality, 'pro' for thorough analysis",
+        )
+        output_schema: Optional[dict] = SchemaField(
+            default=None,
+            description="JSON Schema to enforce structured output. When provided, results are validated and returned as parsed JSON.",
+            advanced=True,
+        )
+        wait_for_completion: bool = SchemaField(
+            default=True,
+            description="Wait for research to complete before returning. Ensures you get results immediately.",
+        )
+        polling_timeout: int = SchemaField(
+            default=600,
+            description="Maximum time to wait for completion in seconds (only if wait_for_completion is True)",
+            advanced=True,
+            ge=1,
+            le=3600,
+        )
+
+    class Output(BlockSchemaOutput):
+        research_id: str = SchemaField(
+            description="Unique identifier for tracking this research request"
+        )
+        status: str = SchemaField(description="Final status of the research")
+        model: str = SchemaField(description="The research model used")
+        instructions: str = SchemaField(
+            description="The research instructions provided"
+        )
+        created_at: int = SchemaField(
+            description="When the research was created (Unix timestamp in ms)"
+        )
+        output_content: Optional[str] = SchemaField(
+            description="Research output as text (only if wait_for_completion was True and completed)"
+        )
+        output_parsed: Optional[dict] = SchemaField(
+            description="Structured JSON output (only if wait_for_completion and outputSchema were provided)"
+        )
+        cost_total: Optional[float] = SchemaField(
+            description="Total cost in USD (only if wait_for_completion was True and completed)"
+        )
+        elapsed_time: Optional[float] = SchemaField(
+            description="Time taken to complete in seconds (only if wait_for_completion was True)"
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="a1f2e3d4-c5b6-4a78-9012-3456789abcde",
+            description="Create research task with optional waiting - explores web and synthesizes findings with citations",
+            categories={BlockCategory.SEARCH, BlockCategory.AI},
+            input_schema=ExaCreateResearchBlock.Input,
+            output_schema=ExaCreateResearchBlock.Output,
+        )
+
+    async def run(
+        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
+    ) -> BlockOutput:
+        url = "https://api.exa.ai/research/v1"
+        headers = {
+            "Content-Type": "application/json",
+            "x-api-key": credentials.api_key.get_secret_value(),
+        }
+
+        payload: Dict[str, Any] = {
+            "model": input_data.model.value,
+            "instructions": input_data.instructions,
+        }
+
+        if input_data.output_schema:
+            payload["outputSchema"] = input_data.output_schema
+
+        response = await Requests().post(url, headers=headers, json=payload)
+        data = response.json()
+
+        research_id = data.get("researchId", "")
+
+        if input_data.wait_for_completion:
+            start_time = time.time()
+            get_url = f"https://api.exa.ai/research/v1/{research_id}"
+            get_headers = {"x-api-key": credentials.api_key.get_secret_value()}
+            check_interval = 10
+
+            while time.time() - start_time < input_data.polling_timeout:
+                poll_response = await Requests().get(url=get_url, headers=get_headers)
+                poll_data = poll_response.json()
+
+                status = poll_data.get("status", "")
+
+                if status in ["completed", "failed", "canceled"]:
+                    elapsed = time.time() - start_time
+                    research = ResearchTaskModel.from_api(poll_data)
+
+                    yield "research_id", research.research_id
+                    yield "status", research.status
+                    yield "model", research.model
+                    yield "instructions", research.instructions
+                    yield "created_at", research.created_at
+                    yield "elapsed_time", elapsed
+
+                    if research.output:
+                        yield "output_content", research.output.content
+                        yield "output_parsed", research.output.parsed
+
+                    if research.cost_dollars:
+                        yield "cost_total", research.cost_dollars.total
+                    return
+
+                await asyncio.sleep(check_interval)
+
+            raise ValueError(
+                f"Research did not complete within {input_data.polling_timeout} seconds"
+            )
+        else:
+            yield "research_id", research_id
+            yield "status", data.get("status", "pending")
+            yield "model", data.get("model", input_data.model.value)
+            yield "instructions", data.get("instructions", input_data.instructions)
+            yield "created_at", data.get("createdAt", 0)
+
+
+class ExaGetResearchBlock(Block):
+    """Get the status and results of a research task."""
+
+    class Input(BlockSchemaInput):
+        credentials: CredentialsMetaInput = exa.credentials_field(
+            description="The Exa integration requires an API Key."
+        )
+        research_id: str = SchemaField(
+            description="The ID of the research task to retrieve",
+            placeholder="01jszdfs0052sg4jc552sg4jc5",
+        )
+        include_events: bool = SchemaField(
+            default=False,
+            description="Include detailed event log of research operations",
+            advanced=True,
+        )
+
+    class Output(BlockSchemaOutput):
+        research_id: str = SchemaField(description="The research task identifier")
+        status: str = SchemaField(
+            description="Current status: pending, running, completed, canceled, or failed"
+        )
+        instructions: str = SchemaField(
+            description="The original research instructions"
+        )
+        model: str = SchemaField(description="The research model used")
+        created_at: int = SchemaField(
+            description="When research was created (Unix timestamp in ms)"
+        )
+        finished_at: Optional[int] = SchemaField(
+            description="When research finished (Unix timestamp in ms, if completed/canceled/failed)"
+        )
+        output_content: Optional[str] = SchemaField(
+            description="Research output as text (if completed)"
+        )
+        output_parsed: Optional[dict] = SchemaField(
+            description="Structured JSON output matching outputSchema (if provided and completed)"
+        )
+        cost_total: Optional[float] = SchemaField(
+            description="Total cost in USD (if completed)"
+        )
+        cost_searches: Optional[int] = SchemaField(
+            description="Number of searches performed (if completed)"
+        )
+        cost_pages: Optional[int] = SchemaField(
+            description="Number of pages crawled (if completed)"
+        )
+        cost_reasoning_tokens: Optional[int] = SchemaField(
+            description="AI tokens used for reasoning (if completed)"
+        )
+        error_message: Optional[str] = SchemaField(
+            description="Error message if research failed"
+        )
+        events: Optional[List[dict]] = SchemaField(
+            description="Detailed event log (if include_events was True)"
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="b2e3f4a5-6789-4bcd-9012-3456789abcde",
+            description="Get status and results of a research task",
+            categories={BlockCategory.SEARCH},
+            input_schema=ExaGetResearchBlock.Input,
+            output_schema=ExaGetResearchBlock.Output,
+        )
+
+    async def run(
+        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
+    ) -> BlockOutput:
+        url = f"https://api.exa.ai/research/v1/{input_data.research_id}"
+        headers = {
+            "x-api-key": credentials.api_key.get_secret_value(),
+        }
+
+        params = {}
+        if input_data.include_events:
+            params["events"] = "true"
+
+        response = await Requests().get(url, headers=headers, params=params)
+        data = response.json()
+
+        research = ResearchTaskModel.from_api(data)
+
+        yield "research_id", research.research_id
+        yield "status", research.status
+        yield "instructions", research.instructions
+        yield "model", research.model
+        yield "created_at", research.created_at
+        yield "finished_at", research.finished_at
+
+        if research.output:
+            yield "output_content", research.output.content
+            yield "output_parsed", research.output.parsed
+
+        if research.cost_dollars:
+            yield "cost_total", research.cost_dollars.total
+            yield "cost_searches", research.cost_dollars.num_searches
+            yield "cost_pages", research.cost_dollars.num_pages
+            yield "cost_reasoning_tokens", research.cost_dollars.reasoning_tokens
+
+        yield "error_message", research.error
+
+        if input_data.include_events:
+            yield "events", data.get("events", [])
+
+
+class ExaWaitForResearchBlock(Block):
+    """Wait for a research task to complete with progress tracking."""
+
+    class Input(BlockSchemaInput):
+        credentials: CredentialsMetaInput = exa.credentials_field(
+            description="The Exa integration requires an API Key."
+        )
+        research_id: str = SchemaField(
+            description="The ID of the research task to wait for",
+            placeholder="01jszdfs0052sg4jc552sg4jc5",
+        )
+        timeout: int = SchemaField(
+            default=600,
+            description="Maximum time to wait in seconds",
+            ge=1,
+            le=3600,
+        )
+        check_interval: int = SchemaField(
+            default=10,
+            description="Seconds between status checks",
+            advanced=True,
+            ge=1,
+            le=60,
+        )
+
+    class Output(BlockSchemaOutput):
+        research_id: str = SchemaField(description="The research task identifier")
+        final_status: str = SchemaField(description="Final status when polling stopped")
+        output_content: Optional[str] = SchemaField(
+            description="Research output as text (if completed)"
+        )
+        output_parsed: Optional[dict] = SchemaField(
+            description="Structured JSON output (if outputSchema was provided and completed)"
+        )
+        cost_total: Optional[float] = SchemaField(description="Total cost in USD")
+        elapsed_time: float = SchemaField(description="Total time waited in seconds")
+        timed_out: bool = SchemaField(
+            description="Whether polling timed out before completion"
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="c3d4e5f6-7890-4abc-9012-3456789abcde",
+            description="Wait for a research task to complete with configurable timeout",
+            categories={BlockCategory.SEARCH},
+            input_schema=ExaWaitForResearchBlock.Input,
+            output_schema=ExaWaitForResearchBlock.Output,
+        )
+
+    async def run(
+        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
+    ) -> BlockOutput:
+        start_time = time.time()
+        url = f"https://api.exa.ai/research/v1/{input_data.research_id}"
+        headers = {
+            "x-api-key": credentials.api_key.get_secret_value(),
+        }
+
+        while time.time() - start_time < input_data.timeout:
+            response = await Requests().get(url, headers=headers)
+            data = response.json()
+
+            status = data.get("status", "")
+
+            if status in ["completed", "failed", "canceled"]:
+                elapsed = time.time() - start_time
+                research = ResearchTaskModel.from_api(data)
+
+                yield "research_id", research.research_id
+                yield "final_status", research.status
+                yield "elapsed_time", elapsed
+                yield "timed_out", False
+
+                if research.output:
+                    yield "output_content", research.output.content
+                    yield "output_parsed", research.output.parsed
+
+                if research.cost_dollars:
+                    yield "cost_total", research.cost_dollars.total
+
+                return
+
+            await asyncio.sleep(input_data.check_interval)
+
+        elapsed = time.time() - start_time
+        response = await Requests().get(url, headers=headers)
+        data = response.json()
+
+        yield "research_id", input_data.research_id
+        yield "final_status", data.get("status", "unknown")
+        yield "elapsed_time", elapsed
+        yield "timed_out", True
+
+
+class ExaListResearchBlock(Block):
+    """List all research tasks with pagination support."""
+
+    class Input(BlockSchemaInput):
+        credentials: CredentialsMetaInput = exa.credentials_field(
+            description="The Exa integration requires an API Key."
+        )
+        cursor: Optional[str] = SchemaField(
+            default=None,
+            description="Cursor for pagination through results",
+            advanced=True,
+        )
+        limit: int = SchemaField(
+            default=10,
+            description="Number of research tasks to return (1-50)",
+            ge=1,
+            le=50,
+            advanced=True,
+        )
+
+    class Output(BlockSchemaOutput):
+        research_tasks: List[ResearchTaskModel] = SchemaField(
+            description="List of research tasks ordered by creation time (newest first)"
+        )
+        research_task: ResearchTaskModel = SchemaField(
+            description="Individual research task (yielded for each task)"
+        )
+        has_more: bool = SchemaField(
+            description="Whether there are more tasks to paginate through"
+        )
+        next_cursor: Optional[str] = SchemaField(
+            description="Cursor for the next page of results"
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="d4e5f6a7-8901-4bcd-9012-3456789abcde",
+            description="List all research tasks with pagination support",
+            categories={BlockCategory.SEARCH},
+            input_schema=ExaListResearchBlock.Input,
+            output_schema=ExaListResearchBlock.Output,
+        )
+
+    async def run(
+        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
+    ) -> BlockOutput:
+        url = "https://api.exa.ai/research/v1"
+        headers = {
+            "x-api-key": credentials.api_key.get_secret_value(),
+        }
+
+        params: Dict[str, Any] = {
+            "limit": input_data.limit,
+        }
+        if input_data.cursor:
+            params["cursor"] = input_data.cursor
+
+        response = await Requests().get(url, headers=headers, params=params)
+        data = response.json()
+
+        tasks = [ResearchTaskModel.from_api(task) for task in data.get("data", [])]
+
+        yield "research_tasks", tasks
+
+        for task in tasks:
+            yield "research_task", task
+
+        yield "has_more", data.get("hasMore", False)
+        yield "next_cursor", data.get("nextCursor")

