wip: fixes

Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>

.dockerignore

@@ -16,7 +16,6 @@
 !autogpt_platform/backend/poetry.lock
 !autogpt_platform/backend/README.md
 !autogpt_platform/backend/.env
-!autogpt_platform/backend/gen_prisma_types_stub.py
 
 # Platform - Market
 !autogpt_platform/market/market/

.github/workflows/claude-dependabot.yml

@@ -74,7 +74,7 @@ jobs:
 
       - name: Generate Prisma Client
         working-directory: autogpt_platform/backend
-        run: poetry run prisma generate && poetry run gen-prisma-stub
+        run: poetry run prisma generate
 
       # Frontend Node.js/pnpm setup (mirrors platform-frontend-ci.yml)
       - name: Set up Node.js

.github/workflows/claude.yml

@@ -90,7 +90,7 @@ jobs:
 
       - name: Generate Prisma Client
         working-directory: autogpt_platform/backend
-        run: poetry run prisma generate && poetry run gen-prisma-stub
+        run: poetry run prisma generate
 
       # Frontend Node.js/pnpm setup (mirrors platform-frontend-ci.yml)
       - name: Set up Node.js

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

@@ -72,7 +72,7 @@ jobs:
 
       - name: Generate Prisma Client
         working-directory: autogpt_platform/backend
-        run: poetry run prisma generate && poetry run gen-prisma-stub
+        run: poetry run prisma generate
 
       # Frontend Node.js/pnpm setup (mirrors platform-frontend-ci.yml)
       - name: Set up Node.js
@@ -108,16 +108,6 @@ jobs:
       #   run: pnpm playwright install --with-deps chromium
 
       # Docker setup for development environment
-      - name: Free up disk space
-        run: |
-          # Remove large unused tools to free disk space for Docker builds
-          sudo rm -rf /usr/share/dotnet
-          sudo rm -rf /usr/local/lib/android
-          sudo rm -rf /opt/ghc
-          sudo rm -rf /opt/hostedtoolcache/CodeQL
-          sudo docker system prune -af
-          df -h
-
       - name: Set up Docker Buildx
         uses: docker/setup-buildx-action@v3
 

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

@@ -134,7 +134,7 @@ jobs:
         run: poetry install
 
       - name: Generate Prisma Client
-        run: poetry run prisma generate && poetry run gen-prisma-stub
+        run: poetry run prisma generate
 
       - id: supabase
         name: Start Supabase

autogpt_platform/Makefile

@@ -12,7 +12,6 @@ reset-db:
 	rm -rf db/docker/volumes/db/data
 	cd backend && poetry run prisma migrate deploy
 	cd backend && poetry run prisma generate
-	cd backend && poetry run gen-prisma-stub
 	
 # View logs for core services
 logs-core:
@@ -34,7 +33,6 @@ init-env:
 migrate:
 	cd backend && poetry run prisma migrate deploy
 	cd backend && poetry run prisma generate
-	cd backend && poetry run gen-prisma-stub
 
 run-backend:
 	cd backend && poetry run app

autogpt_platform/backend/Dockerfile

@@ -48,8 +48,7 @@ RUN poetry install --no-ansi --no-root
 # Generate Prisma client
 COPY autogpt_platform/backend/schema.prisma ./
 COPY autogpt_platform/backend/backend/data/partial_types.py ./backend/data/partial_types.py
-COPY autogpt_platform/backend/gen_prisma_types_stub.py ./
-RUN poetry run prisma generate && poetry run gen-prisma-stub
+RUN poetry run prisma generate
 
 FROM debian:13-slim AS server_dependencies
 

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

@@ -48,7 +48,6 @@ class LibraryAgent(pydantic.BaseModel):
     id: str
     graph_id: str
     graph_version: int
-    owner_user_id: str  # ID of user who owns/created this agent graph
 
     image_url: str | None
 
@@ -164,7 +163,6 @@ class LibraryAgent(pydantic.BaseModel):
             id=agent.id,
             graph_id=agent.agentGraphId,
             graph_version=agent.agentGraphVersion,
-            owner_user_id=agent.userId,
             image_url=agent.imageUrl,
             creator_name=creator_name,
             creator_image_url=creator_image_url,

autogpt_platform/backend/backend/api/features/library/routes_test.py

@@ -42,7 +42,6 @@ async def test_get_library_agents_success(
                 id="test-agent-1",
                 graph_id="test-agent-1",
                 graph_version=1,
-                owner_user_id=test_user_id,
                 name="Test Agent 1",
                 description="Test Description 1",
                 image_url=None,
@@ -65,7 +64,6 @@ async def test_get_library_agents_success(
                 id="test-agent-2",
                 graph_id="test-agent-2",
                 graph_version=1,
-                owner_user_id=test_user_id,
                 name="Test Agent 2",
                 description="Test Description 2",
                 image_url=None,
@@ -140,7 +138,6 @@ async def test_get_favorite_library_agents_success(
                 id="test-agent-1",
                 graph_id="test-agent-1",
                 graph_version=1,
-                owner_user_id=test_user_id,
                 name="Favorite Agent 1",
                 description="Test Favorite Description 1",
                 image_url=None,
@@ -208,7 +205,6 @@ def test_add_agent_to_library_success(
         id="test-library-agent-id",
         graph_id="test-agent-1",
         graph_version=1,
-        owner_user_id=test_user_id,
         name="Test Agent 1",
         description="Test Description 1",
         image_url=None,

autogpt_platform/backend/backend/api/features/store/backfill_embeddings.py

@@ -1,72 +0,0 @@
-#!/usr/bin/env python3
-"""
-CLI script to backfill embeddings for store agents.
-
-Usage:
-    poetry run python -m backend.server.v2.store.backfill_embeddings [--batch-size N]
-"""
-
-import argparse
-import asyncio
-import sys
-
-import prisma
-
-from backend.api.features.store.embeddings import (
-    backfill_missing_embeddings,
-    get_embedding_stats,
-)
-
-
-async def main(batch_size: int = 100) -> int:
-    """Run the backfill process."""
-    # Initialize Prisma client
-    client = prisma.Prisma()
-    await client.connect()
-    prisma.register(client)
-
-    try:
-        # Get current stats
-        print("Current embedding stats:")
-        stats = await get_embedding_stats()
-        print(f"  Total approved: {stats['total_approved']}")
-        print(f"  With embeddings: {stats['with_embeddings']}")
-        print(f"  Without embeddings: {stats['without_embeddings']}")
-        print(f"  Coverage: {stats['coverage_percent']}%")
-
-        if stats["without_embeddings"] == 0:
-            print("\nAll agents already have embeddings. Nothing to do.")
-            return 0
-
-        # Run backfill
-        print(f"\nBackfilling up to {batch_size} embeddings...")
-        result = await backfill_missing_embeddings(batch_size=batch_size)
-        print(f"  Processed: {result['processed']}")
-        print(f"  Success: {result['success']}")
-        print(f"  Failed: {result['failed']}")
-
-        # Get final stats
-        print("\nFinal embedding stats:")
-        stats = await get_embedding_stats()
-        print(f"  Total approved: {stats['total_approved']}")
-        print(f"  With embeddings: {stats['with_embeddings']}")
-        print(f"  Without embeddings: {stats['without_embeddings']}")
-        print(f"  Coverage: {stats['coverage_percent']}%")
-
-        return 0 if result["failed"] == 0 else 1
-
-    finally:
-        await client.disconnect()
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Backfill embeddings for store agents")
-    parser.add_argument(
-        "--batch-size",
-        type=int,
-        default=100,
-        help="Number of embeddings to generate (default: 100)",
-    )
-    args = parser.parse_args()
-
-    sys.exit(asyncio.run(main(batch_size=args.batch_size)))

autogpt_platform/backend/backend/api/features/store/db.py

@@ -1,5 +1,6 @@
 import asyncio
 import logging
+import typing
 from datetime import datetime, timezone
 from typing import Literal
 
@@ -9,7 +10,7 @@ import prisma.errors
 import prisma.models
 import prisma.types
 
-from backend.data.db import transaction
+from backend.data.db import query_raw_with_schema, transaction
 from backend.data.graph import (
     GraphMeta,
     GraphModel,
@@ -29,8 +30,6 @@ from backend.util.settings import Settings
 
 from . import exceptions as store_exceptions
 from . import model as store_model
-from .embeddings import ensure_embedding
-from .hybrid_search import hybrid_search
 
 logger = logging.getLogger(__name__)
 settings = Settings()
@@ -57,62 +56,122 @@ async def get_store_agents(
         f"Getting store agents. featured={featured}, creators={creators}, sorted_by={sorted_by}, search={search_query}, category={category}, page={page}"
     )
 
-    search_used_hybrid = False
-    store_agents: list[store_model.StoreAgent] = []
-    total = 0
-    total_pages = 0
-
     try:
-        # If search_query is provided, try hybrid search (embeddings + tsvector)
+        # If search_query is provided, use full-text search
         if search_query:
-            try:
-                # Use hybrid search combining semantic and lexical signals
-                agents, total = await hybrid_search(
-                    query=search_query,
-                    featured=featured,
-                    creators=creators,
-                    category=category,
-                    sorted_by="relevance",  # Use hybrid scoring for relevance
-                    page=page,
-                    page_size=page_size,
-                )
-                search_used_hybrid = True
+            offset = (page - 1) * page_size
 
-                # Convert hybrid search results (dict format)
-                total_pages = (total + page_size - 1) // page_size
-                store_agents: list[store_model.StoreAgent] = []
-                for agent in agents:
-                    try:
-                        store_agent = store_model.StoreAgent(
-                            slug=agent["slug"],
-                            agent_name=agent["agent_name"],
-                            agent_image=(
-                                agent["agent_image"][0] if agent["agent_image"] else ""
-                            ),
-                            creator=agent["creator_username"] or "Needs Profile",
-                            creator_avatar=agent["creator_avatar"] or "",
-                            sub_heading=agent["sub_heading"],
-                            description=agent["description"],
-                            runs=agent["runs"],
-                            rating=agent["rating"],
-                        )
-                        store_agents.append(store_agent)
-                    except Exception as e:
-                        logger.error(
-                            f"Error parsing Store agent from hybrid search results: {e}"
-                        )
-                        continue
+            # Whitelist allowed order_by columns
+            ALLOWED_ORDER_BY = {
+                "rating": "rating DESC, rank DESC",
+                "runs": "runs DESC, rank DESC",
+                "name": "agent_name ASC, rank ASC",
+                "updated_at": "updated_at DESC, rank DESC",
+            }
 
-            except Exception as hybrid_error:
-                # If hybrid search fails (e.g., missing embeddings table),
-                # fallback to basic search logic below
-                logger.warning(
-                    f"Hybrid search failed, falling back to basic search: {hybrid_error}"
-                )
-                search_used_hybrid = False
+            # Validate and get order clause
+            if sorted_by and sorted_by in ALLOWED_ORDER_BY:
+                order_by_clause = ALLOWED_ORDER_BY[sorted_by]
+            else:
+                order_by_clause = "updated_at DESC, rank DESC"
 
-        if not search_used_hybrid:
-            # Fallback path - use basic search or no search
+            # Build WHERE conditions and parameters list
+            where_parts: list[str] = []
+            params: list[typing.Any] = [search_query]  # $1 - search term
+            param_index = 2  # Start at $2 for next parameter
+
+            # Always filter for available agents
+            where_parts.append("is_available = true")
+
+            if featured:
+                where_parts.append("featured = true")
+
+            if creators and creators:
+                # Use ANY with array parameter
+                where_parts.append(f"creator_username = ANY(${param_index})")
+                params.append(creators)
+                param_index += 1
+
+            if category and category:
+                where_parts.append(f"${param_index} = ANY(categories)")
+                params.append(category)
+                param_index += 1
+
+            sql_where_clause: str = " AND ".join(where_parts) if where_parts else "1=1"
+
+            # Add pagination params
+            params.extend([page_size, offset])
+            limit_param = f"${param_index}"
+            offset_param = f"${param_index + 1}"
+
+            # Execute full-text search query with parameterized values
+            sql_query = f"""
+                SELECT
+                    slug,
+                    agent_name,
+                    agent_image,
+                    creator_username,
+                    creator_avatar,
+                    sub_heading,
+                    description,
+                    runs,
+                    rating,
+                    categories,
+                    featured,
+                    is_available,
+                    updated_at,
+                    ts_rank_cd(search, query) AS rank
+                FROM {{schema_prefix}}"StoreAgent",
+                    plainto_tsquery('english', $1) AS query
+                WHERE {sql_where_clause}
+                    AND search @@ query
+                ORDER BY {order_by_clause}
+                LIMIT {limit_param} OFFSET {offset_param}
+            """
+
+            # Count query for pagination - only uses search term parameter
+            count_query = f"""
+                SELECT COUNT(*) as count
+                FROM {{schema_prefix}}"StoreAgent",
+                    plainto_tsquery('english', $1) AS query
+                WHERE {sql_where_clause}
+                    AND search @@ query
+            """
+
+            # Execute both queries with parameters
+            agents = await query_raw_with_schema(sql_query, *params)
+
+            # For count, use params without pagination (last 2 params)
+            count_params = params[:-2]
+            count_result = await query_raw_with_schema(count_query, *count_params)
+
+            total = count_result[0]["count"] if count_result else 0
+            total_pages = (total + page_size - 1) // page_size
+
+            # Convert raw results to StoreAgent models
+            store_agents: list[store_model.StoreAgent] = []
+            for agent in agents:
+                try:
+                    store_agent = store_model.StoreAgent(
+                        slug=agent["slug"],
+                        agent_name=agent["agent_name"],
+                        agent_image=(
+                            agent["agent_image"][0] if agent["agent_image"] else ""
+                        ),
+                        creator=agent["creator_username"] or "Needs Profile",
+                        creator_avatar=agent["creator_avatar"] or "",
+                        sub_heading=agent["sub_heading"],
+                        description=agent["description"],
+                        runs=agent["runs"],
+                        rating=agent["rating"],
+                    )
+                    store_agents.append(store_agent)
+                except Exception as e:
+                    logger.error(f"Error parsing Store agent from search results: {e}")
+                    continue
+
+        else:
+            # Non-search query path (original logic)
             where_clause: prisma.types.StoreAgentWhereInput = {"is_available": True}
             if featured:
                 where_clause["featured"] = featured
@@ -121,14 +180,6 @@ async def get_store_agents(
             if category:
                 where_clause["categories"] = {"has": category}
 
-            # Add basic text search if search_query provided but hybrid failed
-            if search_query:
-                where_clause["OR"] = [
-                    {"agent_name": {"contains": search_query, "mode": "insensitive"}},
-                    {"sub_heading": {"contains": search_query, "mode": "insensitive"}},
-                    {"description": {"contains": search_query, "mode": "insensitive"}},
-                ]
-
             order_by = []
             if sorted_by == "rating":
                 order_by.append({"rating": "desc"})
@@ -563,7 +614,6 @@ async def get_store_submissions(
         submission_models = []
         for sub in submissions:
             submission_model = store_model.StoreSubmission(
-                listing_id=sub.listing_id,
                 agent_id=sub.agent_id,
                 agent_version=sub.agent_version,
                 name=sub.name,
@@ -617,48 +667,35 @@ async def delete_store_submission(
     submission_id: str,
 ) -> bool:
     """
-    Delete a store submission version as the submitting user.
+    Delete a store listing submission as the submitting user.
 
     Args:
         user_id: ID of the authenticated user
-        submission_id: StoreListingVersion ID to delete
+        submission_id: ID of the submission to be deleted
 
     Returns:
-        bool: True if successfully deleted
+        bool: True if the submission was successfully deleted, False otherwise
     """
+    logger.debug(f"Deleting store submission {submission_id} for user {user_id}")
+
     try:
-        # Find the submission version with ownership check
-        version = await prisma.models.StoreListingVersion.prisma().find_first(
-            where={"id": submission_id}, include={"StoreListing": True}
+        # Verify the submission belongs to this user
+        submission = await prisma.models.StoreListing.prisma().find_first(
+            where={"agentGraphId": submission_id, "owningUserId": user_id}
         )
 
-        if (
-            not version
-            or not version.StoreListing
-            or version.StoreListing.owningUserId != user_id
-        ):
-            raise store_exceptions.SubmissionNotFoundError("Submission not found")
-
-        # Prevent deletion of approved submissions
-        if version.submissionStatus == prisma.enums.SubmissionStatus.APPROVED:
-            raise store_exceptions.InvalidOperationError(
-                "Cannot delete approved submissions"
+        if not submission:
+            logger.warning(f"Submission not found for user {user_id}: {submission_id}")
+            raise store_exceptions.SubmissionNotFoundError(
+                f"Submission not found for this user. User ID: {user_id}, Submission ID: {submission_id}"
             )
 
-        # Delete the version
-        await prisma.models.StoreListingVersion.prisma().delete(
-            where={"id": version.id}
-        )
+        # Delete the submission
+        await prisma.models.StoreListing.prisma().delete(where={"id": submission.id})
 
-        # Clean up empty listing if this was the last version
-        remaining = await prisma.models.StoreListingVersion.prisma().count(
-            where={"storeListingId": version.storeListingId}
+        logger.debug(
+            f"Successfully deleted submission {submission_id} for user {user_id}"
         )
-        if remaining == 0:
-            await prisma.models.StoreListing.prisma().delete(
-                where={"id": version.storeListingId}
-            )
-
         return True
 
     except Exception as e:
@@ -722,15 +759,9 @@ async def create_store_submission(
             logger.warning(
                 f"Agent not found for user {user_id}: {agent_id} v{agent_version}"
             )
-            # Provide more user-friendly error message when agent_id is empty
-            if not agent_id or agent_id.strip() == "":
-                raise store_exceptions.AgentNotFoundError(
-                    "No agent selected. Please select an agent before submitting to the store."
-                )
-            else:
-                raise store_exceptions.AgentNotFoundError(
-                    f"Agent not found for this user. User ID: {user_id}, Agent ID: {agent_id}, Version: {agent_version}"
-                )
+            raise store_exceptions.AgentNotFoundError(
+                f"Agent not found for this user. User ID: {user_id}, Agent ID: {agent_id}, Version: {agent_version}"
+            )
 
         # Check if listing already exists for this agent
         existing_listing = await prisma.models.StoreListing.prisma().find_first(
@@ -802,7 +833,6 @@ async def create_store_submission(
         logger.debug(f"Created store listing for agent {agent_id}")
         # Return submission details
         return store_model.StoreSubmission(
-            listing_id=listing.id,
             agent_id=agent_id,
             agent_version=agent_version,
             name=name,
@@ -914,56 +944,81 @@ async def edit_store_submission(
         # Currently we are not allowing user to update the agent associated with a submission
         # If we allow it in future, then we need a check here to verify the agent belongs to this user.
 
-        # Only allow editing of PENDING submissions
-        if current_version.submissionStatus != prisma.enums.SubmissionStatus.PENDING:
+        # Check if we can edit this submission
+        if current_version.submissionStatus == prisma.enums.SubmissionStatus.REJECTED:
             raise store_exceptions.InvalidOperationError(
-                f"Cannot edit a {current_version.submissionStatus.value.lower()} submission. Only pending submissions can be edited."
+                "Cannot edit a rejected submission"
+            )
+
+        # For APPROVED submissions, we need to create a new version
+        if current_version.submissionStatus == prisma.enums.SubmissionStatus.APPROVED:
+            # Create a new version for the existing listing
+            return await create_store_version(
+                user_id=user_id,
+                agent_id=current_version.agentGraphId,
+                agent_version=current_version.agentGraphVersion,
+                store_listing_id=current_version.storeListingId,
+                name=name,
+                video_url=video_url,
+                agent_output_demo_url=agent_output_demo_url,
+                image_urls=image_urls,
+                description=description,
+                sub_heading=sub_heading,
+                categories=categories,
+                changes_summary=changes_summary,
+                recommended_schedule_cron=recommended_schedule_cron,
+                instructions=instructions,
             )
 
         # For PENDING submissions, we can update the existing version
-        # Update the existing version
-        updated_version = await prisma.models.StoreListingVersion.prisma().update(
-            where={"id": store_listing_version_id},
-            data=prisma.types.StoreListingVersionUpdateInput(
+        elif current_version.submissionStatus == prisma.enums.SubmissionStatus.PENDING:
+            # Update the existing version
+            updated_version = await prisma.models.StoreListingVersion.prisma().update(
+                where={"id": store_listing_version_id},
+                data=prisma.types.StoreListingVersionUpdateInput(
+                    name=name,
+                    videoUrl=video_url,
+                    agentOutputDemoUrl=agent_output_demo_url,
+                    imageUrls=image_urls,
+                    description=description,
+                    categories=categories,
+                    subHeading=sub_heading,
+                    changesSummary=changes_summary,
+                    recommendedScheduleCron=recommended_schedule_cron,
+                    instructions=instructions,
+                ),
+            )
+
+            logger.debug(
+                f"Updated existing version {store_listing_version_id} for agent {current_version.agentGraphId}"
+            )
+
+            if not updated_version:
+                raise DatabaseError("Failed to update store listing version")
+            return store_model.StoreSubmission(
+                agent_id=current_version.agentGraphId,
+                agent_version=current_version.agentGraphVersion,
                 name=name,
-                videoUrl=video_url,
-                agentOutputDemoUrl=agent_output_demo_url,
-                imageUrls=image_urls,
+                sub_heading=sub_heading,
+                slug=current_version.StoreListing.slug,
                 description=description,
-                categories=categories,
-                subHeading=sub_heading,
-                changesSummary=changes_summary,
-                recommendedScheduleCron=recommended_schedule_cron,
                 instructions=instructions,
-            ),
-        )
+                image_urls=image_urls,
+                date_submitted=updated_version.submittedAt or updated_version.createdAt,
+                status=updated_version.submissionStatus,
+                runs=0,
+                rating=0.0,
+                store_listing_version_id=updated_version.id,
+                changes_summary=changes_summary,
+                video_url=video_url,
+                categories=categories,
+                version=updated_version.version,
+            )
 
-        logger.debug(
-            f"Updated existing version {store_listing_version_id} for agent {current_version.agentGraphId}"
-        )
-
-        if not updated_version:
-            raise DatabaseError("Failed to update store listing version")
-        return store_model.StoreSubmission(
-            listing_id=current_version.StoreListing.id,
-            agent_id=current_version.agentGraphId,
-            agent_version=current_version.agentGraphVersion,
-            name=name,
-            sub_heading=sub_heading,
-            slug=current_version.StoreListing.slug,
-            description=description,
-            instructions=instructions,
-            image_urls=image_urls,
-            date_submitted=updated_version.submittedAt or updated_version.createdAt,
-            status=updated_version.submissionStatus,
-            runs=0,
-            rating=0.0,
-            store_listing_version_id=updated_version.id,
-            changes_summary=changes_summary,
-            video_url=video_url,
-            categories=categories,
-            version=updated_version.version,
-        )
+        else:
+            raise store_exceptions.InvalidOperationError(
+                f"Cannot edit submission with status: {current_version.submissionStatus}"
+            )
 
     except (
         store_exceptions.SubmissionNotFoundError,
@@ -1042,78 +1097,38 @@ async def create_store_version(
                 f"Agent not found for this user. User ID: {user_id}, Agent ID: {agent_id}, Version: {agent_version}"
             )
 
-        # Check if there's already a PENDING submission for this agent (any version)
-        existing_pending_submission = (
-            await prisma.models.StoreListingVersion.prisma().find_first(
-                where=prisma.types.StoreListingVersionWhereInput(
-                    storeListingId=store_listing_id,
-                    agentGraphId=agent_id,
-                    submissionStatus=prisma.enums.SubmissionStatus.PENDING,
-                    isDeleted=False,
-                )
+        # Get the latest version number
+        latest_version = listing.Versions[0] if listing.Versions else None
+
+        next_version = (latest_version.version + 1) if latest_version else 1
+
+        # Create a new version for the existing listing
+        new_version = await prisma.models.StoreListingVersion.prisma().create(
+            data=prisma.types.StoreListingVersionCreateInput(
+                version=next_version,
+                agentGraphId=agent_id,
+                agentGraphVersion=agent_version,
+                name=name,
+                videoUrl=video_url,
+                agentOutputDemoUrl=agent_output_demo_url,
+                imageUrls=image_urls,
+                description=description,
+                instructions=instructions,
+                categories=categories,
+                subHeading=sub_heading,
+                submissionStatus=prisma.enums.SubmissionStatus.PENDING,
+                submittedAt=datetime.now(),
+                changesSummary=changes_summary,
+                recommendedScheduleCron=recommended_schedule_cron,
+                storeListingId=store_listing_id,
             )
         )
 
-        # Handle existing pending submission and create new one atomically
-        async with transaction() as tx:
-            # Get the latest version number first
-            latest_listing = await prisma.models.StoreListing.prisma(tx).find_first(
-                where=prisma.types.StoreListingWhereInput(
-                    id=store_listing_id, owningUserId=user_id
-                ),
-                include={"Versions": {"order_by": {"version": "desc"}, "take": 1}},
-            )
-
-            if not latest_listing:
-                raise store_exceptions.ListingNotFoundError(
-                    f"Store listing not found. User ID: {user_id}, Listing ID: {store_listing_id}"
-                )
-
-            latest_version = (
-                latest_listing.Versions[0] if latest_listing.Versions else None
-            )
-            next_version = (latest_version.version + 1) if latest_version else 1
-
-            # If there's an existing pending submission, delete it atomically before creating new one
-            if existing_pending_submission:
-                logger.info(
-                    f"Found existing PENDING submission for agent {agent_id} (was v{existing_pending_submission.agentGraphVersion}, now v{agent_version}), replacing existing submission instead of creating duplicate"
-                )
-                await prisma.models.StoreListingVersion.prisma(tx).delete(
-                    where={"id": existing_pending_submission.id}
-                )
-                logger.debug(
-                    f"Deleted existing pending submission {existing_pending_submission.id}"
-                )
-
-            # Create a new version for the existing listing
-            new_version = await prisma.models.StoreListingVersion.prisma(tx).create(
-                data=prisma.types.StoreListingVersionCreateInput(
-                    version=next_version,
-                    agentGraphId=agent_id,
-                    agentGraphVersion=agent_version,
-                    name=name,
-                    videoUrl=video_url,
-                    agentOutputDemoUrl=agent_output_demo_url,
-                    imageUrls=image_urls,
-                    description=description,
-                    instructions=instructions,
-                    categories=categories,
-                    subHeading=sub_heading,
-                    submissionStatus=prisma.enums.SubmissionStatus.PENDING,
-                    submittedAt=datetime.now(),
-                    changesSummary=changes_summary,
-                    recommendedScheduleCron=recommended_schedule_cron,
-                    storeListingId=store_listing_id,
-                )
-            )
-
         logger.debug(
             f"Created new version for listing {store_listing_id} of agent {agent_id}"
         )
         # Return submission details
         return store_model.StoreSubmission(
-            listing_id=listing.id,
             agent_id=agent_id,
             agent_version=agent_version,
             name=name,
@@ -1549,22 +1564,6 @@ async def review_store_submission(
                     },
                 )
 
-            # Generate embedding for approved listing (non-blocking)
-            try:
-                await ensure_embedding(
-                    version_id=store_listing_version_id,
-                    name=store_listing_version.name,
-                    description=store_listing_version.description,
-                    sub_heading=store_listing_version.subHeading,
-                    categories=store_listing_version.categories or [],
-                )
-            except Exception as e:
-                # Don't fail approval if embedding generation fails
-                logger.warning(
-                    f"Failed to generate embedding for approved listing "
-                    f"{store_listing_version_id}: {e}"
-                )
-
         # If rejecting an approved agent, update the StoreListing accordingly
         if is_rejecting_approved:
             # Check if there are other approved versions
@@ -1709,12 +1708,15 @@ async def review_store_submission(
 
