Merge branch 'dev' into fix/launch-darkly-card

<!-- Clearly explain the need for these changes: -->

Agent blocks require different handling compared to standard blocks,
particularly for:
- Handle ID generation (using direct keys instead of generated IDs)
- Form data storage structure (nested under `inputs` key)
- Field ID parsing (filtering out schema path prefixes)

This PR implements special handling for `BlockUIType.AGENT` throughout
the form rendering and output handling components to ensure agents work
correctly in the flow editor.

### Changes 🏗️

<!-- Concisely describe all of the changes made in this pull request:
-->

- **CustomNode.tsx**: Pass `uiType` prop to `OutputHandler` component
- **FormCreator.tsx**: 
- Store agent form data in `hardcodedValues.inputs` instead of directly
in `hardcodedValues`
- Extract initial values from `hardcodedValues.inputs` for agent blocks
- **OutputHandler.tsx**: 
  - Accept `uiType` prop
- Use direct key as handle ID for agents instead of
`generateHandleId(key)`
- **useMarketplaceAgentsContent.ts**: 
- Fetch full agent details using `getV2GetLibraryAgent` before adding to
builder
- Ensures agent schemas are properly populated (fixes issue where
marketplace endpoint returns empty schemas)
- **AnyOfField.tsx**: Generate handle IDs for agents by filtering out
"root" and "properties" from schema path
- **FieldTemplate.tsx**: Apply same handle ID generation logic for agent
fields

### Checklist 📋

#### For code changes:

- [x] I have clearly listed my changes in the PR description
- [x] I have made a test plan
- [x] I have tested my changes according to the test plan:
- [x] Add an agent block from marketplace and verify it renders
correctly
- [x] Connect inputs/outputs to/from an agent block and verify
connections work
- [x] Fill in form fields for an agent block and verify data persists
correctly
  - [x] Verify agent blocks work in both new and existing flows
- [x] Test that non-agent blocks still work as before (regression test)

.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

.gitignore

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

autogpt_platform/CLAUDE.md

@@ -192,6 +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/`

autogpt_platform/Makefile

@@ -1,4 +1,4 @@
-.PHONY: start-core stop-core logs-core format lint migrate run-backend run-frontend
+.PHONY: start-core stop-core logs-core format lint migrate run-backend run-frontend load-store-agents
 
 # Run just Supabase + Redis + RabbitMQ
 start-core:
@@ -42,7 +42,10 @@ run-frontend:
 
 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:"
@@ -54,4 +57,5 @@ help:
 	@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 "  test-data - Run the test data creator"
+	@echo "  load-store-agents - Load store agents from agents/ folder into test database"

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

@@ -4,11 +4,53 @@ FastAPI dependency functions for JWT-based authentication and authorization.
 These are the high-level dependency functions used in route definitions.
 """
 
+import logging
+
 import fastapi
+from fastapi.security import HTTPAuthorizationCredentials, HTTPBearer
 
 from .jwt_utils import get_jwt_payload, verify_user
 from .models import User
 
+optional_bearer = HTTPBearer(auto_error=False)
+
+# Header name for admin impersonation
+IMPERSONATION_HEADER_NAME = "X-Act-As-User-Id"
+
+logger = logging.getLogger(__name__)
+
+
+def get_optional_user_id(
+    credentials: HTTPAuthorizationCredentials | None = fastapi.Security(
+        optional_bearer
+    ),
+) -> str | None:
+    """
+    Attempts to extract the user ID ("sub" claim) from a Bearer JWT if provided.
+
+    This dependency allows for both authenticated and anonymous access. If a valid bearer token is
+    supplied, it parses the JWT and extracts the user ID. If the token is missing or invalid, it returns None,
+    treating the request as anonymous.
+
+    Args:
+        credentials: Optional HTTPAuthorizationCredentials object from FastAPI Security dependency.
+
+    Returns:
+        The user ID (str) extracted from the JWT "sub" claim, or None if no valid token is present.
+    """
+    if not credentials:
+        return None
+
+    try:
+        # Parse JWT token to get user ID
+        from autogpt_libs.auth.jwt_utils import parse_jwt_token
+
+        payload = parse_jwt_token(credentials.credentials)
+        return payload.get("sub")
+    except Exception as e:
+        logger.debug(f"Auth token validation failed (anonymous access): {e}")
+        return None
+
 
 async def requires_user(jwt_payload: dict = fastapi.Security(get_jwt_payload)) -> User:
     """
@@ -32,16 +74,44 @@ async def requires_admin_user(
     return verify_user(jwt_payload, admin_only=True)
 
 
-async 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
 
@@ -45,6 +46,7 @@ class TestAuthDependencies:
         """Create a test client."""
         return TestClient(app)
 
+    @pytest.mark.asyncio
     async def test_requires_user_with_valid_jwt_payload(self, mocker: MockerFixture):
         """Test requires_user with valid JWT payload."""
         jwt_payload = {"sub": "user-123", "role": "user", "email": "user@example.com"}
@@ -58,6 +60,7 @@ class TestAuthDependencies:
         assert user.user_id == "user-123"
         assert user.role == "user"
 
+    @pytest.mark.asyncio
     async def test_requires_user_with_admin_jwt_payload(self, mocker: MockerFixture):
         """Test requires_user accepts admin users."""
         jwt_payload = {
@@ -73,6 +76,7 @@ class TestAuthDependencies:
         assert user.user_id == "admin-456"
         assert user.role == "admin"
 
+    @pytest.mark.asyncio
     async def test_requires_user_missing_sub(self):
         """Test requires_user with missing user ID."""
         jwt_payload = {"role": "user", "email": "user@example.com"}
@@ -82,6 +86,7 @@ class TestAuthDependencies:
         assert exc_info.value.status_code == 401
         assert "User ID not found" in exc_info.value.detail
 
+    @pytest.mark.asyncio
     async def test_requires_user_empty_sub(self):
         """Test requires_user with empty user ID."""
         jwt_payload = {"sub": "", "role": "user"}
@@ -90,6 +95,7 @@ class TestAuthDependencies:
             await requires_user(jwt_payload)
         assert exc_info.value.status_code == 401
 
+    @pytest.mark.asyncio
     async def test_requires_admin_user_with_admin(self, mocker: MockerFixture):
         """Test requires_admin_user with admin role."""
         jwt_payload = {
@@ -105,6 +111,7 @@ class TestAuthDependencies:
         assert user.user_id == "admin-789"
         assert user.role == "admin"
 
+    @pytest.mark.asyncio
     async def test_requires_admin_user_with_regular_user(self):
         """Test requires_admin_user rejects regular users."""
         jwt_payload = {"sub": "user-123", "role": "user", "email": "user@example.com"}
@@ -114,6 +121,7 @@ class TestAuthDependencies:
         assert exc_info.value.status_code == 403
         assert "Admin access required" in exc_info.value.detail
 
+    @pytest.mark.asyncio
     async def test_requires_admin_user_missing_role(self):
         """Test requires_admin_user with missing role."""
         jwt_payload = {"sub": "user-123", "email": "user@example.com"}
@@ -121,31 +129,40 @@ class TestAuthDependencies:
         with pytest.raises(KeyError):
             await requires_admin_user(jwt_payload)
 
+    @pytest.mark.asyncio
     async def test_get_user_id_with_valid_payload(self, mocker: MockerFixture):
         """Test get_user_id extracts user ID correctly."""
+        request = Mock(spec=Request)
+        request.headers = {}
         jwt_payload = {"sub": "user-id-xyz", "role": "user"}
 
         mocker.patch(
             "autogpt_libs.auth.dependencies.get_jwt_payload", return_value=jwt_payload
         )
-        user_id = await get_user_id(jwt_payload)
+        user_id = await get_user_id(request, jwt_payload)
         assert user_id == "user-id-xyz"
 
+    @pytest.mark.asyncio
     async def test_get_user_id_missing_sub(self):
         """Test get_user_id with missing user ID."""
+        request = Mock(spec=Request)
+        request.headers = {}
         jwt_payload = {"role": "user"}
 
         with pytest.raises(HTTPException) as exc_info:
-            await get_user_id(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
 
+    @pytest.mark.asyncio
     async def test_get_user_id_none_sub(self):
         """Test get_user_id with None user ID."""
+        request = Mock(spec=Request)
+        request.headers = {}
         jwt_payload = {"sub": None, "role": "user"}
 
         with pytest.raises(HTTPException) as exc_info:
-            await get_user_id(jwt_payload)
+            await get_user_id(request, jwt_payload)
         assert exc_info.value.status_code == 401
 
 
@@ -170,6 +187,7 @@ class TestAuthDependenciesIntegration:
 
         return _create_token
 
+    @pytest.mark.asyncio
     async def test_endpoint_auth_enabled_no_token(self):
         """Test endpoints require token when auth is enabled."""
         app = FastAPI()
@@ -184,6 +202,7 @@ class TestAuthDependenciesIntegration:
         response = client.get("/test")
         assert response.status_code == 401
 
+    @pytest.mark.asyncio
     async def test_endpoint_with_valid_token(self, create_token):
         """Test endpoint with valid JWT token."""
         app = FastAPI()
@@ -203,6 +222,7 @@ class TestAuthDependenciesIntegration:
         assert response.status_code == 200
         assert response.json()["user_id"] == "test-user"
 
+    @pytest.mark.asyncio
     async def test_admin_endpoint_requires_admin_role(self, create_token):
         """Test admin endpoint rejects non-admin users."""
         app = FastAPI()
@@ -240,6 +260,7 @@ class TestAuthDependenciesIntegration:
 class TestAuthDependenciesEdgeCases:
     """Edge case tests for authentication dependencies."""
 
+    @pytest.mark.asyncio
     async def test_dependency_with_complex_payload(self):
         """Test dependencies handle complex JWT payloads."""
         complex_payload = {
@@ -263,6 +284,7 @@ class TestAuthDependenciesEdgeCases:
         admin = await requires_admin_user(complex_payload)
         assert admin.role == "admin"
 
+    @pytest.mark.asyncio
     async def test_dependency_with_unicode_in_payload(self):
         """Test dependencies handle unicode in JWT payloads."""
         unicode_payload = {
@@ -276,6 +298,7 @@ class TestAuthDependenciesEdgeCases:
         assert "😀" in user.user_id
         assert user.email == "测试@example.com"
 
+    @pytest.mark.asyncio
     async def test_dependency_with_null_values(self):
         """Test dependencies handle null values in payload."""
         null_payload = {
@@ -290,6 +313,7 @@ class TestAuthDependenciesEdgeCases:
         assert user.user_id == "user-123"
         assert user.email is None
 
+    @pytest.mark.asyncio
     async def test_concurrent_requests_isolation(self):
         """Test that concurrent requests don't interfere with each other."""
         payload1 = {"sub": "user-1", "role": "user"}
@@ -314,6 +338,7 @@ class TestAuthDependenciesEdgeCases:
             ({"sub": "user", "role": "user"}, "Admin access required", True),
         ],
     )
+    @pytest.mark.asyncio
     async def test_dependency_error_cases(
         self, payload, expected_error: str, admin_only: bool
     ):
@@ -325,6 +350,7 @@ class TestAuthDependenciesEdgeCases:
             verify_user(payload, admin_only=admin_only)
         assert expected_error in exc_info.value.detail
 
+    @pytest.mark.asyncio
     async def test_dependency_valid_user(self):
         """Test valid user case for dependency."""
         # Import verify_user to test it directly since dependencies use FastAPI Security
@@ -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/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/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