autogpt_platform/backend/backend/blocks/exa/search.py

@@ -1,32 +1,66 @@
 from datetime import datetime
+from enum import Enum
+from typing import Optional
+
+from exa_py import AsyncExa
 
 from backend.sdk import (
     APIKeyCredentials,
     Block,
     BlockCategory,
     BlockOutput,
-    BlockSchema,
+    BlockSchemaInput,
+    BlockSchemaOutput,
     CredentialsMetaInput,
-    Requests,
     SchemaField,
 )
 
 from ._config import exa
-from .helpers import ContentSettings
+from .helpers import (
+    ContentSettings,
+    CostDollars,
+    ExaSearchResults,
+    process_contents_settings,
+)
+
+
+class ExaSearchTypes(Enum):
+    KEYWORD = "keyword"
+    NEURAL = "neural"
+    FAST = "fast"
+    AUTO = "auto"
+
+
+class ExaSearchCategories(Enum):
+    COMPANY = "company"
+    RESEARCH_PAPER = "research paper"
+    NEWS = "news"
+    PDF = "pdf"
+    GITHUB = "github"
+    TWEET = "tweet"
+    PERSONAL_SITE = "personal site"
+    LINKEDIN_PROFILE = "linkedin profile"
+    FINANCIAL_REPORT = "financial report"
 
 
 class ExaSearchBlock(Block):
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         credentials: CredentialsMetaInput = exa.credentials_field(
             description="The Exa integration requires an API Key."
         )
         query: str = SchemaField(description="The search query")
-        use_auto_prompt: bool = SchemaField(
-            description="Whether to use autoprompt", default=True, advanced=True
+        type: ExaSearchTypes = SchemaField(
+            description="Type of search", default=ExaSearchTypes.AUTO, advanced=True
         )
-        type: str = SchemaField(description="Type of search", default="", advanced=True)
-        category: str = SchemaField(
-            description="Category to search within", default="", advanced=True
+        category: ExaSearchCategories | None = SchemaField(
+            description="Category to search within: company, research paper, news, pdf, github, tweet, personal site, linkedin profile, financial report",
+            default=None,
+            advanced=True,
+        )
+        user_location: str | None = SchemaField(
+            description="The two-letter ISO country code of the user (e.g., 'US')",
+            default=None,
+            advanced=True,
         )
         number_of_results: int = SchemaField(
             description="Number of results to return", default=10, advanced=True
@@ -39,17 +73,17 @@ class ExaSearchBlock(Block):
             default_factory=list,
             advanced=True,
         )
-        start_crawl_date: datetime = SchemaField(
-            description="Start date for crawled content"
+        start_crawl_date: datetime | None = SchemaField(
+            description="Start date for crawled content", advanced=True, default=None
         )
-        end_crawl_date: datetime = SchemaField(
-            description="End date for crawled content"
+        end_crawl_date: datetime | None = SchemaField(
+            description="End date for crawled content", advanced=True, default=None
         )
-        start_published_date: datetime = SchemaField(
-            description="Start date for published content"
+        start_published_date: datetime | None = SchemaField(
+            description="Start date for published content", advanced=True, default=None
         )
-        end_published_date: datetime = SchemaField(
-            description="End date for published content"
+        end_published_date: datetime | None = SchemaField(
+            description="End date for published content", advanced=True, default=None
         )
         include_text: list[str] = SchemaField(
             description="Text patterns to include", default_factory=list, advanced=True
@@ -62,14 +96,30 @@ class ExaSearchBlock(Block):
             default=ContentSettings(),
             advanced=True,
         )
+        moderation: bool = SchemaField(
+            description="Enable content moderation to filter unsafe content from search results",
+            default=False,
+            advanced=True,
+        )
 
-    class Output(BlockSchema):
-        results: list = SchemaField(
-            description="List of search results", default_factory=list
+    class Output(BlockSchemaOutput):
+        results: list[ExaSearchResults] = SchemaField(
+            description="List of search results"
         )
-        error: str = SchemaField(
-            description="Error message if the request failed",
+        result: ExaSearchResults = SchemaField(description="Single search result")
+        context: str = SchemaField(
+            description="A formatted string of the search results ready for LLMs."
         )
+        search_type: str = SchemaField(
+            description="For auto searches, indicates which search type was selected."
+        )
+        resolved_search_type: str = SchemaField(
+            description="The search type that was actually used for this request (neural or keyword)"
+        )
+        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__(
@@ -83,51 +133,76 @@ class ExaSearchBlock(Block):
     async def run(
         self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
     ) -> BlockOutput:
-        url = "https://api.exa.ai/search"
-        headers = {
-            "Content-Type": "application/json",
-            "x-api-key": credentials.api_key.get_secret_value(),
-        }
-
-        payload = {
+        sdk_kwargs = {
             "query": input_data.query,
-            "useAutoprompt": input_data.use_auto_prompt,
-            "numResults": input_data.number_of_results,
-            "contents": input_data.contents.model_dump(),
+            "num_results": input_data.number_of_results,
         }
 