         # Convert to Pydantic model for consistency
         return store_model.StoreSubmission(
-            listing_id=(submission.StoreListing.id if submission.StoreListing else ""),
             agent_id=submission.agentGraphId,
             agent_version=submission.agentGraphVersion,
             name=submission.name,
             sub_heading=submission.subHeading,
-            slug=(submission.StoreListing.slug if submission.StoreListing else ""),
+            slug=(
+                submission.StoreListing.slug
+                if hasattr(submission, "storeListing") and submission.StoreListing
+                else ""
+            ),
             description=submission.description,
             instructions=submission.instructions,
             image_urls=submission.imageUrls or [],
@@ -1816,7 +1818,9 @@ async def get_admin_listings_with_versions(
         where = prisma.types.StoreListingWhereInput(**where_dict)
         include = prisma.types.StoreListingInclude(
             Versions=prisma.types.FindManyStoreListingVersionArgsFromStoreListing(
-                order_by={"version": "desc"}
+                order_by=prisma.types._StoreListingVersion_version_OrderByInput(
+                    version="desc"
+                )
             ),
             OwningUser=True,
         )
@@ -1841,7 +1845,6 @@ async def get_admin_listings_with_versions(
             # If we have versions, turn them into StoreSubmission models
             for version in listing.Versions or []:
                 version_model = store_model.StoreSubmission(
-                    listing_id=listing.id,
                     agent_id=version.agentGraphId,
                     agent_version=version.agentGraphVersion,
                     name=version.name,

autogpt_platform/backend/backend/api/features/store/embeddings.py

@@ -1,533 +0,0 @@
-"""
-Unified Content Embeddings Service
-
-Handles generation and storage of OpenAI embeddings for all content types
-(store listings, blocks, documentation, library agents) to enable semantic/hybrid search.
-"""
-
-import asyncio
-import logging
-from typing import Any
-
-import prisma
-from openai import OpenAI
-from prisma.enums import ContentType
-
-from backend.util.json import dumps
-from backend.util.settings import Settings
-
-logger = logging.getLogger(__name__)
-
-# OpenAI embedding model configuration
-EMBEDDING_MODEL = "text-embedding-3-small"
-EMBEDDING_DIM = 1536
-
-
-def build_searchable_text(
-    name: str,
-    description: str,
-    sub_heading: str,
-    categories: list[str],
-) -> str:
-    """
-    Build searchable text from listing version fields.
-
-    Combines relevant fields into a single string for embedding.
-    """
-    parts = []
-
-    # Name is important - include it
-    if name:
-        parts.append(name)
-
-    # Sub-heading provides context
-    if sub_heading:
-        parts.append(sub_heading)
-
-    # Description is the main content
-    if description:
-        parts.append(description)
-
-    # Categories help with semantic matching
-    if categories:
-        parts.append(" ".join(categories))
-
-    return " ".join(parts)
-
-
-async def generate_embedding(text: str) -> list[float] | None:
-    """
-    Generate embedding for text using OpenAI API.
-
-    Returns None if embedding generation fails.
-    """
-    try:
-        settings = Settings()
-        api_key = settings.secrets.openai_internal_api_key
-        if not api_key:
-            logger.warning("openai_internal_api_key not set, cannot generate embedding")
-            return None
-
-        client = OpenAI(api_key=api_key)
-
-        # Truncate text to avoid token limits (~32k chars for safety)
-        truncated_text = text[:32000]
-
-        response = client.embeddings.create(
-            model=EMBEDDING_MODEL,
-            input=truncated_text,
-        )
-
-        embedding = response.data[0].embedding
-        logger.debug(f"Generated embedding with {len(embedding)} dimensions")
-        return embedding
-
-    except Exception as e:
-        logger.error(f"Failed to generate embedding: {e}")
-        return None
-
-
-async def store_embedding(
-    version_id: str,
-    embedding: list[float],
-    tx: prisma.Prisma | None = None,
-) -> bool:
-    """
-    Store embedding in the database.
-
-    BACKWARD COMPATIBILITY: Maintained for existing store listing usage.
-    Uses raw SQL since Prisma doesn't natively support pgvector.
-    """
-    return await store_content_embedding(
-        content_type=ContentType.STORE_AGENT,
-        content_id=version_id,
-        embedding=embedding,
-        searchable_text="",  # Will be populated from existing data
-        metadata=None,
-        user_id=None,  # Store agents are public
-        tx=tx,
-    )
-
-
-async def store_content_embedding(
-    content_type: ContentType,
-    content_id: str,
-    embedding: list[float],
-    searchable_text: str,
-    metadata: dict | None = None,
-    user_id: str | None = None,
-    tx: prisma.Prisma | None = None,
-) -> bool:
-    """
-    Store embedding in the unified content embeddings table.
-
-    New function for unified content embedding storage.
-    Uses raw SQL since Prisma doesn't natively support pgvector.
-    """
-    try:
-        client = tx if tx else prisma.get_client()
-
-        # Convert embedding to PostgreSQL vector format
-        embedding_str = "[" + ",".join(str(x) for x in embedding) + "]"
-        metadata_json = dumps(metadata or {})
-
-        # Upsert the embedding
-        await client.execute_raw(
-            """
-            INSERT INTO platform."UnifiedContentEmbedding" (
-                "contentType", "contentId", "userId", "embedding", "searchableText", "metadata", "createdAt", "updatedAt"
-            )
-            VALUES ($1, $2, $3, $4::vector, $5, $6::jsonb, NOW(), NOW())
-            ON CONFLICT ("contentType", "contentId", "userId")
-            DO UPDATE SET
-                "embedding" = $4::vector,
-                "searchableText" = $5,
-                "metadata" = $6::jsonb,
-                "updatedAt" = NOW()
-            """,
-            content_type,
-            content_id,
-            user_id,
-            embedding_str,
-            searchable_text,
-            metadata_json,
-        )
-
-        logger.info(f"Stored embedding for {content_type}:{content_id}")
-        return True
-
-    except Exception as e:
-        logger.error(f"Failed to store embedding for {content_type}:{content_id}: {e}")
-        return False
-
-
-async def get_embedding(version_id: str) -> dict[str, Any] | None:
-    """
-    Retrieve embedding record for a listing version.
-
-    BACKWARD COMPATIBILITY: Maintained for existing store listing usage.
-    Returns dict with storeListingVersionId, embedding, timestamps or None if not found.
-    """
-    result = await get_content_embedding(
-        ContentType.STORE_AGENT, version_id, user_id=None
-    )
-    if result:
-        # Transform to old format for backward compatibility
-        return {
-            "storeListingVersionId": result["contentId"],
-            "embedding": result["embedding"],
-            "createdAt": result["createdAt"],
-            "updatedAt": result["updatedAt"],
-        }
-    return None
-
-
-async def get_content_embedding(
-    content_type: ContentType, content_id: str, user_id: str | None = None
-) -> dict[str, Any] | None:
-    """
-    Retrieve embedding record for any content type.
-
-    New function for unified content embedding retrieval.
-    Returns dict with contentType, contentId, embedding, timestamps or None if not found.
-    """
-    try:
-        client = prisma.get_client()
-
-        result = await client.query_raw(
-            """
-            SELECT
-                "contentType",
-                "contentId",
-                "userId",
-                "embedding"::text as "embedding",
-                "searchableText",
-                "metadata",
-                "createdAt",
-                "updatedAt"
-            FROM platform."UnifiedContentEmbedding"
-            WHERE "contentType" = $1 AND "contentId" = $2 AND ("userId" = $3 OR ($3 IS NULL AND "userId" IS NULL))
-            """,
-            content_type,
-            content_id,
-            user_id,
-        )
-
-        if result and len(result) > 0:
-            return result[0]
-        return None
-
-    except Exception as e:
-        logger.error(f"Failed to get embedding for {content_type}:{content_id}: {e}")
-        return None
-
-
-async def ensure_embedding(
-    version_id: str,
-    name: str,
-    description: str,
-    sub_heading: str,
-    categories: list[str],
-    force: bool = False,
-    tx: prisma.Prisma | None = None,
-) -> bool:
-    """
-    Ensure an embedding exists for the listing version.
-
-    Creates embedding if missing. Use force=True to regenerate.
-    Backward-compatible wrapper for store listings.
-
-    Args:
-        version_id: The StoreListingVersion ID
-        name: Agent name
-        description: Agent description
-        sub_heading: Agent sub-heading
-        categories: Agent categories
-        force: Force regeneration even if embedding exists
-        tx: Optional transaction client
-
-    Returns:
-        True if embedding exists/was created, False on failure
-    """
-    try:
-        # Check if embedding already exists
-        if not force:
-            existing = await get_embedding(version_id)
-            if existing and existing.get("embedding"):
-                logger.debug(f"Embedding for version {version_id} already exists")
-                return True
-
-        # Build searchable text for embedding
-        searchable_text = build_searchable_text(
-            name, description, sub_heading, categories
-        )
-
-        # Generate new embedding
-        embedding = await generate_embedding(searchable_text)
-        if embedding is None:
-            logger.warning(f"Could not generate embedding for version {version_id}")
-            return False
-
-        # Store the embedding with metadata using new function
-        metadata = {
-            "name": name,
-            "subHeading": sub_heading,
-            "categories": categories,
-        }
-        return await store_content_embedding(
-            content_type=ContentType.STORE_AGENT,
-            content_id=version_id,
-            embedding=embedding,
-            searchable_text=searchable_text,
-            metadata=metadata,
-            user_id=None,  # Store agents are public
-            tx=tx,
-        )
-
-    except Exception as e:
-        logger.error(f"Failed to ensure embedding for version {version_id}: {e}")
-        return False
-
-
-async def delete_embedding(version_id: str) -> bool:
-    """
-    Delete embedding for a listing version.
-
-    BACKWARD COMPATIBILITY: Maintained for existing store listing usage.
-    Note: This is usually handled automatically by CASCADE delete,
-    but provided for manual cleanup if needed.
-    """
-    return await delete_content_embedding(ContentType.STORE_AGENT, version_id)
-
-
-async def delete_content_embedding(content_type: ContentType, content_id: str) -> bool:
-    """
-    Delete embedding for any content type.
-
-    New function for unified content embedding deletion.
-    Note: This is usually handled automatically by CASCADE delete,
-    but provided for manual cleanup if needed.
-    """
-    try:
-        client = prisma.get_client()
-
-        await client.execute_raw(
-            """
-            DELETE FROM platform."UnifiedContentEmbedding"
-            WHERE "contentType" = $1 AND "contentId" = $2
-            """,
-            content_type,
-            content_id,
-        )
-
-        logger.info(f"Deleted embedding for {content_type}:{content_id}")
-        return True
-
-    except Exception as e:
-        logger.error(f"Failed to delete embedding for {content_type}:{content_id}: {e}")
-        return False
-
-
-async def get_embedding_stats() -> dict[str, Any]:
-    """
-    Get statistics about embedding coverage.
-
-    Returns counts of:
-    - Total approved listing versions
-    - Versions with embeddings
-    - Versions without embeddings
-    """
-    try:
-        client = prisma.get_client()
-
-        # Count approved versions
-        approved_result = await client.query_raw(
-            """
-            SELECT COUNT(*) as count
-            FROM platform."StoreListingVersion"
-            WHERE "submissionStatus" = 'APPROVED'
-            AND "isDeleted" = false
-            """
-        )
-        total_approved = approved_result[0]["count"] if approved_result else 0
-
-        # Count versions with embeddings
-        embedded_result = await client.query_raw(
-            """
-            SELECT COUNT(*) as count
-            FROM platform."StoreListingVersion" slv
-            JOIN platform."UnifiedContentEmbedding" uce ON slv.id = uce."contentId" AND uce."contentType" = 'STORE_AGENT'
-            WHERE slv."submissionStatus" = 'APPROVED'
-            AND slv."isDeleted" = false
-            """
-        )
-        with_embeddings = embedded_result[0]["count"] if embedded_result else 0
-
-        return {
-            "total_approved": total_approved,
-            "with_embeddings": with_embeddings,
-            "without_embeddings": total_approved - with_embeddings,
-            "coverage_percent": (
-                round(with_embeddings / total_approved * 100, 1)
-                if total_approved > 0
-                else 0
-            ),
-        }
-
-    except Exception as e:
-        logger.error(f"Failed to get embedding stats: {e}")
-        return {
-            "total_approved": 0,
-            "with_embeddings": 0,
-            "without_embeddings": 0,
-            "coverage_percent": 0,
-            "error": str(e),
-        }
-
-
-async def backfill_missing_embeddings(batch_size: int = 10) -> dict[str, Any]:
-    """
-    Generate embeddings for approved listings that don't have them.
-
-    Args:
-        batch_size: Number of embeddings to generate in one call
-
-    Returns:
-        Dict with success/failure counts
-    """
-    try:
-        client = prisma.get_client()
-
-        # Find approved versions without embeddings
-        missing = await client.query_raw(
-            """
-            SELECT
-                slv.id,
-                slv.name,
-                slv.description,
-                slv."subHeading",
-                slv.categories
-            FROM platform."StoreListingVersion" slv
-            LEFT JOIN platform."UnifiedContentEmbedding" uce
-                ON slv.id = uce."contentId" AND uce."contentType" = 'STORE_AGENT'
-            WHERE slv."submissionStatus" = 'APPROVED'
-            AND slv."isDeleted" = false
-            AND uce."contentId" IS NULL
-            LIMIT $1
-            """,
-            batch_size,
-        )
-
-        if not missing:
-            return {
-                "processed": 0,
-                "success": 0,
-                "failed": 0,
-                "message": "No missing embeddings",
-            }
-
-        # Process embeddings concurrently for better performance
-        embedding_tasks = [
-            ensure_embedding(
-                version_id=row["id"],
-                name=row["name"],
-                description=row["description"],
-                sub_heading=row["subHeading"],
-                categories=row["categories"] or [],
-            )
-            for row in missing
-        ]
-
-        results = await asyncio.gather(*embedding_tasks, return_exceptions=True)
-
-        success = sum(1 for result in results if result is True)
-        failed = len(results) - success
-
-        return {
-            "processed": len(missing),
-            "success": success,
-            "failed": failed,
-            "message": f"Backfilled {success} embeddings, {failed} failed",
-        }
-
-    except Exception as e:
-        logger.error(f"Failed to backfill embeddings: {e}")
-        return {
-            "processed": 0,
-            "success": 0,
-            "failed": 0,
-            "error": str(e),
-        }
-
-
-async def embed_query(query: str) -> list[float] | None:
-    """
-    Generate embedding for a search query.
-
-    Same as generate_embedding but with clearer intent.
-    """
-    return await generate_embedding(query)
-
-
-def embedding_to_vector_string(embedding: list[float]) -> str:
-    """Convert embedding list to PostgreSQL vector string format."""
-    return "[" + ",".join(str(x) for x in embedding) + "]"
-
-
-async def ensure_content_embedding(
-    content_type: ContentType,
-    content_id: str,
-    searchable_text: str,
-    metadata: dict | None = None,
-    user_id: str | None = None,
-    force: bool = False,
-    tx: prisma.Prisma | None = None,
-) -> bool:
-    """
-    Ensure an embedding exists for any content type.
-
-    Generic function for creating embeddings for store agents, blocks, docs, etc.
-
-    Args:
-        content_type: ContentType enum value (STORE_AGENT, BLOCK, etc.)
-        content_id: Unique identifier for the content
-        searchable_text: Combined text for embedding generation
-        metadata: Optional metadata to store with embedding
-        force: Force regeneration even if embedding exists
-        tx: Optional transaction client
-
-    Returns:
-        True if embedding exists/was created, False on failure
-    """
-    try:
-        # Check if embedding already exists
-        if not force:
-            existing = await get_content_embedding(content_type, content_id, user_id)
-            if existing and existing.get("embedding"):
-                logger.debug(
-                    f"Embedding for {content_type}:{content_id} already exists"
-                )
-                return True
-
-        # Generate new embedding
-        embedding = await generate_embedding(searchable_text)
-        if embedding is None:
-            logger.warning(
-                f"Could not generate embedding for {content_type}:{content_id}"
-            )
-            return False
-
-        # Store the embedding
-        return await store_content_embedding(
-            content_type=content_type,
-            content_id=content_id,
-            embedding=embedding,
-            searchable_text=searchable_text,
-            metadata=metadata or {},
-            user_id=user_id,
-            tx=tx,
-        )
-
-    except Exception as e:
-        logger.error(f"Failed to ensure embedding for {content_type}:{content_id}: {e}")
-        return False

autogpt_platform/backend/backend/api/features/store/embeddings_test.py

@@ -1,359 +0,0 @@
-from unittest.mock import MagicMock, patch
-
-import prisma
-import pytest
-from prisma import Prisma
-from prisma.enums import ContentType
-
-from backend.api.features.store import embeddings
-
-
-@pytest.fixture(autouse=True)
-async def setup_prisma():
-    """Setup Prisma client for tests."""
-    try:
-        Prisma()
-    except prisma.errors.ClientAlreadyRegisteredError:
-        pass
-    yield
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_build_searchable_text():
-    """Test searchable text building from listing fields."""
-    result = embeddings.build_searchable_text(
-        name="AI Assistant",
-        description="A helpful AI assistant for productivity",
-        sub_heading="Boost your productivity",
-        categories=["AI", "Productivity"],
-    )
-
-    expected = "AI Assistant Boost your productivity A helpful AI assistant for productivity AI Productivity"
-    assert result == expected
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_build_searchable_text_empty_fields():
-    """Test searchable text building with empty fields."""
-    result = embeddings.build_searchable_text(
-        name="", description="Test description", sub_heading="", categories=[]
-    )
-
-    assert result == "Test description"
-
-
-@pytest.mark.asyncio(loop_scope="session")
-@patch("backend.api.features.store.embeddings.OpenAI")
-async def test_generate_embedding_success(mock_openai_class):
-    """Test successful embedding generation."""
-    # Mock OpenAI response
-    mock_client = MagicMock()
-    mock_response = MagicMock()
-    mock_response.data = [MagicMock()]
-    mock_response.data[0].embedding = [0.1, 0.2, 0.3] * 512  # 1536 dimensions
-    mock_client.embeddings.create.return_value = mock_response
-    mock_openai_class.return_value = mock_client
-
-    with patch("backend.api.features.store.embeddings.Settings") as mock_settings:
-        mock_settings.return_value.secrets.openai_internal_api_key = "test-key"
-
-        result = await embeddings.generate_embedding("test text")
-
-        assert result is not None
-        assert len(result) == 1536
-        assert result[0] == 0.1
-
-        mock_client.embeddings.create.assert_called_once_with(
-            model="text-embedding-3-small", input="test text"
-        )
-
-
-@pytest.mark.asyncio(loop_scope="session")
-@patch("backend.api.features.store.embeddings.OpenAI")
-async def test_generate_embedding_no_api_key(mock_openai_class):
-    """Test embedding generation without API key."""
-    with patch("backend.api.features.store.embeddings.Settings") as mock_settings:
-        mock_settings.return_value.secrets.openai_internal_api_key = ""
-
-        result = await embeddings.generate_embedding("test text")
-
-        assert result is None
-        mock_openai_class.assert_not_called()
-
-
-@pytest.mark.asyncio(loop_scope="session")
-@patch("backend.api.features.store.embeddings.OpenAI")
-async def test_generate_embedding_api_error(mock_openai_class):
-    """Test embedding generation with API error."""
-    mock_client = MagicMock()
-    mock_client.embeddings.create.side_effect = Exception("API Error")
-    mock_openai_class.return_value = mock_client
-
-    with patch("backend.api.features.store.embeddings.Settings") as mock_settings:
-        mock_settings.return_value.secrets.openai_internal_api_key = "test-key"
-
-        result = await embeddings.generate_embedding("test text")
-
-        assert result is None
-
-
-@pytest.mark.asyncio(loop_scope="session")
-@patch("backend.api.features.store.embeddings.OpenAI")
-async def test_generate_embedding_text_truncation(mock_openai_class):
-    """Test that long text is properly truncated."""
-    mock_client = MagicMock()
-    mock_response = MagicMock()
-    mock_response.data = [MagicMock()]
-    mock_response.data[0].embedding = [0.1] * 1536
-    mock_client.embeddings.create.return_value = mock_response
-    mock_openai_class.return_value = mock_client
-
-    # Create text longer than 32k chars
-    long_text = "a" * 35000
-
-    with patch("backend.api.features.store.embeddings.Settings") as mock_settings:
-        mock_settings.return_value.secrets.openai_internal_api_key = "test-key"
-
-        await embeddings.generate_embedding(long_text)
-
-        # Verify truncated text was sent to API
-        call_args = mock_client.embeddings.create.call_args
-        assert len(call_args.kwargs["input"]) == 32000
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_store_embedding_success(mocker):
-    """Test successful embedding storage."""
-    mock_client = mocker.AsyncMock()
-    mock_client.execute_raw = mocker.AsyncMock()
-
-    embedding = [0.1, 0.2, 0.3]
-
-    result = await embeddings.store_embedding(
-        version_id="test-version-id", embedding=embedding, tx=mock_client
-    )
-
-    assert result is True
-    mock_client.execute_raw.assert_called_once()
-    call_args = mock_client.execute_raw.call_args[0]
-    assert "test-version-id" in call_args
-    assert "[0.1,0.2,0.3]" in call_args
-    assert None in call_args  # userId should be None for store agents
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_store_embedding_database_error(mocker):
-    """Test embedding storage with database error."""
-    mock_client = mocker.AsyncMock()
-    mock_client.execute_raw.side_effect = Exception("Database error")
-
-    embedding = [0.1, 0.2, 0.3]
-
-    result = await embeddings.store_embedding(
-        version_id="test-version-id", embedding=embedding, tx=mock_client
-    )
-
-    assert result is False
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_get_embedding_success(mocker):
-    """Test successful embedding retrieval."""
-    mock_client = mocker.AsyncMock()
-    mock_result = [
-        {
-            "contentType": "STORE_AGENT",
-            "contentId": "test-version-id",
-            "embedding": "[0.1,0.2,0.3]",
-            "searchableText": "Test text",
-            "metadata": {},
-            "createdAt": "2024-01-01T00:00:00Z",
-            "updatedAt": "2024-01-01T00:00:00Z",
-        }
-    ]
-    mock_client.query_raw.return_value = mock_result
-
-    with patch("prisma.get_client", return_value=mock_client):
-        result = await embeddings.get_embedding("test-version-id")
-
-        assert result is not None
-        assert result["storeListingVersionId"] == "test-version-id"
-        assert result["embedding"] == "[0.1,0.2,0.3]"
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_get_embedding_not_found(mocker):
-    """Test embedding retrieval when not found."""
-    mock_client = mocker.AsyncMock()
-    mock_client.query_raw.return_value = []
-
-    with patch("prisma.get_client", return_value=mock_client):
-        result = await embeddings.get_embedding("test-version-id")
-
-        assert result is None
-
-
-@pytest.mark.asyncio(loop_scope="session")
-@patch("backend.api.features.store.embeddings.generate_embedding")
-@patch("backend.api.features.store.embeddings.store_embedding")
-@patch("backend.api.features.store.embeddings.get_embedding")
-async def test_ensure_embedding_already_exists(mock_get, mock_store, mock_generate):
-    """Test ensure_embedding when embedding already exists."""
-    mock_get.return_value = {"embedding": "[0.1,0.2,0.3]"}
-
-    result = await embeddings.ensure_embedding(
-        version_id="test-id",
-        name="Test",
-        description="Test description",
-        sub_heading="Test heading",
-        categories=["test"],
-    )
-
-    assert result is True
-    mock_generate.assert_not_called()
-    mock_store.assert_not_called()
-
-
-@pytest.mark.asyncio(loop_scope="session")
-@patch("backend.api.features.store.embeddings.generate_embedding")
-@patch("backend.api.features.store.embeddings.store_content_embedding")
-@patch("backend.api.features.store.embeddings.get_embedding")
-async def test_ensure_embedding_create_new(mock_get, mock_store, mock_generate):
-    """Test ensure_embedding creating new embedding."""
-    mock_get.return_value = None
-    mock_generate.return_value = [0.1, 0.2, 0.3]
-    mock_store.return_value = True
-
-    result = await embeddings.ensure_embedding(
-        version_id="test-id",
-        name="Test",
-        description="Test description",
-        sub_heading="Test heading",
-        categories=["test"],
-    )
-
-    assert result is True
-    mock_generate.assert_called_once_with("Test Test heading Test description test")
-    mock_store.assert_called_once_with(
-        content_type=ContentType.STORE_AGENT,
-        content_id="test-id",
-        embedding=[0.1, 0.2, 0.3],
-        searchable_text="Test Test heading Test description test",
-        metadata={"name": "Test", "subHeading": "Test heading", "categories": ["test"]},
-        user_id=None,
-        tx=None,
-    )
-
-
-@pytest.mark.asyncio(loop_scope="session")
-@patch("backend.api.features.store.embeddings.generate_embedding")
-@patch("backend.api.features.store.embeddings.get_embedding")
-async def test_ensure_embedding_generation_fails(mock_get, mock_generate):
-    """Test ensure_embedding when generation fails."""
-    mock_get.return_value = None
-    mock_generate.return_value = None
-
-    result = await embeddings.ensure_embedding(
-        version_id="test-id",
-        name="Test",
-        description="Test description",
-        sub_heading="Test heading",
-        categories=["test"],
-    )
-
-    assert result is False
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_get_embedding_stats(mocker):
-    """Test embedding statistics retrieval."""
-    mock_client = mocker.AsyncMock()
-
-    # Mock approved count query
-    mock_approved_result = [{"count": 100}]
-    # Mock embedded count query
-    mock_embedded_result = [{"count": 75}]
-
-    mock_client.query_raw.side_effect = [mock_approved_result, mock_embedded_result]
-
-    with patch("prisma.get_client", return_value=mock_client):
-        result = await embeddings.get_embedding_stats()
-
-        assert result["total_approved"] == 100
-        assert result["with_embeddings"] == 75
-        assert result["without_embeddings"] == 25
-        assert result["coverage_percent"] == 75.0
-
-
-@pytest.mark.asyncio(loop_scope="session")
-@patch("backend.api.features.store.embeddings.ensure_embedding")
-async def test_backfill_missing_embeddings_success(mock_ensure, mocker):
-    """Test backfill with successful embedding generation."""
-    mock_client = mocker.AsyncMock()
-
-    # Mock missing embeddings query
-    mock_missing = [
-        {
-            "id": "version-1",
-            "name": "Agent 1",
-            "description": "Description 1",
-            "subHeading": "Heading 1",
-            "categories": ["AI"],
-        },
-        {
-            "id": "version-2",
-            "name": "Agent 2",
-            "description": "Description 2",
-            "subHeading": "Heading 2",
-            "categories": ["Productivity"],
-        },
-    ]
-    mock_client.query_raw.return_value = mock_missing
-
-    # Mock ensure_embedding to succeed for first, fail for second
-    mock_ensure.side_effect = [True, False]
-
-    with patch("prisma.get_client", return_value=mock_client):
-        result = await embeddings.backfill_missing_embeddings(batch_size=5)
-
-        assert result["processed"] == 2
-        assert result["success"] == 1
-        assert result["failed"] == 1
-        assert mock_ensure.call_count == 2
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_backfill_missing_embeddings_no_missing(mocker):
-    """Test backfill when no embeddings are missing."""
-    mock_client = mocker.AsyncMock()
-    mock_client.query_raw.return_value = []
-
-    with patch("prisma.get_client", return_value=mock_client):
-        result = await embeddings.backfill_missing_embeddings(batch_size=5)
-
-        assert result["processed"] == 0
-        assert result["success"] == 0
-        assert result["failed"] == 0
-        assert result["message"] == "No missing embeddings"
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_embedding_to_vector_string():
-    """Test embedding to PostgreSQL vector string conversion."""
-    embedding = [0.1, 0.2, 0.3, -0.4]
-    result = embeddings.embedding_to_vector_string(embedding)
-    assert result == "[0.1,0.2,0.3,-0.4]"
-
-
-@pytest.mark.asyncio(loop_scope="session")
-async def test_embed_query():
-    """Test embed_query function (alias for generate_embedding)."""
-    with patch(
-        "backend.api.features.store.embeddings.generate_embedding"
-    ) as mock_generate:
-        mock_generate.return_value = [0.1, 0.2, 0.3]
-
-        result = await embeddings.embed_query("test query")
-
-        assert result == [0.1, 0.2, 0.3]
-        mock_generate.assert_called_once_with("test query")