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

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+          "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 @@
+{
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+  "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": {
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+          "x": 872.8268131538296,
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+      "graph_id": "ed2091cf-5b27-45a9-b3ea-42396f95b256",
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+      "webhook_id": null,
+      "webhook": null
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+      "block_id": "363ae599-353e-4804-937e-b2ee3cef3da4",
+      "input_default": {
+        "name": "Generated Image"
+      },
+      "metadata": {
+        "position": {
+          "x": 1453.6844137728922,
+          "y": 963.2466395125115
+        }
+      },
+      "input_links": [
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+          "source_id": "50bc23e9-f2b7-4959-8710-99679ed9eeea",
+          "sink_id": "0d1dec1a-e4ee-4349-9673-449a01bbf14e",
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+          "sink_name": "value",
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+      "webhook": null
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+      "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": {
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+          "y": 962.935949165938
+        }
+      },
+      "input_links": [],
+      "output_links": [
+        {
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+          "source_id": "6f24c45f-1548-4eda-9784-da06ce0abef8",
+          "sink_id": "0d1bca9a-d9b8-4bfd-a19c-fe50b54f4b12",
+          "source_name": "result",
+          "sink_name": "prompt_values_#_TOPIC",
+          "is_static": true
+        }
+      ],
+      "graph_id": "ed2091cf-5b27-45a9-b3ea-42396f95b256",
+      "graph_version": 12,
+      "webhook_id": null,
+      "webhook": null
+    },
+    {
+      "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
+        }
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+      "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",
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+          "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",
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+          "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",
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+          "o1": "openai",
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+          "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": [
+    {
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+      "block_id": "363ae599-353e-4804-937e-b2ee3cef3da4",
+      "input_default": {
+        "name": "Improved Webpage Copy"
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+      "metadata": {
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+          "x": 1039.5884372540172,
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+        }
+      },
+      "input_links": [
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+          "source_id": "c9924577-70d8-4ccb-9106-6f796df09ef9",
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+      "input_default": {
+        "name": "Original Page Analysis",
+        "description": "Analysis of the webpage as it currently stands."
+      },
+      "metadata": {
+        "position": {
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+          "y": -606.5934325506903
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+      },
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+          "is_static": false
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+          "source_id": "b40595c6-dba3-4779-a129-cd4f01fff103",
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+      "block_id": "1f292d4a-41a4-4977-9684-7c8d560b9f91",
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+        "prompt_values": {}
+      },
+      "metadata": {
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+          "is_static": false
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+          "source_id": "c9924577-70d8-4ccb-9106-6f796df09ef9",
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+      "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": {
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+          "y": -0.58403945075381
+        }
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+      "input_links": [
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+          "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": [
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+          "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,
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+      "sink_id": "c9924577-70d8-4ccb-9106-6f796df09ef9",
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+      "sink_id": "130ec496-f75d-4fe2-9cd6-8c00d08ea4a7",
+      "source_name": "response",
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+      "is_static": false
+    },
+    {
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+      "source_id": "b40595c6-dba3-4779-a129-cd4f01fff103",
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+      "source_name": "content",
+      "sink_name": "prompt_values_#_CONTENT",
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+    },
+    {
+      "id": "f979ab78-0903-4f19-a7c2-a419d5d81aef",
+      "source_id": "08612ce2-625b-4c17-accd-3acace7b6477",
+      "sink_id": "cefccd07-fe70-4feb-bf76-46b20aaa5d35",
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+      "sink_id": "c9924577-70d8-4ccb-9106-6f796df09ef9",
+      "source_name": "response",
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+      "is_static": false
+    },
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+      "source_id": "375f8bc3-afd9-4025-ad8e-9aeb329af7ce",
+      "sink_id": "b40595c6-dba3-4779-a129-cd4f01fff103",
+      "source_name": "result",
+      "sink_name": "url",
+      "is_static": true
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+  ],
+  "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",
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+          "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",
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+          "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 @@
+{
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+  "instructions": null,
+  "recommended_schedule_cron": null,
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+      "input_default": {
+        "name": "Address",
+        "value": "USA"
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+          "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
+    },
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+      "block_id": "3146e4fe-2cdd-4f29-bd12-0c9d5bb4deb0",
+      "input_default": {
+        "group": 1,
+        "pattern": "<email>(.*?)<\\/email>"
+      },
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+          "y": 246.091098184158
+        }
+      },
+      "input_links": [
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+          "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": [
+        {
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+          "source_id": "a6e7355e-5bf8-4b09-b11c-a5e140389981",
+          "sink_id": "266b7255-11c4-4b88-99e2-85db31a2e865",
+          "source_name": "negative",
+          "sink_name": "values_#_Result",
+          "is_static": false
+        },
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+          "source_id": "a6e7355e-5bf8-4b09-b11c-a5e140389981",
+          "sink_id": "310c8fab-2ae6-4158-bd48-01dbdc434130",
+          "source_name": "positive",
+          "sink_name": "value",
+          "is_static": false
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+      ],
+      "graph_id": "4c6b68cb-bb75-4044-b1cb-2cee3fd39b26",
+      "graph_version": 29,
+      "webhook_id": null,
+      "webhook": null
+    },
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+      "block_id": "363ae599-353e-4804-937e-b2ee3cef3da4",
+      "input_default": {
+        "name": "Email"
+      },
+      "metadata": {
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+          "y": 246.36913665010354
+        }
+      },
+      "input_links": [
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+          "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
+    },
+    {
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+      "block_id": "87840993-2053-44b7-8da4-187ad4ee518c",
+      "input_default": {},
+      "metadata": {
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+        }
+      },
+      "input_links": [
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+          "source_id": "28b5ddcc-dc20-41cc-ad21-c54ff459f694",
+          "sink_id": "4a41df99-ffe2-4c12-b528-632979c9c030",
+          "source_name": "output",
+          "sink_name": "query",
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+        }
+      ],
+      "output_links": [
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+          "sink_id": "510937b3-0134-4e45-b2ba-05a447bbaf50",
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+          "sink_name": "prompt_values_#_WEBSITE_CONTENT",
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+        }
+      ],
+      "graph_id": "4c6b68cb-bb75-4044-b1cb-2cee3fd39b26",
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+      "webhook_id": null,
+      "webhook": null
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+      "block_id": "c0a8e994-ebf1-4a9c-a4d8-89d09c86741b",
+      "input_default": {
+        "name": "Business Name",
+        "value": "Tim Cook"
+      },
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+          "y": 244.49931152418344
+        }
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+      "input_links": [],
+      "output_links": [
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+          "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
+    },
+    {
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+      "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": [
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+          "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": [
+        {
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+          "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
+    },
+    {
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+      "source_id": "9708a10a-8be0-4c44-abb3-bd0f7c594794",
+      "sink_id": "28b5ddcc-dc20-41cc-ad21-c54ff459f694",
+      "source_name": "result",
+      "sink_name": "values_#_NAME",
+      "is_static": true
+    },
+    {
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+      "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/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

@@ -10,7 +10,12 @@ from backend.blocks.llm import (
     LLMResponse,
     llm_call,
 )
-from backend.data.block import BlockCategory, BlockOutput, BlockSchema
+from backend.data.block import (
+    BlockCategory,
+    BlockOutput,
+    BlockSchemaInput,
+    BlockSchemaOutput,
+)
 from backend.data.model import APIKeyCredentials, NodeExecutionStats, SchemaField
 
 
@@ -23,7 +28,7 @@ class AIConditionBlock(AIBlockBase):
     It provides the same yes/no data pass-through functionality as the standard ConditionBlock.
     """
 
-    class Input(BlockSchema):
+    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)",
@@ -50,7 +55,7 @@ class AIConditionBlock(AIBlockBase):
         )
         credentials: AICredentials = AICredentialsField()
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         result: bool = SchemaField(
             description="The result of the AI condition evaluation (True or False)"
         )

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

@@ -4,9 +4,15 @@ from typing import Any, Literal, Optional
 from e2b_code_interpreter import AsyncSandbox
 from e2b_code_interpreter import Result as E2BExecutionResult
 from e2b_code_interpreter.charts import Chart as E2BExecutionResultChart
-from pydantic import BaseModel, JsonValue, SecretStr
+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,
@@ -61,7 +67,7 @@ class MainCodeExecutionResult(BaseModel):
     jpeg: Optional[str] = None
     pdf: Optional[str] = None
     latex: Optional[str] = None
-    json: Optional[JsonValue] = None  # type: ignore (reportIncompatibleMethodOverride)
+    json_data: Optional[JsonValue] = Field(None, alias="json")
     javascript: Optional[str] = None
     data: Optional[dict] = None
     chart: Optional[Chart] = None
@@ -159,7 +165,7 @@ class ExecuteCodeBlock(Block, BaseE2BExecutorMixin):
     # TODO : Add support to upload and download files
     # NOTE: Currently, you can only customize the CPU and Memory
     #       by creating a pre customized sandbox template
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         credentials: CredentialsMetaInput[
             Literal[ProviderName.E2B], Literal["api_key"]
         ] = CredentialsField(
@@ -217,7 +223,7 @@ class ExecuteCodeBlock(Block, BaseE2BExecutorMixin):
             advanced=True,
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         main_result: MainCodeExecutionResult = SchemaField(
             title="Main Result", description="The main result from the code execution"
         )
@@ -232,7 +238,6 @@ class ExecuteCodeBlock(Block, BaseE2BExecutorMixin):
             description="Standard output logs from execution"
         )
         stderr_logs: str = SchemaField(description="Standard error logs from execution")
-        error: str = SchemaField(description="Error message if execution failed")
 
     def __init__(self):
         super().__init__(
@@ -296,7 +301,7 @@ class ExecuteCodeBlock(Block, BaseE2BExecutorMixin):
 
 
 class InstantiateCodeSandboxBlock(Block, BaseE2BExecutorMixin):
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         credentials: CredentialsMetaInput[
             Literal[ProviderName.E2B], Literal["api_key"]
         ] = CredentialsField(
@@ -346,7 +351,7 @@ class InstantiateCodeSandboxBlock(Block, BaseE2BExecutorMixin):
             advanced=True,
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         sandbox_id: str = SchemaField(description="ID of the sandbox instance")
         response: str = SchemaField(
             title="Text Result",
@@ -356,7 +361,6 @@ class InstantiateCodeSandboxBlock(Block, BaseE2BExecutorMixin):
             description="Standard output logs from execution"
         )
         stderr_logs: str = SchemaField(description="Standard error logs from execution")
-        error: str = SchemaField(description="Error message if execution failed")
 
     def __init__(self):
         super().__init__(
@@ -421,7 +425,7 @@ class InstantiateCodeSandboxBlock(Block, BaseE2BExecutorMixin):
 
 
 class ExecuteCodeStepBlock(Block, BaseE2BExecutorMixin):
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         credentials: CredentialsMetaInput[
             Literal[ProviderName.E2B], Literal["api_key"]
         ] = CredentialsField(
@@ -454,7 +458,7 @@ class ExecuteCodeStepBlock(Block, BaseE2BExecutorMixin):
             default=False,
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         main_result: MainCodeExecutionResult = SchemaField(
             title="Main Result", description="The main result from the code execution"
         )
@@ -469,7 +473,6 @@ class ExecuteCodeStepBlock(Block, BaseE2BExecutorMixin):
             description="Standard output logs from execution"
         )
         stderr_logs: str = SchemaField(description="Standard error logs from execution")
-        error: str = SchemaField(description="Error message if execution failed")
 
     def __init__(self):
         super().__init__(

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")

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"
         )
@@ -62,7 +68,7 @@ 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.",
@@ -86,11 +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__(
@@ -141,11 +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"
@@ -201,7 +206,7 @@ class FindInDictionaryBlock(Block):
 
 
 class RemoveFromDictionaryBlock(Block):
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         dictionary: dict[Any, Any] = SchemaField(
             description="The dictionary to modify."
         )
@@ -210,12 +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__(
@@ -251,19 +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__(
@@ -300,10 +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):
@@ -327,7 +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]",
@@ -343,11 +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__(
@@ -404,7 +406,7 @@ class CreateListBlock(Block):
 
 
 class AddToListBlock(Block):
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         list: List[Any] = SchemaField(
             default_factory=list,
             advanced=False,
@@ -425,11 +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__(
@@ -484,11 +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."
@@ -526,15 +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__(
@@ -561,7 +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."
@@ -574,10 +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__(
@@ -618,17 +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__(
@@ -663,10 +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"
         )
@@ -90,7 +91,6 @@ class DataForSeoKeywordSuggestionsBlock(Block):
         seed_keyword: str = SchemaField(
             description="The seed keyword used for the query"
         )
-        error: str = SchemaField(description="Error message if the API call failed")
 
     def __init__(self):
         super().__init__(
@@ -213,12 +213,12 @@ class DataForSeoKeywordSuggestionsBlock(Block):
 class KeywordSuggestionExtractorBlock(Block):
     """Extracts individual fields from a KeywordSuggestion object."""
 
-    class Input(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"
         )
@@ -98,7 +99,6 @@ class DataForSeoRelatedKeywordsBlock(Block):
         seed_keyword: str = SchemaField(
             description="The seed keyword used for the query"
         )
-        error: str = SchemaField(description="Error message if the API call failed")
 
     def __init__(self):
         super().__init__(
@@ -231,12 +231,12 @@ class DataForSeoRelatedKeywordsBlock(Block):
 class RelatedKeywordExtractorBlock(Block):
     """Extracts individual fields from a RelatedKeyword object."""
 