-        date_field_mapping = {
-            "start_crawl_date": "startCrawlDate",
-            "end_crawl_date": "endCrawlDate",
-            "start_published_date": "startPublishedDate",
-            "end_published_date": "endPublishedDate",
-        }
+        if input_data.type:
+            sdk_kwargs["type"] = input_data.type.value
 
-        # Add dates if they exist
-        for input_field, api_field in date_field_mapping.items():
-            value = getattr(input_data, input_field, None)
-            if value:
-                payload[api_field] = value.strftime("%Y-%m-%dT%H:%M:%S.000Z")
+        if input_data.category:
+            sdk_kwargs["category"] = input_data.category.value
 
-        optional_field_mapping = {
-            "type": "type",
-            "category": "category",
-            "include_domains": "includeDomains",
-            "exclude_domains": "excludeDomains",
-            "include_text": "includeText",
-            "exclude_text": "excludeText",
-        }
+        if input_data.user_location:
+            sdk_kwargs["user_location"] = input_data.user_location
 
-        # Add other fields
-        for input_field, api_field in optional_field_mapping.items():
-            value = getattr(input_data, input_field)
-            if value:  # Only add non-empty values
-                payload[api_field] = value
+        # Handle domains
+        if input_data.include_domains:
+            sdk_kwargs["include_domains"] = input_data.include_domains
+        if input_data.exclude_domains:
+            sdk_kwargs["exclude_domains"] = input_data.exclude_domains
 
-        try:
-            response = await Requests().post(url, headers=headers, json=payload)
-            data = response.json()
-            # Extract just the results array from the response
-            yield "results", data.get("results", [])
-        except Exception as e:
-            yield "error", str(e)
+        # Handle dates
+        if input_data.start_crawl_date:
+            sdk_kwargs["start_crawl_date"] = input_data.start_crawl_date.isoformat()
+        if input_data.end_crawl_date:
+            sdk_kwargs["end_crawl_date"] = input_data.end_crawl_date.isoformat()
+        if input_data.start_published_date:
+            sdk_kwargs["start_published_date"] = (
+                input_data.start_published_date.isoformat()
+            )
+        if input_data.end_published_date:
+            sdk_kwargs["end_published_date"] = input_data.end_published_date.isoformat()
+
+        # Handle text filters
+        if input_data.include_text:
+            sdk_kwargs["include_text"] = input_data.include_text
+        if input_data.exclude_text:
+            sdk_kwargs["exclude_text"] = input_data.exclude_text
+
+        if input_data.moderation:
+            sdk_kwargs["moderation"] = input_data.moderation
+
+        # heck if we need to use search_and_contents
+        content_settings = process_contents_settings(input_data.contents)
+
+        aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
+
+        if content_settings:
+            sdk_kwargs["text"] = content_settings.get("text", False)
+            if "highlights" in content_settings:
+                sdk_kwargs["highlights"] = content_settings["highlights"]
+            if "summary" in content_settings:
+                sdk_kwargs["summary"] = content_settings["summary"]
+            response = await aexa.search_and_contents(**sdk_kwargs)
+        else:
+            response = await aexa.search(**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.resolved_search_type:
+            yield "resolved_search_type", response.resolved_search_type
+
+        if response.cost_dollars:
+            yield "cost_dollars", response.cost_dollars

autogpt_platform/backend/backend/blocks/exa/similar.py

@@ -1,23 +1,30 @@
 from datetime import datetime
-from typing import Any
+from typing import Optional
+
+from exa_py import AsyncExa
 
 from backend.sdk import (
     APIKeyCredentials,
     Block,
     BlockCategory,
     BlockOutput,
-    BlockSchema,
+    BlockSchemaInput,
+    BlockSchemaOutput,
     CredentialsMetaInput,
-    Requests,
     SchemaField,
 )
 
 from ._config import exa
-from .helpers import ContentSettings
+from .helpers import (
+    ContentSettings,
+    CostDollars,
+    ExaSearchResults,
+    process_contents_settings,
+)
 
 
 class ExaFindSimilarBlock(Block):
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         credentials: CredentialsMetaInput = exa.credentials_field(
             description="The Exa integration requires an API Key."
         )
@@ -28,7 +35,7 @@ class ExaFindSimilarBlock(Block):
             description="Number of results to return", default=10, advanced=True
         )
         include_domains: list[str] = SchemaField(
-            description="Domains to include in search",
+            description="List of domains to include in the search. If specified, results will only come from these domains.",
             default_factory=list,
             advanced=True,
         )
@@ -37,17 +44,17 @@ class ExaFindSimilarBlock(Block):
             default_factory=list,
             advanced=True,
         )
-        start_crawl_date: datetime = SchemaField(
-            description="Start date for crawled content"
+        start_crawl_date: Optional[datetime] = SchemaField(
+            description="Start date for crawled content", advanced=True, default=None
         )
-        end_crawl_date: datetime = SchemaField(
-            description="End date for crawled content"
+        end_crawl_date: Optional[datetime] = SchemaField(
+            description="End date for crawled content", advanced=True, default=None
         )
-        start_published_date: datetime = SchemaField(
-            description="Start date for published content"
+        start_published_date: Optional[datetime] = SchemaField(
+            description="Start date for published content", advanced=True, default=None
         )
-        end_published_date: datetime = SchemaField(
-            description="End date for published content"
+        end_published_date: Optional[datetime] = SchemaField(
+            description="End date for published content", advanced=True, default=None
         )
         include_text: list[str] = SchemaField(
             description="Text patterns to include (max 1 string, up to 5 words)",
@@ -64,15 +71,27 @@ class ExaFindSimilarBlock(Block):
             default=ContentSettings(),
             advanced=True,
         )
+        moderation: bool = SchemaField(
+            description="Enable content moderation to filter unsafe content from search results",
+            default=False,
+            advanced=True,
+        )
 
-    class Output(BlockSchema):
-        results: list[Any] = SchemaField(
-            description="List of similar documents with title, URL, published date, author, and score",
-            default_factory=list,
+    class Output(BlockSchemaOutput):
+        results: list[ExaSearchResults] = SchemaField(
+            description="List of similar documents with metadata and content"
         )
-        error: str = SchemaField(
-            description="Error message if the request failed", default=""
+        result: ExaSearchResults = SchemaField(
+            description="Single similar document result"
         )
+        context: str = SchemaField(
+            description="A formatted string of the results ready for LLMs."
+        )
+        request_id: str = SchemaField(description="Unique identifier for the request")
+        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__(
@@ -86,47 +105,65 @@ class ExaFindSimilarBlock(Block):
     async def run(
         self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
     ) -> BlockOutput:
-        url = "https://api.exa.ai/findSimilar"
-        headers = {
-            "Content-Type": "application/json",
-            "x-api-key": credentials.api_key.get_secret_value(),
-        }
-
-        payload = {
+        sdk_kwargs = {
             "url": input_data.url,
-            "numResults": input_data.number_of_results,
-            "contents": input_data.contents.model_dump(),
+            "num_results": input_data.number_of_results,
         }
 
-        optional_field_mapping = {
-            "include_domains": "includeDomains",
-            "exclude_domains": "excludeDomains",
-            "include_text": "includeText",
-            "exclude_text": "excludeText",
-        }
+        # Handle domains
+        if input_data.include_domains:
+            sdk_kwargs["include_domains"] = input_data.include_domains
+        if input_data.exclude_domains:
+            sdk_kwargs["exclude_domains"] = input_data.exclude_domains
 
-        # Add optional fields if they have values
-        for input_field, api_field in optional_field_mapping.items():
-            value = getattr(input_data, input_field)
-            if value:  # Only add non-empty values
-                payload[api_field] = value
+        # Handle dates
+        if input_data.start_crawl_date:
+            sdk_kwargs["start_crawl_date"] = input_data.start_crawl_date.isoformat()
+        if input_data.end_crawl_date:
+            sdk_kwargs["end_crawl_date"] = input_data.end_crawl_date.isoformat()
+        if input_data.start_published_date:
+            sdk_kwargs["start_published_date"] = (
+                input_data.start_published_date.isoformat()
+            )
+        if input_data.end_published_date:
+            sdk_kwargs["end_published_date"] = input_data.end_published_date.isoformat()
 
-        date_field_mapping = {
-            "start_crawl_date": "startCrawlDate",
-            "end_crawl_date": "endCrawlDate",
-            "start_published_date": "startPublishedDate",
-            "end_published_date": "endPublishedDate",
-        }
+        # Handle text filters
+        if input_data.include_text:
+            sdk_kwargs["include_text"] = input_data.include_text
+        if input_data.exclude_text:
+            sdk_kwargs["exclude_text"] = input_data.exclude_text
 
-        # Add dates if they exist
-        for input_field, api_field in date_field_mapping.items():
-            value = getattr(input_data, input_field, None)
-            if value:
-                payload[api_field] = value.strftime("%Y-%m-%dT%H:%M:%S.000Z")
+        if input_data.moderation:
+            sdk_kwargs["moderation"] = input_data.moderation
 