autogpt_platform/backend/backend/api/features/store/hybrid_search.py

@@ -1,377 +0,0 @@
-"""
-Hybrid Search for Store Agents
-
-Combines semantic (embedding) search with lexical (tsvector) search
-for improved relevance in marketplace agent discovery.
-"""
-
-import logging
-from dataclasses import dataclass
-from datetime import datetime
-from typing import Any, Literal
-
-from backend.api.features.store.embeddings import (
-    embed_query,
-    embedding_to_vector_string,
-)
-from backend.data.db import query_raw_with_schema
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class HybridSearchWeights:
-    """Weights for combining search signals."""
-
-    semantic: float = 0.35  # Embedding cosine similarity
-    lexical: float = 0.35  # tsvector ts_rank_cd score
-    category: float = 0.20  # Category match boost
-    recency: float = 0.10  # Newer agents ranked higher
-
-
-DEFAULT_WEIGHTS = HybridSearchWeights()
-
-# Minimum relevance score threshold - agents below this are filtered out
-# With weights (0.35 semantic + 0.35 lexical + 0.20 category + 0.10 recency):
-# - 0.20 means at least ~50% semantic match OR strong lexical match required
-# - Ensures only genuinely relevant results are returned
-# - Recency alone (0.10 max) won't pass the threshold
-DEFAULT_MIN_SCORE = 0.20
-
-
-@dataclass
-class HybridSearchResult:
-    """A single search result with score breakdown."""
-
-    slug: str
-    agent_name: str
-    agent_image: str
-    creator_username: str
-    creator_avatar: str
-    sub_heading: str
-    description: str
-    runs: int
-    rating: float
-    categories: list[str]
-    featured: bool
-    is_available: bool
-    updated_at: datetime
-
-    # Score breakdown (for debugging/tuning)
-    combined_score: float
-    semantic_score: float = 0.0
-    lexical_score: float = 0.0
-    category_score: float = 0.0
-    recency_score: float = 0.0
-
-
-async def hybrid_search(
-    query: str,
-    featured: bool = False,
-    creators: list[str] | None = None,
-    category: str | None = None,
-    sorted_by: (
-        Literal["relevance", "rating", "runs", "name", "updated_at"] | None
-    ) = None,
-    page: int = 1,
-    page_size: int = 20,
-    weights: HybridSearchWeights | None = None,
-    min_score: float | None = None,
-) -> tuple[list[dict[str, Any]], int]:
-    """
-    Perform hybrid search combining semantic and lexical signals.
-
-    Args:
-        query: Search query string
-        featured: Filter for featured agents only
-        creators: Filter by creator usernames
-        category: Filter by category
-        sorted_by: Sort order (relevance uses hybrid scoring)
-        page: Page number (1-indexed)
-        page_size: Results per page
-        weights: Custom weights for search signals
-        min_score: Minimum relevance score threshold (0-1). Results below
-                   this score are filtered out. Defaults to DEFAULT_MIN_SCORE.
-
-    Returns:
-        Tuple of (results list, total count). Returns empty list if no
-        results meet the minimum relevance threshold.
-    """
-    if weights is None:
-        weights = DEFAULT_WEIGHTS
-    if min_score is None:
-        min_score = DEFAULT_MIN_SCORE
-
-    offset = (page - 1) * page_size
-
-    # Generate query embedding
-    query_embedding = await embed_query(query)
-
-    # Build WHERE clause conditions
-    where_parts: list[str] = ["sa.is_available = true"]
-    params: list[Any] = []
-    param_index = 1
-
-    # Add search query for lexical matching
-    params.append(query)
-    query_param = f"${param_index}"
-    param_index += 1
-
-    # Add lowercased query for category matching
-    params.append(query.lower())
-    query_lower_param = f"${param_index}"
-    param_index += 1
-
-    if featured:
-        where_parts.append("sa.featured = true")
-
-    if creators:
-        where_parts.append(f"sa.creator_username = ANY(${param_index})")
-        params.append(creators)
-        param_index += 1
-
-    if category:
-        where_parts.append(f"${param_index} = ANY(sa.categories)")
-        params.append(category)
-        param_index += 1
-
-    where_clause = " AND ".join(where_parts)
-
-    # Determine if we can use hybrid search (have query embedding)
-    use_hybrid = query_embedding is not None
-
-    if use_hybrid:
-        # Add embedding parameter
-        embedding_str = embedding_to_vector_string(query_embedding)
-        params.append(embedding_str)
-        embedding_param = f"${param_index}"
-        param_index += 1
-
-        # Optimized hybrid search query:
-        # 1. Direct join to UnifiedContentEmbedding via contentId=storeListingVersionId (no redundant JOINs)
-        # 2. UNION ALL approach to enable index usage for both lexical and semantic branches
-        # 3. COUNT(*) OVER() to get total count in single query
-        # 4. Simplified category matching with array_to_string
-        sql_query = f"""
-            WITH candidates AS (
-                -- Lexical matches (uses GIN index on search column)
-                SELECT DISTINCT sa."storeListingVersionId"
-                FROM {{schema_prefix}}"StoreAgent" sa
-                WHERE {where_clause}
-                AND sa.search @@ plainto_tsquery('english', {query_param})
-
-                UNION
-
-                -- Semantic matches (uses HNSW index on embedding)
-                SELECT DISTINCT sa."storeListingVersionId"
-                FROM {{schema_prefix}}"StoreAgent" sa
-                INNER JOIN {{schema_prefix}}"UnifiedContentEmbedding" uce
-                    ON sa."storeListingVersionId" = uce."contentId" AND uce."contentType" = 'STORE_AGENT'
-                WHERE {where_clause}
-            ),
-            search_scores AS (
-                SELECT
-                    sa.slug,
-                    sa.agent_name,
-                    sa.agent_image,
-                    sa.creator_username,
-                    sa.creator_avatar,
-                    sa.sub_heading,
-                    sa.description,
-                    sa.runs,
-                    sa.rating,
-                    sa.categories,
-                    sa.featured,
-                    sa.is_available,
-                    sa.updated_at,
-                    -- Semantic score: cosine similarity (1 - distance)
-                    COALESCE(1 - (uce.embedding <=> {embedding_param}::vector), 0) as semantic_score,
-                    -- Lexical score: ts_rank_cd (will be normalized later)
-                    COALESCE(ts_rank_cd(sa.search, plainto_tsquery('english', {query_param})), 0) as lexical_raw,
-                    -- Category match: check if query appears in any category
-                    CASE
-                        WHEN LOWER(array_to_string(sa.categories, ' ')) LIKE '%' || {query_lower_param} || '%'
-                        THEN 1.0
-                        ELSE 0.0
-                    END as category_score,
-                    -- Recency score: exponential decay over 90 days
-                    EXP(-EXTRACT(EPOCH FROM (NOW() - sa.updated_at)) / (90 * 24 * 3600)) as recency_score
-                FROM candidates c
-                INNER JOIN {{schema_prefix}}"StoreAgent" sa
-                    ON c."storeListingVersionId" = sa."storeListingVersionId"
-                LEFT JOIN {{schema_prefix}}"UnifiedContentEmbedding" uce
-                    ON sa."storeListingVersionId" = uce."contentId" AND uce."contentType" = 'STORE_AGENT'
-            ),
-            normalized AS (
-                SELECT
-                    *,
-                    -- Normalize lexical score by max in result set
-                    CASE
-                        WHEN MAX(lexical_raw) OVER () > 0
-                        THEN lexical_raw / MAX(lexical_raw) OVER ()
-                        ELSE 0
-                    END as lexical_score
-                FROM search_scores
-            ),
-            scored AS (
-                SELECT
-                    slug,
-                    agent_name,
-                    agent_image,
-                    creator_username,
-                    creator_avatar,
-                    sub_heading,
-                    description,
-                    runs,
-                    rating,
-                    categories,
-                    featured,
-                    is_available,
-                    updated_at,
-                    semantic_score,
-                    lexical_score,
-                    category_score,
-                    recency_score,
-                    (
-                        {weights.semantic} * semantic_score +
-                        {weights.lexical} * lexical_score +
-                        {weights.category} * category_score +
-                        {weights.recency} * recency_score
-                    ) as combined_score
-                FROM normalized
-            ),
-            filtered AS (
-                SELECT
-                    *,
-                    COUNT(*) OVER () as total_count
-                FROM scored
-                WHERE combined_score >= {min_score}
-            )
-            SELECT * FROM filtered
-            ORDER BY combined_score DESC
-            LIMIT ${param_index} OFFSET ${param_index + 1}
-        """
-
-        # Add pagination params
-        params.extend([page_size, offset])
-
-    else:
-        # Fallback to lexical-only search (existing behavior)
-        logger.warning("Falling back to lexical-only search (no query embedding)")
-
-        sql_query = f"""
-            WITH lexical_scores AS (
-                SELECT
-                    slug,
-                    agent_name,
-                    agent_image,
-                    creator_username,
-                    creator_avatar,
-                    sub_heading,
-                    description,
-                    runs,
-                    rating,
-                    categories,
-                    featured,
-                    is_available,
-                    updated_at,
-                    0.0 as semantic_score,
-                    ts_rank_cd(search, plainto_tsquery('english', {query_param})) as lexical_raw,
-                    CASE
-                        WHEN LOWER(array_to_string(categories, ' ')) LIKE '%' || {query_lower_param} || '%'
-                        THEN 1.0
-                        ELSE 0.0
-                    END as category_score,
-                    EXP(-EXTRACT(EPOCH FROM (NOW() - updated_at)) / (90 * 24 * 3600)) as recency_score
-                FROM {{schema_prefix}}"StoreAgent" sa
-                WHERE {where_clause}
-                AND search @@ plainto_tsquery('english', {query_param})
-            ),
-            normalized AS (
-                SELECT
-                    *,
-                    CASE
-                        WHEN MAX(lexical_raw) OVER () > 0
-                        THEN lexical_raw / MAX(lexical_raw) OVER ()
-                        ELSE 0
-                    END as lexical_score
-                FROM lexical_scores
-            ),
-            scored AS (
-                SELECT
-                    slug,
-                    agent_name,
-                    agent_image,
-                    creator_username,
-                    creator_avatar,
-                    sub_heading,
-                    description,
-                    runs,
-                    rating,
-                    categories,
-                    featured,
-                    is_available,
-                    updated_at,
-                    semantic_score,
-                    lexical_score,
-                    category_score,
-                    recency_score,
-                    (
-                        {weights.lexical} * lexical_score +
-                        {weights.category} * category_score +
-                        {weights.recency} * recency_score
-                    ) as combined_score
-                FROM normalized
-            ),
-            filtered AS (
-                SELECT
-                    *,
-                    COUNT(*) OVER () as total_count
-                FROM scored
-                WHERE combined_score >= {min_score}
-            )
-            SELECT * FROM filtered
-            ORDER BY combined_score DESC
-            LIMIT ${param_index} OFFSET ${param_index + 1}
-        """
-
-        params.extend([page_size, offset])
-
-    try:
-        # Execute search query - includes total_count via window function
-        results = await query_raw_with_schema(sql_query, *params)
-
-        # Extract total count from first result (all rows have same count)
-        total = results[0]["total_count"] if results else 0
-
-        # Remove total_count from results before returning
-        for result in results:
-            result.pop("total_count", None)
-
-        logger.info(
-            f"Hybrid search for '{query}': {len(results)} results, {total} total "
-            f"(hybrid={use_hybrid})"
-        )
-
-        return results, total
-
-    except Exception as e:
-        logger.error(f"Hybrid search failed: {e}")
-        raise
-
-
-async def hybrid_search_simple(
-    query: str,
-    page: int = 1,
-    page_size: int = 20,
-) -> tuple[list[dict[str, Any]], int]:
-    """
-    Simplified hybrid search for common use cases.
-
-    Uses default weights and no filters.
-    """
-    return await hybrid_search(
-        query=query,
-        page=page,
-        page_size=page_size,
-    )

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

@@ -110,7 +110,6 @@ class Profile(pydantic.BaseModel):
 
 
 class StoreSubmission(pydantic.BaseModel):
-    listing_id: str
     agent_id: str
     agent_version: int
     name: str
@@ -165,12 +164,8 @@ class StoreListingsWithVersionsResponse(pydantic.BaseModel):
 
 
 class StoreSubmissionRequest(pydantic.BaseModel):
-    agent_id: str = pydantic.Field(
-        ..., min_length=1, description="Agent ID cannot be empty"
-    )
-    agent_version: int = pydantic.Field(
-        ..., gt=0, description="Agent version must be greater than 0"
-    )
+    agent_id: str
+    agent_version: int
     slug: str
     name: str
     sub_heading: str

autogpt_platform/backend/backend/api/features/store/model_test.py

@@ -138,7 +138,6 @@ def test_creator_details():
 
 def test_store_submission():
     submission = store_model.StoreSubmission(
-        listing_id="listing123",
         agent_id="agent123",
         agent_version=1,
         sub_heading="Test subheading",
@@ -160,7 +159,6 @@ def test_store_submissions_response():
     response = store_model.StoreSubmissionsResponse(
         submissions=[
             store_model.StoreSubmission(
-                listing_id="listing123",
                 agent_id="agent123",
                 agent_version=1,
                 sub_heading="Test subheading",

autogpt_platform/backend/backend/api/features/store/routes_test.py

@@ -521,7 +521,6 @@ def test_get_submissions_success(
     mocked_value = store_model.StoreSubmissionsResponse(
         submissions=[
             store_model.StoreSubmission(
-                listing_id="test-listing-id",
                 name="Test Agent",
                 description="Test agent description",
                 image_urls=["test.jpg"],

autogpt_platform/backend/backend/blocks/airtable/_webhook.py

@@ -6,9 +6,6 @@ import hashlib
 import hmac
 import logging
 from enum import Enum
-from typing import cast
-
-from prisma.types import Serializable
 
 from backend.sdk import (
     BaseWebhooksManager,
@@ -87,9 +84,7 @@ class AirtableWebhookManager(BaseWebhooksManager):
         # update webhook config
         await update_webhook(
             webhook.id,
-            config=cast(
-                dict[str, Serializable], {"base_id": base_id, "cursor": response.cursor}
-            ),
+            config={"base_id": base_id, "cursor": response.cursor},
         )
 
         event_type = "notification"

autogpt_platform/backend/backend/blocks/smart_decision_maker.py

@@ -975,28 +975,10 @@ class SmartDecisionMakerBlock(Block):
         graph_version: int,
         execution_context: ExecutionContext,
         execution_processor: "ExecutionProcessor",
-        nodes_to_skip: set[str] | None = None,
         **kwargs,
     ) -> BlockOutput:
 
         tool_functions = await self._create_tool_node_signatures(node_id)
-        original_tool_count = len(tool_functions)
-
-        # Filter out tools for nodes that should be skipped (e.g., missing optional credentials)
-        if nodes_to_skip:
-            tool_functions = [
-                tf
-                for tf in tool_functions
-                if tf.get("function", {}).get("_sink_node_id") not in nodes_to_skip
-            ]
-
-            # Only raise error if we had tools but they were all filtered out
-            if original_tool_count > 0 and not tool_functions:
-                raise ValueError(
-                    "No available tools to execute - all downstream nodes are unavailable "
-                    "(possibly due to missing optional credentials)"
-                )
-
         yield "tool_functions", json.dumps(tool_functions)
 
         conversation_history = input_data.conversation_history or []

autogpt_platform/backend/backend/data/execution.py

@@ -383,7 +383,6 @@ class GraphExecutionWithNodes(GraphExecution):
         self,
         execution_context: ExecutionContext,
         compiled_nodes_input_masks: Optional[NodesInputMasks] = None,
-        nodes_to_skip: Optional[set[str]] = None,
     ):
         return GraphExecutionEntry(
             user_id=self.user_id,
@@ -391,7 +390,6 @@ class GraphExecutionWithNodes(GraphExecution):
             graph_version=self.graph_version or 0,
             graph_exec_id=self.id,
             nodes_input_masks=compiled_nodes_input_masks,
-            nodes_to_skip=nodes_to_skip or set(),
             execution_context=execution_context,
         )
 
@@ -1147,8 +1145,6 @@ class GraphExecutionEntry(BaseModel):
     graph_id: str
     graph_version: int
     nodes_input_masks: Optional[NodesInputMasks] = None
-    nodes_to_skip: set[str] = Field(default_factory=set)
-    """Node IDs that should be skipped due to optional credentials not being configured."""
     execution_context: ExecutionContext = Field(default_factory=ExecutionContext)
 
 

autogpt_platform/backend/backend/data/graph.py

@@ -94,15 +94,6 @@ class Node(BaseDbModel):
     input_links: list[Link] = []
     output_links: list[Link] = []
 
-    @property
-    def credentials_optional(self) -> bool:
-        """
-        Whether credentials are optional for this node.
-        When True and credentials are not configured, the node will be skipped
-        during execution rather than causing a validation error.
-        """
-        return self.metadata.get("credentials_optional", False)
-
     @property
     def block(self) -> AnyBlockSchema | "_UnknownBlockBase":
         """Get the block for this node. Returns UnknownBlock if block is deleted/missing."""
@@ -335,35 +326,7 @@ class Graph(BaseGraph):
     @computed_field
     @property
     def credentials_input_schema(self) -> dict[str, Any]:
-        schema = self._credentials_input_schema.jsonschema()
-
-        # Determine which credential fields are required based on credentials_optional metadata
-        graph_credentials_inputs = self.aggregate_credentials_inputs()
-        required_fields = []
-
-        # Build a map of node_id -> node for quick lookup
-        all_nodes = {node.id: node for node in self.nodes}
-        for sub_graph in self.sub_graphs:
-            for node in sub_graph.nodes:
-                all_nodes[node.id] = node
-
-        for field_key, (
-            _field_info,
-            node_field_pairs,
-        ) in graph_credentials_inputs.items():
-            # A field is required if ANY node using it has credentials_optional=False
-            is_required = False
-            for node_id, _field_name in node_field_pairs:
-                node = all_nodes.get(node_id)
-                if node and not node.credentials_optional:
-                    is_required = True
-                    break
-
-            if is_required:
-                required_fields.append(field_key)
-
-        schema["required"] = required_fields
-        return schema
+        return self._credentials_input_schema.jsonschema()
 
     @property
     def _credentials_input_schema(self) -> type[BlockSchema]:

autogpt_platform/backend/backend/data/graph_test.py

@@ -396,58 +396,3 @@ async def test_access_store_listing_graph(server: SpinTestServer):
         created_graph.id, created_graph.version, "3e53486c-cf57-477e-ba2a-cb02dc828e1b"
     )
     assert got_graph is not None
-
-
-# ============================================================================
-# Tests for Optional Credentials Feature
-# ============================================================================
-
-
-def test_node_credentials_optional_default():
-    """Test that credentials_optional defaults to False when not set in metadata."""
-    node = Node(
-        id="test_node",
-        block_id=StoreValueBlock().id,
-        input_default={},
-        metadata={},
-    )
-    assert node.credentials_optional is False
-
-
-def test_node_credentials_optional_true():
-    """Test that credentials_optional returns True when explicitly set."""
-    node = Node(
-        id="test_node",
-        block_id=StoreValueBlock().id,
-        input_default={},
-        metadata={"credentials_optional": True},
-    )
-    assert node.credentials_optional is True
-
-
-def test_node_credentials_optional_false():
-    """Test that credentials_optional returns False when explicitly set to False."""
-    node = Node(
-        id="test_node",
-        block_id=StoreValueBlock().id,
-        input_default={},
-        metadata={"credentials_optional": False},
-    )
-    assert node.credentials_optional is False
-
-
-def test_node_credentials_optional_with_other_metadata():
-    """Test that credentials_optional works correctly with other metadata present."""
-    node = Node(
-        id="test_node",
-        block_id=StoreValueBlock().id,
-        input_default={},
-        metadata={
-            "position": {"x": 100, "y": 200},
-            "customized_name": "My Custom Node",
-            "credentials_optional": True,
-        },
-    )
-    assert node.credentials_optional is True
-    assert node.metadata["position"] == {"x": 100, "y": 200}
-    assert node.metadata["customized_name"] == "My Custom Node"

autogpt_platform/backend/backend/executor/manager.py

@@ -178,7 +178,6 @@ async def execute_node(
     execution_processor: "ExecutionProcessor",
     execution_stats: NodeExecutionStats | None = None,
     nodes_input_masks: Optional[NodesInputMasks] = None,
-    nodes_to_skip: Optional[set[str]] = None,
 ) -> BlockOutput:
     """
     Execute a node in the graph. This will trigger a block execution on a node,
@@ -246,7 +245,6 @@ async def execute_node(
         "user_id": user_id,
         "execution_context": execution_context,
         "execution_processor": execution_processor,
-        "nodes_to_skip": nodes_to_skip or set(),
     }
 
     # Last-minute fetch credentials + acquire a system-wide read-write lock to prevent
@@ -544,7 +542,6 @@ class ExecutionProcessor:
         node_exec_progress: NodeExecutionProgress,
         nodes_input_masks: Optional[NodesInputMasks],
         graph_stats_pair: tuple[GraphExecutionStats, threading.Lock],
-        nodes_to_skip: Optional[set[str]] = None,
     ) -> NodeExecutionStats:
         log_metadata = LogMetadata(
             logger=_logger,
@@ -567,7 +564,6 @@ class ExecutionProcessor:
             db_client=db_client,
             log_metadata=log_metadata,
             nodes_input_masks=nodes_input_masks,
-            nodes_to_skip=nodes_to_skip,
         )
         if isinstance(status, BaseException):
             raise status
@@ -613,7 +609,6 @@ class ExecutionProcessor:
         db_client: "DatabaseManagerAsyncClient",
         log_metadata: LogMetadata,
         nodes_input_masks: Optional[NodesInputMasks] = None,
-        nodes_to_skip: Optional[set[str]] = None,
     ) -> ExecutionStatus:
         status = ExecutionStatus.RUNNING
 
@@ -650,7 +645,6 @@ class ExecutionProcessor:
                 execution_processor=self,
                 execution_stats=stats,
                 nodes_input_masks=nodes_input_masks,
-                nodes_to_skip=nodes_to_skip,
             ):
                 await persist_output(output_name, output_data)
 
@@ -962,21 +956,6 @@ class ExecutionProcessor:
 
                 queued_node_exec = execution_queue.get()
 
-                # Check if this node should be skipped due to optional credentials
-                if queued_node_exec.node_id in graph_exec.nodes_to_skip:
-                    log_metadata.info(
-                        f"Skipping node execution {queued_node_exec.node_exec_id} "
-                        f"for node {queued_node_exec.node_id} - optional credentials not configured"
-                    )
-                    # Mark the node as completed without executing
-                    # No outputs will be produced, so downstream nodes won't trigger
-                    update_node_execution_status(
-                        db_client=db_client,
-                        exec_id=queued_node_exec.node_exec_id,
-                        status=ExecutionStatus.COMPLETED,
-                    )
-                    continue
-
                 log_metadata.debug(
                     f"Dispatching node execution {queued_node_exec.node_exec_id} "
                     f"for node {queued_node_exec.node_id}",
@@ -1037,7 +1016,6 @@ class ExecutionProcessor:
                             execution_stats,
                             execution_stats_lock,
                         ),
-                        nodes_to_skip=graph_exec.nodes_to_skip,
                     ),
                     self.node_execution_loop,
                 )

autogpt_platform/backend/backend/executor/utils.py

@@ -239,19 +239,14 @@ async def _validate_node_input_credentials(
     graph: GraphModel,
     user_id: str,
     nodes_input_masks: Optional[NodesInputMasks] = None,
-) -> tuple[dict[str, dict[str, str]], set[str]]:
+) -> dict[str, dict[str, str]]:
     """
-    Checks all credentials for all nodes of the graph and returns structured errors
-    and a set of nodes that should be skipped due to optional missing credentials.
+    Checks all credentials for all nodes of the graph and returns structured errors.
 
     Returns:
-        tuple[
-            dict[node_id, dict[field_name, error_message]]: Credential validation errors per node,
-            set[node_id]: Nodes that should be skipped (optional credentials not configured)
-        ]
+        dict[node_id, dict[field_name, error_message]]: Credential validation errors per node
     """
     credential_errors: dict[str, dict[str, str]] = defaultdict(dict)
-    nodes_to_skip: set[str] = set()
 
     for node in graph.nodes:
         block = node.block
@@ -261,46 +256,27 @@ async def _validate_node_input_credentials(
         if not credentials_fields:
             continue
 
-        # Track if any credential field is missing for this node
-        has_missing_credentials = False
-
         for field_name, credentials_meta_type in credentials_fields.items():
             try:
-                # Check nodes_input_masks first, then input_default
-                field_value = None
                 if (
                     nodes_input_masks
                     and (node_input_mask := nodes_input_masks.get(node.id))
                     and field_name in node_input_mask
                 ):
-                    field_value = node_input_mask[field_name]
+                    credentials_meta = credentials_meta_type.model_validate(
+                        node_input_mask[field_name]
+                    )
                 elif field_name in node.input_default:
-                    # For optional credentials, don't use input_default - treat as missing
-                    # This prevents stale credential IDs from failing validation
-                    if node.credentials_optional:
-                        field_value = None
-                    else:
-                        field_value = node.input_default[field_name]
-
-                # Check if credentials are missing (None, empty, or not present)
-                if field_value is None or (
-                    isinstance(field_value, dict) and not field_value.get("id")
-                ):
-                    has_missing_credentials = True
-                    # If node has credentials_optional flag, mark for skipping instead of error
-                    if node.credentials_optional:
-                        continue  # Don't add error, will be marked for skip after loop
-                    else:
-                        credential_errors[node.id][
-                            field_name
-                        ] = "These credentials are required"
-                        continue
-
-                credentials_meta = credentials_meta_type.model_validate(field_value)
-
+                    credentials_meta = credentials_meta_type.model_validate(
+                        node.input_default[field_name]
+                    )
+                else:
+                    # Missing credentials
+                    credential_errors[node.id][
+                        field_name
+                    ] = "These credentials are required"
+                    continue
             except ValidationError as e:
-                # Validation error means credentials were provided but invalid
-                # This should always be an error, even if optional
                 credential_errors[node.id][field_name] = f"Invalid credentials: {e}"
                 continue
 
@@ -311,7 +287,6 @@ async def _validate_node_input_credentials(
                 )
             except Exception as e:
                 # Handle any errors fetching credentials
-                # If credentials were explicitly configured but unavailable, it's an error
                 credential_errors[node.id][
                     field_name
                 ] = f"Credentials not available: {e}"
@@ -338,19 +313,7 @@ async def _validate_node_input_credentials(
                 ] = "Invalid credentials: type/provider mismatch"
                 continue
 
-        # If node has optional credentials and any are missing, mark for skipping
-        # But only if there are no other errors for this node
-        if (
-            has_missing_credentials
-            and node.credentials_optional
-            and node.id not in credential_errors
-        ):
-            nodes_to_skip.add(node.id)
-            logger.info(
-                f"Node #{node.id} will be skipped: optional credentials not configured"
-            )
-
-    return credential_errors, nodes_to_skip
+    return credential_errors
 
 
 def make_node_credentials_input_map(
@@ -392,25 +355,21 @@ async def validate_graph_with_credentials(
     graph: GraphModel,
     user_id: str,
     nodes_input_masks: Optional[NodesInputMasks] = None,
-) -> tuple[Mapping[str, Mapping[str, str]], set[str]]:
+) -> Mapping[str, Mapping[str, str]]:
     """
-    Validate graph including credentials and return structured errors per node,
-    along with a set of nodes that should be skipped due to optional missing credentials.
+    Validate graph including credentials and return structured errors per node.
 
     Returns:
-        tuple[
-            dict[node_id, dict[field_name, error_message]]: Validation errors per node,
-            set[node_id]: Nodes that should be skipped (optional credentials not configured)
-        ]
+        dict[node_id, dict[field_name, error_message]]: Validation errors per node
     """
     # Get input validation errors
     node_input_errors = GraphModel.validate_graph_get_errors(
         graph, for_run=True, nodes_input_masks=nodes_input_masks
     )
 
-    # Get credential input/availability/validation errors and nodes to skip
-    node_credential_input_errors, nodes_to_skip = (
-        await _validate_node_input_credentials(graph, user_id, nodes_input_masks)
+    # Get credential input/availability/validation errors
+    node_credential_input_errors = await _validate_node_input_credentials(
+        graph, user_id, nodes_input_masks
     )
 
     # Merge credential errors with structural errors
@@ -419,7 +378,7 @@ async def validate_graph_with_credentials(
             node_input_errors[node_id] = {}
         node_input_errors[node_id].update(field_errors)
 
-    return node_input_errors, nodes_to_skip
+    return node_input_errors
 
 
 async def _construct_starting_node_execution_input(
@@ -427,7 +386,7 @@ async def _construct_starting_node_execution_input(
     user_id: str,
     graph_inputs: BlockInput,
     nodes_input_masks: Optional[NodesInputMasks] = None,
-) -> tuple[list[tuple[str, BlockInput]], set[str]]:
+) -> list[tuple[str, BlockInput]]:
     """
     Validates and prepares the input data for executing a graph.
     This function checks the graph for starting nodes, validates the input data
@@ -441,14 +400,11 @@ async def _construct_starting_node_execution_input(
         node_credentials_map: `dict[node_id, dict[input_name, CredentialsMetaInput]]`
 
     Returns:
-        tuple[
-            list[tuple[str, BlockInput]]: A list of tuples, each containing the node ID
-                and the corresponding input data for that node.
-            set[str]: Node IDs that should be skipped (optional credentials not configured)
-        ]
+        list[tuple[str, BlockInput]]: A list of tuples, each containing the node ID and
+            the corresponding input data for that node.
     """
     # Use new validation function that includes credentials
-    validation_errors, nodes_to_skip = await validate_graph_with_credentials(
+    validation_errors = await validate_graph_with_credentials(
         graph, user_id, nodes_input_masks
     )
     n_error_nodes = len(validation_errors)
@@ -489,7 +445,7 @@ async def _construct_starting_node_execution_input(
             "No starting nodes found for the graph, make sure an AgentInput or blocks with no inbound links are present as starting nodes."
         )
 
-    return nodes_input, nodes_to_skip
+    return nodes_input
 
 
 async def validate_and_construct_node_execution_input(
@@ -500,7 +456,7 @@ async def validate_and_construct_node_execution_input(
     graph_credentials_inputs: Optional[Mapping[str, CredentialsMetaInput]] = None,
     nodes_input_masks: Optional[NodesInputMasks] = None,
     is_sub_graph: bool = False,
-) -> tuple[GraphModel, list[tuple[str, BlockInput]], NodesInputMasks, set[str]]:
+) -> tuple[GraphModel, list[tuple[str, BlockInput]], NodesInputMasks]:
     """
     Public wrapper that handles graph fetching, credential mapping, and validation+construction.
     This centralizes the logic used by both scheduler validation and actual execution.
@@ -517,7 +473,6 @@ async def validate_and_construct_node_execution_input(
         GraphModel: Full graph object for the given `graph_id`.
         list[tuple[node_id, BlockInput]]: Starting node IDs with corresponding inputs.
         dict[str, BlockInput]: Node input masks including all passed-in credentials.
-        set[str]: Node IDs that should be skipped (optional credentials not configured).
 