-    class Input(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

@@ -1,13 +1,20 @@
 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 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
@@ -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"
         )
@@ -164,7 +184,7 @@ 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"
@@ -178,7 +198,7 @@ class SendDiscordMessageBlock(Block):
             default="",
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         status: str = SchemaField(
             description="The status of the operation (e.g., 'Message sent', 'Error')"
         )
@@ -310,7 +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')"
@@ -319,7 +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")
 
@@ -399,7 +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"
@@ -436,7 +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")
 
@@ -586,7 +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"
@@ -607,7 +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")
 
@@ -788,7 +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"
@@ -799,7 +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")
 
@@ -913,13 +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)"
         )
@@ -1030,7 +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"
@@ -1041,7 +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")
@@ -1160,3 +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)

autogpt_platform/backend/backend/blocks/exa/websets_items.py

@@ -0,0 +1,591 @@
+"""
+Exa Websets Item Management Blocks
+
+This module provides blocks for managing items within Exa websets, including
+retrieving, listing, deleting, and bulk operations on webset items.
+"""
+
+from typing import Any, Dict, List, Optional
+
+from exa_py import AsyncExa
+from exa_py.websets.types import WebsetItem as SdkWebsetItem
+from exa_py.websets.types import (
+    WebsetItemArticleProperties,
+    WebsetItemCompanyProperties,
+    WebsetItemCustomProperties,
+    WebsetItemPersonProperties,
+    WebsetItemResearchPaperProperties,
+)
+from pydantic import AnyUrl, BaseModel
+
+from backend.sdk import (
+    APIKeyCredentials,
+    Block,
+    BlockCategory,
+    BlockOutput,
+    BlockSchemaInput,
+    BlockSchemaOutput,
+    CredentialsMetaInput,
+    SchemaField,
+)
+
+from ._config import exa
+
+
+# Mirrored model for enrichment results
+class EnrichmentResultModel(BaseModel):
+    """Stable output model mirroring SDK EnrichmentResult."""
+
+    enrichment_id: str
+    format: str
+    result: Optional[List[str]]
+    reasoning: Optional[str]
+    references: List[Dict[str, Any]]
+
+    @classmethod
+    def from_sdk(cls, sdk_enrich) -> "EnrichmentResultModel":
+        """Convert SDK EnrichmentResult to our model."""
+        format_str = (
+            sdk_enrich.format.value
+            if hasattr(sdk_enrich.format, "value")
+            else str(sdk_enrich.format)
+        )
+
+        # Convert references to dicts
+        references_list = []
+        if sdk_enrich.references:
+            for ref in sdk_enrich.references:
+                references_list.append(ref.model_dump(by_alias=True, exclude_none=True))
+
+        return cls(
+            enrichment_id=sdk_enrich.enrichment_id,
+            format=format_str,
+            result=sdk_enrich.result,
+            reasoning=sdk_enrich.reasoning,
+            references=references_list,
+        )
+
+
+# Mirrored model for stability - don't use SDK types directly in block outputs
+class WebsetItemModel(BaseModel):
+    """Stable output model mirroring SDK WebsetItem."""
+
+    id: str
+    url: Optional[AnyUrl]
+    title: str
+    content: str
+    entity_data: Dict[str, Any]
+    enrichments: Dict[str, EnrichmentResultModel]
+    created_at: str
+    updated_at: str
+
+    @classmethod
+    def from_sdk(cls, item: SdkWebsetItem) -> "WebsetItemModel":
+        """Convert SDK WebsetItem to our stable model."""
+        # Extract properties from the union type
+        properties_dict = {}
+        url_value = None
+        title = ""
+        content = ""
+
+        if hasattr(item, "properties") and item.properties:
+            properties_dict = item.properties.model_dump(
+                by_alias=True, exclude_none=True
+            )
+
+            # URL is always available on all property types
+            url_value = item.properties.url
+
+            # Extract title using isinstance checks on the union type
+            if isinstance(item.properties, WebsetItemPersonProperties):
+                title = item.properties.person.name
+                content = ""  # Person type has no content
+            elif isinstance(item.properties, WebsetItemCompanyProperties):
+                title = item.properties.company.name
+                content = item.properties.content or ""
+            elif isinstance(item.properties, WebsetItemArticleProperties):
+                title = item.properties.description
+                content = item.properties.content or ""
+            elif isinstance(item.properties, WebsetItemResearchPaperProperties):
+                title = item.properties.description
+                content = item.properties.content or ""
+            elif isinstance(item.properties, WebsetItemCustomProperties):
+                title = item.properties.description
+                content = item.properties.content or ""
+            else:
+                # Fallback
+                title = item.properties.description
+                content = getattr(item.properties, "content", "")
+
+        # Convert enrichments from list to dict keyed by enrichment_id using Pydantic models
+        enrichments_dict: Dict[str, EnrichmentResultModel] = {}
+        if hasattr(item, "enrichments") and item.enrichments:
+            for sdk_enrich in item.enrichments:
+                enrich_model = EnrichmentResultModel.from_sdk(sdk_enrich)
+                enrichments_dict[enrich_model.enrichment_id] = enrich_model
+
+        return cls(
+            id=item.id,
+            url=url_value,
+            title=title,
+            content=content or "",
+            entity_data=properties_dict,
+            enrichments=enrichments_dict,
+            created_at=item.created_at.isoformat() if item.created_at else "",
+            updated_at=item.updated_at.isoformat() if item.updated_at else "",
+        )
+
+
+class ExaGetWebsetItemBlock(Block):
+    """Get a specific item from a webset by its ID."""
+
+    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",
+        )
+        item_id: str = SchemaField(
+            description="The ID of the specific item to retrieve",
+            placeholder="item-id",
+        )
+
+    class Output(BlockSchemaOutput):
+        item_id: str = SchemaField(description="The unique identifier for the item")
+        url: str = SchemaField(description="The URL of the original source")
+        title: str = SchemaField(description="The title of the item")
+        content: str = SchemaField(description="The main content of the item")
+        entity_data: dict = SchemaField(description="Entity-specific structured data")
+        enrichments: dict = SchemaField(description="Enrichment data added to the item")
+        created_at: str = SchemaField(
+            description="When the item was added to the webset"
+        )
+        updated_at: str = SchemaField(description="When the item was last updated")
+
+    def __init__(self):
+        super().__init__(
+            id="c4a7d9e2-8f3b-4a6c-9d8e-a5b6c7d8e9f0",
+            description="Get a specific item from a webset by its ID",
+            categories={BlockCategory.SEARCH},
+            input_schema=ExaGetWebsetItemBlock.Input,
+            output_schema=ExaGetWebsetItemBlock.Output,
+        )
+
+    async def run(
+        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
+    ) -> BlockOutput:
+        aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
+
+        sdk_item = aexa.websets.items.get(
+            webset_id=input_data.webset_id, id=input_data.item_id
+        )
+
+        item = WebsetItemModel.from_sdk(sdk_item)
+
+        yield "item_id", item.id
+        yield "url", item.url
+        yield "title", item.title
+        yield "content", item.content
+        yield "entity_data", item.entity_data
+        yield "enrichments", item.enrichments
+        yield "created_at", item.created_at
+        yield "updated_at", item.updated_at
+
+
+class ExaListWebsetItemsBlock(Block):
+    """List items in a webset with pagination and optional filtering."""
+
+    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",
+        )
+        limit: int = SchemaField(
+            default=25,
+            description="Number of items to return (1-100)",
+            ge=1,
+            le=100,
+        )
+        cursor: Optional[str] = SchemaField(
+            default=None,
+            description="Cursor for pagination through results",
+            advanced=True,
+        )
+        wait_for_items: bool = SchemaField(
+            default=False,
+            description="Wait for items to be available if webset is still processing",
+            advanced=True,
+        )
+        wait_timeout: int = SchemaField(
+            default=60,
+            description="Maximum time to wait for items in seconds",
+            advanced=True,
+            ge=1,
+            le=300,
+        )
+
+    class Output(BlockSchemaOutput):
+        items: list[WebsetItemModel] = SchemaField(
+            description="List of webset items",
+        )
+        webset_id: str = SchemaField(
+            description="The ID of the webset",
+        )
+        item: WebsetItemModel = SchemaField(
+            description="Individual item (yielded for each item in the list)",
+        )
+        has_more: bool = SchemaField(
+            description="Whether there are more items to paginate through",
+        )
+        next_cursor: Optional[str] = SchemaField(
+            description="Cursor for the next page of results",
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="7b5e8c9f-01a2-43c4-95e6-f7a8b9c0d1e2",
+            description="List items in a webset with pagination support",
+            categories={BlockCategory.SEARCH},
+            input_schema=ExaListWebsetItemsBlock.Input,
+            output_schema=ExaListWebsetItemsBlock.Output,
+        )
+
+    async def run(
+        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
+    ) -> BlockOutput:
+        aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
+
+        if input_data.wait_for_items:
+            import asyncio
+            import time
+
+            start_time = time.time()
+            interval = 2
+            response = None
+
+            while time.time() - start_time < input_data.wait_timeout:
+                response = aexa.websets.items.list(
+                    webset_id=input_data.webset_id,
+                    cursor=input_data.cursor,
+                    limit=input_data.limit,
+                )
+
+                if response.data:
+                    break
+
+                await asyncio.sleep(interval)
+                interval = min(interval * 1.2, 10)
+
+            if not response:
+                response = aexa.websets.items.list(
+                    webset_id=input_data.webset_id,
+                    cursor=input_data.cursor,
+                    limit=input_data.limit,
+                )
+        else:
+            response = aexa.websets.items.list(
+                webset_id=input_data.webset_id,
+                cursor=input_data.cursor,
+                limit=input_data.limit,
+            )
+
+        items = [WebsetItemModel.from_sdk(item) for item in response.data]
+
+        yield "items", items
+
+        for item in items:
+            yield "item", item
+
+        yield "has_more", response.has_more
+        yield "next_cursor", response.next_cursor
+        yield "webset_id", input_data.webset_id
+
+
+class ExaDeleteWebsetItemBlock(Block):
+    """Delete a specific item 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",
+        )
+        item_id: str = SchemaField(
+            description="The ID of the item to delete",
+            placeholder="item-id",
+        )
+
+    class Output(BlockSchemaOutput):
+        item_id: str = SchemaField(description="The ID of the deleted item")
+        success: str = SchemaField(description="Whether the deletion was successful")
+
+    def __init__(self):
+        super().__init__(
+            id="12c57fbe-c270-4877-a2b6-d2d05529ba79",
+            description="Delete a specific item from a webset",
+            categories={BlockCategory.SEARCH},
+            input_schema=ExaDeleteWebsetItemBlock.Input,
+            output_schema=ExaDeleteWebsetItemBlock.Output,
+        )
+
+    async def run(
+        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
+    ) -> BlockOutput:
+        aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
+
+        deleted_item = aexa.websets.items.delete(
+            webset_id=input_data.webset_id, id=input_data.item_id
+        )
+
+        yield "item_id", deleted_item.id
+        yield "success", "true"
+
+
+class ExaBulkWebsetItemsBlock(Block):
+    """Get all items from a webset in a single operation (with size limits)."""
+
+    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",
+        )
+        max_items: int = SchemaField(
+            default=100,
+            description="Maximum number of items to retrieve (1-1000). Note: Large values may take longer.",
+            ge=1,
+            le=1000,
+        )
+        include_enrichments: bool = SchemaField(
+            default=True,
+            description="Include enrichment data for each item",
+        )
+        include_content: bool = SchemaField(
+            default=True,
+            description="Include full content for each item",
+        )
+
+    class Output(BlockSchemaOutput):
+        items: list[WebsetItemModel] = SchemaField(
+            description="All items from the webset"
+        )
+        item: WebsetItemModel = SchemaField(
+            description="Individual item (yielded for each item)"
+        )
+        total_retrieved: int = SchemaField(
+            description="Total number of items retrieved"
+        )
+        truncated: bool = SchemaField(
+            description="Whether results were truncated due to max_items limit"
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="dbd619f5-476e-4395-af9a-a7a7c0fb8c4e",
+            description="Get all items from a webset in bulk (with configurable limits)",
+            categories={BlockCategory.SEARCH},
+            input_schema=ExaBulkWebsetItemsBlock.Input,
+            output_schema=ExaBulkWebsetItemsBlock.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())
+
+        all_items: List[WebsetItemModel] = []
+        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
+
+            item = WebsetItemModel.from_sdk(sdk_item)
+
+            if not input_data.include_enrichments:
+                item.enrichments = {}
+            if not input_data.include_content:
+                item.content = ""
+
+            all_items.append(item)
+
+        yield "items", all_items
+
+        for item in all_items:
+            yield "item", item
+
+        yield "total_retrieved", len(all_items)
+        yield "truncated", len(all_items) >= input_data.max_items
+
+
+class ExaWebsetItemsSummaryBlock(Block):
+    """Get a summary of items in a webset without retrieving all data."""
+
+    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",
+        )
+        sample_size: int = SchemaField(
+            default=5,
+            description="Number of sample items to include",
+            ge=0,
+            le=10,
+        )
+
+    class Output(BlockSchemaOutput):
+        total_items: int = SchemaField(
+            description="Total number of items in the webset"
+        )
+        entity_type: str = SchemaField(description="Type of entities in the webset")
+        sample_items: list[WebsetItemModel] = SchemaField(
+            description="Sample of items from the webset"
+        )
+        enrichment_columns: list[str] = SchemaField(
+            description="List of enrichment columns available"
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="db7813ad-10bd-4652-8623-5667d6fecdd5",
+            description="Get a summary of webset items without retrieving all data",
+            categories={BlockCategory.SEARCH},
+            input_schema=ExaWebsetItemsSummaryBlock.Input,
+            output_schema=ExaWebsetItemsSummaryBlock.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())
+
+        webset = aexa.websets.get(id=input_data.webset_id)
+
+        entity_type = "unknown"
+        if webset.searches:
+            first_search = webset.searches[0]
+            if first_search.entity:
+                # The entity is a union type, extract type field
+                entity_dict = first_search.entity.model_dump(by_alias=True)
+                entity_type = entity_dict.get("type", "unknown")
+
+        # Get enrichment columns
+        enrichment_columns = []
+        if webset.enrichments:
+            enrichment_columns = [
+                e.title if e.title else e.description for e in webset.enrichments
+            ]
+
+        # Get sample items if requested
+        sample_items: List[WebsetItemModel] = []
+        if input_data.sample_size > 0:
+            items_response = aexa.websets.items.list(
+                webset_id=input_data.webset_id, limit=input_data.sample_size
+            )
+            # Convert to our stable models
+            sample_items = [
+                WebsetItemModel.from_sdk(item) for item in items_response.data
+            ]
+
+        total_items = 0
+        if webset.searches:
+            for search in webset.searches:
+                if search.progress:
+                    total_items += search.progress.found
+
+        yield "total_items", total_items
+        yield "entity_type", entity_type
+        yield "sample_items", sample_items
+        yield "enrichment_columns", enrichment_columns
+
+
+class ExaGetNewItemsBlock(Block):
+    """Get items added to a webset since a specific cursor (incremental processing helper)."""
+
+    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",
+        )
+        since_cursor: Optional[str] = SchemaField(
+            default=None,
+            description="Cursor from previous run - only items after this will be returned. Leave empty on first run.",
+            placeholder="cursor-from-previous-run",
+        )
+        max_items: int = SchemaField(
+            default=100,
+            description="Maximum number of new items to retrieve",
+            ge=1,
+            le=1000,
+        )
+
+    class Output(BlockSchemaOutput):
+        new_items: list[WebsetItemModel] = SchemaField(
+            description="Items added since the cursor"
+        )
+        item: WebsetItemModel = SchemaField(
+            description="Individual item (yielded for each new item)"
+        )
+        count: int = SchemaField(description="Number of new items found")
+        next_cursor: Optional[str] = SchemaField(
+            description="Save this cursor for the next run to get only newer items"
+        )
+        has_more: bool = SchemaField(
+            description="Whether there are more new items beyond max_items"
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="3ff9bdf5-9613-4d21-8a60-90eb8b69c414",
+            description="Get items added since a cursor - enables incremental processing without reprocessing",
+            categories={BlockCategory.SEARCH, BlockCategory.DATA},
+            input_schema=ExaGetNewItemsBlock.Input,
+            output_schema=ExaGetNewItemsBlock.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())
+
+        # Get items starting from cursor
+        response = aexa.websets.items.list(
+            webset_id=input_data.webset_id,
+            cursor=input_data.since_cursor,
+            limit=input_data.max_items,
+        )
+
+        # Convert SDK items to our stable models
+        new_items = [WebsetItemModel.from_sdk(item) for item in response.data]
+
+        # Yield the full list
+        yield "new_items", new_items
+
+        # Yield individual items for processing
+        for item in new_items:
+            yield "item", item
+
+        # Yield metadata for next run
+        yield "count", len(new_items)
+        yield "next_cursor", response.next_cursor
+        yield "has_more", response.has_more