-        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)
+        # check if we need to use find_similar_and_contents
+        content_settings = process_contents_settings(input_data.contents)
+
+        aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
+
+        if content_settings:
+            # Use find_similar_and_contents when contents are requested
+            sdk_kwargs["text"] = content_settings.get("text", False)
+            if "highlights" in content_settings:
+                sdk_kwargs["highlights"] = content_settings["highlights"]
+            if "summary" in content_settings:
+                sdk_kwargs["summary"] = content_settings["summary"]
+            response = await aexa.find_similar_and_contents(**sdk_kwargs)
+        else:
+            response = await aexa.find_similar(**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.cost_dollars:
+            yield "cost_dollars", response.cost_dollars

autogpt_platform/backend/backend/blocks/exa/webhook_blocks.py

@@ -9,7 +9,8 @@ from backend.sdk import (
     Block,
     BlockCategory,
     BlockOutput,
-    BlockSchema,
+    BlockSchemaInput,
+    BlockSchemaOutput,
     BlockType,
     BlockWebhookConfig,
     CredentialsMetaInput,
@@ -84,7 +85,7 @@ class ExaWebsetWebhookBlock(Block):
     including creation, updates, searches, and exports.
     """
 
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         credentials: CredentialsMetaInput = exa.credentials_field(
             description="Exa API credentials for webhook management"
         )
@@ -104,7 +105,7 @@ class ExaWebsetWebhookBlock(Block):
             description="Webhook payload data", default={}, hidden=True
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         event_type: str = SchemaField(description="Type of event that occurred")
         event_id: str = SchemaField(description="Unique identifier for this event")
         webset_id: str = SchemaField(description="ID of the affected webset")
@@ -131,45 +132,33 @@ class ExaWebsetWebhookBlock(Block):
 
     async def run(self, input_data: Input, **kwargs) -> BlockOutput:
         """Process incoming Exa webhook payload."""
-        try:
-            payload = input_data.payload
+        payload = input_data.payload
 
-            # Extract event details
-            event_type = payload.get("eventType", "unknown")
-            event_id = payload.get("eventId", "")
+        # Extract event details
+        event_type = payload.get("eventType", "unknown")
+        event_id = payload.get("eventId", "")
 
-            # Get webset ID from payload or input
-            webset_id = payload.get("websetId", input_data.webset_id)
+        # Get webset ID from payload or input
+        webset_id = payload.get("websetId", input_data.webset_id)
 
-            # Check if we should process this event based on filter
-            should_process = self._should_process_event(
-                event_type, input_data.event_filter
-            )
+        # Check if we should process this event based on filter
+        should_process = self._should_process_event(event_type, input_data.event_filter)
 
-            if not should_process:
-                # Skip events that don't match our filter
-                return
+        if not should_process:
+            # Skip events that don't match our filter
+            return
 
-            # Extract event data
-            event_data = payload.get("data", {})
-            timestamp = payload.get("occurredAt", payload.get("createdAt", ""))
-            metadata = payload.get("metadata", {})
+        # Extract event data
+        event_data = payload.get("data", {})
+        timestamp = payload.get("occurredAt", payload.get("createdAt", ""))
+        metadata = payload.get("metadata", {})
 
-            yield "event_type", event_type
-            yield "event_id", event_id
-            yield "webset_id", webset_id
-            yield "data", event_data
-            yield "timestamp", timestamp
-            yield "metadata", metadata
-
-        except Exception as e:
-            # Handle errors gracefully
-            yield "event_type", "error"
-            yield "event_id", ""
-            yield "webset_id", input_data.webset_id
-            yield "data", {"error": str(e)}
-            yield "timestamp", ""
-            yield "metadata", {}
+        yield "event_type", event_type
+        yield "event_id", event_id
+        yield "webset_id", webset_id
+        yield "data", event_data
+        yield "timestamp", timestamp
+        yield "metadata", metadata
 
     def _should_process_event(
         self, event_type: str, event_filter: WebsetEventFilter