     Raises:
         NotFoundError: If the graph is not found.
@@ -559,16 +514,14 @@ async def validate_and_construct_node_execution_input(
         nodes_input_masks or {},
     )
 
-    starting_nodes_input, nodes_to_skip = (
-        await _construct_starting_node_execution_input(
-            graph=graph,
-            user_id=user_id,
-            graph_inputs=graph_inputs,
-            nodes_input_masks=nodes_input_masks,
-        )
+    starting_nodes_input = await _construct_starting_node_execution_input(
+        graph=graph,
+        user_id=user_id,
+        graph_inputs=graph_inputs,
+        nodes_input_masks=nodes_input_masks,
     )
 
-    return graph, starting_nodes_input, nodes_input_masks, nodes_to_skip
+    return graph, starting_nodes_input, nodes_input_masks
 
 
 def _merge_nodes_input_masks(
@@ -826,9 +779,6 @@ async def add_graph_execution(
 
         # Use existing execution's compiled input masks
         compiled_nodes_input_masks = graph_exec.nodes_input_masks or {}
-        # For resumed executions, nodes_to_skip was already determined at creation time
-        # TODO: Consider storing nodes_to_skip in DB if we need to preserve it across resumes
-        nodes_to_skip: set[str] = set()
 
         logger.info(f"Resuming graph execution #{graph_exec.id} for graph #{graph_id}")
     else:
@@ -837,7 +787,7 @@ async def add_graph_execution(
         )
 
         # Create new execution
-        graph, starting_nodes_input, compiled_nodes_input_masks, nodes_to_skip = (
+        graph, starting_nodes_input, compiled_nodes_input_masks = (
             await validate_and_construct_node_execution_input(
                 graph_id=graph_id,
                 user_id=user_id,
@@ -886,7 +836,6 @@ async def add_graph_execution(
     try:
         graph_exec_entry = graph_exec.to_graph_execution_entry(
             compiled_nodes_input_masks=compiled_nodes_input_masks,
-            nodes_to_skip=nodes_to_skip,
             execution_context=execution_context,
         )
         logger.info(f"Publishing execution {graph_exec.id} to execution queue")

autogpt_platform/backend/backend/executor/utils_test.py

@@ -367,13 +367,10 @@ async def test_add_graph_execution_is_repeatable(mocker: MockerFixture):
     )
 
     # Setup mock returns
-    # The function returns (graph, starting_nodes_input, compiled_nodes_input_masks, nodes_to_skip)
-    nodes_to_skip: set[str] = set()
     mock_validate.return_value = (
         mock_graph,
         starting_nodes_input,
         compiled_nodes_input_masks,
-        nodes_to_skip,
     )
     mock_prisma.is_connected.return_value = True
     mock_edb.create_graph_execution = mocker.AsyncMock(return_value=mock_graph_exec)
@@ -459,212 +456,3 @@ async def test_add_graph_execution_is_repeatable(mocker: MockerFixture):
     # Both executions should succeed (though they create different objects)
     assert result1 == mock_graph_exec
     assert result2 == mock_graph_exec_2
-
-
-# ============================================================================
-# Tests for Optional Credentials Feature
-# ============================================================================
-
-
-@pytest.mark.asyncio
-async def test_validate_node_input_credentials_returns_nodes_to_skip(
-    mocker: MockerFixture,
-):
-    """
-    Test that _validate_node_input_credentials returns nodes_to_skip set
-    for nodes with credentials_optional=True and missing credentials.
-    """
-    from backend.executor.utils import _validate_node_input_credentials
-
-    # Create a mock node with credentials_optional=True
-    mock_node = mocker.MagicMock()
-    mock_node.id = "node-with-optional-creds"
-    mock_node.credentials_optional = True
-    mock_node.input_default = {}  # No credentials configured
-
-    # Create a mock block with credentials field
-    mock_block = mocker.MagicMock()
-    mock_credentials_field_type = mocker.MagicMock()
-    mock_block.input_schema.get_credentials_fields.return_value = {
-        "credentials": mock_credentials_field_type
-    }
-    mock_node.block = mock_block
-
-    # Create mock graph
-    mock_graph = mocker.MagicMock()
-    mock_graph.nodes = [mock_node]
-
-    # Call the function
-    errors, nodes_to_skip = await _validate_node_input_credentials(
-        graph=mock_graph,
-        user_id="test-user-id",
-        nodes_input_masks=None,
-    )
-
-    # Node should be in nodes_to_skip, not in errors
-    assert mock_node.id in nodes_to_skip
-    assert mock_node.id not in errors
-
-
-@pytest.mark.asyncio
-async def test_validate_node_input_credentials_required_missing_creds_error(
-    mocker: MockerFixture,
-):
-    """
-    Test that _validate_node_input_credentials returns errors
-    for nodes with credentials_optional=False and missing credentials.
-    """
-    from backend.executor.utils import _validate_node_input_credentials
-
-    # Create a mock node with credentials_optional=False (required)
-    mock_node = mocker.MagicMock()
-    mock_node.id = "node-with-required-creds"
-    mock_node.credentials_optional = False
-    mock_node.input_default = {}  # No credentials configured
-
-    # Create a mock block with credentials field
-    mock_block = mocker.MagicMock()
-    mock_credentials_field_type = mocker.MagicMock()
-    mock_block.input_schema.get_credentials_fields.return_value = {
-        "credentials": mock_credentials_field_type
-    }
-    mock_node.block = mock_block
-
-    # Create mock graph
-    mock_graph = mocker.MagicMock()
-    mock_graph.nodes = [mock_node]
-
-    # Call the function
-    errors, nodes_to_skip = await _validate_node_input_credentials(
-        graph=mock_graph,
-        user_id="test-user-id",
-        nodes_input_masks=None,
-    )
-
-    # Node should be in errors, not in nodes_to_skip
-    assert mock_node.id in errors
-    assert "credentials" in errors[mock_node.id]
-    assert "required" in errors[mock_node.id]["credentials"].lower()
-    assert mock_node.id not in nodes_to_skip
-
-
-@pytest.mark.asyncio
-async def test_validate_graph_with_credentials_returns_nodes_to_skip(
-    mocker: MockerFixture,
-):
-    """
-    Test that validate_graph_with_credentials returns nodes_to_skip set
-    from _validate_node_input_credentials.
-    """
-    from backend.executor.utils import validate_graph_with_credentials
-
-    # Mock _validate_node_input_credentials to return specific values
-    mock_validate = mocker.patch(
-        "backend.executor.utils._validate_node_input_credentials"
-    )
-    expected_errors = {"node1": {"field": "error"}}
-    expected_nodes_to_skip = {"node2", "node3"}
-    mock_validate.return_value = (expected_errors, expected_nodes_to_skip)
-
-    # Mock GraphModel with validate_graph_get_errors method
-    mock_graph = mocker.MagicMock()
-    mock_graph.validate_graph_get_errors.return_value = {}
-
-    # Call the function
-    errors, nodes_to_skip = await validate_graph_with_credentials(
-        graph=mock_graph,
-        user_id="test-user-id",
-        nodes_input_masks=None,
-    )
-
-    # Verify nodes_to_skip is passed through
-    assert nodes_to_skip == expected_nodes_to_skip
-    assert "node1" in errors
-
-
-@pytest.mark.asyncio
-async def test_add_graph_execution_with_nodes_to_skip(mocker: MockerFixture):
-    """
-    Test that add_graph_execution properly passes nodes_to_skip
-    to the graph execution entry.
-    """
-    from backend.data.execution import GraphExecutionWithNodes
-    from backend.executor.utils import add_graph_execution
-
-    # Mock data
-    graph_id = "test-graph-id"
-    user_id = "test-user-id"
-    inputs = {"test_input": "test_value"}
-    graph_version = 1
-
-    # Mock the graph object
-    mock_graph = mocker.MagicMock()
-    mock_graph.version = graph_version
-
-    # Starting nodes and masks
-    starting_nodes_input = [("node1", {"input1": "value1"})]
-    compiled_nodes_input_masks = {}
-    nodes_to_skip = {"skipped-node-1", "skipped-node-2"}
-
-    # Mock the graph execution object
-    mock_graph_exec = mocker.MagicMock(spec=GraphExecutionWithNodes)
-    mock_graph_exec.id = "execution-id-123"
-    mock_graph_exec.node_executions = []
-
-    # Track what's passed to to_graph_execution_entry
-    captured_kwargs = {}
-
-    def capture_to_entry(**kwargs):
-        captured_kwargs.update(kwargs)
-        return mocker.MagicMock()
-
-    mock_graph_exec.to_graph_execution_entry.side_effect = capture_to_entry
-
-    # Setup mocks
-    mock_validate = mocker.patch(
-        "backend.executor.utils.validate_and_construct_node_execution_input"
-    )
-    mock_edb = mocker.patch("backend.executor.utils.execution_db")
-    mock_prisma = mocker.patch("backend.executor.utils.prisma")
-    mock_udb = mocker.patch("backend.executor.utils.user_db")
-    mock_gdb = mocker.patch("backend.executor.utils.graph_db")
-    mock_get_queue = mocker.patch("backend.executor.utils.get_async_execution_queue")
-    mock_get_event_bus = mocker.patch(
-        "backend.executor.utils.get_async_execution_event_bus"
-    )
-
-    # Setup returns - include nodes_to_skip in the tuple
-    mock_validate.return_value = (
-        mock_graph,
-        starting_nodes_input,
-        compiled_nodes_input_masks,
-        nodes_to_skip,  # This should be passed through
-    )
-    mock_prisma.is_connected.return_value = True
-    mock_edb.create_graph_execution = mocker.AsyncMock(return_value=mock_graph_exec)
-    mock_edb.update_graph_execution_stats = mocker.AsyncMock(
-        return_value=mock_graph_exec
-    )
-    mock_edb.update_node_execution_status_batch = mocker.AsyncMock()
-
-    mock_user = mocker.MagicMock()
-    mock_user.timezone = "UTC"
-    mock_settings = mocker.MagicMock()
-    mock_settings.human_in_the_loop_safe_mode = True
-
-    mock_udb.get_user_by_id = mocker.AsyncMock(return_value=mock_user)
-    mock_gdb.get_graph_settings = mocker.AsyncMock(return_value=mock_settings)
-    mock_get_queue.return_value = mocker.AsyncMock()
-    mock_get_event_bus.return_value = mocker.MagicMock(publish=mocker.AsyncMock())
-
-    # Call the function
-    await add_graph_execution(
-        graph_id=graph_id,
-        user_id=user_id,
-        inputs=inputs,
-        graph_version=graph_version,
-    )
-
-    # Verify nodes_to_skip was passed to to_graph_execution_entry
-    assert "nodes_to_skip" in captured_kwargs
-    assert captured_kwargs["nodes_to_skip"] == nodes_to_skip

autogpt_platform/backend/gen_prisma_types_stub.py

@@ -1,227 +0,0 @@
-#!/usr/bin/env python3
-"""
-Generate a lightweight stub for prisma/types.py that collapses all exported
-symbols to Any. This prevents Pyright from spending time/budget on Prisma's
-query DSL types while keeping runtime behavior unchanged.
-
-Usage:
-    poetry run gen-prisma-stub
-
-This script automatically finds the prisma package location and generates
-the types.pyi stub file in the same directory as types.py.
-"""
-
-from __future__ import annotations
-
-import ast
-import importlib.util
-import sys
-from pathlib import Path
-from typing import Iterable, Set
-
-
-def _iter_assigned_names(target: ast.expr) -> Iterable[str]:
-    """Extract names from assignment targets (handles tuple unpacking)."""
-    if isinstance(target, ast.Name):
-        yield target.id
-    elif isinstance(target, (ast.Tuple, ast.List)):
-        for elt in target.elts:
-            yield from _iter_assigned_names(elt)
-
-
-def _is_private(name: str) -> bool:
-    """Check if a name is private (starts with _ but not __)."""
-    return name.startswith("_") and not name.startswith("__")
-
-
-def _is_safe_type_alias(node: ast.Assign) -> bool:
-    """Check if an assignment is a safe type alias that shouldn't be stubbed.
-
-    Safe types are:
-    - Literal types (don't cause type budget issues)
-    - Simple type references (SortMode, SortOrder, etc.)
-    - TypeVar definitions
-    """
-    if not node.value:
-        return False
-
-    # Check if it's a Subscript (like Literal[...], Union[...], TypeVar[...])
-    if isinstance(node.value, ast.Subscript):
-        # Get the base type name
-        if isinstance(node.value.value, ast.Name):
-            base_name = node.value.value.id
-            # Literal types are safe
-            if base_name == "Literal":
-                return True
-            # TypeVar is safe
-            if base_name == "TypeVar":
-                return True
-        elif isinstance(node.value.value, ast.Attribute):
-            # Handle typing_extensions.Literal etc.
-            if node.value.value.attr == "Literal":
-                return True
-
-    # Check if it's a simple Name reference (like SortMode = _types.SortMode)
-    if isinstance(node.value, ast.Attribute):
-        return True
-
-    # Check if it's a Call (like TypeVar(...))
-    if isinstance(node.value, ast.Call):
-        if isinstance(node.value.func, ast.Name):
-            if node.value.func.id == "TypeVar":
-                return True
-
-    return False
-
-
-def collect_top_level_symbols(
-    tree: ast.Module, source_lines: list[str]
-) -> tuple[Set[str], Set[str], list[str], Set[str]]:
-    """Collect all top-level symbols from an AST module.
-
-    Returns:
-        Tuple of (class_names, function_names, safe_variable_sources, unsafe_variable_names)
-        safe_variable_sources contains the actual source code lines for safe variables
-    """
-    classes: Set[str] = set()
-    functions: Set[str] = set()
-    safe_variable_sources: list[str] = []
-    unsafe_variables: Set[str] = set()
-
-    for node in tree.body:
-        if isinstance(node, ast.ClassDef):
-            if not _is_private(node.name):
-                classes.add(node.name)
-        elif isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef)):
-            if not _is_private(node.name):
-                functions.add(node.name)
-        elif isinstance(node, ast.Assign):
-            is_safe = _is_safe_type_alias(node)
-            names = []
-            for t in node.targets:
-                for n in _iter_assigned_names(t):
-                    if not _is_private(n):
-                        names.append(n)
-            if names:
-                if is_safe:
-                    # Extract the source code for this assignment
-                    start_line = node.lineno - 1  # 0-indexed
-                    end_line = node.end_lineno if node.end_lineno else node.lineno
-                    source = "\n".join(source_lines[start_line:end_line])
-                    safe_variable_sources.append(source)
-                else:
-                    unsafe_variables.update(names)
-        elif isinstance(node, ast.AnnAssign) and node.target:
-            # Annotated assignments are always stubbed
-            for n in _iter_assigned_names(node.target):
-                if not _is_private(n):
-                    unsafe_variables.add(n)
-
-    return classes, functions, safe_variable_sources, unsafe_variables
-
-
-def find_prisma_types_path() -> Path:
-    """Find the prisma types.py file in the installed package."""
-    spec = importlib.util.find_spec("prisma")
-    if spec is None or spec.origin is None:
-        raise RuntimeError("Could not find prisma package. Is it installed?")
-
-    prisma_dir = Path(spec.origin).parent
-    types_path = prisma_dir / "types.py"
-
-    if not types_path.exists():
-        raise RuntimeError(f"prisma/types.py not found at {types_path}")
-
-    return types_path
-
-
-def generate_stub(src_path: Path, stub_path: Path) -> int:
-    """Generate the .pyi stub file from the source types.py."""
-    code = src_path.read_text(encoding="utf-8", errors="ignore")
-    source_lines = code.splitlines()
-    tree = ast.parse(code, filename=str(src_path))
-    classes, functions, safe_variable_sources, unsafe_variables = (
-        collect_top_level_symbols(tree, source_lines)
-    )
-
-    header = """\
-# -*- coding: utf-8 -*-
-# Auto-generated stub file - DO NOT EDIT
-# Generated by gen_prisma_types_stub.py
-#
-# This stub intentionally collapses complex Prisma query DSL types to Any.
-# Prisma's generated types can explode Pyright's type inference budgets
-# on large schemas. We collapse them to Any so the rest of the codebase
-# can remain strongly typed while keeping runtime behavior unchanged.
-#
-# Safe types (Literal, TypeVar, simple references) are preserved from the
-# original types.py to maintain proper type checking where possible.
-
-from __future__ import annotations
-from typing import Any
-from typing_extensions import Literal
-
-# Re-export commonly used typing constructs that may be imported from this module
-from typing import TYPE_CHECKING, TypeVar, Generic, Union, Optional, List, Dict
-
-# Base type alias for stubbed Prisma types - allows any dict structure
-_PrismaDict = dict[str, Any]
-
-"""
-
-    lines = [header]
-
-    # Include safe variable definitions (Literal types, TypeVars, etc.)
-    lines.append("# Safe type definitions preserved from original types.py")
-    for source in safe_variable_sources:
-        lines.append(source)
-    lines.append("")
-
-    # Stub all classes and unsafe variables uniformly as dict[str, Any] aliases
-    # This allows:
-    # 1. Use in type annotations: x: SomeType
-    # 2. Constructor calls: SomeType(...)
-    # 3. Dict literal assignments: x: SomeType = {...}
-    lines.append(
-        "# Stubbed types (collapsed to dict[str, Any] to prevent type budget exhaustion)"
-    )
-    all_stubbed = sorted(classes | unsafe_variables)
-    for name in all_stubbed:
-        lines.append(f"{name} = _PrismaDict")
-
-    lines.append("")
-
-    # Stub functions
-    for name in sorted(functions):
-        lines.append(f"def {name}(*args: Any, **kwargs: Any) -> Any: ...")
-
-    lines.append("")
-
-    stub_path.write_text("\n".join(lines), encoding="utf-8")
-    return (
-        len(classes)
-        + len(functions)
-        + len(safe_variable_sources)
-        + len(unsafe_variables)
-    )
-
-
-def main() -> None:
-    """Main entry point."""
-    try:
-        types_path = find_prisma_types_path()
-        stub_path = types_path.with_suffix(".pyi")
-
-        print(f"Found prisma types.py at: {types_path}")
-        print(f"Generating stub at: {stub_path}")
-
-        num_symbols = generate_stub(types_path, stub_path)
-        print(f"Generated {stub_path.name} with {num_symbols} Any-typed symbols")
-
-    except Exception as e:
-        print(f"Error: {e}", file=sys.stderr)
-        sys.exit(1)
-
-
-if __name__ == "__main__":
-    main()

autogpt_platform/backend/linter.py

@@ -25,9 +25,6 @@ def run(*command: str) -> None:
 
 
 def lint():
-    # Generate Prisma types stub before running pyright to prevent type budget exhaustion
-    run("gen-prisma-stub")
-
     lint_step_args: list[list[str]] = [
         ["ruff", "check", *TARGET_DIRS, "--exit-zero"],
         ["ruff", "format", "--diff", "--check", LIBS_DIR],
@@ -52,6 +49,4 @@ def format():
     run("ruff", "format", LIBS_DIR)
     run("isort", "--profile", "black", BACKEND_DIR)
     run("black", BACKEND_DIR)
-    # Generate Prisma types stub before running pyright to prevent type budget exhaustion
-    run("gen-prisma-stub")
     run("pyright", *TARGET_DIRS)

autogpt_platform/backend/migrations/20260109181714_add_docs_embedding/migration.sql

@@ -1,35 +0,0 @@
--- CreateExtension in public schema (standard location for pgvector)
-CREATE EXTENSION IF NOT EXISTS "vector" WITH SCHEMA "public";
-
--- Grant usage on public schema to platform users
-GRANT USAGE ON SCHEMA public TO postgres;
-
--- CreateEnum
-CREATE TYPE "ContentType" AS ENUM ('STORE_AGENT', 'BLOCK', 'INTEGRATION', 'DOCUMENTATION', 'LIBRARY_AGENT');
-
--- CreateTable
-CREATE TABLE "UnifiedContentEmbedding" (
-    "id" TEXT NOT NULL,
-    "createdAt" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP,
-    "updatedAt" TIMESTAMP(3) NOT NULL,
-    "contentType" "ContentType" NOT NULL,
-    "contentId" TEXT NOT NULL,
-    "userId" TEXT,
-    "embedding" public.vector(1536) NOT NULL,
-    "searchableText" TEXT NOT NULL,
-    "metadata" JSONB NOT NULL DEFAULT '{}',
-
-    CONSTRAINT "UnifiedContentEmbedding_pkey" PRIMARY KEY ("id")
-);
-
--- CreateIndex
-CREATE INDEX "UnifiedContentEmbedding_contentType_idx" ON "UnifiedContentEmbedding"("contentType");
-
--- CreateIndex
-CREATE INDEX "UnifiedContentEmbedding_userId_idx" ON "UnifiedContentEmbedding"("userId");
-
--- CreateIndex
-CREATE INDEX "UnifiedContentEmbedding_contentType_userId_idx" ON "UnifiedContentEmbedding"("contentType", "userId");
-
--- CreateIndex
-CREATE UNIQUE INDEX "UnifiedContentEmbedding_contentType_contentId_userId_key" ON "UnifiedContentEmbedding"("contentType", "contentId", "userId");

autogpt_platform/backend/pyproject.toml

@@ -117,7 +117,6 @@ lint = "linter:lint"
 test = "run_tests:test"
 load-store-agents = "test.load_store_agents:run"
 export-api-schema = "backend.cli.generate_openapi_json:main"
-gen-prisma-stub = "gen_prisma_types_stub:main"
 oauth-tool = "backend.cli.oauth_tool:cli"
 
 [tool.isort]
@@ -135,9 +134,6 @@ ignore_patterns = []
 [tool.pytest.ini_options]
 asyncio_mode = "auto"
 asyncio_default_fixture_loop_scope = "session"
-# Disable syrupy plugin to avoid conflict with pytest-snapshot
-# Both provide --snapshot-update argument causing ArgumentError
-addopts = "-p no:syrupy"
 filterwarnings = [
     "ignore:'audioop' is deprecated:DeprecationWarning:discord.player",
     "ignore:invalid escape sequence:DeprecationWarning:tweepy.api",

autogpt_platform/backend/schema.prisma

@@ -1,15 +1,14 @@
 datasource db {
-  provider   = "postgresql"
-  url        = env("DATABASE_URL")
-  directUrl  = env("DIRECT_URL")
-  extensions = [pgvector(map: "vector")]
+  provider  = "postgresql"
+  url       = env("DATABASE_URL")
+  directUrl = env("DIRECT_URL")
 }
 
 generator client {
   provider               = "prisma-client-py"
   recursive_type_depth   = -1
   interface              = "asyncio"
-  previewFeatures        = ["views", "fullTextSearch", "postgresqlExtensions"]
+  previewFeatures        = ["views", "fullTextSearch"]
   partial_type_generator = "backend/data/partial_types.py"
 }
 
@@ -128,8 +127,8 @@ model BuilderSearchHistory {
   updatedAt DateTime @default(now()) @updatedAt
 
   searchQuery String
-  filter      String[] @default([])
-  byCreator   String[] @default([])
+  filter      String[]  @default([])
+  byCreator   String[]  @default([])
 
   userId String
   User   User   @relation(fields: [userId], references: [id], onDelete: Cascade)
@@ -722,25 +721,26 @@ view StoreAgent {
   storeListingVersionId String
   updated_at            DateTime
 
-  slug              String
-  agent_name        String
-  agent_video       String?
-  agent_output_demo String?
-  agent_image       String[]
+  slug                String
+  agent_name          String
+  agent_video         String?
+  agent_output_demo   String?
+  agent_image         String[]
 
-  featured           Boolean                  @default(false)
-  creator_username   String?
-  creator_avatar     String?
-  sub_heading        String
-  description        String
-  categories         String[]
-  runs               Int
-  rating             Float
-  versions           String[]
-  agentGraphVersions String[]
-  agentGraphId       String
-  is_available       Boolean                  @default(true)
-  useForOnboarding   Boolean                  @default(false)
+  featured         Boolean                  @default(false)
+  creator_username String?
+  creator_avatar   String?
+  sub_heading      String
+  description      String
+  categories       String[]
+  search           Unsupported("tsvector")? @default(dbgenerated("''::tsvector"))
+  runs                Int
+  rating              Float
+  versions            String[]
+  agentGraphVersions  String[]
+  agentGraphId        String
+  is_available        Boolean                  @default(true)
+  useForOnboarding    Boolean                  @default(false)
 
   // Materialized views used (refreshed every 15 minutes via pg_cron):
   // - mv_agent_run_counts - Pre-aggregated agent execution counts by agentGraphId
@@ -856,14 +856,14 @@ model StoreListingVersion {
   AgentGraph        AgentGraph @relation(fields: [agentGraphId, agentGraphVersion], references: [id, version])
 
   // Content fields
-  name               String
-  subHeading         String
-  videoUrl           String?
-  agentOutputDemoUrl String?
-  imageUrls          String[]
-  description        String
-  instructions       String?
-  categories         String[]
+  name                String
+  subHeading          String
+  videoUrl            String?
+  agentOutputDemoUrl  String?
+  imageUrls           String[]
+  description         String
+  instructions        String?
+  categories          String[]
 
   isFeatured Boolean @default(false)
 
@@ -899,9 +899,6 @@ model StoreListingVersion {
   // Reviews for this specific version
   Reviews StoreListingReview[]
 
-  // Note: Embeddings now stored in UnifiedContentEmbedding table
-  // Use contentType=STORE_AGENT and contentId=storeListingVersionId
-
   @@unique([storeListingId, version])
   @@index([storeListingId, submissionStatus, isAvailable])
   @@index([submissionStatus])
@@ -909,42 +906,6 @@ model StoreListingVersion {
   @@index([agentGraphId, agentGraphVersion]) // Non-unique index for efficient lookups
 }
 
-
-// Content type enum for unified search across store agents, blocks, docs
-// Note: BLOCK/INTEGRATION are file-based (Python classes), not DB records
-// DOCUMENTATION are file-based (.md files), not DB records  
-// Only STORE_AGENT and LIBRARY_AGENT are stored in database
-enum ContentType {
-  STORE_AGENT     // Database: StoreListingVersion
-  BLOCK           // File-based: Python classes in /backend/blocks/
-  INTEGRATION     // File-based: Python classes (blocks with credentials)
-  DOCUMENTATION   // File-based: .md/.mdx files
-  LIBRARY_AGENT   // Database: User's personal agents
-}
-
-// Unified embeddings table for all searchable content types
-// Supports both public content (userId=null) and user-specific content (userId=userID)
-model UnifiedContentEmbedding {
-  id          String      @id @default(uuid())
-  createdAt   DateTime    @default(now())
-  updatedAt   DateTime    @updatedAt
-  
-  // Content identification
-  contentType ContentType
-  contentId   String      // DB ID (storeListingVersionId) or file identifier (block.id, file_path)
-  userId      String?     // NULL for public content (store, blocks, docs), userId for private content (library agents)
-  
-  // Search data  
-  embedding      Unsupported("public.vector(1536)")  // pgvector embedding from public schema
-  searchableText String                        // Combined text for search and fallback
-  metadata       Json        @default("{}")    // Content-specific metadata
-  
-  @@unique([contentType, contentId, userId])  // Allow same content for different users
-  @@index([contentType])
-  @@index([userId])
-  @@index([contentType, userId])
-}
-
 model StoreListingReview {
   id        String   @id @default(uuid())
   createdAt DateTime @default(now())
@@ -1037,16 +998,16 @@ model OAuthApplication {
   updatedAt DateTime @updatedAt
 
   // Application metadata
-  name             String
-  description      String?
-  logoUrl          String? // URL to app logo stored in GCS
-  clientId         String  @unique
-  clientSecret     String // Hashed with Scrypt (same as API keys)
-  clientSecretSalt String // Salt for Scrypt hashing
+  name               String
+  description        String?
+  logoUrl            String? // URL to app logo stored in GCS
+  clientId           String @unique
+  clientSecret       String // Hashed with Scrypt (same as API keys)
+  clientSecretSalt   String // Salt for Scrypt hashing
 
   // OAuth configuration
   redirectUris String[] // Allowed callback URLs
-  grantTypes   String[]           @default(["authorization_code", "refresh_token"])
+  grantTypes   String[] @default(["authorization_code", "refresh_token"])
   scopes       APIKeyPermission[] // Which permissions the app can request
 
   // Application management

autogpt_platform/backend/snapshots/grph_single

@@ -2,7 +2,6 @@
   "created_at": "2025-09-04T13:37:00",
   "credentials_input_schema": {
     "properties": {},
-    "required": [],
     "title": "TestGraphCredentialsInputSchema",
     "type": "object"
   },

autogpt_platform/backend/snapshots/grphs_all

@@ -2,7 +2,6 @@
   {
     "credentials_input_schema": {
       "properties": {},
-      "required": [],
       "title": "TestGraphCredentialsInputSchema",
       "type": "object"
     },

autogpt_platform/backend/snapshots/lib_agts_search

@@ -4,7 +4,6 @@
       "id": "test-agent-1",
       "graph_id": "test-agent-1",
       "graph_version": 1,
-      "owner_user_id": "3e53486c-cf57-477e-ba2a-cb02dc828e1a",
       "image_url": null,
       "creator_name": "Test Creator",
       "creator_image_url": "",
@@ -42,7 +41,6 @@
       "id": "test-agent-2",
       "graph_id": "test-agent-2",
       "graph_version": 1,
-      "owner_user_id": "3e53486c-cf57-477e-ba2a-cb02dc828e1a",
       "image_url": null,
       "creator_name": "Test Creator",
       "creator_image_url": "",

autogpt_platform/backend/snapshots/sub_success

@@ -1,7 +1,6 @@
 {
   "submissions": [
     {
-      "listing_id": "test-listing-id",
       "agent_id": "test-agent-id",
       "agent_version": 1,
       "name": "Test Agent",

autogpt_platform/docker-compose.platform.yml

@@ -37,7 +37,7 @@ services:
       context: ../
       dockerfile: autogpt_platform/backend/Dockerfile
       target: migrate
-    command: ["sh", "-c", "poetry run prisma generate && poetry run gen-prisma-stub && poetry run prisma migrate deploy"]
+    command: ["sh", "-c", "poetry run prisma generate && poetry run prisma migrate deploy"]
     develop:
       watch:
         - path: ./

autogpt_platform/frontend/package.json

@@ -46,15 +46,14 @@
     "@radix-ui/react-scroll-area": "1.2.10",
     "@radix-ui/react-select": "2.2.6",
     "@radix-ui/react-separator": "1.1.7",
-    "@radix-ui/react-slider": "1.3.6",
     "@radix-ui/react-slot": "1.2.3",
     "@radix-ui/react-switch": "1.2.6",
     "@radix-ui/react-tabs": "1.1.13",
     "@radix-ui/react-toast": "1.2.15",
     "@radix-ui/react-tooltip": "1.2.8",
-    "@rjsf/core": "6.1.2",
-    "@rjsf/utils": "6.1.2",
-    "@rjsf/validator-ajv8": "6.1.2",
+    "@rjsf/core": "5.24.13",
+    "@rjsf/utils": "5.24.13",
+    "@rjsf/validator-ajv8": "5.24.13",
     "@sentry/nextjs": "10.27.0",
     "@supabase/ssr": "0.7.0",
     "@supabase/supabase-js": "2.78.0",
@@ -92,6 +91,7 @@
     "react-currency-input-field": "4.0.3",
     "react-day-picker": "9.11.1",
     "react-dom": "18.3.1",
+    "react-drag-drop-files": "2.4.0",
     "react-hook-form": "7.66.0",
     "react-icons": "5.5.0",
     "react-markdown": "9.0.3",

autogpt_platform/frontend/src/app/(platform)/auth/integrations/oauth_callback/route.ts

@@ -1,4 +1,4 @@
-import { OAuthPopupResultMessage } from "./types";
+import { OAuthPopupResultMessage } from "@/components/renderers/input-renderer/fields/CredentialField/models/OAuthCredentialModal/useOAuthCredentialModal";
 import { NextResponse } from "next/server";
 
 // This route is intended to be used as the callback for integration OAuth flows,

autogpt_platform/frontend/src/app/(platform)/auth/integrations/oauth_callback/types.ts

@@ -1,11 +0,0 @@
-export type OAuthPopupResultMessage = { message_type: "oauth_popup_result" } & (
-  | {
-      success: true;
-      code: string;
-      state: string;
-    }
-  | {
-      success: false;
-      message: string;
-    }
-);

autogpt_platform/frontend/src/app/(platform)/build/components/BuilderActions/components/RunInputDialog/RunInputDialog.tsx

@@ -5,7 +5,7 @@ import { useGraphStore } from "@/app/(platform)/build/stores/graphStore";
 import { Button } from "@/components/atoms/Button/Button";
 import { ClockIcon, PlayIcon } from "@phosphor-icons/react";
 import { Text } from "@/components/atoms/Text/Text";
-import { FormRenderer } from "@/components/renderers/InputRenderer/FormRenderer";
+import { FormRenderer } from "@/components/renderers/input-renderer/FormRenderer";
 import { useRunInputDialog } from "./useRunInputDialog";
 import { CronSchedulerDialog } from "../CronSchedulerDialog/CronSchedulerDialog";
 
@@ -66,7 +66,6 @@ export const RunInputDialog = ({
                     formContext={{
                       showHandles: false,
                       size: "large",
-                      showOptionalToggle: false,
                     }}
                   />
                 </div>

autogpt_platform/frontend/src/app/(platform)/build/components/BuilderActions/components/RunInputDialog/useRunInputDialog.ts