autogpt_platform/backend/backend/blocks/exa/websets_monitor.py

@@ -0,0 +1,600 @@
+"""
+Exa Websets Monitor Management Blocks
+
+This module provides blocks for creating and managing monitors that automatically
+keep websets updated with fresh data on a schedule.
+"""
+
+from enum import Enum
+from typing import Optional
+
+from exa_py import AsyncExa
+from exa_py.websets.types import Monitor as SdkMonitor
+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 MonitorModel(BaseModel):
+    """Stable output model mirroring SDK Monitor."""
+
+    id: str
+    status: str
+    webset_id: str
+    behavior_type: str
+    behavior_config: dict
+    cron_expression: str
+    timezone: str
+    next_run_at: str
+    last_run: dict
+    metadata: dict
+    created_at: str
+    updated_at: str
+
+    @classmethod
+    def from_sdk(cls, monitor: SdkMonitor) -> "MonitorModel":
+        """Convert SDK Monitor to our stable model."""
+        # Extract behavior information
+        behavior_dict = monitor.behavior.model_dump(by_alias=True, exclude_none=True)
+        behavior_type = behavior_dict.get("type", "unknown")
+        behavior_config = behavior_dict.get("config", {})
+
+        # Extract cadence information
+        cadence_dict = monitor.cadence.model_dump(by_alias=True, exclude_none=True)
+        cron_expr = cadence_dict.get("cron", "")
+        timezone = cadence_dict.get("timezone", "Etc/UTC")
+
+        # Extract last run information
+        last_run_dict = {}
+        if monitor.last_run:
+            last_run_dict = monitor.last_run.model_dump(
+                by_alias=True, exclude_none=True
+            )
+
+        # Handle status enum
+        status_str = (
+            monitor.status.value
+            if hasattr(monitor.status, "value")
+            else str(monitor.status)
+        )
+
+        return cls(
+            id=monitor.id,
+            status=status_str,
+            webset_id=monitor.webset_id,
+            behavior_type=behavior_type,
+            behavior_config=behavior_config,
+            cron_expression=cron_expr,
+            timezone=timezone,
+            next_run_at=monitor.next_run_at.isoformat() if monitor.next_run_at else "",
+            last_run=last_run_dict,
+            metadata=monitor.metadata or {},
+            created_at=monitor.created_at.isoformat() if monitor.created_at else "",
+            updated_at=monitor.updated_at.isoformat() if monitor.updated_at else "",
+        )
+
+
+class MonitorStatus(str, Enum):
+    """Status of a monitor."""
+
+    ENABLED = "enabled"
+    DISABLED = "disabled"
+    PAUSED = "paused"
+
+
+class MonitorBehaviorType(str, Enum):
+    """Type of behavior for a monitor."""
+
+    SEARCH = "search"  # Run new searches
+    REFRESH = "refresh"  # Refresh existing items
+
+
+class SearchBehavior(str, Enum):
+    """How search results interact with existing items."""
+
+    APPEND = "append"
+    OVERRIDE = "override"
+
+
+class ExaCreateMonitorBlock(Block):
+    """Create a monitor to automatically keep a webset updated on a schedule."""
+
+    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 monitor",
+            placeholder="webset-id-or-external-id",
+        )
+
+        # Schedule configuration
+        cron_expression: str = SchemaField(
+            description="Cron expression for scheduling (5 fields, max once per day)",
+            placeholder="0 9 * * 1",  # Every Monday at 9 AM
+        )
+        timezone: str = SchemaField(
+            default="Etc/UTC",
+            description="IANA timezone for the schedule",
+            placeholder="America/New_York",
+            advanced=True,
+        )
+
+        # Behavior configuration
+        behavior_type: MonitorBehaviorType = SchemaField(
+            default=MonitorBehaviorType.SEARCH,
+            description="Type of monitor behavior (search for new items or refresh existing)",
+        )
+
+        # Search configuration (for SEARCH behavior)
+        search_query: Optional[str] = SchemaField(
+            default=None,
+            description="Search query for finding new items (required for search behavior)",
+            placeholder="AI startups that raised funding in the last week",
+        )
+        search_count: int = SchemaField(
+            default=10,
+            description="Number of items to find in each search",
+            ge=1,
+            le=100,
+        )
+        search_criteria: list[str] = SchemaField(
+            default_factory=list,
+            description="Criteria that items must meet",
+            advanced=True,
+        )
+        search_behavior: SearchBehavior = SchemaField(
+            default=SearchBehavior.APPEND,
+            description="How new results interact with existing items",
+            advanced=True,
+        )
+        entity_type: Optional[str] = SchemaField(
+            default=None,
+            description="Type of entity to search for (company, person, etc.)",
+            advanced=True,
+        )
+
+        # Refresh configuration (for REFRESH behavior)
+        refresh_content: bool = SchemaField(
+            default=True,
+            description="Refresh content from source URLs (for refresh behavior)",
+            advanced=True,
+        )
+        refresh_enrichments: bool = SchemaField(
+            default=True,
+            description="Re-run enrichments on items (for refresh behavior)",
+            advanced=True,
+        )
+
+        # Metadata
+        metadata: Optional[dict] = SchemaField(
+            default=None,
+            description="Metadata to attach to the monitor",
+            advanced=True,
+        )
+
+    class Output(BlockSchemaOutput):
+        monitor_id: str = SchemaField(
+            description="The unique identifier for the created monitor"
+        )
+        webset_id: str = SchemaField(description="The webset this monitor belongs to")
+        status: str = SchemaField(description="Status of the monitor")
+        behavior_type: str = SchemaField(description="Type of monitor behavior")
+        next_run_at: Optional[str] = SchemaField(
+            description="When the monitor will next run"
+        )
+        cron_expression: str = SchemaField(description="The schedule cron expression")
+        timezone: str = SchemaField(description="The timezone for scheduling")
+        created_at: str = SchemaField(description="When the monitor was created")
+
+    def __init__(self):
+        super().__init__(
+            id="f8a9b0c1-d2e3-4567-890a-bcdef1234567",
+            description="Create automated monitors to keep websets updated with fresh data on a schedule",
+            categories={BlockCategory.SEARCH},
+            input_schema=ExaCreateMonitorBlock.Input,
+            output_schema=ExaCreateMonitorBlock.Output,
+            test_input={
+                "credentials": TEST_CREDENTIALS_INPUT,
+                "webset_id": "test-webset",
+                "cron_expression": "0 9 * * 1",
+                "behavior_type": MonitorBehaviorType.SEARCH,
+                "search_query": "AI startups",
+                "search_count": 10,
+            },
+            test_output=[
+                ("monitor_id", "monitor-123"),
+                ("webset_id", "test-webset"),
+                ("status", "enabled"),
+                ("behavior_type", "search"),
+                ("next_run_at", "2024-01-01T00:00:00"),
+                ("cron_expression", "0 9 * * 1"),
+                ("timezone", "Etc/UTC"),
+                ("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 monitor object
+        mock_monitor = MagicMock()
+        mock_monitor.id = "monitor-123"
+        mock_monitor.status = MagicMock(value="enabled")
+        mock_monitor.webset_id = "test-webset"
+        mock_monitor.next_run_at = datetime.fromisoformat("2024-01-01T00:00:00")
+        mock_monitor.created_at = datetime.fromisoformat("2024-01-01T00:00:00")
+        mock_monitor.updated_at = datetime.fromisoformat("2024-01-01T00:00:00")
+        mock_monitor.metadata = {}
+        mock_monitor.last_run = None
+
+        # Mock behavior
+        mock_behavior = MagicMock()
+        mock_behavior.model_dump = MagicMock(
+            return_value={"type": "search", "config": {}}
+        )
+        mock_monitor.behavior = mock_behavior
+
+        # Mock cadence
+        mock_cadence = MagicMock()
+        mock_cadence.model_dump = MagicMock(
+            return_value={"cron": "0 9 * * 1", "timezone": "Etc/UTC"}
+        )
+        mock_monitor.cadence = mock_cadence
+
+        return {
+            "_get_client": lambda *args, **kwargs: MagicMock(
+                websets=MagicMock(
+                    monitors=MagicMock(create=lambda *args, **kwargs: mock_monitor)
+                )
+            )
+        }
+
+    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())
+
+        # Build the payload
+        payload = {
+            "websetId": input_data.webset_id,
+            "cadence": {
+                "cron": input_data.cron_expression,
+                "timezone": input_data.timezone,
+            },
+        }
+
+        # Build behavior configuration based on type
+        if input_data.behavior_type == MonitorBehaviorType.SEARCH:
+            behavior_config = {
+                "query": input_data.search_query or "",
+                "count": input_data.search_count,
+                "behavior": input_data.search_behavior.value,
+            }
+
+            if input_data.search_criteria:
+                behavior_config["criteria"] = [
+                    {"description": c} for c in input_data.search_criteria
+                ]
+
+            if input_data.entity_type:
+                behavior_config["entity"] = {"type": input_data.entity_type}
+
+            payload["behavior"] = {
+                "type": "search",
+                "config": behavior_config,
+            }
+        else:
+            # REFRESH behavior
+            payload["behavior"] = {
+                "type": "refresh",
+                "config": {
+                    "content": input_data.refresh_content,
+                    "enrichments": input_data.refresh_enrichments,
+                },
+            }
+
+        # Add metadata if provided
+        if input_data.metadata:
+            payload["metadata"] = input_data.metadata
+
+        sdk_monitor = aexa.websets.monitors.create(params=payload)
+
+        monitor = MonitorModel.from_sdk(sdk_monitor)
+
+        # Yield all fields
+        yield "monitor_id", monitor.id
+        yield "webset_id", monitor.webset_id
+        yield "status", monitor.status
+        yield "behavior_type", monitor.behavior_type
+        yield "next_run_at", monitor.next_run_at
+        yield "cron_expression", monitor.cron_expression
+        yield "timezone", monitor.timezone
+        yield "created_at", monitor.created_at
+
+
+class ExaGetMonitorBlock(Block):
+    """Get the details and status of a monitor."""
+
+    class Input(BlockSchemaInput):
+        credentials: CredentialsMetaInput = exa.credentials_field(
+            description="The Exa integration requires an API Key."
+        )
+        monitor_id: str = SchemaField(
+            description="The ID of the monitor to retrieve",
+            placeholder="monitor-id",
+        )
+
+    class Output(BlockSchemaOutput):
+        monitor_id: str = SchemaField(
+            description="The unique identifier for the monitor"
+        )
+        webset_id: str = SchemaField(description="The webset this monitor belongs to")
+        status: str = SchemaField(description="Current status of the monitor")
+        behavior_type: str = SchemaField(description="Type of monitor behavior")
+        behavior_config: dict = SchemaField(
+            description="Configuration for the monitor behavior"
+        )
+        cron_expression: str = SchemaField(description="The schedule cron expression")
+        timezone: str = SchemaField(description="The timezone for scheduling")
+        next_run_at: Optional[str] = SchemaField(
+            description="When the monitor will next run"
+        )
+        last_run: Optional[dict] = SchemaField(
+            description="Information about the last run"
+        )
+        created_at: str = SchemaField(description="When the monitor was created")
+        updated_at: str = SchemaField(description="When the monitor was last updated")
+        metadata: dict = SchemaField(description="Metadata attached to the monitor")
+
+    def __init__(self):
+        super().__init__(
+            id="5c852a2d-d505-4a56-b711-7def8dd14e72",
+            description="Get the details and status of a webset monitor",
+            categories={BlockCategory.SEARCH},
+            input_schema=ExaGetMonitorBlock.Input,
+            output_schema=ExaGetMonitorBlock.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_monitor = aexa.websets.monitors.get(monitor_id=input_data.monitor_id)
+
+        monitor = MonitorModel.from_sdk(sdk_monitor)
+
+        # Yield all fields
+        yield "monitor_id", monitor.id
+        yield "webset_id", monitor.webset_id
+        yield "status", monitor.status
+        yield "behavior_type", monitor.behavior_type
+        yield "behavior_config", monitor.behavior_config
+        yield "cron_expression", monitor.cron_expression
+        yield "timezone", monitor.timezone
+        yield "next_run_at", monitor.next_run_at
+        yield "last_run", monitor.last_run
+        yield "created_at", monitor.created_at
+        yield "updated_at", monitor.updated_at
+        yield "metadata", monitor.metadata
+
+
+class ExaUpdateMonitorBlock(Block):
+    """Update a monitor's configuration."""
+
+    class Input(BlockSchemaInput):
+        credentials: CredentialsMetaInput = exa.credentials_field(
+            description="The Exa integration requires an API Key."
+        )
+        monitor_id: str = SchemaField(
+            description="The ID of the monitor to update",
+            placeholder="monitor-id",
+        )
+        status: Optional[MonitorStatus] = SchemaField(
+            default=None,
+            description="New status for the monitor",
+        )
+        cron_expression: Optional[str] = SchemaField(
+            default=None,
+            description="New cron expression for scheduling",
+        )
+        timezone: Optional[str] = SchemaField(
+            default=None,
+            description="New timezone for the schedule",
+            advanced=True,
+        )
+        metadata: Optional[dict] = SchemaField(
+            default=None,
+            description="New metadata for the monitor",
+            advanced=True,
+        )
+
+    class Output(BlockSchemaOutput):
+        monitor_id: str = SchemaField(
+            description="The unique identifier for the monitor"
+        )
+        status: str = SchemaField(description="Updated status of the monitor")
+        next_run_at: Optional[str] = SchemaField(
+            description="When the monitor will next run"
+        )
+        updated_at: str = SchemaField(description="When the monitor was updated")
+        success: str = SchemaField(description="Whether the update was successful")
+
+    def __init__(self):
+        super().__init__(
+            id="245102c3-6af3-4515-a308-c2210b7939d2",
+            description="Update a monitor's status, schedule, or metadata",
+            categories={BlockCategory.SEARCH},
+            input_schema=ExaUpdateMonitorBlock.Input,
+            output_schema=ExaUpdateMonitorBlock.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())
+
+        # Build update payload
+        payload = {}
+
+        if input_data.status is not None:
+            payload["status"] = input_data.status.value
+
+        if input_data.cron_expression is not None or input_data.timezone is not None:
+            cadence = {}
+            if input_data.cron_expression:
+                cadence["cron"] = input_data.cron_expression
+            if input_data.timezone:
+                cadence["timezone"] = input_data.timezone
+            payload["cadence"] = cadence
+
+        if input_data.metadata is not None:
+            payload["metadata"] = input_data.metadata
+
+        sdk_monitor = aexa.websets.monitors.update(
+            monitor_id=input_data.monitor_id, params=payload
+        )
+
+        # Convert to our stable model
+        monitor = MonitorModel.from_sdk(sdk_monitor)
+
+        # Yield fields
+        yield "monitor_id", monitor.id
+        yield "status", monitor.status
+        yield "next_run_at", monitor.next_run_at
+        yield "updated_at", monitor.updated_at
+        yield "success", "true"
+
+
+class ExaDeleteMonitorBlock(Block):
+    """Delete a monitor from a webset."""
+
+    class Input(BlockSchemaInput):
+        credentials: CredentialsMetaInput = exa.credentials_field(
+            description="The Exa integration requires an API Key."
+        )
+        monitor_id: str = SchemaField(
+            description="The ID of the monitor to delete",
+            placeholder="monitor-id",
+        )
+
+    class Output(BlockSchemaOutput):
+        monitor_id: str = SchemaField(description="The ID of the deleted monitor")
+        success: str = SchemaField(description="Whether the deletion was successful")
+
+    def __init__(self):
+        super().__init__(
+            id="f16f9b10-0c4d-4db8-997d-7b96b6026094",
+            description="Delete a monitor from a webset",
+            categories={BlockCategory.SEARCH},
+            input_schema=ExaDeleteMonitorBlock.Input,
+            output_schema=ExaDeleteMonitorBlock.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_monitor = aexa.websets.monitors.delete(monitor_id=input_data.monitor_id)
+
+        yield "monitor_id", deleted_monitor.id
+        yield "success", "true"
+
+
+class ExaListMonitorsBlock(Block):
+    """List all monitors with pagination."""
+
+    class Input(BlockSchemaInput):
+        credentials: CredentialsMetaInput = exa.credentials_field(
+            description="The Exa integration requires an API Key."
+        )
+        webset_id: Optional[str] = SchemaField(
+            default=None,
+            description="Filter monitors by webset ID",
+            placeholder="webset-id",
+        )
+        limit: int = SchemaField(
+            default=25,
+            description="Number of monitors to return",
+            ge=1,
+            le=100,
+        )
+        cursor: Optional[str] = SchemaField(
+            default=None,
+            description="Cursor for pagination",
+            advanced=True,
+        )
+
+    class Output(BlockSchemaOutput):
+        monitors: list[dict] = SchemaField(description="List of monitors")
+        monitor: dict = SchemaField(
+            description="Individual monitor (yielded for each monitor)"
+        )
+        has_more: bool = SchemaField(
+            description="Whether there are more monitors to paginate through"
+        )
+        next_cursor: Optional[str] = SchemaField(
+            description="Cursor for the next page of results"
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="f06e2b38-5397-4e8f-aa85-491149dd98df",
+            description="List all monitors with optional webset filtering",
+            categories={BlockCategory.SEARCH},
+            input_schema=ExaListMonitorsBlock.Input,
+            output_schema=ExaListMonitorsBlock.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.monitors.list(
+            cursor=input_data.cursor,
+            limit=input_data.limit,
+            webset_id=input_data.webset_id,
+        )
+
+        # Convert SDK monitors to our stable models
+        monitors = [MonitorModel.from_sdk(m) for m in response.data]
+
+        # Yield the full list
+        yield "monitors", [m.model_dump() for m in monitors]
+
+        # Yield individual monitors for graph chaining
+        for monitor in monitors:
+            yield "monitor", monitor.model_dump()
+
+        # Yield pagination metadata
+        yield "has_more", response.has_more
+        yield "next_cursor", response.next_cursor