autogpt_platform/backend/backend/blocks/exa/websets_enrichment.py

@@ -0,0 +1,554 @@
+"""
+Exa Websets Enrichment Management Blocks
+
+This module provides blocks for creating and managing enrichments on webset items,
+allowing extraction of additional structured data from existing items.
+"""
+
+from enum import Enum
+from typing import Any, Dict, List, Optional
+
+from exa_py import AsyncExa
+from exa_py.websets.types import WebsetEnrichment as SdkWebsetEnrichment
+from pydantic import BaseModel
+
+from backend.sdk import (
+    APIKeyCredentials,
+    Block,
+    BlockCategory,
+    BlockOutput,
+    BlockSchemaInput,
+    BlockSchemaOutput,
+    CredentialsMetaInput,
+    Requests,
+    SchemaField,
+)
+
+from ._config import exa
+
+
+# Mirrored model for stability
+class WebsetEnrichmentModel(BaseModel):
+    """Stable output model mirroring SDK WebsetEnrichment."""
+
+    id: str
+    webset_id: str
+    status: str
+    title: Optional[str]
+    description: str
+    format: str
+    options: List[str]
+    instructions: Optional[str]
+    metadata: Dict[str, Any]
+    created_at: str
+    updated_at: str
+
+    @classmethod
+    def from_sdk(cls, enrichment: SdkWebsetEnrichment) -> "WebsetEnrichmentModel":
+        """Convert SDK WebsetEnrichment to our stable model."""
+        # Extract options
+        options_list = []
+        if enrichment.options:
+            for option in enrichment.options:
+                option_dict = option.model_dump(by_alias=True)
+                options_list.append(option_dict.get("label", ""))
+
+        return cls(
+            id=enrichment.id,
+            webset_id=enrichment.webset_id,
+            status=(
+                enrichment.status.value
+                if hasattr(enrichment.status, "value")
+                else str(enrichment.status)
+            ),
+            title=enrichment.title,
+            description=enrichment.description,
+            format=(
+                enrichment.format.value
+                if enrichment.format and hasattr(enrichment.format, "value")
+                else "text"
+            ),
+            options=options_list,
+            instructions=enrichment.instructions,
+            metadata=enrichment.metadata if enrichment.metadata else {},
+            created_at=(
+                enrichment.created_at.isoformat() if enrichment.created_at else ""
+            ),
+            updated_at=(
+                enrichment.updated_at.isoformat() if enrichment.updated_at else ""
+            ),
+        )
+
+
+class EnrichmentFormat(str, Enum):
+    """Format types for enrichment responses."""
+
+    TEXT = "text"  # Free text response
+    DATE = "date"  # Date/datetime format
+    NUMBER = "number"  # Numeric value
+    OPTIONS = "options"  # Multiple choice from provided options
+    EMAIL = "email"  # Email address format
+    PHONE = "phone"  # Phone number format
+
+
+class ExaCreateEnrichmentBlock(Block):
+    """Create a new enrichment to extract additional data from webset items."""
+
+    class Input(BlockSchemaInput):
+        credentials: CredentialsMetaInput = exa.credentials_field(
+            description="The Exa integration requires an API Key."
+        )
+        webset_id: str = SchemaField(
+            description="The ID or external ID of the Webset",
+            placeholder="webset-id-or-external-id",
+        )
+        description: str = SchemaField(
+            description="What data to extract from each item",
+            placeholder="Extract the company's main product or service offering",
+        )
+        title: Optional[str] = SchemaField(
+            default=None,
+            description="Short title for this enrichment (auto-generated if not provided)",
+            placeholder="Main Product",
+        )
+        format: EnrichmentFormat = SchemaField(
+            default=EnrichmentFormat.TEXT,
+            description="Expected format of the extracted data",
+        )
+        options: list[str] = SchemaField(
+            default_factory=list,
+            description="Available options when format is 'options'",
+            placeholder='["B2B", "B2C", "Both", "Unknown"]',
+            advanced=True,
+        )
+        apply_to_existing: bool = SchemaField(
+            default=True,
+            description="Apply this enrichment to existing items in the webset",
+        )
+        metadata: Optional[dict] = SchemaField(
+            default=None,
+            description="Metadata to attach to the enrichment",
+            advanced=True,
+        )
+        wait_for_completion: bool = SchemaField(
+            default=False,
+            description="Wait for the enrichment to complete on existing items",
+        )
+        polling_timeout: int = SchemaField(
+            default=300,
+            description="Maximum time to wait for completion in seconds",
+            advanced=True,
+            ge=1,
+            le=600,
+        )
+
+    class Output(BlockSchemaOutput):
+        enrichment_id: str = SchemaField(
+            description="The unique identifier for the created enrichment"
+        )
+        webset_id: str = SchemaField(
+            description="The webset this enrichment belongs to"
+        )
+        status: str = SchemaField(description="Current status of the enrichment")
+        title: str = SchemaField(description="Title of the enrichment")
+        description: str = SchemaField(
+            description="Description of what data is extracted"
+        )
+        format: str = SchemaField(description="Format of the extracted data")
+        instructions: str = SchemaField(
+            description="Generated instructions for the enrichment"
+        )
+        items_enriched: Optional[int] = SchemaField(
+            description="Number of items enriched (if wait_for_completion was True)"
+        )
+        completion_time: Optional[float] = SchemaField(
+            description="Time taken to complete in seconds (if wait_for_completion was True)"
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="71146ae8-0cb1-4a15-8cde-eae30de71cb6",
+            description="Create enrichments to extract additional structured data from webset items",
+            categories={BlockCategory.AI, BlockCategory.SEARCH},
+            input_schema=ExaCreateEnrichmentBlock.Input,
+            output_schema=ExaCreateEnrichmentBlock.Output,
+        )
+
+    async def run(
+        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
+    ) -> BlockOutput:
+        import time
+
+        # Build the payload
+        payload: dict[str, Any] = {
+            "description": input_data.description,
+            "format": input_data.format.value,
+        }
+
+        # Add title if provided
+        if input_data.title:
+            payload["title"] = input_data.title
+
+        # Add options for 'options' format
+        if input_data.format == EnrichmentFormat.OPTIONS and input_data.options:
+            payload["options"] = [{"label": opt} for opt in input_data.options]
+
+        # Add metadata if provided
+        if input_data.metadata:
+            payload["metadata"] = input_data.metadata
+
+        start_time = time.time()
+
+        # Use AsyncExa SDK
+        aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
+
+        sdk_enrichment = aexa.websets.enrichments.create(
+            webset_id=input_data.webset_id, params=payload
+        )
+
+        enrichment_id = sdk_enrichment.id
+        status = (
+            sdk_enrichment.status.value
+            if hasattr(sdk_enrichment.status, "value")
+            else str(sdk_enrichment.status)
+        )
+
+        # If wait_for_completion is True and apply_to_existing is True, poll for completion
+        if input_data.wait_for_completion and input_data.apply_to_existing:
+            import asyncio
+
+            poll_interval = 5
+            max_interval = 30
+            poll_start = time.time()
+            items_enriched = 0
+
+            while time.time() - poll_start < input_data.polling_timeout:
+                current_enrich = aexa.websets.enrichments.get(
+                    webset_id=input_data.webset_id, id=enrichment_id
+                )
+                current_status = (
+                    current_enrich.status.value
+                    if hasattr(current_enrich.status, "value")
+                    else str(current_enrich.status)
+                )
+
+                if current_status in ["completed", "failed", "cancelled"]:
+                    # Estimate items from webset searches
+                    webset = aexa.websets.get(id=input_data.webset_id)
+                    if webset.searches:
+                        for search in webset.searches:
+                            if search.progress:
+                                items_enriched += search.progress.found
+                    completion_time = time.time() - start_time
+
+                    yield "enrichment_id", enrichment_id
+                    yield "webset_id", input_data.webset_id
+                    yield "status", current_status
+                    yield "title", sdk_enrichment.title
+                    yield "description", input_data.description
+                    yield "format", input_data.format.value
+                    yield "instructions", sdk_enrichment.instructions
+                    yield "items_enriched", items_enriched
+                    yield "completion_time", completion_time
+                    return
+
+                await asyncio.sleep(poll_interval)
+                poll_interval = min(poll_interval * 1.5, max_interval)
+
+            # Timeout
+            completion_time = time.time() - start_time
+            yield "enrichment_id", enrichment_id
+            yield "webset_id", input_data.webset_id
+            yield "status", status
+            yield "title", sdk_enrichment.title
+            yield "description", input_data.description
+            yield "format", input_data.format.value
+            yield "instructions", sdk_enrichment.instructions
+            yield "items_enriched", 0
+            yield "completion_time", completion_time
+        else:
+            yield "enrichment_id", enrichment_id
+            yield "webset_id", input_data.webset_id
+            yield "status", status
+            yield "title", sdk_enrichment.title
+            yield "description", input_data.description
+            yield "format", input_data.format.value
+            yield "instructions", sdk_enrichment.instructions
+
+
+class ExaGetEnrichmentBlock(Block):
+    """Get the status and details of a webset enrichment."""
+
+    class Input(BlockSchemaInput):