@@ -8,7 +8,7 @@ import {
 import { parseAsInteger, parseAsString, useQueryStates } from "nuqs";
 import { useMemo, useState } from "react";
 import { uiSchema } from "../../../FlowEditor/nodes/uiSchema";
-import { isCredentialFieldSchema } from "@/components/renderers/InputRenderer/custom/CredentialField/helpers";
+import { isCredentialFieldSchema } from "@/components/renderers/input-renderer/fields/CredentialField/helpers";
 
 export const useRunInputDialog = ({
   setIsOpen,
@@ -66,7 +66,7 @@ export const useRunInputDialog = ({
         if (isCredentialFieldSchema(fieldSchema)) {
           dynamicUiSchema[fieldName] = {
             ...dynamicUiSchema[fieldName],
-            "ui:field": "custom/credential_field",
+            "ui:field": "credentials",
           };
         }
       });
@@ -76,18 +76,12 @@ export const useRunInputDialog = ({
   }, [credentialsSchema]);
 
   const handleManualRun = async () => {
-    // Filter out incomplete credentials (those without a valid id)
-    // RJSF auto-populates const values (provider, type) but not id field
-    const validCredentials = Object.fromEntries(
-      Object.entries(credentialValues).filter(([_, cred]) => cred && cred.id),
-    );
-
     await executeGraph({
       graphId: flowID ?? "",
       graphVersion: flowVersion || null,
       data: {
         inputs: inputValues,
-        credentials_inputs: validCredentials,
+        credentials_inputs: credentialValues,
         source: "builder",
       },
     });

autogpt_platform/frontend/src/app/(platform)/build/components/DraftRecoveryDialog/DraftRecoveryPopup.tsx

@@ -12,59 +12,16 @@ import {
 import { useDraftRecoveryPopup } from "./useDraftRecoveryPopup";
 import { Text } from "@/components/atoms/Text/Text";
 import { AnimatePresence, motion } from "framer-motion";
-import { DraftDiff } from "@/lib/dexie/draft-utils";
 
 interface DraftRecoveryPopupProps {
   isInitialLoadComplete: boolean;
 }
 
-function formatDiffSummary(diff: DraftDiff | null): string {
-  if (!diff) return "";
-
-  const parts: string[] = [];
-
-  // Node changes
-  const nodeChanges: string[] = [];
-  if (diff.nodes.added > 0) nodeChanges.push(`+${diff.nodes.added}`);
-  if (diff.nodes.removed > 0) nodeChanges.push(`-${diff.nodes.removed}`);
-  if (diff.nodes.modified > 0) nodeChanges.push(`~${diff.nodes.modified}`);
-
-  if (nodeChanges.length > 0) {
-    parts.push(
-      `${nodeChanges.join("/")} block${diff.nodes.added + diff.nodes.removed + diff.nodes.modified !== 1 ? "s" : ""}`,
-    );
-  }
-
-  // Edge changes
-  const edgeChanges: string[] = [];
-  if (diff.edges.added > 0) edgeChanges.push(`+${diff.edges.added}`);
-  if (diff.edges.removed > 0) edgeChanges.push(`-${diff.edges.removed}`);
-  if (diff.edges.modified > 0) edgeChanges.push(`~${diff.edges.modified}`);
-
-  if (edgeChanges.length > 0) {
-    parts.push(
-      `${edgeChanges.join("/")} connection${diff.edges.added + diff.edges.removed + diff.edges.modified !== 1 ? "s" : ""}`,
-    );
-  }
-
-  return parts.join(", ");
-}
-
 export function DraftRecoveryPopup({
   isInitialLoadComplete,
 }: DraftRecoveryPopupProps) {
-  const {
-    isOpen,
-    popupRef,
-    nodeCount,
-    edgeCount,
-    diff,
-    savedAt,
-    onLoad,
-    onDiscard,
-  } = useDraftRecoveryPopup(isInitialLoadComplete);
-
-  const diffSummary = formatDiffSummary(diff);
+  const { isOpen, popupRef, nodeCount, edgeCount, savedAt, onLoad, onDiscard } =
+    useDraftRecoveryPopup(isInitialLoadComplete);
 
   return (
     <AnimatePresence>
@@ -115,9 +72,10 @@ export function DraftRecoveryPopup({
                 variant="small"
                 className="text-amber-700 dark:text-amber-400"
               >
-                {diffSummary ||
-                  `${nodeCount} block${nodeCount !== 1 ? "s" : ""}, ${edgeCount} connection${edgeCount !== 1 ? "s" : ""}`}{" "}
-                • {formatTimeAgo(new Date(savedAt).toISOString())}
+                {nodeCount} block{nodeCount !== 1 ? "s" : ""}, {edgeCount}{" "}
+                connection
+                {edgeCount !== 1 ? "s" : ""} •{" "}
+                {formatTimeAgo(new Date(savedAt).toISOString())}
               </Text>
             </div>
 

autogpt_platform/frontend/src/app/(platform)/build/components/DraftRecoveryDialog/useDraftRecoveryPopup.tsx

@@ -9,7 +9,6 @@ export const useDraftRecoveryPopup = (isInitialLoadComplete: boolean) => {
     savedAt,
     nodeCount,
     edgeCount,
-    diff,
     loadDraft: onLoad,
     discardDraft: onDiscard,
   } = useDraftManager(isInitialLoadComplete);
@@ -55,7 +54,6 @@ export const useDraftRecoveryPopup = (isInitialLoadComplete: boolean) => {
     isOpen,
     nodeCount,
     edgeCount,
-    diff,
     savedAt,
     onLoad,
     onDiscard,

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

@@ -97,9 +97,6 @@ export const Flow = () => {
           onConnect={onConnect}
           onEdgesChange={onEdgesChange}
           onNodeDragStop={onNodeDragStop}
-          onNodeContextMenu={(event) => {
-            event.preventDefault();
-          }}
           maxZoom={2}
           minZoom={0.1}
           onDragOver={onDragOver}

autogpt_platform/frontend/src/app/(platform)/build/components/FlowEditor/Flow/helpers/resolve-collision.ts

@@ -48,6 +48,8 @@ export const resolveCollisions: CollisionAlgorithm = (
     const width = (node.width ?? node.measured?.width ?? 0) + margin * 2;
     const height = (node.height ?? node.measured?.height ?? 0) + margin * 2;
 
+    console.log("width", width);
+    console.log("height", height);
     const x = node.position.x - margin;
     const y = node.position.y - margin;
 

autogpt_platform/frontend/src/app/(platform)/build/components/FlowEditor/Flow/useDraftManager.ts

@@ -7,12 +7,7 @@ import {
   DraftData,
 } from "@/services/builder-draft/draft-service";
 import { BuilderDraft } from "@/lib/dexie/db";
-import {
-  cleanNodes,
-  cleanEdges,
-  calculateDraftDiff,
-  DraftDiff,
-} from "@/lib/dexie/draft-utils";
+import { cleanNodes, cleanEdges } from "@/lib/dexie/draft-utils";
 import { useNodeStore } from "../../../stores/nodeStore";
 import { useEdgeStore } from "../../../stores/edgeStore";
 import { useGraphStore } from "../../../stores/graphStore";
@@ -24,7 +19,6 @@ const AUTO_SAVE_INTERVAL_MS = 15000; // 15 seconds
 interface DraftRecoveryState {
   isOpen: boolean;
   draft: BuilderDraft | null;
-  diff: DraftDiff | null;
 }
 
 /**
@@ -37,7 +31,6 @@ export function useDraftManager(isInitialLoadComplete: boolean) {
   const [state, setState] = useState<DraftRecoveryState>({
     isOpen: false,
     draft: null,
-    diff: null,
   });
 
   const [{ flowID, flowVersion }] = useQueryStates({
@@ -214,16 +207,9 @@ export function useDraftManager(isInitialLoadComplete: boolean) {
       );
 
       if (isDifferent && (draft.nodes.length > 0 || draft.edges.length > 0)) {
-        const diff = calculateDraftDiff(
-          draft.nodes,
-          draft.edges,
-          currentNodes,
-          currentEdges,
-        );
         setState({
           isOpen: true,
           draft,
-          diff,
         });
       } else {
         await draftService.deleteDraft(effectiveFlowId);
@@ -245,7 +231,6 @@ export function useDraftManager(isInitialLoadComplete: boolean) {
     setState({
       isOpen: false,
       draft: null,
-      diff: null,
     });
   }, [flowID]);
 
@@ -257,10 +242,8 @@ export function useDraftManager(isInitialLoadComplete: boolean) {
     try {
       useNodeStore.getState().setNodes(draft.nodes);
       useEdgeStore.getState().setEdges(draft.edges);
-      draft.nodes.forEach((node) => {
-        useNodeStore.getState().syncHardcodedValuesWithHandleIds(node.id);
-      });
 
+      // Restore nodeCounter to prevent ID conflicts when adding new nodes
       if (draft.nodeCounter !== undefined) {
         useNodeStore.setState({ nodeCounter: draft.nodeCounter });
       }
@@ -284,7 +267,6 @@ export function useDraftManager(isInitialLoadComplete: boolean) {
       setState({
         isOpen: false,
         draft: null,
-        diff: null,
       });
     } catch (error) {
       console.error("[DraftRecovery] Failed to load draft:", error);
@@ -293,7 +275,7 @@ export function useDraftManager(isInitialLoadComplete: boolean) {
 
   const discardDraft = useCallback(async () => {
     if (!state.draft) {
-      setState({ isOpen: false, draft: null, diff: null });
+      setState({ isOpen: false, draft: null });
       return;
     }
 
@@ -303,7 +285,7 @@ export function useDraftManager(isInitialLoadComplete: boolean) {
       console.error("[DraftRecovery] Failed to discard draft:", error);
     }
 
-    setState({ isOpen: false, draft: null, diff: null });
+    setState({ isOpen: false, draft: null });
   }, [state.draft]);
 
   return {
@@ -312,7 +294,6 @@ export function useDraftManager(isInitialLoadComplete: boolean) {
     savedAt: state.draft?.savedAt ?? 0,
     nodeCount: state.draft?.nodes.length ?? 0,
     edgeCount: state.draft?.edges.length ?? 0,
-    diff: state.diff,
     loadDraft,
     discardDraft,
   };

autogpt_platform/frontend/src/app/(platform)/build/components/FlowEditor/Flow/useFlow.ts

@@ -121,14 +121,6 @@ export const useFlow = () => {
     if (customNodes.length > 0) {
       useNodeStore.getState().setNodes([]);
       addNodes(customNodes);
-
-      // Sync hardcoded values with handle IDs.
-      // If a key–value field has a key without a value, the backend omits it from hardcoded values.
-      // But if a handleId exists for that key, it causes inconsistency.
-      // This ensures hardcoded values stay in sync with handle IDs.
-      customNodes.forEach((node) => {
-        useNodeStore.getState().syncHardcodedValuesWithHandleIds(node.id);
-      });
     }
   }, [customNodes, addNodes]);
 

autogpt_platform/frontend/src/app/(platform)/build/components/FlowEditor/edges/useCustomEdge.ts

@@ -1,17 +1,12 @@
-import {
-  Connection as RFConnection,
-  EdgeChange,
-  applyEdgeChanges,
-} from "@xyflow/react";
+import { Connection as RFConnection, EdgeChange } from "@xyflow/react";
 import { useEdgeStore } from "@/app/(platform)/build/stores/edgeStore";
 import { useCallback } from "react";
 import { useNodeStore } from "../../../stores/nodeStore";
-import { CustomEdge } from "./CustomEdge";
 
 export const useCustomEdge = () => {
   const edges = useEdgeStore((s) => s.edges);
   const addEdge = useEdgeStore((s) => s.addEdge);
-  const setEdges = useEdgeStore((s) => s.setEdges);
+  const removeEdge = useEdgeStore((s) => s.removeEdge);
 
   const onConnect = useCallback(
     (conn: RFConnection) => {
@@ -50,10 +45,14 @@ export const useCustomEdge = () => {
   );
 
   const onEdgesChange = useCallback(
-    (changes: EdgeChange<CustomEdge>[]) => {
-      setEdges(applyEdgeChanges(changes, edges));
+    (changes: EdgeChange[]) => {
+      changes.forEach((change) => {
+        if (change.type === "remove") {
+          removeEdge(change.id);
+        }
+      });
     },
-    [edges, setEdges],
+    [removeEdge],
   );
 
   return { edges, onConnect, onEdgesChange };

autogpt_platform/frontend/src/app/(platform)/build/components/FlowEditor/handlers/NodeHandle.tsx

@@ -1,32 +1,26 @@
 import { CircleIcon } from "@phosphor-icons/react";
 import { Handle, Position } from "@xyflow/react";
-import { useEdgeStore } from "../../../stores/edgeStore";
-import { cleanUpHandleId } from "@/components/renderers/InputRenderer/helpers";
-import { cn } from "@/lib/utils";
 
-const InputNodeHandle = ({
+const NodeHandle = ({
   handleId,
-  nodeId,
+  isConnected,
+  side,
 }: {
   handleId: string;
-  nodeId: string;
+  isConnected: boolean;
+  side: "left" | "right";
 }) => {
-  const cleanedHandleId = cleanUpHandleId(handleId);
-  const isInputConnected = useEdgeStore((state) =>
-    state.isInputConnected(nodeId ?? "", cleanedHandleId),
-  );
-
   return (
     <Handle
-      type={"target"}
-      position={Position.Left}
-      id={cleanedHandleId}
-      className={"-ml-6 mr-2"}
+      type={side === "left" ? "target" : "source"}
+      position={side === "left" ? Position.Left : Position.Right}
+      id={handleId}
+      className={side === "left" ? "-ml-4 mr-2" : "-mr-2 ml-2"}
     >
       <div className="pointer-events-none">
         <CircleIcon
           size={16}
-          weight={isInputConnected ? "fill" : "duotone"}
+          weight={isConnected ? "fill" : "duotone"}
           className={"text-gray-400 opacity-100"}
         />
       </div>
@@ -34,35 +28,4 @@ const InputNodeHandle = ({
   );
 };
 
-const OutputNodeHandle = ({
-  field_name,
-  nodeId,
-  hexColor,
-}: {
-  field_name: string;
-  nodeId: string;
-  hexColor: string;
-}) => {
-  const isOutputConnected = useEdgeStore((state) =>
-    state.isOutputConnected(nodeId, field_name),
-  );
-  return (
-    <Handle
-      type={"source"}
-      position={Position.Right}
-      id={field_name}
-      className={"-mr-2 ml-2"}
-    >
-      <div className="pointer-events-none">
-        <CircleIcon
-          size={16}
-          weight={"duotone"}
-          color={isOutputConnected ? hexColor : "gray"}
-          className={cn("text-gray-400 opacity-100")}
-        />
-      </div>
-    </Handle>
-  );
-};
-
-export { InputNodeHandle, OutputNodeHandle };
+export default NodeHandle;

autogpt_platform/frontend/src/app/(platform)/build/components/FlowEditor/handlers/helpers.ts

@@ -1,4 +1,31 @@
-// Here we are handling single level of nesting, if need more in future then i will update it
+/**
+ * Handle ID Types for different input structures
+ *
+ * Examples:
+ * SIMPLE: "message"
+ * NESTED: "config.api_key"
+ * ARRAY: "items_$_0", "items_$_1"
+ * KEY_VALUE: "headers_#_Authorization", "params_#_limit"
+ *
+ * Note: All handle IDs are sanitized to remove spaces and special characters.
+ * Spaces become underscores, and special characters are removed.
+ * Example: "user name" becomes "user_name", "email@domain.com" becomes "emaildomaincom"
+ */
+export enum HandleIdType {
+  SIMPLE = "SIMPLE",
+  NESTED = "NESTED",
+  ARRAY = "ARRAY",
+  KEY_VALUE = "KEY_VALUE",
+}
+
+const fromRjsfId = (id: string): string => {
+  if (!id) return "";
+  const parts = id.split("_");
+  const filtered = parts.filter(
+    (p) => p !== "root" && p !== "properties" && p.length > 0,
+  );
+  return filtered.join("_") || "";
+};
 
 const sanitizeForHandleId = (str: string): string => {
   if (!str) return "";
@@ -11,53 +38,51 @@ const sanitizeForHandleId = (str: string): string => {
     .replace(/^_|_$/g, ""); // Remove leading/trailing underscores
 };
 
-const cleanTitleId = (id: string): string => {
-  if (!id) return "";
-
-  if (id.endsWith("_title")) {
-    id = id.slice(0, -6);
-  }
-  const parts = id.split("_");
-  const filtered = parts.filter(
-    (p) => p !== "root" && p !== "properties" && p.length > 0,
-  );
-  const filtered_id = filtered.join("_") || "";
-  return filtered_id;
-};
-
-export const generateHandleIdFromTitleId = (
+export const generateHandleId = (
   fieldKey: string,
-  {
-    isObjectProperty,
-    isAdditionalProperty,
-    isArrayItem,
-  }: {
-    isArrayItem?: boolean;
-    isObjectProperty?: boolean;
-    isAdditionalProperty?: boolean;
-  } = {
-    isArrayItem: false,
-    isObjectProperty: false,
-    isAdditionalProperty: false,
-  },
+  nestedValues: string[] = [],
+  type: HandleIdType = HandleIdType.SIMPLE,
 ): string => {
   if (!fieldKey) return "";
 
-  const filteredKey = cleanTitleId(fieldKey);
-  if (isAdditionalProperty || isArrayItem) {
-    return filteredKey;
-  }
-  const cleanedKey = sanitizeForHandleId(filteredKey);
+  fieldKey = fromRjsfId(fieldKey);
+  fieldKey = sanitizeForHandleId(fieldKey);
 
-  if (isObjectProperty) {
-    // "config_api_key" -> "config.api_key"
-    const parts = cleanedKey.split("_");
-    if (parts.length >= 2) {
-      const baseName = parts[0];
-      const propertyName = parts.slice(1).join("_");
-      return `${baseName}.${propertyName}`;
-    }
+  if (type === HandleIdType.SIMPLE || nestedValues.length === 0) {
+    return fieldKey;
   }
 
-  return cleanedKey;
+  const sanitizedNestedValues = nestedValues.map((value) =>
+    sanitizeForHandleId(value),
+  );
+
+  switch (type) {
+    case HandleIdType.NESTED:
+      return [fieldKey, ...sanitizedNestedValues].join(".");
+
+    case HandleIdType.ARRAY:
+      return [fieldKey, ...sanitizedNestedValues].join("_$_");
+
+    case HandleIdType.KEY_VALUE:
+      return [fieldKey, ...sanitizedNestedValues].join("_#_");
+
+    default:
+      return fieldKey;
+  }
+};
+
+export const parseKeyValueHandleId = (
+  handleId: string,
+  type: HandleIdType,
+): string => {
+  if (type === HandleIdType.KEY_VALUE) {
+    return handleId.split("_#_")[1];
+  } else if (type === HandleIdType.ARRAY) {
+    return handleId.split("_$_")[1];
+  } else if (type === HandleIdType.NESTED) {
+    return handleId.split(".")[1];
+  } else if (type === HandleIdType.SIMPLE) {
+    return handleId.split("_")[1];
+  }
+  return "";
 };

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

@@ -1,25 +1,24 @@
-import { AgentExecutionStatus } from "@/app/api/__generated__/models/agentExecutionStatus";
-import { BlockCost } from "@/app/api/__generated__/models/blockCost";
-import { BlockInfoCategoriesItem } from "@/app/api/__generated__/models/blockInfoCategoriesItem";
-import { NodeExecutionResult } from "@/app/api/__generated__/models/nodeExecutionResult";
-import { NodeModelMetadata } from "@/app/api/__generated__/models/nodeModelMetadata";
-import { preprocessInputSchema } from "@/components/renderers/InputRenderer/utils/input-schema-pre-processor";
-import { cn } from "@/lib/utils";
-import { RJSFSchema } from "@rjsf/utils";
-import { NodeProps, Node as XYNode } from "@xyflow/react";
 import React from "react";
+import { Node as XYNode, NodeProps } from "@xyflow/react";
+import { RJSFSchema } from "@rjsf/utils";
 import { BlockUIType } from "../../../types";
-import { FormCreator } from "../FormCreator";
-import { OutputHandler } from "../OutputHandler";
-import { AyrshareConnectButton } from "./components/AyrshareConnectButton";
-import { NodeAdvancedToggle } from "./components/NodeAdvancedToggle";
-import { NodeContainer } from "./components/NodeContainer";
-import { NodeExecutionBadge } from "./components/NodeExecutionBadge";
-import { NodeHeader } from "./components/NodeHeader";
-import { NodeDataRenderer } from "./components/NodeOutput/NodeOutput";
-import { NodeRightClickMenu } from "./components/NodeRightClickMenu";
 import { StickyNoteBlock } from "./components/StickyNoteBlock";
+import { BlockInfoCategoriesItem } from "@/app/api/__generated__/models/blockInfoCategoriesItem";
+import { BlockCost } from "@/app/api/__generated__/models/blockCost";
+import { AgentExecutionStatus } from "@/app/api/__generated__/models/agentExecutionStatus";
+import { NodeExecutionResult } from "@/app/api/__generated__/models/nodeExecutionResult";
+import { NodeContainer } from "./components/NodeContainer";
+import { NodeHeader } from "./components/NodeHeader";
+import { FormCreator } from "../FormCreator";
+import { preprocessInputSchema } from "@/components/renderers/input-renderer/utils/input-schema-pre-processor";
+import { OutputHandler } from "../OutputHandler";
+import { NodeAdvancedToggle } from "./components/NodeAdvancedToggle";
+import { NodeDataRenderer } from "./components/NodeOutput/NodeOutput";
+import { NodeExecutionBadge } from "./components/NodeExecutionBadge";
+import { cn } from "@/lib/utils";
 import { WebhookDisclaimer } from "./components/WebhookDisclaimer";
+import { AyrshareConnectButton } from "./components/AyrshareConnectButton";
+import { NodeModelMetadata } from "@/app/api/__generated__/models/nodeModelMetadata";
 
 export type CustomNodeData = {
   hardcodedValues: {
@@ -89,7 +88,7 @@ export const CustomNode: React.FC<NodeProps<CustomNode>> = React.memo(
 
     // Currently all blockTypes design are similar - that's why i am using the same component for all of them
     // If in future - if we need some drastic change in some blockTypes design - we can create separate components for them
-    const node = (
+    return (
       <NodeContainer selected={selected} nodeId={nodeId} hasErrors={hasErrors}>
         <div className="rounded-xlarge bg-white">
           <NodeHeader data={data} nodeId={nodeId} />
@@ -100,7 +99,7 @@ export const CustomNode: React.FC<NodeProps<CustomNode>> = React.memo(
             nodeId={nodeId}
             uiType={data.uiType}
             className={cn(
-              "bg-white px-4",
+              "bg-white pr-6",
               isWebhook && "pointer-events-none opacity-50",
             )}
             showHandles={showHandles}
@@ -118,15 +117,6 @@ export const CustomNode: React.FC<NodeProps<CustomNode>> = React.memo(
         <NodeExecutionBadge nodeId={nodeId} />
       </NodeContainer>
     );
-
-    return (
-      <NodeRightClickMenu
-        nodeId={nodeId}
-        subGraphID={data.hardcodedValues?.graph_id}
-      >
-        {node}
-      </NodeRightClickMenu>
-    );
   },
 );
 

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

@@ -8,7 +8,7 @@ export const NodeAdvancedToggle = ({ nodeId }: { nodeId: string }) => {
   );
   const setShowAdvanced = useNodeStore((state) => state.setShowAdvanced);
   return (
-    <div className="flex items-center justify-between gap-2 rounded-b-xlarge border-t border-zinc-200 bg-white px-5 py-3.5">
+    <div className="flex items-center justify-between gap-2 rounded-b-xlarge border-t border-slate-200/50 bg-white px-5 py-3.5">
       <Text variant="body" className="font-medium text-slate-700">
         Advanced
       </Text>

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

@@ -22,7 +22,7 @@ export const NodeContainer = ({
   return (
     <div
       className={cn(
-        "z-12 w-[350px] rounded-xlarge ring-1 ring-slate-200/60",
+        "z-12 max-w-[370px] rounded-xlarge ring-1 ring-slate-200/60",
         selected && "shadow-lg ring-2 ring-slate-200",
         status && nodeStyleBasedOnStatus[status],
         hasErrors ? nodeStyleBasedOnStatus[AgentExecutionStatus.FAILED] : "",

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

@@ -1,31 +1,26 @@
-import { useCopyPasteStore } from "@/app/(platform)/build/stores/copyPasteStore";
-import { useNodeStore } from "@/app/(platform)/build/stores/nodeStore";
+import { Separator } from "@/components/__legacy__/ui/separator";
 import {
   DropdownMenu,
+  DropdownMenuContent,
+  DropdownMenuItem,
   DropdownMenuTrigger,
 } from "@/components/molecules/DropdownMenu/DropdownMenu";
-import {
-  SecondaryDropdownMenuContent,
-  SecondaryDropdownMenuItem,
-  SecondaryDropdownMenuSeparator,
-} from "@/components/molecules/SecondaryMenu/SecondaryMenu";
-import {
-  ArrowSquareOutIcon,
-  CopyIcon,
-  DotsThreeOutlineVerticalIcon,
-  TrashIcon,
-} from "@phosphor-icons/react";
+import { DotsThreeOutlineVerticalIcon } from "@phosphor-icons/react";
+import { Copy, Trash2, ExternalLink } from "lucide-react";
+import { useNodeStore } from "@/app/(platform)/build/stores/nodeStore";
+import { useCopyPasteStore } from "@/app/(platform)/build/stores/copyPasteStore";
 import { useReactFlow } from "@xyflow/react";
 
-type Props = {
+export const NodeContextMenu = ({
+  nodeId,
+  subGraphID,
+}: {
   nodeId: string;
   subGraphID?: string;
-};
-
-export const NodeContextMenu = ({ nodeId, subGraphID }: Props) => {
+}) => {
   const { deleteElements } = useReactFlow();
 
-  function handleCopy() {
+  const handleCopy = () => {
     useNodeStore.setState((state) => ({
       nodes: state.nodes.map((node) => ({
         ...node,
@@ -35,47 +30,47 @@ export const NodeContextMenu = ({ nodeId, subGraphID }: Props) => {
 
     useCopyPasteStore.getState().copySelectedNodes();
     useCopyPasteStore.getState().pasteNodes();
-  }
+  };
 
-  function handleDelete() {
+  const handleDelete = () => {
     deleteElements({ nodes: [{ id: nodeId }] });
-  }
+  };
 
   return (
     <DropdownMenu>
       <DropdownMenuTrigger className="py-2">
         <DotsThreeOutlineVerticalIcon size={16} weight="fill" />
       </DropdownMenuTrigger>
-      <SecondaryDropdownMenuContent side="right" align="start">
-        <SecondaryDropdownMenuItem onClick={handleCopy}>
-          <CopyIcon size={20} className="mr-2 dark:text-gray-100" />
-          <span className="dark:text-gray-100">Copy</span>
-        </SecondaryDropdownMenuItem>
-        <SecondaryDropdownMenuSeparator />
+      <DropdownMenuContent
+        side="right"
+        align="start"
+        className="rounded-xlarge"
+      >
+        <DropdownMenuItem onClick={handleCopy} className="hover:rounded-xlarge">
+          <Copy className="mr-2 h-4 w-4" />
+          Copy Node
+        </DropdownMenuItem>
 
         {subGraphID && (
-          <>
-            <SecondaryDropdownMenuItem
-              onClick={() => window.open(`/build?flowID=${subGraphID}`)}
-            >
-              <ArrowSquareOutIcon
-                size={20}
-                className="mr-2 dark:text-gray-100"
-              />
-              <span className="dark:text-gray-100">Open agent</span>
-            </SecondaryDropdownMenuItem>
-            <SecondaryDropdownMenuSeparator />
-          </>
+          <DropdownMenuItem
+            onClick={() => window.open(`/build?flowID=${subGraphID}`)}
+            className="hover:rounded-xlarge"
+          >
+            <ExternalLink className="mr-2 h-4 w-4" />
+            Open Agent
+          </DropdownMenuItem>
         )}
 
-        <SecondaryDropdownMenuItem variant="destructive" onClick={handleDelete}>
-          <TrashIcon
-            size={20}
-            className="mr-2 text-red-500 dark:text-red-400"
-          />
-          <span className="dark:text-red-400">Delete</span>
-        </SecondaryDropdownMenuItem>
-      </SecondaryDropdownMenuContent>
+        <Separator className="my-2" />
+
+        <DropdownMenuItem
+          onClick={handleDelete}
+          className="text-red-600 hover:rounded-xlarge"
+        >
+          <Trash2 className="mr-2 h-4 w-4" />
+          Delete
+        </DropdownMenuItem>
+      </DropdownMenuContent>
     </DropdownMenu>
   );
 };

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

@@ -1,30 +1,29 @@
-import { useNodeStore } from "@/app/(platform)/build/stores/nodeStore";
 import { Text } from "@/components/atoms/Text/Text";
+import { beautifyString, cn } from "@/lib/utils";
+import { NodeCost } from "./NodeCost";
+import { NodeBadges } from "./NodeBadges";
+import { NodeContextMenu } from "./NodeContextMenu";
+import { CustomNodeData } from "../CustomNode";
+import { useNodeStore } from "@/app/(platform)/build/stores/nodeStore";
+import { useState } from "react";
 import {
   Tooltip,
   TooltipContent,
   TooltipProvider,
   TooltipTrigger,
 } from "@/components/atoms/Tooltip/BaseTooltip";
-import { beautifyString, cn } from "@/lib/utils";
-import { useState } from "react";
-import { CustomNodeData } from "../CustomNode";
-import { NodeBadges } from "./NodeBadges";
-import { NodeContextMenu } from "./NodeContextMenu";
-import { NodeCost } from "./NodeCost";
 
-type Props = {
+export const NodeHeader = ({
+  data,
+  nodeId,
+}: {
   data: CustomNodeData;
   nodeId: string;
-};
-
-export const NodeHeader = ({ data, nodeId }: Props) => {
+}) => {
   const updateNodeData = useNodeStore((state) => state.updateNodeData);
   const title = (data.metadata?.customized_name as string) || data.title;
   const [isEditingTitle, setIsEditingTitle] = useState(false);
-  const [editedTitle, setEditedTitle] = useState(
-    beautifyString(title).replace("Block", "").trim(),
-  );
+  const [editedTitle, setEditedTitle] = useState(title);
 
   const handleTitleEdit = () => {
     updateNodeData(nodeId, {
@@ -42,7 +41,7 @@ export const NodeHeader = ({ data, nodeId }: Props) => {
   };
 
   return (
-    <div className="flex h-auto flex-col gap-1 rounded-xlarge border-b border-zinc-200 bg-gradient-to-r from-slate-50/80 to-white/90 px-4 py-4 pt-3">
+    <div className="flex h-auto flex-col gap-1 rounded-xlarge border-b border-slate-200/50 bg-gradient-to-r from-slate-50/80 to-white/90 px-4 py-4 pt-3">
       {/* Title row with context menu */}
       <div className="flex items-start justify-between gap-2">
         <div className="flex min-w-0 flex-1 items-center gap-2">
@@ -69,12 +68,12 @@ export const NodeHeader = ({ data, nodeId }: Props) => {
                   <TooltipTrigger asChild>
                     <div>
                       <Text variant="large-semibold" className="line-clamp-1">
-                        {beautifyString(title).replace("Block", "").trim()}
+                        {beautifyString(title)}
                       </Text>
                     </div>
                   </TooltipTrigger>
                   <TooltipContent>
-                    <p>{beautifyString(title).replace("Block", "").trim()}</p>
+                    <p>{beautifyString(title)}</p>
                   </TooltipContent>
                 </Tooltip>
               </TooltipProvider>