autogpt_platform/backend/backend/blocks/exa/websets_polling.py

@@ -0,0 +1,600 @@
+"""
+Exa Websets Polling Blocks
+
+This module provides dedicated polling blocks for waiting on webset operations
+to complete, with progress tracking and timeout management.
+"""
+
+import asyncio
+import time
+from enum import Enum
+from typing import Any, Dict
+
+from exa_py import AsyncExa
+from pydantic import BaseModel
+
+from backend.sdk import (
+    APIKeyCredentials,
+    Block,
+    BlockCategory,
+    BlockOutput,
+    BlockSchemaInput,
+    BlockSchemaOutput,
+    CredentialsMetaInput,
+    SchemaField,
+)
+
+from ._config import exa
+
+# Import WebsetItemModel for use in enrichment samples
+# This is safe as websets_items doesn't import from websets_polling
+from .websets_items import WebsetItemModel
+
+
+# Model for sample enrichment data
+class SampleEnrichmentModel(BaseModel):
+    """Sample enrichment result for display."""
+
+    item_id: str
+    item_title: str
+    enrichment_data: Dict[str, Any]
+
+
+class WebsetTargetStatus(str, Enum):
+    IDLE = "idle"
+    COMPLETED = "completed"
+    RUNNING = "running"
+    PAUSED = "paused"
+    ANY_COMPLETE = "any_complete"  # Either idle or completed
+
+
+class ExaWaitForWebsetBlock(Block):
+    """Wait for a webset to reach a specific status with progress tracking."""
+
+    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 monitor",
+            placeholder="webset-id-or-external-id",
+        )
+        target_status: WebsetTargetStatus = SchemaField(
+            default=WebsetTargetStatus.IDLE,
+            description="Status to wait for (idle=all operations complete, completed=search done, running=actively processing)",
+        )
+        timeout: int = SchemaField(
+            default=300,
+            description="Maximum time to wait in seconds",
+            ge=1,
+            le=1800,  # 30 minutes max
+        )
+        check_interval: int = SchemaField(
+            default=5,
+            description="Initial interval between status checks in seconds",
+            advanced=True,
+            ge=1,
+            le=60,
+        )
+        max_interval: int = SchemaField(
+            default=30,
+            description="Maximum interval between checks (for exponential backoff)",
+            advanced=True,
+            ge=5,
+            le=120,
+        )
+        include_progress: bool = SchemaField(
+            default=True,
+            description="Include detailed progress information in output",
+        )
+
+    class Output(BlockSchemaOutput):
+        webset_id: str = SchemaField(description="The webset ID that was monitored")
+        final_status: str = SchemaField(description="The final status of the webset")
+        elapsed_time: float = SchemaField(description="Total time elapsed in seconds")
+        item_count: int = SchemaField(description="Number of items found")
+        search_progress: dict = SchemaField(
+            description="Detailed search progress information"
+        )
+        enrichment_progress: dict = SchemaField(
+            description="Detailed enrichment progress information"
+        )
+        timed_out: bool = SchemaField(description="Whether the operation timed out")
+
+    def __init__(self):
+        super().__init__(
+            id="619d71e8-b72a-434d-8bd4-23376dd0342c",
+            description="Wait for a webset to reach a specific status with progress tracking",
+            categories={BlockCategory.SEARCH},
+            input_schema=ExaWaitForWebsetBlock.Input,
+            output_schema=ExaWaitForWebsetBlock.Output,
+        )
+
+    async def run(
+        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
+    ) -> BlockOutput:
+        start_time = time.time()
+        aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
+
+        try:
+            if input_data.target_status in [
+                WebsetTargetStatus.IDLE,
+                WebsetTargetStatus.ANY_COMPLETE,
+            ]:
+                final_webset = aexa.websets.wait_until_idle(
+                    id=input_data.webset_id,
+                    timeout=input_data.timeout,
+                    poll_interval=input_data.check_interval,
+                )
+
+                elapsed = time.time() - start_time
+
+                status_str = (
+                    final_webset.status.value
+                    if hasattr(final_webset.status, "value")
+                    else str(final_webset.status)
+                )
+
+                item_count = 0
+                if final_webset.searches:
+                    for search in final_webset.searches:
+                        if search.progress:
+                            item_count += search.progress.found
+
+                # Extract progress if requested
+                search_progress = {}
+                enrichment_progress = {}
+                if input_data.include_progress:
+                    webset_dict = final_webset.model_dump(
+                        by_alias=True, exclude_none=True
+                    )
+                    search_progress = self._extract_search_progress(webset_dict)
+                    enrichment_progress = self._extract_enrichment_progress(webset_dict)
+
+                yield "webset_id", input_data.webset_id
+                yield "final_status", status_str
+                yield "elapsed_time", elapsed
+                yield "item_count", item_count
+                if input_data.include_progress:
+                    yield "search_progress", search_progress
+                    yield "enrichment_progress", enrichment_progress
+                yield "timed_out", False
+            else:
+                # For other status targets, manually poll
+                interval = input_data.check_interval
+                while time.time() - start_time < input_data.timeout:
+                    # Get current webset status
+                    webset = aexa.websets.get(id=input_data.webset_id)
+                    current_status = (
+                        webset.status.value
+                        if hasattr(webset.status, "value")
+                        else str(webset.status)
+                    )
+
+                    # Check if target status reached
+                    if current_status == input_data.target_status.value:
+                        elapsed = time.time() - start_time
+
+                        # Estimate item count from search progress
+                        item_count = 0
+                        if webset.searches:
+                            for search in webset.searches:
+                                if search.progress:
+                                    item_count += search.progress.found
+
+                        search_progress = {}
+                        enrichment_progress = {}
+                        if input_data.include_progress:
+                            webset_dict = webset.model_dump(
+                                by_alias=True, exclude_none=True
+                            )
+                            search_progress = self._extract_search_progress(webset_dict)
+                            enrichment_progress = self._extract_enrichment_progress(
+                                webset_dict
+                            )
+
+                        yield "webset_id", input_data.webset_id
+                        yield "final_status", current_status
+                        yield "elapsed_time", elapsed
+                        yield "item_count", item_count
+                        if input_data.include_progress:
+                            yield "search_progress", search_progress
+                            yield "enrichment_progress", enrichment_progress
+                        yield "timed_out", False
+                        return
+
+                    # Wait before next check with exponential backoff
+                    await asyncio.sleep(interval)
+                    interval = min(interval * 1.5, input_data.max_interval)
+
+                # Timeout reached
+                elapsed = time.time() - start_time
+                webset = aexa.websets.get(id=input_data.webset_id)
+                final_status = (
+                    webset.status.value
+                    if hasattr(webset.status, "value")
+                    else str(webset.status)
+                )
+
+                item_count = 0
+                if webset.searches:
+                    for search in webset.searches:
+                        if search.progress:
+                            item_count += search.progress.found
+
+                search_progress = {}
+                enrichment_progress = {}
+                if input_data.include_progress:
+                    webset_dict = webset.model_dump(by_alias=True, exclude_none=True)
+                    search_progress = self._extract_search_progress(webset_dict)
+                    enrichment_progress = self._extract_enrichment_progress(webset_dict)
+
+                yield "webset_id", input_data.webset_id
+                yield "final_status", final_status
+                yield "elapsed_time", elapsed
+                yield "item_count", item_count
+                if input_data.include_progress:
+                    yield "search_progress", search_progress
+                    yield "enrichment_progress", enrichment_progress
+                yield "timed_out", True
+
+        except asyncio.TimeoutError:
+            raise ValueError(
+                f"Polling timed out after {input_data.timeout} seconds"
+            ) from None
+
+    def _extract_search_progress(self, webset_data: dict) -> dict:
+        """Extract search progress information from webset data."""
+        progress = {}
+        searches = webset_data.get("searches", [])
+
+        for idx, search in enumerate(searches):
+            search_id = search.get("id", f"search_{idx}")
+            search_progress = search.get("progress", {})
+
+            progress[search_id] = {
+                "status": search.get("status", "unknown"),
+                "found": search_progress.get("found", 0),
+                "analyzed": search_progress.get("analyzed", 0),
+                "completion": search_progress.get("completion", 0),
+                "time_left": search_progress.get("timeLeft", 0),
+            }
+
+        return progress
+
+    def _extract_enrichment_progress(self, webset_data: dict) -> dict:
+        """Extract enrichment progress information from webset data."""
+        progress = {}
+        enrichments = webset_data.get("enrichments", [])
+
+        for idx, enrichment in enumerate(enrichments):
+            enrich_id = enrichment.get("id", f"enrichment_{idx}")
+
+            progress[enrich_id] = {
+                "status": enrichment.get("status", "unknown"),
+                "title": enrichment.get("title", ""),
+                "description": enrichment.get("description", ""),
+            }
+
+        return progress
+
+
+class ExaWaitForSearchBlock(Block):
+    """Wait for a specific webset search to complete with progress tracking."""
+
+    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",
+        )
+        search_id: str = SchemaField(
+            description="The ID of the search to monitor",
+            placeholder="search-id",
+        )
+        timeout: int = SchemaField(
+            default=300,
+            description="Maximum time to wait in seconds",
+            ge=1,
+            le=1800,
+        )
+        check_interval: int = SchemaField(
+            default=5,
+            description="Initial interval between status checks in seconds",
+            advanced=True,
+            ge=1,
+            le=60,
+        )
+
+    class Output(BlockSchemaOutput):
+        search_id: str = SchemaField(description="The search ID that was monitored")
+        final_status: str = SchemaField(description="The final status of the search")
+        items_found: int = SchemaField(
+            description="Number of items found by the search"
+        )
+        items_analyzed: int = SchemaField(description="Number of items analyzed")
+        completion_percentage: int = SchemaField(
+            description="Completion percentage (0-100)"
+        )
+        elapsed_time: float = SchemaField(description="Total time elapsed in seconds")
+        recall_info: dict = SchemaField(
+            description="Information about expected results and confidence"
+        )
+        timed_out: bool = SchemaField(description="Whether the operation timed out")
+
+    def __init__(self):
+        super().__init__(
+            id="14da21ae-40a1-41bc-a111-c8e5c9ef012b",
+            description="Wait for a specific webset search to complete with progress tracking",
+            categories={BlockCategory.SEARCH},
+            input_schema=ExaWaitForSearchBlock.Input,
+            output_schema=ExaWaitForSearchBlock.Output,
+        )
+
+    async def run(
+        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
+    ) -> BlockOutput:
+        start_time = time.time()
+        interval = input_data.check_interval
+        max_interval = 30
+        # Use AsyncExa SDK
+        aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
+
+        try:
+            while time.time() - start_time < input_data.timeout:
+                # Get current search status using SDK
+                search = aexa.websets.searches.get(
+                    webset_id=input_data.webset_id, id=input_data.search_id
+                )
+
+                # Extract status
+                status = (
+                    search.status.value
+                    if hasattr(search.status, "value")
+                    else str(search.status)
+                )
+
+                # Check if search is complete
+                if status in ["completed", "failed", "canceled"]:
+                    elapsed = time.time() - start_time
+
+                    # Extract progress information
+                    progress_dict = {}
+                    if search.progress:
+                        progress_dict = search.progress.model_dump(
+                            by_alias=True, exclude_none=True
+                        )
+
+                    # Extract recall information
+                    recall_info = {}
+                    if search.recall:
+                        recall_dict = search.recall.model_dump(
+                            by_alias=True, exclude_none=True
+                        )
+                        expected = recall_dict.get("expected", {})
+                        recall_info = {
+                            "expected_total": expected.get("total", 0),
+                            "confidence": expected.get("confidence", ""),
+                            "min_expected": expected.get("bounds", {}).get("min", 0),
+                            "max_expected": expected.get("bounds", {}).get("max", 0),
+                            "reasoning": recall_dict.get("reasoning", ""),
+                        }
+
+                    yield "search_id", input_data.search_id
+                    yield "final_status", status
+                    yield "items_found", progress_dict.get("found", 0)
+                    yield "items_analyzed", progress_dict.get("analyzed", 0)
+                    yield "completion_percentage", progress_dict.get("completion", 0)
+                    yield "elapsed_time", elapsed
+                    yield "recall_info", recall_info
+                    yield "timed_out", False
+
+                    return
+
+                # Wait before next check with exponential backoff
+                await asyncio.sleep(interval)
+                interval = min(interval * 1.5, max_interval)
+
+            # Timeout reached
+            elapsed = time.time() - start_time
+
+            # Get last known status
+            search = aexa.websets.searches.get(
+                webset_id=input_data.webset_id, id=input_data.search_id
+            )
+            final_status = (
+                search.status.value
+                if hasattr(search.status, "value")
+                else str(search.status)
+            )
+
+            progress_dict = {}
+            if search.progress:
+                progress_dict = search.progress.model_dump(
+                    by_alias=True, exclude_none=True
+                )
+
+            yield "search_id", input_data.search_id
+            yield "final_status", final_status
+            yield "items_found", progress_dict.get("found", 0)
+            yield "items_analyzed", progress_dict.get("analyzed", 0)
+            yield "completion_percentage", progress_dict.get("completion", 0)
+            yield "elapsed_time", elapsed
+            yield "timed_out", True
+
+        except asyncio.TimeoutError:
+            raise ValueError(
+                f"Search polling timed out after {input_data.timeout} seconds"
+            ) from None
+
+
+class ExaWaitForEnrichmentBlock(Block):
+    """Wait for a webset enrichment to complete with progress tracking."""
+
+    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 monitor",
+            placeholder="enrichment-id",
+        )
+        timeout: int = SchemaField(
+            default=300,
+            description="Maximum time to wait in seconds",
+            ge=1,
+            le=1800,
+        )
+        check_interval: int = SchemaField(
+            default=5,
+            description="Initial interval between status checks in seconds",
+            advanced=True,
+            ge=1,
+            le=60,
+        )
+        sample_results: bool = SchemaField(
+            default=True,
+            description="Include sample enrichment results in output",
+        )
+
+    class Output(BlockSchemaOutput):
+        enrichment_id: str = SchemaField(
+            description="The enrichment ID that was monitored"
+        )
+        final_status: str = SchemaField(
+            description="The final status of the enrichment"
+        )
+        items_enriched: int = SchemaField(
+            description="Number of items successfully enriched"
+        )
+        enrichment_title: str = SchemaField(
+            description="Title/description of the enrichment"
+        )
+        elapsed_time: float = SchemaField(description="Total time elapsed in seconds")
+        sample_data: list[SampleEnrichmentModel] = SchemaField(
+            description="Sample of enriched data (if requested)"
+        )
+        timed_out: bool = SchemaField(description="Whether the operation timed out")
+
+    def __init__(self):
+        super().__init__(
+            id="a11865c3-ac80-4721-8a40-ac4e3b71a558",
+            description="Wait for a webset enrichment to complete with progress tracking",
+            categories={BlockCategory.SEARCH},
+            input_schema=ExaWaitForEnrichmentBlock.Input,
+            output_schema=ExaWaitForEnrichmentBlock.Output,
+        )
+
+    async def run(
+        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
+    ) -> BlockOutput:
+        start_time = time.time()
+        interval = input_data.check_interval
+        max_interval = 30
+        # Use AsyncExa SDK
+        aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
+
+        try:
+            while time.time() - start_time < input_data.timeout:
+                # Get current enrichment status using SDK
+                enrichment = aexa.websets.enrichments.get(
+                    webset_id=input_data.webset_id, id=input_data.enrichment_id
+                )
+
+                # Extract status
+                status = (
+                    enrichment.status.value
+                    if hasattr(enrichment.status, "value")
+                    else str(enrichment.status)
+                )
+
+                # Check if enrichment is complete
+                if status in ["completed", "failed", "canceled"]:
+                    elapsed = time.time() - start_time
+
+                    # Get sample enriched items if requested
+                    sample_data = []
+                    items_enriched = 0
+
+                    if input_data.sample_results and status == "completed":
+                        sample_data, items_enriched = (
+                            await self._get_sample_enrichments(
+                                input_data.webset_id, input_data.enrichment_id, aexa
+                            )
+                        )
+
+                    yield "enrichment_id", input_data.enrichment_id
+                    yield "final_status", status
+                    yield "items_enriched", items_enriched
+                    yield "enrichment_title", enrichment.title or enrichment.description or ""
+                    yield "elapsed_time", elapsed
+                    if input_data.sample_results:
+                        yield "sample_data", sample_data
+                    yield "timed_out", False
+
+                    return
+
+                # Wait before next check with exponential backoff
+                await asyncio.sleep(interval)
+                interval = min(interval * 1.5, max_interval)
+
+            # Timeout reached
+            elapsed = time.time() - start_time
+
+            # Get last known status
+            enrichment = aexa.websets.enrichments.get(
+                webset_id=input_data.webset_id, id=input_data.enrichment_id
+            )
+            final_status = (
+                enrichment.status.value
+                if hasattr(enrichment.status, "value")
+                else str(enrichment.status)
+            )
+            title = enrichment.title or enrichment.description or ""
+
+            yield "enrichment_id", input_data.enrichment_id
+            yield "final_status", final_status
+            yield "items_enriched", 0
+            yield "enrichment_title", title
+            yield "elapsed_time", elapsed
+            yield "timed_out", True
+
+        except asyncio.TimeoutError:
+            raise ValueError(
+                f"Enrichment polling timed out after {input_data.timeout} seconds"
+            ) from None
+
+    async def _get_sample_enrichments(
+        self, webset_id: str, enrichment_id: str, aexa: AsyncExa
+    ) -> tuple[list[SampleEnrichmentModel], int]:
+        """Get sample enriched data and count."""
+        # Get a few items to see enrichment results using SDK
+        response = aexa.websets.items.list(webset_id=webset_id, limit=5)
+
+        sample_data: list[SampleEnrichmentModel] = []
+        enriched_count = 0
+
+        for sdk_item in response.data:
+            # Convert to our WebsetItemModel first
+            item = WebsetItemModel.from_sdk(sdk_item)
+
+            # Check if this item has the enrichment we're looking for
+            if enrichment_id in item.enrichments:
+                enriched_count += 1
+                enrich_model = item.enrichments[enrichment_id]
+
+                # Create sample using our typed model
+                sample = SampleEnrichmentModel(
+                    item_id=item.id,
+                    item_title=item.title,
+                    enrichment_data=enrich_model.model_dump(exclude_none=True),
+                )
+                sample_data.append(sample)
+
+        return sample_data, enriched_count