+        credentials: CredentialsMetaInput = exa.credentials_field(
+            description="The Exa integration requires an API Key."
+        )
+        webset_id: str = SchemaField(
+            description="The ID or external ID of the Webset",
+            placeholder="webset-id-or-external-id",
+        )
+        enrichment_id: str = SchemaField(
+            description="The ID of the enrichment to retrieve",
+            placeholder="enrichment-id",
+        )
+
+    class Output(BlockSchemaOutput):
+        enrichment_id: str = SchemaField(
+            description="The unique identifier for the enrichment"
+        )
+        status: str = SchemaField(description="Current status of the enrichment")
+        title: str = SchemaField(description="Title of the enrichment")
+        description: str = SchemaField(
+            description="Description of what data is extracted"
+        )
+        format: str = SchemaField(description="Format of the extracted data")
+        options: list[str] = SchemaField(
+            description="Available options (for 'options' format)"
+        )
+        instructions: str = SchemaField(
+            description="Generated instructions for the enrichment"
+        )
+        created_at: str = SchemaField(description="When the enrichment was created")
+        updated_at: str = SchemaField(
+            description="When the enrichment was last updated"
+        )
+        metadata: dict = SchemaField(description="Metadata attached to the enrichment")
+
+    def __init__(self):
+        super().__init__(
+            id="b8c9d0e1-f2a3-4567-89ab-cdef01234567",
+            description="Get the status and details of a webset enrichment",
+            categories={BlockCategory.SEARCH},
+            input_schema=ExaGetEnrichmentBlock.Input,
+            output_schema=ExaGetEnrichmentBlock.Output,
+        )
+
+    async def run(
+        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
+    ) -> BlockOutput:
+        # Use AsyncExa SDK
+        aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
+
+        sdk_enrichment = aexa.websets.enrichments.get(
+            webset_id=input_data.webset_id, id=input_data.enrichment_id
+        )
+
+        enrichment = WebsetEnrichmentModel.from_sdk(sdk_enrichment)
+
+        yield "enrichment_id", enrichment.id
+        yield "status", enrichment.status
+        yield "title", enrichment.title
+        yield "description", enrichment.description
+        yield "format", enrichment.format
+        yield "options", enrichment.options
+        yield "instructions", enrichment.instructions
+        yield "created_at", enrichment.created_at
+        yield "updated_at", enrichment.updated_at
+        yield "metadata", enrichment.metadata
+
+
+class ExaUpdateEnrichmentBlock(Block):
+    """Update an existing enrichment configuration."""
+
+    class Input(BlockSchemaInput):
+        credentials: CredentialsMetaInput = exa.credentials_field(
+            description="The Exa integration requires an API Key."
+        )
+        webset_id: str = SchemaField(
+            description="The ID or external ID of the Webset",
+            placeholder="webset-id-or-external-id",
+        )
+        enrichment_id: str = SchemaField(
+            description="The ID of the enrichment to update",
+            placeholder="enrichment-id",
+        )
+        description: Optional[str] = SchemaField(
+            default=None,
+            description="New description for what data to extract",
+        )
+        format: Optional[EnrichmentFormat] = SchemaField(
+            default=None,
+            description="New format for the extracted data",
+        )
+        options: Optional[list[str]] = SchemaField(
+            default=None,
+            description="New options when format is 'options'",
+        )
+        metadata: Optional[dict] = SchemaField(
+            default=None,
+            description="New metadata to attach to the enrichment",
+        )
+
+    class Output(BlockSchemaOutput):
+        enrichment_id: str = SchemaField(
+            description="The unique identifier for the enrichment"
+        )
+        status: str = SchemaField(description="Current status of the enrichment")
+        title: str = SchemaField(description="Title of the enrichment")
+        description: str = SchemaField(description="Updated description")
+        format: str = SchemaField(description="Updated format")
+        success: str = SchemaField(description="Whether the update was successful")
+
+    def __init__(self):
+        super().__init__(
+            id="c8d5c5fb-9684-4a29-bd2a-5b38d71776c9",
+            description="Update an existing enrichment configuration",
+            categories={BlockCategory.SEARCH},
+            input_schema=ExaUpdateEnrichmentBlock.Input,
+            output_schema=ExaUpdateEnrichmentBlock.Output,
+        )
+
+    async def run(
+        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
+    ) -> BlockOutput:
+        url = f"https://api.exa.ai/websets/v0/websets/{input_data.webset_id}/enrichments/{input_data.enrichment_id}"
+        headers = {
+            "Content-Type": "application/json",
+            "x-api-key": credentials.api_key.get_secret_value(),
+        }
+
+        # Build the update payload
+        payload = {}
+
+        if input_data.description is not None:
+            payload["description"] = input_data.description
+
+        if input_data.format is not None:
+            payload["format"] = input_data.format.value
+
+        if input_data.options is not None:
+            payload["options"] = [{"label": opt} for opt in input_data.options]
+
+        if input_data.metadata is not None:
+            payload["metadata"] = input_data.metadata
+
+        try:
+            response = await Requests().patch(url, headers=headers, json=payload)
+            data = response.json()
+
+            yield "enrichment_id", data.get("id", "")
+            yield "status", data.get("status", "")
+            yield "title", data.get("title", "")
+            yield "description", data.get("description", "")
+            yield "format", data.get("format", "")
+            yield "success", "true"
+
+        except ValueError as e:
+            # Re-raise user input validation errors
+            raise ValueError(f"Failed to update enrichment: {e}") from e
+        # Let all other exceptions propagate naturally
+
+
+class ExaDeleteEnrichmentBlock(Block):
+    """Delete an enrichment from a webset."""
+
+    class Input(BlockSchemaInput):
+        credentials: CredentialsMetaInput = exa.credentials_field(
+            description="The Exa integration requires an API Key."
+        )
+        webset_id: str = SchemaField(
+            description="The ID or external ID of the Webset",
+            placeholder="webset-id-or-external-id",
+        )
+        enrichment_id: str = SchemaField(
+            description="The ID of the enrichment to delete",
+            placeholder="enrichment-id",
+        )
+
+    class Output(BlockSchemaOutput):
+        enrichment_id: str = SchemaField(description="The ID of the deleted enrichment")
+        success: str = SchemaField(description="Whether the deletion was successful")
+
+    def __init__(self):
+        super().__init__(
+            id="b250de56-2ca6-4237-a7b8-b5684892189f",
+            description="Delete an enrichment from a webset",
+            categories={BlockCategory.SEARCH},
+            input_schema=ExaDeleteEnrichmentBlock.Input,
+            output_schema=ExaDeleteEnrichmentBlock.Output,
+        )
+
+    async def run(
+        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
+    ) -> BlockOutput:
+        # Use AsyncExa SDK
+        aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
+
+        deleted_enrichment = aexa.websets.enrichments.delete(
+            webset_id=input_data.webset_id, id=input_data.enrichment_id
+        )
+
+        yield "enrichment_id", deleted_enrichment.id
+        yield "success", "true"
+
+
+class ExaCancelEnrichmentBlock(Block):
+    """Cancel a running enrichment operation."""
+
+    class Input(BlockSchemaInput):
+        credentials: CredentialsMetaInput = exa.credentials_field(
+            description="The Exa integration requires an API Key."
+        )
+        webset_id: str = SchemaField(
+            description="The ID or external ID of the Webset",
+            placeholder="webset-id-or-external-id",
+        )
+        enrichment_id: str = SchemaField(
+            description="The ID of the enrichment to cancel",
+            placeholder="enrichment-id",
+        )
+
+    class Output(BlockSchemaOutput):
+        enrichment_id: str = SchemaField(
+            description="The ID of the canceled enrichment"
+        )
+        status: str = SchemaField(description="Status after cancellation")
+        items_enriched_before_cancel: int = SchemaField(
+            description="Approximate number of items enriched before cancellation"
+        )
+        success: str = SchemaField(
+            description="Whether the cancellation was successful"
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="7e1f8f0f-b6ab-43b3-bd1d-0c534a649295",
+            description="Cancel a running enrichment operation",
+            categories={BlockCategory.SEARCH},
+            input_schema=ExaCancelEnrichmentBlock.Input,
+            output_schema=ExaCancelEnrichmentBlock.Output,
+        )
+
+    async def run(
+        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
+    ) -> BlockOutput:
+        # Use AsyncExa SDK
+        aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
+
+        canceled_enrichment = aexa.websets.enrichments.cancel(
+            webset_id=input_data.webset_id, id=input_data.enrichment_id
+        )
+
+        # Try to estimate how many items were enriched before cancellation
+        items_enriched = 0
+        items_response = aexa.websets.items.list(
+            webset_id=input_data.webset_id, limit=100
+        )
+
+        for sdk_item in items_response.data:
+            # Check if this enrichment is present
+            for enrich_result in sdk_item.enrichments:
+                if enrich_result.enrichment_id == input_data.enrichment_id:
+                    items_enriched += 1
+                    break
+
+        status = (
+            canceled_enrichment.status.value
+            if hasattr(canceled_enrichment.status, "value")
+            else str(canceled_enrichment.status)
+        )
+
+        yield "enrichment_id", canceled_enrichment.id
+        yield "status", status
+        yield "items_enriched_before_cancel", items_enriched
+        yield "success", "true"