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

@@ -23,7 +23,7 @@ export const NodeDataRenderer = ({ nodeId }: { nodeId: string }) => {
   }
 
   return (
-    <div className="flex flex-col gap-3 rounded-b-xl border-t border-zinc-200 px-4 py-4">
+    <div className="flex flex-col gap-3 rounded-b-xl border-t border-slate-200/50 px-4 py-4">
       <div className="flex items-center justify-between">
         <Text variant="body-medium" className="!font-semibold text-slate-700">
           Node Output

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

@@ -151,7 +151,7 @@ export const NodeDataViewer: FC<NodeDataViewerProps> = ({
         </div>
 
         <div className="flex justify-end pt-4">
-          {outputItems.length > 1 && (
+          {outputItems.length > 0 && (
             <OutputActions
               items={outputItems.map((item) => ({
                 value: item.value,

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

@@ -1,104 +0,0 @@
-import { useCopyPasteStore } from "@/app/(platform)/build/stores/copyPasteStore";
-import { useNodeStore } from "@/app/(platform)/build/stores/nodeStore";
-import {
-  SecondaryMenuContent,
-  SecondaryMenuItem,
-  SecondaryMenuSeparator,
-} from "@/components/molecules/SecondaryMenu/SecondaryMenu";
-import { ArrowSquareOutIcon, CopyIcon, TrashIcon } from "@phosphor-icons/react";
-import * as ContextMenu from "@radix-ui/react-context-menu";
-import { useReactFlow } from "@xyflow/react";
-import { useEffect, useRef } from "react";
-import { CustomNode } from "../CustomNode";
-
-type Props = {
-  nodeId: string;
-  subGraphID?: string;
-  children: React.ReactNode;
-};
-
-const DOUBLE_CLICK_TIMEOUT = 300;
-
-export function NodeRightClickMenu({ nodeId, subGraphID, children }: Props) {
-  const { deleteElements } = useReactFlow<CustomNode>();
-  const lastRightClickTime = useRef<number>(0);
-  const containerRef = useRef<HTMLDivElement>(null);
-
-  function copyNode() {
-    useNodeStore.setState((state) => ({
-      nodes: state.nodes.map((node) => ({
-        ...node,
-        selected: node.id === nodeId,
-      })),
-    }));
-
-    useCopyPasteStore.getState().copySelectedNodes();
-    useCopyPasteStore.getState().pasteNodes();
-  }
-
-  function deleteNode() {
-    deleteElements({ nodes: [{ id: nodeId }] });
-  }
-
-  useEffect(() => {
-    const container = containerRef.current;
-    if (!container) return;
-
-    function handleContextMenu(e: MouseEvent) {
-      const now = Date.now();
-      const timeSinceLastClick = now - lastRightClickTime.current;
-
-      if (timeSinceLastClick < DOUBLE_CLICK_TIMEOUT) {
-        e.stopImmediatePropagation();
-        lastRightClickTime.current = 0;
-        return;
-      }
-
-      lastRightClickTime.current = now;
-    }
-
-    container.addEventListener("contextmenu", handleContextMenu, true);
-
-    return () => {
-      container.removeEventListener("contextmenu", handleContextMenu, true);
-    };
-  }, []);
-
-  return (
-    <ContextMenu.Root>
-      <ContextMenu.Trigger asChild>
-        <div ref={containerRef}>{children}</div>
-      </ContextMenu.Trigger>
-      <SecondaryMenuContent>
-        <SecondaryMenuItem onSelect={copyNode}>
-          <CopyIcon size={20} className="mr-2 dark:text-gray-100" />
-          <span className="dark:text-gray-100">Copy</span>
-        </SecondaryMenuItem>
-        <SecondaryMenuSeparator />
-
-        {subGraphID && (
-          <>
-            <SecondaryMenuItem
-              onClick={() => window.open(`/build?flowID=${subGraphID}`)}
-            >
-              <ArrowSquareOutIcon
-                size={20}
-                className="mr-2 dark:text-gray-100"
-              />
-              <span className="dark:text-gray-100">Open agent</span>
-            </SecondaryMenuItem>
-            <SecondaryMenuSeparator />
-          </>
-        )}
-
-        <SecondaryMenuItem variant="destructive" onSelect={deleteNode}>
-          <TrashIcon
-            size={20}
-            className="mr-2 text-red-500 dark:text-red-400"
-          />
-          <span className="dark:text-red-400">Delete</span>
-        </SecondaryMenuItem>
-      </SecondaryMenuContent>
-    </ContextMenu.Root>
-  );
-}

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

@@ -1,6 +1,6 @@
 import { useMemo } from "react";
 import { FormCreator } from "../../FormCreator";
-import { preprocessInputSchema } from "@/components/renderers/InputRenderer/utils/input-schema-pre-processor";
+import { preprocessInputSchema } from "@/components/renderers/input-renderer/utils/input-schema-pre-processor";
 import { CustomNodeData } from "../CustomNode";
 import { Text } from "@/components/atoms/Text/Text";
 import { cn } from "@/lib/utils";

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

@@ -3,7 +3,7 @@ import React from "react";
 import { uiSchema } from "./uiSchema";
 import { useNodeStore } from "../../../stores/nodeStore";
 import { BlockUIType } from "../../types";
-import { FormRenderer } from "@/components/renderers/InputRenderer/FormRenderer";
+import { FormRenderer } from "@/components/renderers/input-renderer/FormRenderer";
 
 export const FormCreator = React.memo(
   ({

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

@@ -4,7 +4,7 @@ import { CaretDownIcon, InfoIcon } from "@phosphor-icons/react";
 import { RJSFSchema } from "@rjsf/utils";
 import { useState } from "react";
 
-import { OutputNodeHandle } from "../handlers/NodeHandle";
+import NodeHandle from "../handlers/NodeHandle";
 import {
   Tooltip,
   TooltipContent,
@@ -13,6 +13,7 @@ import {
 } from "@/components/atoms/Tooltip/BaseTooltip";
 import { useEdgeStore } from "@/app/(platform)/build/stores/edgeStore";
 import { getTypeDisplayInfo } from "./helpers";
+import { generateHandleId } from "../handlers/helpers";
 import { BlockUIType } from "../../types";
 
 export const OutputHandler = ({
@@ -28,73 +29,8 @@ export const OutputHandler = ({
   const properties = outputSchema?.properties || {};
   const [isOutputVisible, setIsOutputVisible] = useState(true);
 
-  const showHandles = uiType !== BlockUIType.OUTPUT;
-
-  const renderOutputHandles = (
-    schema: RJSFSchema,
-    keyPrefix: string = "",
-    titlePrefix: string = "",
-  ): React.ReactNode[] => {
-    return Object.entries(schema).map(
-      ([key, fieldSchema]: [string, RJSFSchema]) => {
-        const fullKey = keyPrefix ? `${keyPrefix}_#_${key}` : key;
-        const fieldTitle = titlePrefix + (fieldSchema?.title || key);
-
-        const isConnected = isOutputConnected(nodeId, fullKey);
-        const shouldShow = isConnected || isOutputVisible;
-        const { displayType, colorClass, hexColor } =
-          getTypeDisplayInfo(fieldSchema);
-
-        return shouldShow ? (
-          <div key={fullKey} className="flex flex-col items-end gap-2">
-            <div className="relative flex items-center gap-2">
-              {fieldSchema?.description && (
-                <TooltipProvider>
-                  <Tooltip>
-                    <TooltipTrigger asChild>
-                      <span
-                        style={{ marginLeft: 6, cursor: "pointer" }}
-                        aria-label="info"
-                        tabIndex={0}
-                      >
-                        <InfoIcon />
-                      </span>
-                    </TooltipTrigger>
-                    <TooltipContent>{fieldSchema?.description}</TooltipContent>
-                  </Tooltip>
-                </TooltipProvider>
-              )}
-              <Text variant="body" className="text-slate-700">
-                {fieldTitle}
-              </Text>
-              <Text variant="small" as="span" className={colorClass}>
-                ({displayType})
-              </Text>
-
-              {showHandles && (
-                <OutputNodeHandle
-                  field_name={fullKey}
-                  nodeId={nodeId}
-                  hexColor={hexColor}
-                />
-              )}
-            </div>
-
-            {/* Recursively render nested properties */}
-            {fieldSchema?.properties &&
-              renderOutputHandles(
-                fieldSchema.properties,
-                fullKey,
-                `${fieldTitle}.`,
-              )}
-          </div>
-        ) : null;
-      },
-    );
-  };
-
   return (
-    <div className="flex flex-col items-end justify-between gap-2 rounded-b-xlarge border-t border-zinc-200 bg-white py-3.5">
+    <div className="flex flex-col items-end justify-between gap-2 rounded-b-xlarge border-t border-slate-200/50 bg-white py-3.5">
       <Button
         variant="ghost"
         className="mr-4 h-fit min-w-0 p-0 hover:border-transparent hover:bg-transparent"
@@ -113,9 +49,50 @@ export const OutputHandler = ({
         </Text>
       </Button>
 
-      <div className="flex flex-col items-end gap-2">
-        {renderOutputHandles(properties)}
-      </div>
+      {
+        <div className="flex flex-col items-end gap-2">
+          {Object.entries(properties).map(([key, property]: [string, any]) => {
+            const isConnected = isOutputConnected(nodeId, key);
+            const shouldShow = isConnected || isOutputVisible;
+            const { displayType, colorClass } = getTypeDisplayInfo(property);
+
+            return shouldShow ? (
+              <div key={key} className="relative flex items-center gap-2">
+                {property?.description && (
+                  <TooltipProvider>
+                    <Tooltip>
+                      <TooltipTrigger asChild>
+                        <span
+                          style={{ marginLeft: 6, cursor: "pointer" }}
+                          aria-label="info"
+                          tabIndex={0}
+                        >
+                          <InfoIcon />
+                        </span>
+                      </TooltipTrigger>
+                      <TooltipContent>{property?.description}</TooltipContent>
+                    </Tooltip>
+                  </TooltipProvider>
+                )}
+                <Text variant="body" className="text-slate-700">
+                  {property?.title || key}{" "}
+                </Text>
+                <Text variant="small" as="span" className={colorClass}>
+                  ({displayType})
+                </Text>
+
+                <NodeHandle
+                  handleId={
+                    uiType === BlockUIType.AGENT ? key : generateHandleId(key)
+                  }
+                  isConnected={isConnected}
+                  side="right"
+                />
+              </div>
+            ) : null;
+          })}
+        </div>
+      }
     </div>
   );
 };

autogpt_platform/frontend/src/app/(platform)/build/components/FlowEditor/nodes/helpers.ts

@@ -92,38 +92,14 @@ export const getTypeDisplayInfo = (schema: any) => {
   if (schema?.type === "string" && schema?.format) {
     const formatMap: Record<
       string,
-      { displayType: string; colorClass: string; hexColor: string }
+      { displayType: string; colorClass: string }
     > = {
-      file: {
-        displayType: "file",
-        colorClass: "!text-green-500",
-        hexColor: "#22c55e",
-      },
-      date: {
-        displayType: "date",
-        colorClass: "!text-blue-500",
-        hexColor: "#3b82f6",
-      },
-      time: {
-        displayType: "time",
-        colorClass: "!text-blue-500",
-        hexColor: "#3b82f6",
-      },
-      "date-time": {
-        displayType: "datetime",
-        colorClass: "!text-blue-500",
-        hexColor: "#3b82f6",
-      },
-      "long-text": {
-        displayType: "text",
-        colorClass: "!text-green-500",
-        hexColor: "#22c55e",
-      },
-      "short-text": {
-        displayType: "text",
-        colorClass: "!text-green-500",
-        hexColor: "#22c55e",
-      },
+      file: { displayType: "file", colorClass: "!text-green-500" },
+      date: { displayType: "date", colorClass: "!text-blue-500" },
+      time: { displayType: "time", colorClass: "!text-blue-500" },
+      "date-time": { displayType: "datetime", colorClass: "!text-blue-500" },
+      "long-text": { displayType: "text", colorClass: "!text-green-500" },
+      "short-text": { displayType: "text", colorClass: "!text-green-500" },
     };
 
     const formatInfo = formatMap[schema.format];
@@ -155,23 +131,10 @@ export const getTypeDisplayInfo = (schema: any) => {
     any: "!text-gray-500",
   };
 
-  const hexColorMap: Record<string, string> = {
-    string: "#22c55e",
-    number: "#3b82f6",
-    integer: "#3b82f6",
-    boolean: "#eab308",
-    object: "#a855f7",
-    array: "#6366f1",
-    null: "#6b7280",
-    any: "#6b7280",
-  };
-
   const colorClass = colorMap[schema?.type] || "!text-gray-500";
-  const hexColor = hexColorMap[schema?.type] || "#6b7280";
 
   return {
     displayType,
     colorClass,
-    hexColor,
   };
 };

autogpt_platform/frontend/src/app/(platform)/build/components/FlowEditor/nodes/uiSchema.ts

@@ -1,6 +1,6 @@
 export const uiSchema = {
   credentials: {
-    "ui:field": "custom/credential_field",
+    "ui:field": "credentials",
     provider: { "ui:widget": "hidden" },
     type: { "ui:widget": "hidden" },
     id: { "ui:autofocus": true },

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

@@ -1,57 +0,0 @@
-import { useBlockMenuStore } from "@/app/(platform)/build/stores/blockMenuStore";
-import { FilterChip } from "../FilterChip";
-import { categories } from "./constants";
-import { FilterSheet } from "../FilterSheet/FilterSheet";
-import { GetV2BuilderSearchFilterAnyOfItem } from "@/app/api/__generated__/models/getV2BuilderSearchFilterAnyOfItem";
-
-export const BlockMenuFilters = () => {
-  const {
-    filters,
-    addFilter,
-    removeFilter,
-    categoryCounts,
-    creators,
-    addCreator,
-    removeCreator,
-  } = useBlockMenuStore();
-
-  const handleFilterClick = (filter: GetV2BuilderSearchFilterAnyOfItem) => {
-    if (filters.includes(filter)) {
-      removeFilter(filter);
-    } else {
-      addFilter(filter);
-    }
-  };
-
-  const handleCreatorClick = (creator: string) => {
-    if (creators.includes(creator)) {
-      removeCreator(creator);
-    } else {
-      addCreator(creator);
-    }
-  };
-
-  return (
-    <div className="flex flex-wrap gap-2">
-      <FilterSheet categories={categories} />
-      {creators.length > 0 &&
-        creators.map((creator) => (
-          <FilterChip
-            key={creator}
-            name={"Created by " + creator.slice(0, 10) + "..."}
-            selected={creators.includes(creator)}
-            onClick={() => handleCreatorClick(creator)}
-          />
-        ))}
-      {categories.map((category) => (
-        <FilterChip
-          key={category.key}
-          name={category.name}
-          selected={filters.includes(category.key)}
-          onClick={() => handleFilterClick(category.key)}
-          number={categoryCounts[category.key] ?? 0}
-        />
-      ))}
-    </div>
-  );
-};

autogpt_platform/frontend/src/app/(platform)/build/components/NewControlPanel/NewBlockMenu/BlockMenuFilters/constants.ts

@@ -1,15 +0,0 @@
-import { GetV2BuilderSearchFilterAnyOfItem } from "@/app/api/__generated__/models/getV2BuilderSearchFilterAnyOfItem";
-import { CategoryKey } from "./types";
-
-export const categories: Array<{ key: CategoryKey; name: string }> = [
-  { key: GetV2BuilderSearchFilterAnyOfItem.blocks, name: "Blocks" },
-  {
-    key: GetV2BuilderSearchFilterAnyOfItem.integrations,
-    name: "Integrations",
-  },
-  {
-    key: GetV2BuilderSearchFilterAnyOfItem.marketplace_agents,
-    name: "Marketplace agents",
-  },
-  { key: GetV2BuilderSearchFilterAnyOfItem.my_agents, name: "My agents" },
-];

autogpt_platform/frontend/src/app/(platform)/build/components/NewControlPanel/NewBlockMenu/BlockMenuFilters/types.ts

@@ -1,26 +0,0 @@
-import { GetV2BuilderSearchFilterAnyOfItem } from "@/app/api/__generated__/models/getV2BuilderSearchFilterAnyOfItem";
-
-export type DefaultStateType =
-  | "suggestion"
-  | "all_blocks"
-  | "input_blocks"
-  | "action_blocks"
-  | "output_blocks"
-  | "integrations"
-  | "marketplace_agents"
-  | "my_agents";
-
-export type CategoryKey = GetV2BuilderSearchFilterAnyOfItem;
-
-export interface Filters {
-  categories: {
-    blocks: boolean;
-    integrations: boolean;
-    marketplace_agents: boolean;
-    my_agents: boolean;
-    providers: boolean;
-  };
-  createdBy: string[];
-}
-
-export type CategoryCounts = Record<CategoryKey, number>;

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

@@ -1,14 +1,111 @@
 import { Text } from "@/components/atoms/Text/Text";
+import { useBlockMenuSearch } from "./useBlockMenuSearch";
+import { InfiniteScroll } from "@/components/contextual/InfiniteScroll/InfiniteScroll";
+import { LoadingSpinner } from "@/components/__legacy__/ui/loading";
+import { SearchResponseItemsItem } from "@/app/api/__generated__/models/searchResponseItemsItem";
+import { MarketplaceAgentBlock } from "../MarketplaceAgentBlock";
+import { Block } from "../Block";
+import { UGCAgentBlock } from "../UGCAgentBlock";
+import { getSearchItemType } from "./helper";
+import { useBlockMenuStore } from "../../../../stores/blockMenuStore";
 import { blockMenuContainerStyle } from "../style";
-import { BlockMenuFilters } from "../BlockMenuFilters/BlockMenuFilters";
-import { BlockMenuSearchContent } from "../BlockMenuSearchContent/BlockMenuSearchContent";
+import { cn } from "@/lib/utils";
+import { NoSearchResult } from "../NoSearchResult";
 
 export const BlockMenuSearch = () => {
+  const {
+    searchResults,
+    isFetchingNextPage,
+    fetchNextPage,
+    hasNextPage,
+    searchLoading,
+    handleAddLibraryAgent,
+    handleAddMarketplaceAgent,
+    addingLibraryAgentId,
+    addingMarketplaceAgentSlug,
+  } = useBlockMenuSearch();
+  const { searchQuery } = useBlockMenuStore();
+
+  if (searchLoading) {
+    return (
+      <div
+        className={cn(
+          blockMenuContainerStyle,
+          "flex items-center justify-center",
+        )}
+      >
+        <LoadingSpinner className="size-13" />
+      </div>
+    );
+  }
+
+  if (searchResults.length === 0) {
+    return <NoSearchResult />;
+  }
+
   return (
     <div className={blockMenuContainerStyle}>
-      <BlockMenuFilters />
       <Text variant="body-medium">Search results</Text>
-      <BlockMenuSearchContent />
+      <InfiniteScroll
+        isFetchingNextPage={isFetchingNextPage}
+        fetchNextPage={fetchNextPage}
+        hasNextPage={hasNextPage}
+        loader={<LoadingSpinner className="size-13" />}
+        className="space-y-2.5"
+      >
+        {searchResults.map((item: SearchResponseItemsItem, index: number) => {
+          const { type, data } = getSearchItemType(item);
+          // backend give support to these 3 types only [right now] - we need to give support to integration and ai agent types in follow up PRs
+          switch (type) {
+            case "store_agent":
+              return (
+                <MarketplaceAgentBlock
+                  key={index}
+                  slug={data.slug}
+                  highlightedText={searchQuery}
+                  title={data.agent_name}
+                  image_url={data.agent_image}
+                  creator_name={data.creator}
+                  number_of_runs={data.runs}
+                  loading={addingMarketplaceAgentSlug === data.slug}
+                  onClick={() =>
+                    handleAddMarketplaceAgent({
+                      creator_name: data.creator,
+                      slug: data.slug,
+                    })
+                  }
+                />
+              );
+            case "block":
+              return (
+                <Block
+                  key={index}
+                  title={data.name}
+                  highlightedText={searchQuery}
+                  description={data.description}
+                  blockData={data}
+                />
+              );
+
+            case "library_agent":
+              return (
+                <UGCAgentBlock
+                  key={index}
+                  title={data.name}
+                  highlightedText={searchQuery}
+                  image_url={data.image_url}
+                  version={data.graph_version}
+                  edited_time={data.updated_at}
+                  isLoading={addingLibraryAgentId === data.id}
+                  onClick={() => handleAddLibraryAgent(data)}
+                />
+              );
+
+            default:
+              return null;
+          }
+        })}
+      </InfiniteScroll>
     </div>
   );
 };

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

@@ -23,19 +23,9 @@ import { LibraryAgent } from "@/app/api/__generated__/models/libraryAgent";
 import { getQueryClient } from "@/lib/react-query/queryClient";
 import { useToast } from "@/components/molecules/Toast/use-toast";
 import * as Sentry from "@sentry/nextjs";
-import { GetV2BuilderSearchFilterAnyOfItem } from "@/app/api/__generated__/models/getV2BuilderSearchFilterAnyOfItem";
-
-export const useBlockMenuSearchContent = () => {
-  const {
-    searchQuery,
-    searchId,
-    setSearchId,
-    filters,
-    setCreatorsList,
-    creators,
-    setCategoryCounts,
-  } = useBlockMenuStore();
 
+export const useBlockMenuSearch = () => {
+  const { searchQuery, searchId, setSearchId } = useBlockMenuStore();
   const { toast } = useToast();
   const { addAgentToBuilder, addLibraryAgentToBuilder } =
     useAddAgentToBuilder();
@@ -67,8 +57,6 @@ export const useBlockMenuSearchContent = () => {
       page_size: 8,
       search_query: searchQuery,
       search_id: searchId,
-      filter: filters.length > 0 ? filters : undefined,
-      by_creator: creators.length > 0 ? creators : undefined,
     },
     {
       query: { getNextPageParam: getPaginationNextPageNumber },
@@ -110,26 +98,6 @@ export const useBlockMenuSearchContent = () => {
     }
   }, [searchQueryData, searchId, setSearchId]);
 
-  // from all the results, we need to get all the unique creators
-  useEffect(() => {
-    if (!searchQueryData?.pages?.length) {
-      return;
-    }
-    const latestData = okData(searchQueryData.pages.at(-1));
-    setCategoryCounts(
-      (latestData?.total_items as Record<
-        GetV2BuilderSearchFilterAnyOfItem,
-        number
-      >) || {
-        blocks: 0,
-        integrations: 0,
-        marketplace_agents: 0,
-        my_agents: 0,
-      },
-    );
-    setCreatorsList(latestData?.items || []);
-  }, [searchQueryData]);
-
   useEffect(() => {
     if (searchId && !searchQuery) {
       resetSearchSession();

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

@@ -1,108 +0,0 @@
-import { SearchResponseItemsItem } from "@/app/api/__generated__/models/searchResponseItemsItem";
-import { LoadingSpinner } from "@/components/atoms/LoadingSpinner/LoadingSpinner";
-import { InfiniteScroll } from "@/components/contextual/InfiniteScroll/InfiniteScroll";
-import { getSearchItemType } from "./helper";
-import { MarketplaceAgentBlock } from "../MarketplaceAgentBlock";
-import { Block } from "../Block";
-import { UGCAgentBlock } from "../UGCAgentBlock";
-import { useBlockMenuSearchContent } from "./useBlockMenuSearchContent";
-import { useBlockMenuStore } from "@/app/(platform)/build/stores/blockMenuStore";
-import { cn } from "@/lib/utils";
-import { blockMenuContainerStyle } from "../style";
-import { NoSearchResult } from "../NoSearchResult";
-
-export const BlockMenuSearchContent = () => {
-  const {
-    searchResults,
-    isFetchingNextPage,
-    fetchNextPage,
-    hasNextPage,
-    searchLoading,
-    handleAddLibraryAgent,
-    handleAddMarketplaceAgent,
-    addingLibraryAgentId,
-    addingMarketplaceAgentSlug,
-  } = useBlockMenuSearchContent();
-
-  const { searchQuery } = useBlockMenuStore();
-
-  if (searchLoading) {
-    return (
-      <div
-        className={cn(
-          blockMenuContainerStyle,
-          "flex items-center justify-center",
-        )}
-      >
-        <LoadingSpinner className="size-13" />
-      </div>
-    );
-  }
-
-  if (searchResults.length === 0) {
-    return <NoSearchResult />;
-  }
-
-  return (
-    <InfiniteScroll
-      isFetchingNextPage={isFetchingNextPage}
-      fetchNextPage={fetchNextPage}
-      hasNextPage={hasNextPage}
-      loader={<LoadingSpinner className="size-13" />}
-      className="space-y-2.5"
-    >
-      {searchResults.map((item: SearchResponseItemsItem, index: number) => {
-        const { type, data } = getSearchItemType(item);
-        // backend give support to these 3 types only [right now] - we need to give support to integration and ai agent types in follow up PRs
-        switch (type) {
-          case "store_agent":
-            return (
-              <MarketplaceAgentBlock
-                key={index}
-                slug={data.slug}
-                highlightedText={searchQuery}
-                title={data.agent_name}
-                image_url={data.agent_image}
-                creator_name={data.creator}
-                number_of_runs={data.runs}
-                loading={addingMarketplaceAgentSlug === data.slug}
-                onClick={() =>
-                  handleAddMarketplaceAgent({
-                    creator_name: data.creator,
-                    slug: data.slug,
-                  })
-                }
-              />
-            );
-          case "block":
-            return (
-              <Block
-                key={index}
-                title={data.name}
-                highlightedText={searchQuery}
-                description={data.description}
-                blockData={data}
-              />
-            );
-
-          case "library_agent":
-            return (
-              <UGCAgentBlock
-                key={index}
-                title={data.name}
-                highlightedText={searchQuery}
-                image_url={data.image_url}
-                version={data.graph_version}
-                edited_time={data.updated_at}
-                isLoading={addingLibraryAgentId === data.id}
-                onClick={() => handleAddLibraryAgent(data)}
-              />
-            );
-
-          default:
-            return null;
-        }
-      })}
-    </InfiniteScroll>
-  );
-};

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

@@ -1,9 +1,7 @@
 import { Button } from "@/components/__legacy__/ui/button";
 import { cn } from "@/lib/utils";
-import { XIcon } from "@phosphor-icons/react";
-import { AnimatePresence, motion } from "framer-motion";
-
-import React, { ButtonHTMLAttributes, useState } from "react";
+import { X } from "lucide-react";
+import React, { ButtonHTMLAttributes } from "react";
 
 interface Props extends ButtonHTMLAttributes<HTMLButtonElement> {
   selected?: boolean;
@@ -18,51 +16,39 @@ export const FilterChip: React.FC<Props> = ({
   className,
   ...rest
 }) => {
-  const [isHovered, setIsHovered] = useState(false);
   return (
-    <AnimatePresence mode="wait">
-      <Button
-        onMouseEnter={() => setIsHovered(true)}
-        onMouseLeave={() => setIsHovered(false)}
+    <Button
+      className={cn(
+        "group w-fit space-x-1 rounded-[1.5rem] border border-zinc-300 bg-transparent px-[0.625rem] py-[0.375rem] shadow-none transition-transform duration-300 ease-in-out",
+        "hover:border-violet-500 hover:bg-transparent focus:ring-0 disabled:cursor-not-allowed",
+        selected && "border-0 bg-violet-700 hover:border",
+        className,
+      )}
+      {...rest}
+    >
+      <span
         className={cn(
-          "group w-fit space-x-1 rounded-[1.5rem] border border-zinc-300 bg-transparent px-[0.625rem] py-[0.375rem] shadow-none",
-          "hover:border-violet-500 hover:bg-transparent focus:ring-0 disabled:cursor-not-allowed",
-          selected && "border-0 bg-violet-700 hover:border",
-          className,
+          "font-sans text-sm font-medium leading-[1.375rem] text-zinc-600 group-hover:text-zinc-600 group-disabled:text-zinc-400",
+          selected && "text-zinc-50",
         )}
-        {...rest}
       >
-        <span
-          className={cn(
-            "font-sans text-sm font-medium leading-[1.375rem] text-zinc-600 group-hover:text-zinc-600 group-disabled:text-zinc-400",
-            selected && "text-zinc-50",
+        {name}
+      </span>
+      {selected && (
+        <>
+          <span className="flex h-4 w-4 items-center justify-center rounded-full bg-zinc-50 transition-all duration-300 ease-in-out group-hover:hidden">
+            <X
+              className="h-3 w-3 rounded-full text-violet-700"
+              strokeWidth={2}
+            />
+          </span>
+          {number !== undefined && (
+            <span className="hidden h-[1.375rem] items-center rounded-[1.25rem] bg-violet-700 p-[0.375rem] text-zinc-50 transition-all duration-300 ease-in-out animate-in fade-in zoom-in group-hover:flex">
+              {number > 100 ? "100+" : number}
+            </span>
           )}
-        >
-          {name}
-        </span>
-        {selected && !isHovered && (
-          <motion.span
-            initial={{ opacity: 0.5, scale: 0.5, filter: "blur(20px)" }}
-            animate={{ opacity: 1, scale: 1, filter: "blur(0px)" }}
-            exit={{ opacity: 0.5, scale: 0.5, filter: "blur(20px)" }}
-            transition={{ duration: 0.3, type: "spring", bounce: 0.2 }}
-            className="flex h-4 w-4 items-center justify-center rounded-full bg-zinc-50"
-          >
-            <XIcon size={12} weight="bold" className="text-violet-700" />
-          </motion.span>
-        )}
-        {number !== undefined && isHovered && (
-          <motion.span
-            initial={{ opacity: 0.5, scale: 0.5, filter: "blur(10px)" }}
-            animate={{ opacity: 1, scale: 1, filter: "blur(0px)" }}
-            exit={{ opacity: 0.5, scale: 0.5, filter: "blur(10px)" }}
-            transition={{ duration: 0.3, type: "spring", bounce: 0.2 }}
-            className="flex h-[1.375rem] items-center rounded-[1.25rem] bg-violet-700 p-[0.375rem] text-zinc-50"
-          >
-            {number > 100 ? "100+" : number}
-          </motion.span>
-        )}
-      </Button>
-    </AnimatePresence>
+        </>
+      )}
+    </Button>
   );
 };