autogpt_platform/backend/backend/blocks/exa/websets_search.py

@@ -0,0 +1,650 @@
+"""
+Exa Websets Search Management Blocks
+
+This module provides blocks for creating and managing searches within websets,
+including adding new searches, checking status, and canceling operations.
+"""
+
+from enum import Enum
+from typing import Any, Dict, List, Optional
+
+from exa_py import AsyncExa
+from exa_py.websets.types import WebsetSearch as SdkWebsetSearch
+from pydantic import BaseModel
+
+from backend.sdk import (
+    APIKeyCredentials,
+    Block,
+    BlockCategory,
+    BlockOutput,
+    BlockSchemaInput,
+    BlockSchemaOutput,
+    CredentialsMetaInput,
+    SchemaField,
+)
+
+from ._config import exa
+
+
+# Mirrored model for stability
+class WebsetSearchModel(BaseModel):
+    """Stable output model mirroring SDK WebsetSearch."""
+
+    id: str
+    webset_id: str
+    status: str
+    query: str
+    entity_type: str
+    criteria: List[Dict[str, Any]]
+    count: int
+    behavior: str
+    progress: Dict[str, Any]
+    recall: Optional[Dict[str, Any]]
+    created_at: str
+    updated_at: str
+    canceled_at: Optional[str]
+    canceled_reason: Optional[str]
+    metadata: Dict[str, Any]
+
+    @classmethod
+    def from_sdk(cls, search: SdkWebsetSearch) -> "WebsetSearchModel":
+        """Convert SDK WebsetSearch to our stable model."""
+        # Extract entity type
+        entity_type = "auto"
+        if search.entity:
+            entity_dict = search.entity.model_dump(by_alias=True)
+            entity_type = entity_dict.get("type", "auto")
+
+        # Convert criteria
+        criteria = [c.model_dump(by_alias=True) for c in search.criteria]
+
+        # Convert progress
+        progress_dict = {}
+        if search.progress:
+            progress_dict = search.progress.model_dump(by_alias=True)
+
+        # Convert recall
+        recall_dict = None
+        if search.recall:
+            recall_dict = search.recall.model_dump(by_alias=True)
+
+        return cls(
+            id=search.id,
+            webset_id=search.webset_id,
+            status=(
+                search.status.value
+                if hasattr(search.status, "value")
+                else str(search.status)
+            ),
+            query=search.query,
+            entity_type=entity_type,
+            criteria=criteria,
+            count=search.count,
+            behavior=search.behavior.value if search.behavior else "override",
+            progress=progress_dict,
+            recall=recall_dict,
+            created_at=search.created_at.isoformat() if search.created_at else "",
+            updated_at=search.updated_at.isoformat() if search.updated_at else "",
+            canceled_at=search.canceled_at.isoformat() if search.canceled_at else None,
+            canceled_reason=(
+                search.canceled_reason.value if search.canceled_reason else None
+            ),
+            metadata=search.metadata if search.metadata else {},
+        )
+
+
+class SearchBehavior(str, Enum):
+    """Behavior for how new search results interact with existing items."""
+
+    OVERRIDE = "override"  # Replace existing items
+    APPEND = "append"  # Add to existing items
+    MERGE = "merge"  # Merge with existing items
+
+
+class SearchEntityType(str, Enum):
+    COMPANY = "company"
+    PERSON = "person"
+    ARTICLE = "article"
+    RESEARCH_PAPER = "research_paper"
+    CUSTOM = "custom"
+    AUTO = "auto"
+
+
+class ExaCreateWebsetSearchBlock(Block):
+    """Add a new search to an existing 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",
+        )
+        query: str = SchemaField(
+            description="Search query describing what to find",
+            placeholder="Engineering managers at Fortune 500 companies",
+        )
+        count: int = SchemaField(
+            default=10,
+            description="Number of items to find",
+            ge=1,
+            le=1000,
+        )
+
+        # Entity configuration
+        entity_type: SearchEntityType = SchemaField(
+            default=SearchEntityType.AUTO,
+            description="Type of entity to search for",
+        )
+        entity_description: Optional[str] = SchemaField(
+            default=None,
+            description="Description for custom entity type",
+            advanced=True,
+        )
+
+        # Criteria for verification
+        criteria: list[str] = SchemaField(
+            default_factory=list,
+            description="List of criteria that items must meet. If not provided, auto-detected from query.",
+            advanced=True,
+        )
+
+        # Advanced search options
+        behavior: SearchBehavior = SchemaField(
+            default=SearchBehavior.APPEND,
+            description="How new results interact with existing items",
+            advanced=True,
+        )
+        recall: bool = SchemaField(
+            default=True,
+            description="Enable recall estimation for expected results",
+            advanced=True,
+        )
+
+        # Exclude sources
+        exclude_source_ids: list[str] = SchemaField(
+            default_factory=list,
+            description="IDs of imports/websets to exclude from results",
+            advanced=True,
+        )
+        exclude_source_types: list[str] = SchemaField(
+            default_factory=list,
+            description="Types of sources to exclude ('import' or 'webset')",
+            advanced=True,
+        )
+
+        # Scope sources
+        scope_source_ids: list[str] = SchemaField(
+            default_factory=list,
+            description="IDs of imports/websets to limit search scope to",
+            advanced=True,
+        )
+        scope_source_types: list[str] = SchemaField(
+            default_factory=list,
+            description="Types of scope sources ('import' or 'webset')",
+            advanced=True,
+        )
+        scope_relationships: list[str] = SchemaField(
+            default_factory=list,
+            description="Relationship definitions for hop searches",
+            advanced=True,
+        )
+        scope_relationship_limits: list[int] = SchemaField(
+            default_factory=list,
+            description="Limits on related entities to find",
+            advanced=True,
+        )
+
+        metadata: Optional[dict] = SchemaField(
+            default=None,
+            description="Metadata to attach to the search",
+            advanced=True,
+        )
+
+        # Polling options
+        wait_for_completion: bool = SchemaField(
+            default=False,
+            description="Wait for the search to complete before returning",
+        )
+        polling_timeout: int = SchemaField(
+            default=300,
+            description="Maximum time to wait for completion in seconds",
+            advanced=True,
+            ge=1,
+            le=600,
+        )
+
+    class Output(BlockSchemaOutput):
+        search_id: str = SchemaField(
+            description="The unique identifier for the created search"
+        )
+        webset_id: str = SchemaField(description="The webset this search belongs to")
+        status: str = SchemaField(description="Current status of the search")
+        query: str = SchemaField(description="The search query")
+        expected_results: dict = SchemaField(
+            description="Recall estimation of expected results"
+        )
+        items_found: Optional[int] = SchemaField(
+            description="Number of items found (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="342ff776-2e2c-4cdb-b392-4eeb34b21d5f",
+            description="Add a new search to an existing webset to find more items",
+            categories={BlockCategory.SEARCH},
+            input_schema=ExaCreateWebsetSearchBlock.Input,
+            output_schema=ExaCreateWebsetSearchBlock.Output,
+        )
+
+    async def run(
+        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
+    ) -> BlockOutput:
+        import time
+
+        # Build the payload
+        payload = {
+            "query": input_data.query,
+            "count": input_data.count,
+            "behavior": input_data.behavior.value,
+            "recall": input_data.recall,
+        }
+
+        # Add entity configuration
+        if input_data.entity_type != SearchEntityType.AUTO:
+            entity = {"type": input_data.entity_type.value}
+            if (
+                input_data.entity_type == SearchEntityType.CUSTOM
+                and input_data.entity_description
+            ):
+                entity["description"] = input_data.entity_description
+            payload["entity"] = entity
+
+        # Add criteria if provided
+        if input_data.criteria:
+            payload["criteria"] = [{"description": c} for c in input_data.criteria]
+
+        # Add exclude sources
+        if input_data.exclude_source_ids:
+            exclude_list = []
+            for idx, src_id in enumerate(input_data.exclude_source_ids):
+                src_type = "import"
+                if input_data.exclude_source_types and idx < len(
+                    input_data.exclude_source_types
+                ):
+                    src_type = input_data.exclude_source_types[idx]
+                exclude_list.append({"source": src_type, "id": src_id})
+            payload["exclude"] = exclude_list
+
+        # Add scope sources
+        if input_data.scope_source_ids:
+            scope_list: list[dict[str, Any]] = []
+            for idx, src_id in enumerate(input_data.scope_source_ids):
+                scope_item: dict[str, Any] = {"source": "import", "id": src_id}
+
+                if input_data.scope_source_types and idx < len(
+                    input_data.scope_source_types
+                ):
+                    scope_item["source"] = input_data.scope_source_types[idx]
+
+                # Add relationship if provided
+                if input_data.scope_relationships and idx < len(
+                    input_data.scope_relationships
+                ):
+                    relationship: dict[str, Any] = {
+                        "definition": input_data.scope_relationships[idx]
+                    }
+                    if input_data.scope_relationship_limits and idx < len(
+                        input_data.scope_relationship_limits
+                    ):
+                        relationship["limit"] = input_data.scope_relationship_limits[
+                            idx
+                        ]
+                    scope_item["relationship"] = relationship
+
+                scope_list.append(scope_item)
+            payload["scope"] = scope_list
+
+        # Add metadata if provided
+        if input_data.metadata:
+            payload["metadata"] = input_data.metadata
+
+        start_time = time.time()
+
+        aexa = AsyncExa(api_key=credentials.api_key.get_secret_value())
+
+        sdk_search = aexa.websets.searches.create(
+            webset_id=input_data.webset_id, params=payload
+        )
+
+        search_id = sdk_search.id
+        status = (
+            sdk_search.status.value
+            if hasattr(sdk_search.status, "value")
+            else str(sdk_search.status)
+        )
+
+        # Extract expected results from recall
+        expected_results = {}
+        if sdk_search.recall:
+            recall_dict = sdk_search.recall.model_dump(by_alias=True)
+            expected = recall_dict.get("expected", {})