autogpt_platform/backend/backend/blocks/exa/websets_import_export.py

@@ -0,0 +1,676 @@
+"""
+Exa Websets Import/Export Management Blocks
+
+This module provides blocks for importing data into websets from CSV files
+and exporting webset data in various formats.
+"""
+
+import csv
+import json
+from enum import Enum
+from io import StringIO
+from typing import Optional, Union
+
+from exa_py import AsyncExa
+from exa_py.websets.types import CreateImportResponse
+from exa_py.websets.types import Import as SdkImport
+from pydantic import BaseModel
+
+from backend.sdk import (
+    APIKeyCredentials,
+    Block,
+    BlockCategory,
+    BlockOutput,
+    BlockSchemaInput,
+    BlockSchemaOutput,
+    CredentialsMetaInput,
+    SchemaField,
+)
+
+from ._config import exa
+from ._test import TEST_CREDENTIALS, TEST_CREDENTIALS_INPUT
+
+
+# Mirrored model for stability - don't use SDK types directly in block outputs
+class ImportModel(BaseModel):
+    """Stable output model mirroring SDK Import."""
+
+    id: str
+    status: str
+    title: str
+    format: str
+    entity_type: str
+    count: int
+    upload_url: Optional[str]  # Only in CreateImportResponse
+    upload_valid_until: Optional[str]  # Only in CreateImportResponse
+    failed_reason: str
+    failed_message: str
+    metadata: dict
+    created_at: str
+    updated_at: str
+
+    @classmethod
+    def from_sdk(
+        cls, import_obj: Union[SdkImport, CreateImportResponse]
+    ) -> "ImportModel":
+        """Convert SDK Import or CreateImportResponse to our stable model."""
+        # Extract entity type from union (may be None)
+        entity_type = "unknown"
+        if import_obj.entity:
+            entity_dict = import_obj.entity.model_dump(by_alias=True, exclude_none=True)
+            entity_type = entity_dict.get("type", "unknown")
+
+        # Handle status enum
+        status_str = (
+            import_obj.status.value
+            if hasattr(import_obj.status, "value")
+            else str(import_obj.status)
+        )
+
+        # Handle format enum
+        format_str = (
+            import_obj.format.value
+            if hasattr(import_obj.format, "value")
+            else str(import_obj.format)
+        )
+
+        # Handle failed_reason enum (may be None or enum)
+        failed_reason_str = ""
+        if import_obj.failed_reason:
+            failed_reason_str = (
+                import_obj.failed_reason.value
+                if hasattr(import_obj.failed_reason, "value")
+                else str(import_obj.failed_reason)
+            )
+
+        return cls(
+            id=import_obj.id,
+            status=status_str,
+            title=import_obj.title or "",
+            format=format_str,
+            entity_type=entity_type,
+            count=int(import_obj.count or 0),
+            upload_url=getattr(
+                import_obj, "upload_url", None
+            ),  # Only in CreateImportResponse
+            upload_valid_until=getattr(
+                import_obj, "upload_valid_until", None
+            ),  # Only in CreateImportResponse
+            failed_reason=failed_reason_str,
+            failed_message=import_obj.failed_message or "",
+            metadata=import_obj.metadata or {},
+            created_at=(
+                import_obj.created_at.isoformat() if import_obj.created_at else ""
+            ),
+            updated_at=(
+                import_obj.updated_at.isoformat() if import_obj.updated_at else ""
+            ),
+        )
+
+
+class ImportFormat(str, Enum):
+    """Supported import formats."""
+
+    CSV = "csv"
+    # JSON = "json"  # Future support
+
+
+class ImportEntityType(str, Enum):
+    """Entity types for imports."""
+
+    COMPANY = "company"
+    PERSON = "person"
+    ARTICLE = "article"
+    RESEARCH_PAPER = "research_paper"
+    CUSTOM = "custom"
+
+
+class ExportFormat(str, Enum):
+    """Supported export formats."""
+
+    JSON = "json"
+    CSV = "csv"
+    JSON_LINES = "jsonl"
+
+
+class ExaCreateImportBlock(Block):
+    """Create an import to load external data that can be used with websets."""
+
+    class Input(BlockSchemaInput):
+        credentials: CredentialsMetaInput = exa.credentials_field(
+            description="The Exa integration requires an API Key."
+        )
+        title: str = SchemaField(
+            description="Title for this import",
+            placeholder="Customer List Import",
+        )
+        csv_data: str = SchemaField(
+            description="CSV data to import (as a string)",
+            placeholder="name,url\nAcme Corp,https://acme.com\nExample Inc,https://example.com",
+        )
+        entity_type: ImportEntityType = SchemaField(
+            default=ImportEntityType.COMPANY,
+            description="Type of entities being imported",
+        )
+        entity_description: Optional[str] = SchemaField(
+            default=None,
+            description="Description for custom entity type",
+            advanced=True,
+        )
+        identifier_column: int = SchemaField(
+            default=0,
+            description="Column index containing the identifier (0-based)",
+            ge=0,
+        )
+        url_column: Optional[int] = SchemaField(
+            default=None,
+            description="Column index containing URLs (optional)",
+            ge=0,
+            advanced=True,
+        )
+        metadata: Optional[dict] = SchemaField(
+            default=None,
+            description="Metadata to attach to the import",
+            advanced=True,
+        )
+
+    class Output(BlockSchemaOutput):
+        import_id: str = SchemaField(
+            description="The unique identifier for the created import"
+        )
+        status: str = SchemaField(description="Current status of the import")
+        title: str = SchemaField(description="Title of the import")
+        count: int = SchemaField(description="Number of items in the import")
+        entity_type: str = SchemaField(description="Type of entities imported")
+        upload_url: Optional[str] = SchemaField(
+            description="Upload URL for CSV data (only if csv_data not provided in request)"
+        )
+        upload_valid_until: Optional[str] = SchemaField(
+            description="Expiration time for upload URL (only if upload_url is provided)"
+        )
+        created_at: str = SchemaField(description="When the import was created")
+
+    def __init__(self):
+        super().__init__(
+            id="020a35d8-8a53-4e60-8b60-1de5cbab1df3",
+            description="Import CSV data to use with websets for targeted searches",
+            categories={BlockCategory.DATA},
+            input_schema=ExaCreateImportBlock.Input,
+            output_schema=ExaCreateImportBlock.Output,
+            test_input={
+                "credentials": TEST_CREDENTIALS_INPUT,
+                "title": "Test Import",
+                "csv_data": "name,url\nAcme,https://acme.com",
+                "entity_type": ImportEntityType.COMPANY,
+                "identifier_column": 0,
+            },
+            test_output=[
+                ("import_id", "import-123"),
+                ("status", "pending"),
+                ("title", "Test Import"),
+                ("count", 1),
+                ("entity_type", "company"),
+                ("upload_url", None),
+                ("upload_valid_until", None),
+                ("created_at", "2024-01-01T00:00:00"),
+            ],
+            test_credentials=TEST_CREDENTIALS,
+            test_mock=self._create_test_mock(),
+        )
+
+    @staticmethod
+    def _create_test_mock():
+        """Create test mocks for the AsyncExa SDK."""
+        from datetime import datetime
+        from unittest.mock import MagicMock
+
+        # Create mock SDK import object
+        mock_import = MagicMock()
+        mock_import.id = "import-123"
+        mock_import.status = MagicMock(value="pending")
+        mock_import.title = "Test Import"
+        mock_import.format = MagicMock(value="csv")
+        mock_import.count = 1
+        mock_import.upload_url = None
+        mock_import.upload_valid_until = None
+        mock_import.failed_reason = None
+        mock_import.failed_message = ""
+        mock_import.metadata = {}
+        mock_import.created_at = datetime.fromisoformat("2024-01-01T00:00:00")
+        mock_import.updated_at = datetime.fromisoformat("2024-01-01T00:00:00")
+
+        # Mock entity
+        mock_entity = MagicMock()
+        mock_entity.model_dump = MagicMock(return_value={"type": "company"})
+        mock_import.entity = mock_entity
+
+        return {
+            "_get_client": lambda *args, **kwargs: MagicMock(
+                websets=MagicMock(
+                    imports=MagicMock(create=lambda *args, **kwargs: mock_import)
+                )
+            )
+        }
+
+    def _get_client(self, api_key: str) -> AsyncExa:
+        """Get Exa client (separated for testing)."""
+        return AsyncExa(api_key=api_key)
+
+    async def run(
+        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
+    ) -> BlockOutput:
+        aexa = self._get_client(credentials.api_key.get_secret_value())
+
+        csv_reader = csv.reader(StringIO(input_data.csv_data))
+        rows = list(csv_reader)
+        count = len(rows) - 1 if len(rows) > 1 else 0
+
+        size = len(input_data.csv_data.encode("utf-8"))
+
+        payload = {
+            "title": input_data.title,
+            "format": ImportFormat.CSV.value,
+            "count": count,
+            "size": size,
+            "csv": {
+                "identifier": input_data.identifier_column,
+            },
+        }
+
+        # Add URL column if specified
+        if input_data.url_column is not None:
+            payload["csv"]["url"] = input_data.url_column
+
+        # Add entity configuration
+        entity = {"type": input_data.entity_type.value}
+        if (
+            input_data.entity_type == ImportEntityType.CUSTOM
+            and input_data.entity_description
+        ):
+            entity["description"] = input_data.entity_description
+        payload["entity"] = entity
+
+        # Add metadata if provided
+        if input_data.metadata:
+            payload["metadata"] = input_data.metadata
+
+        sdk_import = aexa.websets.imports.create(
+            params=payload, csv_data=input_data.csv_data
+        )
+
+        import_obj = ImportModel.from_sdk(sdk_import)
+
+        yield "import_id", import_obj.id
+        yield "status", import_obj.status
+        yield "title", import_obj.title
+        yield "count", import_obj.count
+        yield "entity_type", import_obj.entity_type
+        yield "upload_url", import_obj.upload_url
+        yield "upload_valid_until", import_obj.upload_valid_until
+        yield "created_at", import_obj.created_at
+
+
+class ExaGetImportBlock(Block):
+    """Get the status and details of an import."""
+
+    class Input(BlockSchemaInput):
+        credentials: CredentialsMetaInput = exa.credentials_field(
+            description="The Exa integration requires an API Key."
+        )
+        import_id: str = SchemaField(
+            description="The ID of the import to retrieve",
+            placeholder="import-id",
+        )
+
+    class Output(BlockSchemaOutput):
+        import_id: str = SchemaField(description="The unique identifier for the import")
+        status: str = SchemaField(description="Current status of the import")
+        title: str = SchemaField(description="Title of the import")
+        format: str = SchemaField(description="Format of the imported data")
+        entity_type: str = SchemaField(description="Type of entities imported")
+        count: int = SchemaField(description="Number of items imported")
+        upload_url: Optional[str] = SchemaField(
+            description="Upload URL for CSV data (if import not yet uploaded)"
+        )
+        upload_valid_until: Optional[str] = SchemaField(
+            description="Expiration time for upload URL (if applicable)"
+        )
+        failed_reason: Optional[str] = SchemaField(
+            description="Reason for failure (if applicable)"
+        )
+        failed_message: Optional[str] = SchemaField(
+            description="Detailed failure message (if applicable)"