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

@@ -1,156 +0,0 @@
-import { FilterChip } from "../FilterChip";
-import { cn } from "@/lib/utils";
-import { CategoryKey } from "../BlockMenuFilters/types";
-import { AnimatePresence, motion } from "framer-motion";
-import { XIcon } from "@phosphor-icons/react";
-import { Button } from "@/components/atoms/Button/Button";
-import { Text } from "@/components/atoms/Text/Text";
-import { Separator } from "@/components/__legacy__/ui/separator";
-import { Checkbox } from "@/components/__legacy__/ui/checkbox";
-import { useFilterSheet } from "./useFilterSheet";
-import { INITIAL_CREATORS_TO_SHOW } from "./constant";
-
-export function FilterSheet({
-  categories,
-}: {
-  categories: Array<{ key: CategoryKey; name: string }>;
-}) {
-  const {
-    isOpen,
-    localCategories,
-    localCreators,
-    displayedCreatorsCount,
-    handleLocalCategoryChange,
-    handleToggleShowMoreCreators,
-    handleLocalCreatorChange,
-    handleClearFilters,
-    handleCloseButton,
-    handleApplyFilters,
-    hasLocalActiveFilters,
-    visibleCreators,
-    creators,
-    handleOpenFilters,
-    hasActiveFilters,
-  } = useFilterSheet();
-
-  return (
-    <div className="m-0 inline w-fit p-0">
-      <FilterChip
-        name={hasActiveFilters() ? "Edit filters" : "All filters"}
-        onClick={handleOpenFilters}
-      />
-
-      <AnimatePresence>
-        {isOpen && (
-          <motion.div
-            className={cn(
-              "absolute bottom-2 left-2 top-2 z-20 w-3/4 max-w-[22.5rem] space-y-4 overflow-hidden rounded-[0.75rem] bg-white pb-4 shadow-[0_4px_12px_2px_rgba(0,0,0,0.1)]",
-            )}
-            initial={{ x: "-100%", filter: "blur(10px)" }}
-            animate={{ x: 0, filter: "blur(0px)" }}
-            exit={{ x: "-110%", filter: "blur(10px)" }}
-            transition={{ duration: 0.4, type: "spring", bounce: 0.2 }}
-          >
-            {/* Top section */}
-            <div className="flex items-center justify-between px-5 pt-4">
-              <Text variant="body">Filters</Text>
-              <Button
-                className="p-0"
-                variant="ghost"
-                size="icon"
-                onClick={handleCloseButton}
-              >
-                <XIcon size={20} />
-              </Button>
-            </div>
-
-            <Separator className="h-[1px] w-full text-zinc-300" />
-
-            {/* Category section */}
-            <div className="space-y-4 px-5">
-              <Text variant="large">Categories</Text>
-              <div className="space-y-2">
-                {categories.map((category) => (
-                  <div
-                    key={category.key}
-                    className="flex items-center space-x-2"
-                  >
-                    <Checkbox
-                      id={category.key}
-                      checked={localCategories.includes(category.key)}
-                      onCheckedChange={() =>
-                        handleLocalCategoryChange(category.key)
-                      }
-                      className="border border-[#D4D4D4] shadow-none data-[state=checked]:border-none data-[state=checked]:bg-violet-700 data-[state=checked]:text-white"
-                    />
-                    <label
-                      htmlFor={category.key}
-                      className="font-sans text-sm leading-[1.375rem] text-zinc-600"
-                    >
-                      {category.name}
-                    </label>
-                  </div>
-                ))}
-              </div>
-            </div>
-
-            {/* Created by section */}
-            <div className="space-y-4 px-5">
-              <p className="font-sans text-base font-medium text-zinc-800">
-                Created by
-              </p>
-              <div className="space-y-2">
-                {visibleCreators.map((creator, i) => (
-                  <div key={i} className="flex items-center space-x-2">
-                    <Checkbox
-                      id={`creator-${creator}`}
-                      checked={localCreators.includes(creator)}
-                      onCheckedChange={() => handleLocalCreatorChange(creator)}
-                      className="border border-[#D4D4D4] shadow-none data-[state=checked]:border-none data-[state=checked]:bg-violet-700 data-[state=checked]:text-white"
-                    />
-                    <label
-                      htmlFor={`creator-${creator}`}
-                      className="font-sans text-sm leading-[1.375rem] text-zinc-600"
-                    >
-                      {creator}
-                    </label>
-                  </div>
-                ))}
-              </div>
-              {creators.length > INITIAL_CREATORS_TO_SHOW && (
-                <Button
-                  variant={"link"}
-                  className="m-0 p-0 font-sans text-sm font-medium leading-[1.375rem] text-zinc-800 underline hover:text-zinc-600"
-                  onClick={handleToggleShowMoreCreators}
-                >
-                  {displayedCreatorsCount < creators.length ? "More" : "Less"}
-                </Button>
-              )}
-            </div>
-
-            {/* Footer section */}
-            <div className="fixed bottom-0 flex w-full justify-between gap-3 border-t border-zinc-200 bg-white px-5 py-3">
-              <Button
-                size="small"
-                variant={"outline"}
-                onClick={handleClearFilters}
-                className="rounded-[8px] px-2 py-1.5"
-              >
-                Clear
-              </Button>
-
-              <Button
-                size="small"
-                onClick={handleApplyFilters}
-                disabled={!hasLocalActiveFilters()}
-                className="rounded-[8px] px-2 py-1.5"
-              >
-                Apply filters
-              </Button>
-            </div>
-          </motion.div>
-        )}
-      </AnimatePresence>
-    </div>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/build/components/NewControlPanel/NewBlockMenu/FilterSheet/constant.ts

@@ -1 +0,0 @@
-export const INITIAL_CREATORS_TO_SHOW = 5;

autogpt_platform/frontend/src/app/(platform)/build/components/NewControlPanel/NewBlockMenu/FilterSheet/useFilterSheet.ts

@@ -1,100 +0,0 @@
-import { useBlockMenuStore } from "@/app/(platform)/build/stores/blockMenuStore";
-import { useState } from "react";
-import { INITIAL_CREATORS_TO_SHOW } from "./constant";
-import { GetV2BuilderSearchFilterAnyOfItem } from "@/app/api/__generated__/models/getV2BuilderSearchFilterAnyOfItem";
-
-export const useFilterSheet = () => {
-  const { filters, creators_list, creators, setFilters, setCreators } =
-    useBlockMenuStore();
-
-  const [isOpen, setIsOpen] = useState(false);
-  const [localCategories, setLocalCategories] =
-    useState<GetV2BuilderSearchFilterAnyOfItem[]>(filters);
-  const [localCreators, setLocalCreators] = useState<string[]>(creators);
-  const [displayedCreatorsCount, setDisplayedCreatorsCount] = useState(
-    INITIAL_CREATORS_TO_SHOW,
-  );
-
-  const handleLocalCategoryChange = (
-    category: GetV2BuilderSearchFilterAnyOfItem,
-  ) => {
-    setLocalCategories((prev) => {
-      if (prev.includes(category)) {
-        return prev.filter((c) => c !== category);
-      }
-      return [...prev, category];
-    });
-  };
-
-  const hasActiveFilters = () => {
-    return filters.length > 0 || creators.length > 0;
-  };
-
-  const handleToggleShowMoreCreators = () => {
-    if (displayedCreatorsCount < creators.length) {
-      setDisplayedCreatorsCount(creators.length);
-    } else {
-      setDisplayedCreatorsCount(INITIAL_CREATORS_TO_SHOW);
-    }
-  };
-
-  const handleLocalCreatorChange = (creator: string) => {
-    setLocalCreators((prev) => {
-      if (prev.includes(creator)) {
-        return prev.filter((c) => c !== creator);
-      }
-      return [...prev, creator];
-    });
-  };
-
-  const handleClearFilters = () => {
-    setLocalCategories([]);
-    setLocalCreators([]);
-    setDisplayedCreatorsCount(INITIAL_CREATORS_TO_SHOW);
-  };
-
-  const handleCloseButton = () => {
-    setIsOpen(false);
-    setLocalCategories(filters);
-    setLocalCreators(creators);
-    setDisplayedCreatorsCount(INITIAL_CREATORS_TO_SHOW);
-  };
-
-  const handleApplyFilters = () => {
-    setFilters(localCategories);
-    setCreators(localCreators);
-    setIsOpen(false);
-  };
-
-  const handleOpenFilters = () => {
-    setIsOpen(true);
-    setLocalCategories(filters);
-    setLocalCreators(creators);
-  };
-
-  const hasLocalActiveFilters = () => {
-    return localCategories.length > 0 || localCreators.length > 0;
-  };
-
-  const visibleCreators = creators_list.slice(0, displayedCreatorsCount);
-
-  return {
-    creators,
-    isOpen,
-    setIsOpen,
-    localCategories,
-    localCreators,
-    displayedCreatorsCount,
-    setDisplayedCreatorsCount,
-    handleLocalCategoryChange,
-    handleToggleShowMoreCreators,
-    handleLocalCreatorChange,
-    handleClearFilters,
-    handleCloseButton,
-    handleOpenFilters,
-    handleApplyFilters,
-    hasLocalActiveFilters,
-    visibleCreators,
-    hasActiveFilters,
-  };
-};

autogpt_platform/frontend/src/app/(platform)/build/stores/blockMenuStore.ts

@@ -1,30 +1,12 @@
 import { create } from "zustand";
 import { DefaultStateType } from "../components/NewControlPanel/NewBlockMenu/types";
-import { SearchResponseItemsItem } from "@/app/api/__generated__/models/searchResponseItemsItem";
-import { getSearchItemType } from "../components/NewControlPanel/NewBlockMenu/BlockMenuSearchContent/helper";
-import { StoreAgent } from "@/app/api/__generated__/models/storeAgent";
-import { GetV2BuilderSearchFilterAnyOfItem } from "@/app/api/__generated__/models/getV2BuilderSearchFilterAnyOfItem";
 
 type BlockMenuStore = {
   searchQuery: string;
   searchId: string | undefined;
   defaultState: DefaultStateType;
   integration: string | undefined;
-  filters: GetV2BuilderSearchFilterAnyOfItem[];
-  creators: string[];
-  creators_list: string[];
-  categoryCounts: Record<GetV2BuilderSearchFilterAnyOfItem, number>;
 
-  setCategoryCounts: (
-    counts: Record<GetV2BuilderSearchFilterAnyOfItem, number>,
-  ) => void;
-  setCreatorsList: (searchData: SearchResponseItemsItem[]) => void;
-  addCreator: (creator: string) => void;
-  setCreators: (creators: string[]) => void;
-  removeCreator: (creator: string) => void;
-  addFilter: (filter: GetV2BuilderSearchFilterAnyOfItem) => void;
-  setFilters: (filters: GetV2BuilderSearchFilterAnyOfItem[]) => void;
-  removeFilter: (filter: GetV2BuilderSearchFilterAnyOfItem) => void;
   setSearchQuery: (query: string) => void;
   setSearchId: (id: string | undefined) => void;
   setDefaultState: (state: DefaultStateType) => void;
@@ -37,44 +19,11 @@ export const useBlockMenuStore = create<BlockMenuStore>((set) => ({
   searchId: undefined,
   defaultState: DefaultStateType.SUGGESTION,
   integration: undefined,
-  filters: [],
-  creators: [], // creator filters that are applied to the search results
-  creators_list: [], // all creators that are available to filter by
-  categoryCounts: {
-    blocks: 0,
-    integrations: 0,
-    marketplace_agents: 0,
-    my_agents: 0,
-  },
 
-  setCategoryCounts: (counts) => set({ categoryCounts: counts }),
-  setCreatorsList: (searchData) => {
-    const marketplaceAgents = searchData.filter((item) => {
-      return getSearchItemType(item).type === "store_agent";
-    }) as StoreAgent[];
-
-    const newCreators = marketplaceAgents.map((agent) => agent.creator);
-
-    set((state) => ({
-      creators_list: Array.from(
-        new Set([...state.creators_list, ...newCreators]),
-      ),
-    }));
-  },
-  setCreators: (creators) => set({ creators }),
-  setFilters: (filters) => set({ filters }),
   setSearchQuery: (query) => set({ searchQuery: query }),
   setSearchId: (id) => set({ searchId: id }),
   setDefaultState: (state) => set({ defaultState: state }),
   setIntegration: (integration) => set({ integration }),
-  addFilter: (filter) =>
-    set((state) => ({ filters: [...state.filters, filter] })),
-  removeFilter: (filter) =>
-    set((state) => ({ filters: state.filters.filter((f) => f !== filter) })),
-  addCreator: (creator) =>
-    set((state) => ({ creators: [...state.creators, creator] })),
-  removeCreator: (creator) =>
-    set((state) => ({ creators: state.creators.filter((c) => c !== creator) })),
   reset: () =>
     set({
       searchQuery: "",

autogpt_platform/frontend/src/app/(platform)/build/stores/edgeStore.ts

@@ -4,7 +4,6 @@ import { CustomEdge } from "../components/FlowEditor/edges/CustomEdge";
 import { customEdgeToLink, linkToCustomEdge } from "../components/helper";
 import { MarkerType } from "@xyflow/react";
 import { NodeExecutionResult } from "@/app/api/__generated__/models/nodeExecutionResult";
-import { cleanUpHandleId } from "@/components/renderers/InputRenderer/helpers";
 
 type EdgeStore = {
   edges: CustomEdge[];
@@ -14,8 +13,6 @@ type EdgeStore = {
   removeEdge: (edgeId: string) => void;
   upsertMany: (edges: CustomEdge[]) => void;
 
-  removeEdgesByHandlePrefix: (nodeId: string, handlePrefix: string) => void;
-
   getNodeEdges: (nodeId: string) => CustomEdge[];
   isInputConnected: (nodeId: string, handle: string) => boolean;
   isOutputConnected: (nodeId: string, handle: string) => boolean;
@@ -82,27 +79,11 @@ export const useEdgeStore = create<EdgeStore>((set, get) => ({
       return { edges: Array.from(byKey.values()) };
     }),
 
-  removeEdgesByHandlePrefix: (nodeId, handlePrefix) =>
-    set((state) => ({
-      edges: state.edges.filter(
-        (e) =>
-          !(
-            e.target === nodeId &&
-            e.targetHandle &&
-            e.targetHandle.startsWith(handlePrefix)
-          ),
-      ),
-    })),
-
   getNodeEdges: (nodeId) =>
     get().edges.filter((e) => e.source === nodeId || e.target === nodeId),
 
-  isInputConnected: (nodeId, handle) => {
-    const cleanedHandle = cleanUpHandleId(handle);
-    return get().edges.some(
-      (e) => e.target === nodeId && e.targetHandle === cleanedHandle,
-    );
-  },
+  isInputConnected: (nodeId, handle) =>
+    get().edges.some((e) => e.target === nodeId && e.targetHandle === handle),
 
   isOutputConnected: (nodeId, handle) =>
     get().edges.some((e) => e.source === nodeId && e.sourceHandle === handle),
@@ -124,15 +105,15 @@ export const useEdgeStore = create<EdgeStore>((set, get) => ({
     targetNodeId: string,
     executionResult: NodeExecutionResult,
   ) => {
-    set((state) => {
-      let hasChanges = false;
-
-      const newEdges = state.edges.map((edge) => {
+    set((state) => ({
+      edges: state.edges.map((edge) => {
         if (edge.target !== targetNodeId) {
           return edge;
         }
 
-        const beadData = new Map(edge.data?.beadData ?? new Map());
+        const beadData =
+          edge.data?.beadData ??
+          new Map<string, NodeExecutionResult["status"]>();
 
         const inputValue = edge.targetHandle
           ? executionResult.input_data[edge.targetHandle]
@@ -156,11 +137,6 @@ export const useEdgeStore = create<EdgeStore>((set, get) => ({
           beadUp = beadDown + 1;
         }
 
-        if (edge.data?.beadUp === beadUp && edge.data?.beadDown === beadDown) {
-          return edge;
-        }
-
-        hasChanges = true;
         return {
           ...edge,
           data: {
@@ -170,10 +146,8 @@ export const useEdgeStore = create<EdgeStore>((set, get) => ({
             beadData,
           },
         };
-      });
-
-      return hasChanges ? { edges: newEdges } : state;
-    });
+      }),
+    }));
   },
 
   resetEdgeBeads: () => {

autogpt_platform/frontend/src/app/(platform)/build/stores/nodeStore.ts

@@ -13,10 +13,6 @@ import { useHistoryStore } from "./historyStore";
 import { useEdgeStore } from "./edgeStore";
 import { BlockUIType } from "../components/types";
 import { pruneEmptyValues } from "@/lib/utils";
-import {
-  ensurePathExists,
-  parseHandleIdToPath,
-} from "@/components/renderers/InputRenderer/helpers";
 
 // Minimum movement (in pixels) required before logging position change to history
 // Prevents spamming history with small movements when clicking on inputs inside blocks
@@ -66,11 +62,6 @@ type NodeStore = {
     errors: { [key: string]: string },
   ) => void;
   clearAllNodeErrors: () => void; // Add this
-
-  syncHardcodedValuesWithHandleIds: (nodeId: string) => void;
-
-  // Credentials optional helpers
-  setCredentialsOptional: (nodeId: string, optional: boolean) => void;
 };
 
 export const useNodeStore = create<NodeStore>((set, get) => ({
@@ -229,9 +220,6 @@ export const useNodeStore = create<NodeStore>((set, get) => ({
         ...(node.data.metadata?.customized_name !== undefined && {
           customized_name: node.data.metadata.customized_name,
         }),
-        ...(node.data.metadata?.credentials_optional !== undefined && {
-          credentials_optional: node.data.metadata.credentials_optional,
-        }),
       },
     };
   },
@@ -317,61 +305,4 @@ export const useNodeStore = create<NodeStore>((set, get) => ({
       })),
     }));
   },
-
-  syncHardcodedValuesWithHandleIds: (nodeId: string) => {
-    const node = get().nodes.find((n) => n.id === nodeId);
-    if (!node) return;
-
-    const handleIds = useEdgeStore.getState().getAllHandleIdsOfANode(nodeId);
-    const additionalHandles = handleIds.filter((h) => h.includes("_#_"));
-
-    if (additionalHandles.length === 0) return;
-
-    const hardcodedValues = JSON.parse(
-      JSON.stringify(node.data.hardcodedValues || {}),
-    );
-
-    let modified = false;
-
-    additionalHandles.forEach((handleId) => {
-      const segments = parseHandleIdToPath(handleId);
-      if (ensurePathExists(hardcodedValues, segments)) {
-        modified = true;
-      }
-    });
-
-    if (modified) {
-      set((state) => ({
-        nodes: state.nodes.map((n) =>
-          n.id === nodeId ? { ...n, data: { ...n.data, hardcodedValues } } : n,
-        ),
-      }));
-    }
-  },
-
-  setCredentialsOptional: (nodeId: string, optional: boolean) => {
-    set((state) => ({
-      nodes: state.nodes.map((n) =>
-        n.id === nodeId
-          ? {
-              ...n,
-              data: {
-                ...n.data,
-                metadata: {
-                  ...n.data.metadata,
-                  credentials_optional: optional,
-                },
-              },
-            }
-          : n,
-      ),
-    }));
-
-    const newState = {
-      nodes: get().nodes,
-      edges: useEdgeStore.getState().edges,
-    };
-
-    useHistoryStore.getState().pushState(newState);
-  },
 }));

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

@@ -34,7 +34,6 @@ type Props = {
   onSelectCredentials: (newValue?: CredentialsMetaInput) => void;
   onLoaded?: (loaded: boolean) => void;
   readOnly?: boolean;
-  isOptional?: boolean;
   showTitle?: boolean;
 };
 
@@ -46,7 +45,6 @@ export function CredentialsInput({
   siblingInputs,
   onLoaded,
   readOnly = false,
-  isOptional = false,
   showTitle = true,
 }: Props) {
   const hookData = useCredentialsInput({
@@ -56,7 +54,6 @@ export function CredentialsInput({
     siblingInputs,
     onLoaded,
     readOnly,
-    isOptional,
   });
 
   if (!isLoaded(hookData)) {
@@ -97,14 +94,7 @@ export function CredentialsInput({
     <div className={cn("mb-6", className)}>
       {showTitle && (
         <div className="mb-2 flex items-center gap-2">
-          <Text variant="large-medium">
-            {displayName} credentials
-            {isOptional && (
-              <span className="ml-1 text-sm font-normal text-gray-500">
-                (optional)
-              </span>
-            )}
-          </Text>
+          <Text variant="large-medium">{displayName} credentials</Text>
           {schema.description && (
             <InformationTooltip description={schema.description} />
           )}
@@ -113,16 +103,14 @@ export function CredentialsInput({
 
       {hasCredentialsToShow ? (
         <>
-          {(credentialsToShow.length > 1 || isOptional) && !readOnly ? (
+          {credentialsToShow.length > 1 && !readOnly ? (
             <CredentialsSelect
               credentials={credentialsToShow}
               provider={provider}
               displayName={displayName}
               selectedCredentials={selectedCredential}
               onSelectCredential={handleCredentialSelect}
-              onClearCredential={() => onSelectCredential(undefined)}
               readOnly={readOnly}
-              allowNone={isOptional}
             />
           ) : (
             <div className="mb-4 space-y-2">
@@ -155,7 +143,6 @@ export function CredentialsInput({
               size="small"
               onClick={handleActionButtonClick}
               className="w-fit"
-              type="button"
             >
               {actionButtonText}
             </Button>
@@ -168,7 +155,6 @@ export function CredentialsInput({
             size="small"
             onClick={handleActionButtonClick}
             className="w-fit"
-            type="button"
           >
             {actionButtonText}
           </Button>

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

@@ -23,9 +23,7 @@ interface Props {
   displayName: string;
   selectedCredentials?: CredentialsMetaInput;
   onSelectCredential: (credentialId: string) => void;
-  onClearCredential?: () => void;
   readOnly?: boolean;
-  allowNone?: boolean;
 }
 
 export function CredentialsSelect({
@@ -34,30 +32,20 @@ export function CredentialsSelect({
   displayName,
   selectedCredentials,
   onSelectCredential,
-  onClearCredential,
   readOnly = false,
-  allowNone = true,
 }: Props) {
-  // Auto-select first credential if none is selected (only if allowNone is false)
+  // Auto-select first credential if none is selected
   useEffect(() => {
-    if (!allowNone && !selectedCredentials && credentials.length > 0) {
+    if (!selectedCredentials && credentials.length > 0) {
       onSelectCredential(credentials[0].id);
     }
-  }, [allowNone, selectedCredentials, credentials, onSelectCredential]);
-
-  const handleValueChange = (value: string) => {
-    if (value === "__none__") {
-      onClearCredential?.();
-    } else {
-      onSelectCredential(value);
-    }
-  };
+  }, [selectedCredentials, credentials, onSelectCredential]);
 
   return (
     <div className="mb-4 w-full">
       <Select
-        value={selectedCredentials?.id || (allowNone ? "__none__" : "")}
-        onValueChange={handleValueChange}
+        value={selectedCredentials?.id || ""}
+        onValueChange={(value) => onSelectCredential(value)}
       >
         <SelectTrigger className="h-auto min-h-12 w-full rounded-medium border-zinc-200 p-0 pr-4 shadow-none">
           {selectedCredentials ? (
@@ -82,15 +70,6 @@ export function CredentialsSelect({
           )}
         </SelectTrigger>
         <SelectContent>
-          {allowNone && (
-            <SelectItem key="__none__" value="__none__">
-              <div className="flex items-center gap-2">
-                <Text variant="body" className="tracking-tight text-gray-500">
-                  None (skip this credential)
-                </Text>
-              </div>
-            </SelectItem>
-          )}
           {credentials.map((credential) => (
             <SelectItem key={credential.id} value={credential.id}>
               <div className="flex items-center gap-2">

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

@@ -22,7 +22,6 @@ type Params = {
   siblingInputs?: Record<string, any>;
   onLoaded?: (loaded: boolean) => void;
   readOnly?: boolean;
-  isOptional?: boolean;
 };
 
 export function useCredentialsInput({
@@ -32,7 +31,6 @@ export function useCredentialsInput({
   siblingInputs,
   onLoaded,
   readOnly = false,
-  isOptional = false,
 }: Params) {
   const [isAPICredentialsModalOpen, setAPICredentialsModalOpen] =
     useState(false);
@@ -101,20 +99,13 @@ export function useCredentialsInput({
       : null;
   }, [credentials]);
 
-  // Auto-select the one available credential (only if not optional)
+  // Auto-select the one available credential
   useEffect(() => {
     if (readOnly) return;
-    if (isOptional) return; // Don't auto-select when credential is optional
     if (singleCredential && !selectedCredential) {
       onSelectCredential(singleCredential);
     }
-  }, [
-    singleCredential,
-    selectedCredential,
-    onSelectCredential,
-    readOnly,
-    isOptional,
-  ]);
+  }, [singleCredential, selectedCredential, onSelectCredential, readOnly]);
 
   if (
     !credentials ||

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

@@ -8,7 +8,6 @@ import { WebhookTriggerBanner } from "../WebhookTriggerBanner/WebhookTriggerBann
 
 export function ModalRunSection() {
   const {
-    agent,
     defaultRunType,
     presetName,
     setPresetName,
@@ -25,11 +24,6 @@ export function ModalRunSection() {
   const inputFields = Object.entries(agentInputFields || {});
   const credentialFields = Object.entries(agentCredentialsInputFields || {});
 
-  // Get the list of required credentials from the schema
-  const requiredCredentials = new Set(
-    (agent.credentials_input_schema?.required as string[]) || [],
-  );
-
   return (
     <div className="flex flex-col gap-4">
       {defaultRunType === "automatic-trigger" ||
@@ -105,12 +99,14 @@ export function ModalRunSection() {
                   schema={
                     { ...inputSubSchema, discriminator: undefined } as any
                   }
-                  selectedCredentials={inputCredentials?.[key]}
+                  selectedCredentials={
+                    (inputCredentials && inputCredentials[key]) ??
+                    inputSubSchema.default
+                  }
                   onSelectCredentials={(value) =>
                     setInputCredentialsValue(key, value)
                   }
                   siblingInputs={inputValues}
-                  isOptional={!requiredCredentials.has(key)}
                 />
               ),
             )}

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

@@ -163,21 +163,15 @@ export function useAgentRunModal(
   }, [agentInputSchema.required, inputValues]);
 
   const [allCredentialsAreSet, missingCredentials] = useMemo(() => {
-    // Only check required credentials from schema, not all properties
-    // Credentials marked as optional in node metadata won't be in the required array
-    const requiredCredentials = new Set(
-      (agent.credentials_input_schema?.required as string[]) || [],
+    const availableCredentials = new Set(Object.keys(inputCredentials));
+    const allCredentials = new Set(
+      Object.keys(agentCredentialsInputFields || {}) ?? [],
+    );
+    const missing = [...allCredentials].filter(
+      (key) => !availableCredentials.has(key),
     );
-
-    // Check if required credentials have valid id (not just key existence)
-    // A credential is valid only if it has an id field set
-    const missing = [...requiredCredentials].filter((key) => {
-      const cred = inputCredentials[key];
-      return !cred || !cred.id;
-    });
-
     return [missing.length === 0, missing];
-  }, [agent.credentials_input_schema, inputCredentials]);
+  }, [agentCredentialsInputFields, inputCredentials]);
 
   const credentialsRequired = useMemo(
     () => Object.keys(agentCredentialsInputFields || {}).length > 0,
@@ -245,18 +239,12 @@ export function useAgentRunModal(
       });
     } else {
       // Manual execution
-      // Filter out incomplete credentials (optional ones not selected)
-      // Only send credentials that have a valid id field
-      const validCredentials = Object.fromEntries(
-        Object.entries(inputCredentials).filter(([_, cred]) => cred && cred.id),
-      );
-
       executeGraphMutation.mutate({
         graphId: agent.graph_id,
         graphVersion: agent.graph_version,
         data: {
           inputs: inputValues,
-          credentials_inputs: validCredentials,
+          credentials_inputs: inputCredentials,
           source: "library",
         },
       });