+            expected_results = {
+                "total": expected.get("total", 0),
+                "confidence": expected.get("confidence", ""),
+                "min": expected.get("bounds", {}).get("min", 0),
+                "max": expected.get("bounds", {}).get("max", 0),
+                "reasoning": recall_dict.get("reasoning", ""),
+            }
+
+        # If wait_for_completion is True, poll for completion
+        if input_data.wait_for_completion:
+            import asyncio
+
+            poll_interval = 5
+            max_interval = 30
+            poll_start = time.time()
+
+            while time.time() - poll_start < input_data.polling_timeout:
+                current_search = aexa.websets.searches.get(
+                    webset_id=input_data.webset_id, id=search_id
+                )
+                current_status = (
+                    current_search.status.value
+                    if hasattr(current_search.status, "value")
+                    else str(current_search.status)
+                )
+
+                if current_status in ["completed", "failed", "cancelled"]:
+                    items_found = 0
+                    if current_search.progress:
+                        items_found = current_search.progress.found
+                    completion_time = time.time() - start_time
+
+                    yield "search_id", search_id
+                    yield "webset_id", input_data.webset_id
+                    yield "status", current_status
+                    yield "query", input_data.query
+                    yield "expected_results", expected_results
+                    yield "items_found", items_found
+                    yield "completion_time", completion_time
+                    return
+
+                await asyncio.sleep(poll_interval)
+                poll_interval = min(poll_interval * 1.5, max_interval)
+
+            # Timeout - yield what we have
+            yield "search_id", search_id
+            yield "webset_id", input_data.webset_id
+            yield "status", status
+            yield "query", input_data.query
+            yield "expected_results", expected_results
+            yield "items_found", 0
+            yield "completion_time", time.time() - start_time
+        else:
+            yield "search_id", search_id
+            yield "webset_id", input_data.webset_id
+            yield "status", status
+            yield "query", input_data.query
+            yield "expected_results", expected_results
+
+
+class ExaGetWebsetSearchBlock(Block):
+    """Get the status and details of a webset search."""
+
+    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",
+        )
+        search_id: str = SchemaField(
+            description="The ID of the search to retrieve",
+            placeholder="search-id",
+        )
+
+    class Output(BlockSchemaOutput):
+        search_id: str = SchemaField(description="The unique identifier for the search")
+        status: str = SchemaField(description="Current status of the search")
+        query: str = SchemaField(description="The search query")
+        entity_type: str = SchemaField(description="Type of entity being searched")
+        criteria: list[dict] = SchemaField(description="Criteria used for verification")
+        progress: dict = SchemaField(description="Search progress information")
+        recall: dict = SchemaField(description="Recall estimation information")
+        created_at: str = SchemaField(description="When the search was created")
+        updated_at: str = SchemaField(description="When the search was last updated")
+        canceled_at: Optional[str] = SchemaField(
+            description="When the search was canceled (if applicable)"
+        )
+        canceled_reason: Optional[str] = SchemaField(
+            description="Reason for cancellation (if applicable)"
+        )
+        metadata: dict = SchemaField(description="Metadata attached to the search")
+
+    def __init__(self):
+        super().__init__(
+            id="4fa3e627-a0ff-485f-8732-52148051646c",
+            description="Get the status and details of a webset search",
+            categories={BlockCategory.SEARCH},
+            input_schema=ExaGetWebsetSearchBlock.Input,
+            output_schema=ExaGetWebsetSearchBlock.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_search = aexa.websets.searches.get(
+            webset_id=input_data.webset_id, id=input_data.search_id
+        )
+
+        search = WebsetSearchModel.from_sdk(sdk_search)
+
+        # Extract progress information
+        progress_info = {
+            "found": search.progress.get("found", 0),
+            "analyzed": search.progress.get("analyzed", 0),
+            "completion": search.progress.get("completion", 0),
+            "time_left": search.progress.get("timeLeft", 0),
+        }
+
+        # Extract recall information
+        recall_data = {}
+        if search.recall:
+            expected = search.recall.get("expected", {})
+            recall_data = {
+                "expected_total": expected.get("total", 0),
+                "confidence": expected.get("confidence", ""),
+                "min_expected": expected.get("bounds", {}).get("min", 0),
+                "max_expected": expected.get("bounds", {}).get("max", 0),
+                "reasoning": search.recall.get("reasoning", ""),
+            }
+
+        yield "search_id", search.id
+        yield "status", search.status
+        yield "query", search.query
+        yield "entity_type", search.entity_type
+        yield "criteria", search.criteria
+        yield "progress", progress_info
+        yield "recall", recall_data
+        yield "created_at", search.created_at
+        yield "updated_at", search.updated_at
+        yield "canceled_at", search.canceled_at
+        yield "canceled_reason", search.canceled_reason
+        yield "metadata", search.metadata
+
+
+class ExaCancelWebsetSearchBlock(Block):
+    """Cancel a running webset search."""
+
+    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",
+        )
+        search_id: str = SchemaField(
+            description="The ID of the search to cancel",
+            placeholder="search-id",
+        )
+
+    class Output(BlockSchemaOutput):
+        search_id: str = SchemaField(description="The ID of the canceled search")
+        status: str = SchemaField(description="Status after cancellation")
+        items_found_before_cancel: int = SchemaField(
+            description="Number of items found before cancellation"
+        )
+        success: str = SchemaField(
+            description="Whether the cancellation was successful"
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="74ef9f1e-ae89-4c7f-9d7d-d217214815b4",
+            description="Cancel a running webset search",
+            categories={BlockCategory.SEARCH},
+            input_schema=ExaCancelWebsetSearchBlock.Input,
+            output_schema=ExaCancelWebsetSearchBlock.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_search = aexa.websets.searches.cancel(
+            webset_id=input_data.webset_id, id=input_data.search_id
+        )
+
+        # Extract items found before cancellation
+        items_found = 0
+        if canceled_search.progress:
+            items_found = canceled_search.progress.found
+
+        status = (
+            canceled_search.status.value
+            if hasattr(canceled_search.status, "value")
+            else str(canceled_search.status)
+        )
+
+        yield "search_id", canceled_search.id
+        yield "status", status
+        yield "items_found_before_cancel", items_found
+        yield "success", "true"
+
+
+class ExaFindOrCreateSearchBlock(Block):
+    """Find existing search by query or create new one (prevents duplicate searches)."""
+
+    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",
+        )
+        query: str = SchemaField(
+            description="Search query to find or create",
+            placeholder="AI companies in San Francisco",
+        )
+        count: int = SchemaField(
+            default=10,
+            description="Number of items to find (only used if creating new search)",
+            ge=1,
+            le=1000,
+        )
+        entity_type: SearchEntityType = SchemaField(
+            default=SearchEntityType.AUTO,
+            description="Entity type (only used if creating)",
+            advanced=True,
+        )
+        behavior: SearchBehavior = SchemaField(
+            default=SearchBehavior.OVERRIDE,
+            description="Search behavior (only used if creating)",
+            advanced=True,
+        )
+
+    class Output(BlockSchemaOutput):
+        search_id: str = SchemaField(description="The search ID (existing or new)")
+        webset_id: str = SchemaField(description="The webset ID")
+        status: str = SchemaField(description="Current search status")
+        query: str = SchemaField(description="The search query")
+        was_created: bool = SchemaField(
+            description="True if search was newly created, False if already existed"
+        )
+        items_found: int = SchemaField(
+            description="Number of items found (0 if still running)"
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="cbdb05ac-cb73-4b03-a493-6d34e9a011da",
+            description="Find existing search by query or create new - prevents duplicate searches in workflows",
+            categories={BlockCategory.SEARCH},
+            input_schema=ExaFindOrCreateSearchBlock.Input,
+            output_schema=ExaFindOrCreateSearchBlock.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())
+
+        # Get webset to check existing searches
+        webset = aexa.websets.get(id=input_data.webset_id)
+
+        # Look for existing search with same query
+        existing_search = None
+        if webset.searches:
+            for search in webset.searches:
+                if search.query.strip().lower() == input_data.query.strip().lower():
+                    existing_search = search
+                    break
+
+        if existing_search:
+            # Found existing search
+            search = WebsetSearchModel.from_sdk(existing_search)
+
+            yield "search_id", search.id
+            yield "webset_id", input_data.webset_id
+            yield "status", search.status
+            yield "query", search.query
+            yield "was_created", False
+            yield "items_found", search.progress.get("found", 0)
+        else:
+            # Create new search
+            payload: Dict[str, Any] = {
+                "query": input_data.query,
+                "count": input_data.count,
+                "behavior": input_data.behavior.value,
+            }
+
+            # Add entity if not auto
+            if input_data.entity_type != SearchEntityType.AUTO:
+                payload["entity"] = {"type": input_data.entity_type.value}
+
+            sdk_search = aexa.websets.searches.create(
+                webset_id=input_data.webset_id, params=payload
+            )
+
+            search = WebsetSearchModel.from_sdk(sdk_search)
+
+            yield "search_id", search.id
+            yield "webset_id", input_data.webset_id
+            yield "status", search.status
+            yield "query", search.query
+            yield "was_created", True
+            yield "items_found", 0  # Newly created, no items yet