+        )
+        created_at: str = SchemaField(description="When the import was created")
+        updated_at: str = SchemaField(description="When the import was last updated")
+        metadata: dict = SchemaField(description="Metadata attached to the import")
+
+    def __init__(self):
+        super().__init__(
+            id="236663c8-a8dc-45f7-a050-2676bb0a3dd2",
+            description="Get the status and details of an import",
+            categories={BlockCategory.DATA},
+            input_schema=ExaGetImportBlock.Input,
+            output_schema=ExaGetImportBlock.Output,
+        )
+
+    async def run(
+        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
+    ) -> BlockOutput:
+        # Use AsyncExa SDK
+        aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
+
+        sdk_import = aexa.websets.imports.get(import_id=input_data.import_id)
+
+        import_obj = ImportModel.from_sdk(sdk_import)
+
+        # Yield all fields
+        yield "import_id", import_obj.id
+        yield "status", import_obj.status
+        yield "title", import_obj.title
+        yield "format", import_obj.format
+        yield "entity_type", import_obj.entity_type
+        yield "count", import_obj.count
+        yield "upload_url", import_obj.upload_url
+        yield "upload_valid_until", import_obj.upload_valid_until
+        yield "failed_reason", import_obj.failed_reason
+        yield "failed_message", import_obj.failed_message
+        yield "created_at", import_obj.created_at
+        yield "updated_at", import_obj.updated_at
+        yield "metadata", import_obj.metadata
+
+
+class ExaListImportsBlock(Block):
+    """List all imports with pagination."""
+
+    class Input(BlockSchemaInput):
+        credentials: CredentialsMetaInput = exa.credentials_field(
+            description="The Exa integration requires an API Key."
+        )
+        limit: int = SchemaField(
+            default=25,
+            description="Number of imports to return",
+            ge=1,
+            le=100,
+        )
+        cursor: Optional[str] = SchemaField(
+            default=None,
+            description="Cursor for pagination",
+            advanced=True,
+        )
+
+    class Output(BlockSchemaOutput):
+        imports: list[dict] = SchemaField(description="List of imports")
+        import_item: dict = SchemaField(
+            description="Individual import (yielded for each import)"
+        )
+        has_more: bool = SchemaField(
+            description="Whether there are more imports to paginate through"
+        )
+        next_cursor: Optional[str] = SchemaField(
+            description="Cursor for the next page of results"
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="65323630-f7e9-4692-a624-184ba14c0686",
+            description="List all imports with pagination support",
+            categories={BlockCategory.DATA},
+            input_schema=ExaListImportsBlock.Input,
+            output_schema=ExaListImportsBlock.Output,
+        )
+
+    async def run(
+        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
+    ) -> BlockOutput:
+        # Use AsyncExa SDK
+        aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
+
+        response = aexa.websets.imports.list(
+            cursor=input_data.cursor,
+            limit=input_data.limit,
+        )
+
+        # Convert SDK imports to our stable models
+        imports = [ImportModel.from_sdk(i) for i in response.data]
+
+        yield "imports", [i.model_dump() for i in imports]
+
+        for import_obj in imports:
+            yield "import_item", import_obj.model_dump()
+
+        yield "has_more", response.has_more
+        yield "next_cursor", response.next_cursor
+
+
+class ExaDeleteImportBlock(Block):
+    """Delete an import."""
+
+    class Input(BlockSchemaInput):
+        credentials: CredentialsMetaInput = exa.credentials_field(
+            description="The Exa integration requires an API Key."
+        )
+        import_id: str = SchemaField(
+            description="The ID of the import to delete",
+            placeholder="import-id",
+        )
+
+    class Output(BlockSchemaOutput):
+        import_id: str = SchemaField(description="The ID of the deleted import")
+        success: str = SchemaField(description="Whether the deletion was successful")
+
+    def __init__(self):
+        super().__init__(
+            id="81ae30ed-c7ba-4b5d-8483-b726846e570c",
+            description="Delete an import",
+            categories={BlockCategory.DATA},
+            input_schema=ExaDeleteImportBlock.Input,
+            output_schema=ExaDeleteImportBlock.Output,
+        )
+
+    async def run(
+        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
+    ) -> BlockOutput:
+        # Use AsyncExa SDK
+        aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
+
+        deleted_import = aexa.websets.imports.delete(import_id=input_data.import_id)
+
+        yield "import_id", deleted_import.id
+        yield "success", "true"
+
+
+class ExaExportWebsetBlock(Block):
+    """Export all data from a webset in various formats."""
+
+    class Input(BlockSchemaInput):
+        credentials: CredentialsMetaInput = exa.credentials_field(
+            description="The Exa integration requires an API Key."
+        )
+        webset_id: str = SchemaField(
+            description="The ID or external ID of the Webset to export",
+            placeholder="webset-id-or-external-id",
+        )
+        format: ExportFormat = SchemaField(
+            default=ExportFormat.JSON,
+            description="Export format",
+        )
+        include_content: bool = SchemaField(
+            default=True,
+            description="Include full content in export",
+        )
+        include_enrichments: bool = SchemaField(
+            default=True,
+            description="Include enrichment data in export",
+        )
+        max_items: int = SchemaField(
+            default=100,
+            description="Maximum number of items to export",
+            ge=1,
+            le=100,
+        )
+
+    class Output(BlockSchemaOutput):
+        export_data: str = SchemaField(
+            description="Exported data in the requested format"
+        )
+        item_count: int = SchemaField(description="Number of items exported")
+        total_items: int = SchemaField(
+            description="Total number of items in the webset"
+        )
+        truncated: bool = SchemaField(
+            description="Whether the export was truncated due to max_items limit"
+        )
+        format: str = SchemaField(description="Format of the exported data")
+
+    def __init__(self):
+        super().__init__(
+            id="5da9d0fd-4b5b-4318-8302-8f71d0ccce9d",
+            description="Export webset data in JSON, CSV, or JSON Lines format",
+            categories={BlockCategory.DATA},
+            input_schema=ExaExportWebsetBlock.Input,
+            output_schema=ExaExportWebsetBlock.Output,
+            test_input={
+                "credentials": TEST_CREDENTIALS_INPUT,
+                "webset_id": "test-webset",
+                "format": ExportFormat.JSON,
+                "include_content": True,
+                "include_enrichments": True,
+                "max_items": 10,
+            },
+            test_output=[
+                ("export_data", str),
+                ("item_count", 2),
+                ("total_items", 2),
+                ("truncated", False),
+                ("format", "json"),
+            ],
+            test_credentials=TEST_CREDENTIALS,
+            test_mock=self._create_test_mock(),
+        )
+
+    @staticmethod
+    def _create_test_mock():
+        """Create test mocks for the AsyncExa SDK."""
+        from unittest.mock import MagicMock
+
+        # Create mock webset items
+        mock_item1 = MagicMock()
+        mock_item1.model_dump = MagicMock(
+            return_value={
+                "id": "item-1",
+                "url": "https://example.com",
+                "title": "Test Item 1",
+            }
+        )
+
+        mock_item2 = MagicMock()
+        mock_item2.model_dump = MagicMock(
+            return_value={
+                "id": "item-2",
+                "url": "https://example.org",
+                "title": "Test Item 2",
+            }
+        )
+
+        # Create mock iterator
+        mock_items = [mock_item1, mock_item2]
+
+        return {
+            "_get_client": lambda *args, **kwargs: MagicMock(
+                websets=MagicMock(
+                    items=MagicMock(list_all=lambda *args, **kwargs: iter(mock_items))
+                )
+            )
+        }
+
+    def _get_client(self, api_key: str) -> AsyncExa:
+        """Get Exa client (separated for testing)."""
+        return AsyncExa(api_key=api_key)
+
+    async def run(
+        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
+    ) -> BlockOutput:
+        # Use AsyncExa SDK
+        aexa = self._get_client(credentials.api_key.get_secret_value())
+
+        try:
+            all_items = []
+
+            # Use SDK's list_all iterator to fetch items
+            item_iterator = aexa.websets.items.list_all(
+                webset_id=input_data.webset_id, limit=input_data.max_items
+            )
+
+            for sdk_item in item_iterator:
+                if len(all_items) >= input_data.max_items:
+                    break
+
+                # Convert to dict for export
+                item_dict = sdk_item.model_dump(by_alias=True, exclude_none=True)
+                all_items.append(item_dict)
+
+            # Calculate total and truncated
+            total_items = len(all_items)  # SDK doesn't provide total count
+            truncated = len(all_items) >= input_data.max_items
+
+            # Process items based on include flags
+            if not input_data.include_content:
+                for item in all_items:
+                    item.pop("content", None)
+
+            if not input_data.include_enrichments:
+                for item in all_items:
+                    item.pop("enrichments", None)
+
+            # Format the export data
+            export_data = ""
+
+            if input_data.format == ExportFormat.JSON:
+                export_data = json.dumps(all_items, indent=2, default=str)
+
+            elif input_data.format == ExportFormat.JSON_LINES:
+                lines = [json.dumps(item, default=str) for item in all_items]
+                export_data = "\n".join(lines)
+
+            elif input_data.format == ExportFormat.CSV:
+                # Extract all unique keys for CSV headers
+                all_keys = set()
+                for item in all_items:
+                    all_keys.update(self._flatten_dict(item).keys())
+
+                # Create CSV
+                output = StringIO()
+                writer = csv.DictWriter(output, fieldnames=sorted(all_keys))
+                writer.writeheader()
+
+                for item in all_items:
+                    flat_item = self._flatten_dict(item)
+                    writer.writerow(flat_item)
+
+                export_data = output.getvalue()
+
+            yield "export_data", export_data
+            yield "item_count", len(all_items)
+            yield "total_items", total_items
+            yield "truncated", truncated
+            yield "format", input_data.format.value
+
+        except ValueError as e:
+            # Re-raise user input validation errors
+            raise ValueError(f"Failed to export webset: {e}") from e
+        # Let all other exceptions propagate naturally
+
+    def _flatten_dict(self, d: dict, parent_key: str = "", sep: str = "_") -> dict:
+        """Flatten nested dictionaries for CSV export."""
+        items = []
+        for k, v in d.items():
+            new_key = f"{parent_key}{sep}{k}" if parent_key else k
+            if isinstance(v, dict):
+                items.extend(self._flatten_dict(v, new_key, sep=sep).items())
+            elif isinstance(v, list):
+                # Convert lists to JSON strings for CSV
+                items.append((new_key, json.dumps(v, default=str)))
+            else:
+                items.append((new_key, v))
+        return dict(items)