autogpt_platform/backend/backend/blocks/fal/ai_video_generator.py

@@ -10,7 +10,13 @@ from backend.blocks.fal._auth import (
     FalCredentialsField,
     FalCredentialsInput,
 )
-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.request import ClientResponseError, Requests
 
@@ -24,7 +30,7 @@ class FalModel(str, Enum):
 
 
 class AIVideoGeneratorBlock(Block):
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         prompt: str = SchemaField(
             description="Description of the video to generate.",
             placeholder="A dog running in a field.",
@@ -36,7 +42,7 @@ class AIVideoGeneratorBlock(Block):
         )
         credentials: FalCredentialsInput = FalCredentialsField()
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         video_url: str = SchemaField(description="The URL of the generated video.")
         error: str = SchemaField(
             description="Error message if video generation failed."

autogpt_platform/backend/backend/blocks/firecrawl/crawl.py

@@ -9,7 +9,8 @@ from backend.sdk import (
     Block,
     BlockCategory,
     BlockOutput,
-    BlockSchema,
+    BlockSchemaInput,
+    BlockSchemaOutput,
     CredentialsMetaInput,
     SchemaField,
 )
@@ -19,7 +20,7 @@ from ._format_utils import convert_to_format_options
 
 
 class FirecrawlCrawlBlock(Block):
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         credentials: CredentialsMetaInput = firecrawl.credentials_field()
         url: str = SchemaField(description="The URL to crawl")
         limit: int = SchemaField(description="The number of pages to crawl", default=10)
@@ -39,7 +40,7 @@ class FirecrawlCrawlBlock(Block):
             description="The format of the crawl", default=[ScrapeFormat.MARKDOWN]
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         data: list[dict[str, Any]] = SchemaField(description="The result of the crawl")
         markdown: str = SchemaField(description="The markdown of the crawl")
         html: str = SchemaField(description="The html of the crawl")
@@ -55,6 +56,10 @@ class FirecrawlCrawlBlock(Block):
         change_tracking: dict[str, Any] = SchemaField(
             description="The change tracking of the crawl"
         )
+        error: str = SchemaField(
+            description="Error message if the crawl failed",
+            default="",
+        )
 
     def __init__(self):
         super().__init__(

autogpt_platform/backend/backend/blocks/firecrawl/extract.py

@@ -9,7 +9,8 @@ from backend.sdk import (
     BlockCost,
     BlockCostType,
     BlockOutput,
-    BlockSchema,
+    BlockSchemaInput,
+    BlockSchemaOutput,
     CredentialsMetaInput,
     SchemaField,
     cost,
@@ -20,7 +21,7 @@ from ._config import firecrawl
 
 @cost(BlockCost(2, BlockCostType.RUN))
 class FirecrawlExtractBlock(Block):
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         credentials: CredentialsMetaInput = firecrawl.credentials_field()
         urls: list[str] = SchemaField(
             description="The URLs to crawl - at least one is required. Wildcards are supported. (/*)"
@@ -37,8 +38,12 @@ class FirecrawlExtractBlock(Block):
             default=False,
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         data: dict[str, Any] = SchemaField(description="The result of the crawl")
+        error: str = SchemaField(
+            description="Error message if the extraction failed",
+            default="",
+        )
 
     def __init__(self):
         super().__init__(

autogpt_platform/backend/backend/blocks/firecrawl/map.py

@@ -7,7 +7,8 @@ from backend.sdk import (
     Block,
     BlockCategory,
     BlockOutput,
-    BlockSchema,
+    BlockSchemaInput,
+    BlockSchemaOutput,
     CredentialsMetaInput,
     SchemaField,
 )
@@ -16,16 +17,20 @@ from ._config import firecrawl
 
 
 class FirecrawlMapWebsiteBlock(Block):
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         credentials: CredentialsMetaInput = firecrawl.credentials_field()
 
         url: str = SchemaField(description="The website url to map")
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         links: list[str] = SchemaField(description="List of URLs found on the website")
         results: list[dict[str, Any]] = SchemaField(
             description="List of search results with url, title, and description"
         )
+        error: str = SchemaField(
+            description="Error message if the map failed",
+            default="",
+        )
 
     def __init__(self):
         super().__init__(

autogpt_platform/backend/backend/blocks/firecrawl/scrape.py

@@ -8,7 +8,8 @@ from backend.sdk import (
     Block,
     BlockCategory,
     BlockOutput,
-    BlockSchema,
+    BlockSchemaInput,
+    BlockSchemaOutput,
     CredentialsMetaInput,
     SchemaField,
 )
@@ -18,7 +19,7 @@ from ._format_utils import convert_to_format_options
 
 
 class FirecrawlScrapeBlock(Block):
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         credentials: CredentialsMetaInput = firecrawl.credentials_field()
         url: str = SchemaField(description="The URL to crawl")
         limit: int = SchemaField(description="The number of pages to crawl", default=10)
@@ -38,7 +39,7 @@ class FirecrawlScrapeBlock(Block):
             description="The format of the crawl", default=[ScrapeFormat.MARKDOWN]
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         data: dict[str, Any] = SchemaField(description="The result of the crawl")
         markdown: str = SchemaField(description="The markdown of the crawl")
         html: str = SchemaField(description="The html of the crawl")
@@ -54,6 +55,10 @@ class FirecrawlScrapeBlock(Block):
         change_tracking: dict[str, Any] = SchemaField(
             description="The change tracking of the crawl"
         )
+        error: str = SchemaField(
+            description="Error message if the scrape failed",
+            default="",
+        )
 
     def __init__(self):
         super().__init__(

autogpt_platform/backend/backend/blocks/firecrawl/search.py

@@ -9,7 +9,8 @@ from backend.sdk import (
     Block,
     BlockCategory,
     BlockOutput,
-    BlockSchema,
+    BlockSchemaInput,
+    BlockSchemaOutput,
     CredentialsMetaInput,
     SchemaField,
 )
@@ -19,7 +20,7 @@ from ._format_utils import convert_to_format_options
 
 
 class FirecrawlSearchBlock(Block):
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         credentials: CredentialsMetaInput = firecrawl.credentials_field()
         query: str = SchemaField(description="The query to search for")
         limit: int = SchemaField(description="The number of pages to crawl", default=10)
@@ -35,9 +36,13 @@ class FirecrawlSearchBlock(Block):
             description="Returns the content of the search if specified", default=[]
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         data: dict[str, Any] = SchemaField(description="The result of the search")
         site: dict[str, Any] = SchemaField(description="The site of the search")
+        error: str = SchemaField(
+            description="Error message if the search failed",
+            default="",
+        )
 
     def __init__(self):
         super().__init__(

autogpt_platform/backend/backend/blocks/flux_kontext.py

@@ -5,7 +5,13 @@ 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,
@@ -57,7 +63,7 @@ class AspectRatio(str, Enum):
 
 
 class AIImageEditorBlock(Block):
-    class Input(BlockSchema):
+    class Input(BlockSchemaInput):
         credentials: CredentialsMetaInput[
             Literal[ProviderName.REPLICATE], Literal["api_key"]
         ] = CredentialsField(
@@ -90,11 +96,10 @@ class AIImageEditorBlock(Block):
             title="Model",
         )
 
-    class Output(BlockSchema):
+    class Output(BlockSchemaOutput):
         output_image: MediaFileType = SchemaField(
             description="URL of the transformed image"
         )
-        error: str = SchemaField(description="Error message if generation failed")
 
     def __init__(self):
         super().__init__(