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

@@ -1,25 +1,17 @@
 "use client";
 
 import { getV1GetGraphVersion } from "@/app/api/__generated__/endpoints/graphs/graphs";
-import {
-  getGetV2ListLibraryAgentsQueryKey,
-  useDeleteV2DeleteLibraryAgent,
-} from "@/app/api/__generated__/endpoints/library/library";
 import { GraphExecutionJobInfo } from "@/app/api/__generated__/models/graphExecutionJobInfo";
 import { GraphExecutionMeta } from "@/app/api/__generated__/models/graphExecutionMeta";
 import { LibraryAgent } from "@/app/api/__generated__/models/libraryAgent";
 import { LibraryAgentPreset } from "@/app/api/__generated__/models/libraryAgentPreset";
 import { Button } from "@/components/atoms/Button/Button";
 import { Text } from "@/components/atoms/Text/Text";
-import { Dialog } from "@/components/molecules/Dialog/Dialog";
 import { ShowMoreText } from "@/components/molecules/ShowMoreText/ShowMoreText";
 import { useToast } from "@/components/molecules/Toast/use-toast";
 import { exportAsJSONFile } from "@/lib/utils";
 import { formatDate } from "@/lib/utils/time";
-import { useQueryClient } from "@tanstack/react-query";
 import Link from "next/link";
-import { useRouter } from "next/navigation";
-import { useState } from "react";
 import { RunAgentModal } from "../modals/RunAgentModal/RunAgentModal";
 import { RunDetailCard } from "../selected-views/RunDetailCard/RunDetailCard";
 import { EmptyTasksIllustration } from "./EmptyTasksIllustration";
@@ -38,41 +30,6 @@ export function EmptyTasks({
   onScheduleCreated,
 }: Props) {
   const { toast } = useToast();
-  const queryClient = useQueryClient();
-  const router = useRouter();
-  const [showDeleteDialog, setShowDeleteDialog] = useState(false);
-  const [isDeletingAgent, setIsDeletingAgent] = useState(false);
-
-  const { mutateAsync: deleteAgent } = useDeleteV2DeleteLibraryAgent();
-
-  async function handleDeleteAgent() {
-    if (!agent.id) return;
-
-    setIsDeletingAgent(true);
-
-    try {
-      await deleteAgent({ libraryAgentId: agent.id });
-
-      await queryClient.refetchQueries({
-        queryKey: getGetV2ListLibraryAgentsQueryKey(),
-      });
-
-      toast({ title: "Agent deleted" });
-      setShowDeleteDialog(false);
-      router.push("/library");
-    } catch (error: unknown) {
-      toast({
-        title: "Failed to delete agent",
-        description:
-          error instanceof Error
-            ? error.message
-            : "An unexpected error occurred.",
-        variant: "destructive",
-      });
-    } finally {
-      setIsDeletingAgent(false);
-    }
-  }
 
   async function handleExport() {
     try {
@@ -190,50 +147,9 @@ export function EmptyTasks({
             <Button variant="secondary" size="small" onClick={handleExport}>
               Export agent to file
             </Button>
-            <Button
-              variant="secondary"
-              size="small"
-              onClick={() => setShowDeleteDialog(true)}
-            >
-              Delete agent
-            </Button>
           </div>
         </div>
       </div>
-
-      <Dialog
-        controlled={{
-          isOpen: showDeleteDialog,
-          set: setShowDeleteDialog,
-        }}
-        styling={{ maxWidth: "32rem" }}
-        title="Delete agent"
-      >
-        <Dialog.Content>
-          <div>
-            <Text variant="large">
-              Are you sure you want to delete this agent? This action cannot be
-              undone.
-            </Text>
-            <Dialog.Footer>
-              <Button
-                variant="secondary"
-                disabled={isDeletingAgent}
-                onClick={() => setShowDeleteDialog(false)}
-              >
-                Cancel
-              </Button>
-              <Button
-                variant="destructive"
-                onClick={handleDeleteAgent}
-                loading={isDeletingAgent}
-              >
-                Delete Agent
-              </Button>
-            </Dialog.Footer>
-          </div>
-        </Dialog.Content>
-      </Dialog>
     </div>
   );
 }

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

@@ -83,9 +83,7 @@ function renderCode(
         </div>
       )}
       <pre className="overflow-x-auto rounded-md bg-muted p-3">
-        <code className="whitespace-pre-wrap break-words font-mono text-sm">
-          {codeValue}
-        </code>
+        <code className="font-mono text-sm">{codeValue}</code>
       </pre>
     </div>
   );

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

@@ -13,7 +13,7 @@ import { LoadingSelectedContent } from "../LoadingSelectedContent";
 import { RunDetailCard } from "../RunDetailCard/RunDetailCard";
 import { RunDetailHeader } from "../RunDetailHeader/RunDetailHeader";
 import { SelectedViewLayout } from "../SelectedViewLayout";
-import { SelectedScheduleActions } from "./components/SelectedScheduleActions/SelectedScheduleActions";
+import { SelectedScheduleActions } from "./components/SelectedScheduleActions";
 import { useSelectedScheduleView } from "./useSelectedScheduleView";
 
 interface Props {

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

@@ -0,0 +1,40 @@
+import { LibraryAgent } from "@/app/api/__generated__/models/libraryAgent";
+import { Button } from "@/components/atoms/Button/Button";
+import { EyeIcon } from "@phosphor-icons/react";
+import { AgentActionsDropdown } from "../../AgentActionsDropdown";
+import { useScheduleDetailHeader } from "../../RunDetailHeader/useScheduleDetailHeader";
+import { SelectedActionsWrap } from "../../SelectedActionsWrap";
+
+type Props = {
+  agent: LibraryAgent;
+  scheduleId: string;
+  onDeleted?: () => void;
+};
+
+export function SelectedScheduleActions({ agent, scheduleId }: Props) {
+  const { openInBuilderHref } = useScheduleDetailHeader(
+    agent.graph_id,
+    scheduleId,
+    agent.graph_version,
+  );
+
+  return (
+    <>
+      <SelectedActionsWrap>
+        {openInBuilderHref && (
+          <Button
+            variant="icon"
+            size="icon"
+            as="NextLink"
+            href={openInBuilderHref}
+            target="_blank"
+            aria-label="View scheduled task details"
+          >
+            <EyeIcon weight="bold" size={18} className="text-zinc-700" />
+          </Button>
+        )}
+        <AgentActionsDropdown agent={agent} scheduleId={scheduleId} />
+      </SelectedActionsWrap>
+    </>
+  );
+}

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

@@ -1,96 +0,0 @@
-"use client";
-
-import { LibraryAgent } from "@/app/api/__generated__/models/libraryAgent";
-import { Button } from "@/components/atoms/Button/Button";
-import { LoadingSpinner } from "@/components/atoms/LoadingSpinner/LoadingSpinner";
-import { Text } from "@/components/atoms/Text/Text";
-import { Dialog } from "@/components/molecules/Dialog/Dialog";
-import { EyeIcon, TrashIcon } from "@phosphor-icons/react";
-import { AgentActionsDropdown } from "../../../AgentActionsDropdown";
-import { SelectedActionsWrap } from "../../../SelectedActionsWrap";
-import { useSelectedScheduleActions } from "./useSelectedScheduleActions";
-
-type Props = {
-  agent: LibraryAgent;
-  scheduleId: string;
-  onDeleted?: () => void;
-};
-
-export function SelectedScheduleActions({
-  agent,
-  scheduleId,
-  onDeleted,
-}: Props) {
-  const {
-    openInBuilderHref,
-    showDeleteDialog,
-    setShowDeleteDialog,
-    handleDelete,
-    isDeleting,
-  } = useSelectedScheduleActions({ agent, scheduleId, onDeleted });
-
-  return (
-    <>
-      <SelectedActionsWrap>
-        {openInBuilderHref && (
-          <Button
-            variant="icon"
-            size="icon"
-            as="NextLink"
-            href={openInBuilderHref}
-            target="_blank"
-            aria-label="View scheduled task details"
-          >
-            <EyeIcon weight="bold" size={18} className="text-zinc-700" />
-          </Button>
-        )}
-        <Button
-          variant="icon"
-          size="icon"
-          aria-label="Delete schedule"
-          onClick={() => setShowDeleteDialog(true)}
-          disabled={isDeleting}
-        >
-          {isDeleting ? (
-            <LoadingSpinner size="small" />
-          ) : (
-            <TrashIcon weight="bold" size={18} />
-          )}
-        </Button>
-        <AgentActionsDropdown agent={agent} scheduleId={scheduleId} />
-      </SelectedActionsWrap>
-
-      <Dialog
-        controlled={{
-          isOpen: showDeleteDialog,
-          set: setShowDeleteDialog,
-        }}
-        styling={{ maxWidth: "32rem" }}
-        title="Delete schedule"
-      >
-        <Dialog.Content>
-          <Text variant="large">
-            Are you sure you want to delete this schedule? This action cannot be
-            undone.
-          </Text>
-          <Dialog.Footer>
-            <Button
-              variant="secondary"
-              onClick={() => setShowDeleteDialog(false)}
-              disabled={isDeleting}
-            >
-              Cancel
-            </Button>
-            <Button
-              variant="destructive"
-              onClick={handleDelete}
-              loading={isDeleting}
-            >
-              Delete Schedule
-            </Button>
-          </Dialog.Footer>
-        </Dialog.Content>
-      </Dialog>
-    </>
-  );
-}

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

@@ -1,65 +0,0 @@
-"use client";
-
-import {
-  getGetV1ListExecutionSchedulesForAGraphQueryOptions,
-  useDeleteV1DeleteExecutionSchedule,
-} from "@/app/api/__generated__/endpoints/schedules/schedules";
-import { LibraryAgent } from "@/app/api/__generated__/models/libraryAgent";
-import { useToast } from "@/components/molecules/Toast/use-toast";
-import { useQueryClient } from "@tanstack/react-query";
-import { useState } from "react";
-
-interface UseSelectedScheduleActionsProps {
-  agent: LibraryAgent;
-  scheduleId: string;
-  onDeleted?: () => void;
-}
-
-export function useSelectedScheduleActions({
-  agent,
-  scheduleId,
-  onDeleted,
-}: UseSelectedScheduleActionsProps) {
-  const { toast } = useToast();
-  const queryClient = useQueryClient();
-  const [showDeleteDialog, setShowDeleteDialog] = useState(false);
-
-  const deleteMutation = useDeleteV1DeleteExecutionSchedule({
-    mutation: {
-      onSuccess: () => {
-        toast({ title: "Schedule deleted" });
-        queryClient.invalidateQueries({
-          queryKey: getGetV1ListExecutionSchedulesForAGraphQueryOptions(
-            agent.graph_id,
-          ).queryKey,
-        });
-        setShowDeleteDialog(false);
-        onDeleted?.();
-      },
-      onError: (error: unknown) =>
-        toast({
-          title: "Failed to delete schedule",
-          description:
-            error instanceof Error
-              ? error.message
-              : "An unexpected error occurred.",
-          variant: "destructive",
-        }),
-    },
-  });
-
-  function handleDelete() {
-    if (!scheduleId) return;
-    deleteMutation.mutate({ scheduleId });
-  }
-
-  const openInBuilderHref = `/build?flowID=${agent.graph_id}&flowVersion=${agent.graph_version}`;
-
-  return {
-    openInBuilderHref,
-    showDeleteDialog,
-    setShowDeleteDialog,
-    handleDelete,
-    isDeleting: deleteMutation.isPending,
-  };
-}

autogpt_platform/frontend/src/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/hooks/useMarketplaceUpdate.ts

@@ -40,17 +40,15 @@ export function useMarketplaceUpdate({ agent }: UseMarketplaceUpdateProps) {
     },
   );
 
-  // Get user's submissions - only fetch if user is the creator
-  const { data: submissionsData, isLoading: isSubmissionsLoading } =
-    useGetV2ListMySubmissions(
-      { page: 1, page_size: 50 },
-      {
-        query: {
-          // Only fetch if user is the creator
-          enabled: !!(user?.id && agent?.owner_user_id === user.id),
-        },
+  // Get user's submissions to check for pending submissions
+  const { data: submissionsData } = useGetV2ListMySubmissions(
+    { page: 1, page_size: 50 }, // Get enough to cover recent submissions
+    {
+      query: {
+        enabled: !!user?.id, // Only fetch if user is authenticated
       },
-    );
+    },
+  );
 
   const updateToLatestMutation = usePatchV2UpdateLibraryAgent({
     mutation: {
@@ -80,36 +78,11 @@ export function useMarketplaceUpdate({ agent }: UseMarketplaceUpdateProps) {
   // Check if marketplace has a newer version than user's current version
   const marketplaceUpdateInfo = React.useMemo(() => {
     const storeAgent = okData(storeAgentData) as any;
-
-    if (!agent || isSubmissionsLoading) {
+    if (!agent || !storeAgent) {
       return {
         hasUpdate: false,
         latestVersion: undefined,
         isUserCreator: false,
-        hasPublishUpdate: false,
-      };
-    }
-
-    const isUserCreator = agent?.owner_user_id === user?.id;
-
-    // Check if there's a pending submission for this specific agent version
-    const submissionsResponse = okData(submissionsData) as any;
-    const hasPendingSubmissionForCurrentVersion =
-      isUserCreator &&
-      submissionsResponse?.submissions?.some(
-        (submission: StoreSubmission) =>
-          submission.agent_id === agent.graph_id &&
-          submission.agent_version === agent.graph_version &&
-          submission.status === "PENDING",
-      );
-
-    if (!storeAgent) {
-      return {
-        hasUpdate: false,
-        latestVersion: undefined,
-        isUserCreator,
-        hasPublishUpdate:
-          isUserCreator && !hasPendingSubmissionForCurrentVersion,
       };
     }
 
@@ -124,15 +97,29 @@ export function useMarketplaceUpdate({ agent }: UseMarketplaceUpdateProps) {
           )
         : undefined;
 
-    // Show publish update button if:
-    // 1. User is the creator
-    // 2. No pending submission for current version
-    // 3. Either: agent not published yet OR local version is newer than marketplace
+    // Determine if the user is the creator of this agent
+    // Compare current user ID with the marketplace listing creator ID
+    const isUserCreator =
+      user?.id && agent.marketplace_listing?.creator.id === user.id;
+
+    // Check if there's a pending submission for this specific agent version
+    const submissionsResponse = okData(submissionsData) as any;
+    const hasPendingSubmissionForCurrentVersion =
+      isUserCreator &&
+      submissionsResponse?.submissions?.some(
+        (submission: StoreSubmission) =>
+          submission.agent_id === agent.graph_id &&
+          submission.agent_version === agent.graph_version &&
+          submission.status === "PENDING",
+      );
+
+    // If user is creator and their version is newer than marketplace, show publish update banner
+    // BUT only if there's no pending submission for this version
     const hasPublishUpdate =
       isUserCreator &&
       !hasPendingSubmissionForCurrentVersion &&
-      (latestMarketplaceVersion === undefined || // Not published yet
-        agent.graph_version > latestMarketplaceVersion); // Or local version is newer
+      latestMarketplaceVersion !== undefined &&
+      agent.graph_version > latestMarketplaceVersion;
 
     // If marketplace version is newer than user's version, show update banner
     // This applies to both creators and non-creators
@@ -146,7 +133,7 @@ export function useMarketplaceUpdate({ agent }: UseMarketplaceUpdateProps) {
       isUserCreator,
       hasPublishUpdate,
     };
-  }, [agent, storeAgentData, user, submissionsData, isSubmissionsLoading]);
+  }, [agent, storeAgentData, user, submissionsData]);
 
   const handlePublishUpdate = () => {
     setModalOpen(true);

autogpt_platform/frontend/src/app/(platform)/library/components/FavoritesSection/FavoritesSection.tsx

@@ -1,17 +1,16 @@
 "use client";
 
-import { LibraryAgent } from "@/app/api/__generated__/models/libraryAgent";
-import { Text } from "@/components/atoms/Text/Text";
-import { InfiniteScroll } from "@/components/contextual/InfiniteScroll/InfiniteScroll";
-import { HeartIcon } from "@phosphor-icons/react";
+import React from "react";
 import { useFavoriteAgents } from "../../hooks/useFavoriteAgents";
-import { LibraryAgentCard } from "../LibraryAgentCard/LibraryAgentCard";
+import LibraryAgentCard from "../LibraryAgentCard/LibraryAgentCard";
+import { useGetFlag, Flag } from "@/services/feature-flags/use-get-flag";
+import { Heart } from "lucide-react";
+import { Skeleton } from "@/components/__legacy__/ui/skeleton";
+import { InfiniteScroll } from "@/components/contextual/InfiniteScroll/InfiniteScroll";
+import { LibraryAgent } from "@/app/api/__generated__/models/libraryAgent";
 
-interface Props {
-  searchTerm: string;
-}
-
-export function FavoritesSection({ searchTerm }: Props) {
+export default function FavoritesSection() {
+  const isAgentFavoritingEnabled = useGetFlag(Flag.AGENT_FAVORITING);
   const {
     allAgents: favoriteAgents,
     agentLoading: isLoading,
@@ -19,50 +18,60 @@ export function FavoritesSection({ searchTerm }: Props) {
     hasNextPage,
     fetchNextPage,
     isFetchingNextPage,
-  } = useFavoriteAgents({ searchTerm });
+  } = useFavoriteAgents();
 
-  if (isLoading || favoriteAgents.length === 0) {
+  // Only show this section if the feature flag is enabled
+  if (!isAgentFavoritingEnabled) {
+    return null;
+  }
+
+  // Don't show the section if there are no favorites
+  if (!isLoading && favoriteAgents.length === 0) {
     return null;
   }
 
   return (
-    <div className="!mb-8">
-      <div className="mb-3 flex items-center gap-2 p-2">
-        <HeartIcon className="h-5 w-5" weight="fill" />
-        <div className="flex items-baseline gap-2">
-          <Text variant="h4">Favorites</Text>
-          {!isLoading && (
-            <Text
-              variant="body"
-              data-testid="agents-count"
-              className="relative bottom-px text-zinc-500"
-            >
-              {agentCount}
-            </Text>
-          )}
-        </div>
+    <div className="mb-8">
+      <div className="flex items-center gap-[10px] p-2 pb-[10px]">
+        <Heart className="h-5 w-5 fill-red-500 text-red-500" />
+        <span className="font-poppin text-[18px] font-semibold leading-[28px] text-neutral-800">
+          Favorites
+        </span>
+        {!isLoading && (
+          <span className="font-sans text-[14px] font-normal leading-6">
+            {agentCount} {agentCount === 1 ? "agent" : "agents"}
+          </span>
+        )}
       </div>
 
       <div className="relative">
-        <InfiniteScroll
-          isFetchingNextPage={isFetchingNextPage}
-          fetchNextPage={fetchNextPage}
-          hasNextPage={hasNextPage}
-          loader={
-            <div className="flex h-8 w-full items-center justify-center">
-              <div className="h-6 w-6 animate-spin rounded-full border-b-2 border-t-2 border-neutral-800" />
-            </div>
-          }
-        >
+        {isLoading ? (
           <div className="grid grid-cols-1 gap-4 sm:grid-cols-2 lg:grid-cols-3 xl:grid-cols-4">
-            {favoriteAgents.map((agent: LibraryAgent) => (
-              <LibraryAgentCard key={agent.id} agent={agent} />
+            {[...Array(4)].map((_, i) => (
+              <Skeleton key={i} className="h-48 w-full rounded-lg" />
             ))}
           </div>
-        </InfiniteScroll>
+        ) : (
+          <InfiniteScroll
+            isFetchingNextPage={isFetchingNextPage}
+            fetchNextPage={fetchNextPage}
+            hasNextPage={hasNextPage}
+            loader={
+              <div className="flex h-8 w-full items-center justify-center">
+                <div className="h-6 w-6 animate-spin rounded-full border-b-2 border-t-2 border-neutral-800" />
+              </div>
+            }
+          >
+            <div className="grid grid-cols-1 gap-4 sm:grid-cols-2 lg:grid-cols-3 xl:grid-cols-4">
+              {favoriteAgents.map((agent: LibraryAgent) => (
+                <LibraryAgentCard key={agent.id} agent={agent} />
+              ))}
+            </div>
+          </InfiniteScroll>
+        )}
       </div>
 
-      {favoriteAgents.length > 0 && <div className="!mt-10 border-t" />}
+      {favoriteAgents.length > 0 && <div className="mt-6 border-t pt-6" />}
     </div>
   );
 }

autogpt_platform/frontend/src/app/(platform)/library/components/LibraryActionHeader/LibraryActionHeader.tsx

@@ -1,28 +1,34 @@
-import { LibrarySearchBar } from "../LibrarySearchBar/LibrarySearchBar";
+// import LibraryNotificationDropdown from "./library-notification-dropdown";
 import LibraryUploadAgentDialog from "../LibraryUploadAgentDialog/LibraryUploadAgentDialog";
+import LibrarySearchBar from "../LibrarySearchBar/LibrarySearchBar";
 
-interface Props {
-  setSearchTerm: (value: string) => void;
-}
+type LibraryActionHeaderProps = Record<string, never>;
 
-export function LibraryActionHeader({ setSearchTerm }: Props) {
+/**
+ * LibraryActionHeader component - Renders a header with search, notifications and filters
+ */
+const LibraryActionHeader: React.FC<LibraryActionHeaderProps> = ({}) => {
   return (
     <>
-      <div className="mb-[32px] hidden items-center justify-center gap-4 md:flex">
-        <LibrarySearchBar setSearchTerm={setSearchTerm} />
+      <div className="mb-[32px] hidden items-start justify-between md:flex">
+        {/* <LibraryNotificationDropdown /> */}
+        <LibrarySearchBar />
         <LibraryUploadAgentDialog />
       </div>
 
       {/* Mobile and tablet */}
       <div className="flex flex-col gap-4 p-4 pt-[52px] md:hidden">
         <div className="flex w-full justify-between">
+          {/* <LibraryNotificationDropdown /> */}
           <LibraryUploadAgentDialog />
         </div>
 
         <div className="flex items-center justify-center">
-          <LibrarySearchBar setSearchTerm={setSearchTerm} />
+          <LibrarySearchBar />
         </div>
       </div>
     </>
   );
-}
+};
+
+export default LibraryActionHeader;

autogpt_platform/frontend/src/app/(platform)/library/components/LibraryActionSubHeader/LibraryActionSubHeader.tsx

@@ -1,28 +1,28 @@
 "use client";
 
-import { LibraryAgentSort } from "@/app/api/__generated__/models/libraryAgentSort";
-import { Text } from "@/components/atoms/Text/Text";
-import { LibrarySortMenu } from "../LibrarySortMenu/LibrarySortMenu";
+import LibrarySortMenu from "../LibrarySortMenu/LibrarySortMenu";
 
-interface Props {
+interface LibraryActionSubHeaderProps {
   agentCount: number;
-  setLibrarySort: (value: LibraryAgentSort) => void;
 }
 
-export function LibraryActionSubHeader({ agentCount, setLibrarySort }: Props) {
+export default function LibraryActionSubHeader({
+  agentCount,
+}: LibraryActionSubHeaderProps) {
   return (
-    <div className="flex items-baseline justify-between">
-      <div className="flex items-baseline gap-4">
-        <Text variant="h4">My agents</Text>
-        <Text
-          variant="body"
+    <div className="flex items-center justify-between pb-[10px]">
+      <div className="flex items-center gap-[10px] p-2">
+        <span className="font-poppin w-[96px] text-[18px] font-semibold leading-[28px] text-neutral-800">
+          My agents
+        </span>
+        <span
+          className="w-[70px] font-sans text-[14px] font-normal leading-6"
           data-testid="agents-count"
-          className="text-zinc-500"
         >
-          {agentCount}
-        </Text>
+          {agentCount} agents
+        </span>
       </div>
-      <LibrarySortMenu setLibrarySort={setLibrarySort} />
+      <LibrarySortMenu />
     </div>
   );
 }

autogpt_platform/frontend/src/app/(platform)/library/components/LibraryAgentCard/LibraryAgentCard.tsx

@@ -1,126 +1,332 @@
 "use client";
 
-import { Text } from "@/components/atoms/Text/Text";
-import { CaretCircleRightIcon } from "@phosphor-icons/react";
+import Link from "next/link";
 import Image from "next/image";
-import NextLink from "next/link";
+import { Heart } from "@phosphor-icons/react";
+import { useState, useEffect } from "react";
+import { getQueryClient } from "@/lib/react-query/queryClient";
+import { InfiniteData } from "@tanstack/react-query";
 
 import { LibraryAgent } from "@/app/api/__generated__/models/libraryAgent";
+import {
+  getV2ListLibraryAgentsResponse,
+  getV2ListFavoriteLibraryAgentsResponse,
+} from "@/app/api/__generated__/endpoints/library/library";
+import BackendAPI, { LibraryAgentID } from "@/lib/autogpt-server-api";
+import { cn } from "@/lib/utils";
+import { useToast } from "@/components/molecules/Toast/use-toast";
+import { Flag, useGetFlag } from "@/services/feature-flags/use-get-flag";
 import Avatar, {
   AvatarFallback,
   AvatarImage,
 } from "@/components/atoms/Avatar/Avatar";
-import { Link } from "@/components/atoms/Link/Link";
-import { AgentCardMenu } from "./components/AgentCardMenu";
-import { FavoriteButton } from "./components/FavoriteButton";
-import { useLibraryAgentCard } from "./useLibraryAgentCard";
 
-interface Props {
+interface LibraryAgentCardProps {
   agent: LibraryAgent;
 }
 
-export function LibraryAgentCard({ agent }: Props) {
-  const { id, name, graph_id, can_access_graph, image_url } = agent;
-
-  const {
-    isFromMarketplace,
-    isFavorite,
-    profile,
+export default function LibraryAgentCard({
+  agent: {
+    id,
+    name,
+    description,
+    graph_id,
+    can_access_graph,
     creator_image_url,
-    handleToggleFavorite,
-  } = useLibraryAgentCard({ agent });
+    image_url,
+    is_favorite,
+  },
+}: LibraryAgentCardProps) {
+  const isAgentFavoritingEnabled = useGetFlag(Flag.AGENT_FAVORITING);
+  const [isFavorite, setIsFavorite] = useState(is_favorite);
+  const [isUpdating, setIsUpdating] = useState(false);
+  const { toast } = useToast();
+  const api = new BackendAPI();
+  const queryClient = getQueryClient();
+
+  // Sync local state with prop when it changes (e.g., after query invalidation)
+  useEffect(() => {
+    setIsFavorite(is_favorite);
+  }, [is_favorite]);
+
+  const updateQueryData = (newIsFavorite: boolean) => {
+    // Update the agent in all library agent queries
+    queryClient.setQueriesData(
+      { queryKey: ["/api/library/agents"] },
+      (
+        oldData:
+          | InfiniteData<getV2ListLibraryAgentsResponse, number | undefined>
+          | undefined,
+      ) => {
+        if (!oldData?.pages) return oldData;
+
+        return {
+          ...oldData,
+          pages: oldData.pages.map((page) => {
+            if (page.status !== 200) return page;
+
+            return {
+              ...page,
+              data: {
+                ...page.data,
+                agents: page.data.agents.map((agent: LibraryAgent) =>
+                  agent.id === id
+                    ? { ...agent, is_favorite: newIsFavorite }
+                    : agent,
+                ),
+              },
+            };
+          }),
+        };
+      },
+    );
+
+    // Update or remove from favorites query based on new state
+    queryClient.setQueriesData(
+      { queryKey: ["/api/library/agents/favorites"] },
+      (
+        oldData:
+          | InfiniteData<
+              getV2ListFavoriteLibraryAgentsResponse,
+              number | undefined
+            >
+          | undefined,
+      ) => {
+        if (!oldData?.pages) return oldData;
+
+        if (newIsFavorite) {
+          // Add to favorites if not already there
+          const exists = oldData.pages.some(
+            (page) =>
+              page.status === 200 &&
+              page.data.agents.some((agent: LibraryAgent) => agent.id === id),
+          );
+
+          if (!exists) {
+            const firstPage = oldData.pages[0];
+            if (firstPage?.status === 200) {
+              const updatedAgent = {
+                id,
+                name,
+                description,
+                graph_id,
+                can_access_graph,
+                creator_image_url,
+                image_url,
+                is_favorite: true,
+              };
+
+              return {
+                ...oldData,
+                pages: [
+                  {
+                    ...firstPage,
+                    data: {
+                      ...firstPage.data,
+                      agents: [updatedAgent, ...firstPage.data.agents],
+                      pagination: {
+                        ...firstPage.data.pagination,
+                        total_items: firstPage.data.pagination.total_items + 1,
+                      },
+                    },
+                  },
+                  ...oldData.pages.slice(1).map((page) =>
+                    page.status === 200
+                      ? {
+                          ...page,
+                          data: {
+                            ...page.data,
+                            pagination: {
+                              ...page.data.pagination,
+                              total_items: page.data.pagination.total_items + 1,
+                            },
+                          },
+                        }
+                      : page,
+                  ),
+                ],
+              };
+            }
+          }
+        } else {
+          // Remove from favorites
+          let removedCount = 0;
+          return {
+            ...oldData,
+            pages: oldData.pages.map((page) => {
+              if (page.status !== 200) return page;
+
+              const filteredAgents = page.data.agents.filter(
+                (agent: LibraryAgent) => agent.id !== id,
+              );
+
+              if (filteredAgents.length < page.data.agents.length) {
+                removedCount = 1;
+              }
+
+              return {
+                ...page,
+                data: {
+                  ...page.data,
+                  agents: filteredAgents,
+                  pagination: {
+                    ...page.data.pagination,
+                    total_items:
+                      page.data.pagination.total_items - removedCount,
+                  },
+                },
+              };
+            }),
+          };
+        }
+
+        return oldData;
+      },
+    );
+  };
+
+  const handleToggleFavorite = async (e: React.MouseEvent) => {
+    e.preventDefault(); // Prevent navigation when clicking the heart
+    e.stopPropagation();
+
+    if (isUpdating || !isAgentFavoritingEnabled) return;
+
+    const newIsFavorite = !isFavorite;
+
+    // Optimistic update
+    setIsFavorite(newIsFavorite);
+    updateQueryData(newIsFavorite);
+
+    setIsUpdating(true);
+    try {
+      await api.updateLibraryAgent(id as LibraryAgentID, {
+        is_favorite: newIsFavorite,
+      });
+
+      toast({
+        title: newIsFavorite ? "Added to favorites" : "Removed from favorites",
+        description: `${name} has been ${newIsFavorite ? "added to" : "removed from"} your favorites.`,
+      });
+    } catch (error) {
+      // Revert on error
+      console.error("Failed to update favorite status:", error);
+      setIsFavorite(!newIsFavorite);
+      updateQueryData(!newIsFavorite);
+
+      toast({
+        title: "Error",
+        description: "Failed to update favorite status. Please try again.",
+        variant: "destructive",
+      });
+    } finally {
+      setIsUpdating(false);
+    }
+  };
 
   return (
     <div
       data-testid="library-agent-card"
       data-agent-id={id}
-      className="group relative inline-flex h-[10.625rem] w-full max-w-[25rem] flex-col items-start justify-start gap-2.5 rounded-medium border border-zinc-100 bg-white transition-all duration-300 hover:shadow-md"
+      className="group inline-flex w-full max-w-[434px] flex-col items-start justify-start gap-2.5 rounded-[26px] bg-white transition-all duration-300 hover:shadow-lg dark:bg-transparent dark:hover:shadow-gray-700"
     >
-      <NextLink href={`/library/agents/${id}`} className="flex-shrink-0">
-        <div className="relative flex items-center gap-2 px-4 pt-3">
-          <Avatar className="h-4 w-4 rounded-full">
+      <Link
+        href={`/library/agents/${id}`}
+        className="relative h-[200px] w-full overflow-hidden rounded-[20px]"
+      >
+        {!image_url ? (
+          <div
+            className={`h-full w-full ${
+              [
+                "bg-gradient-to-r from-green-200 to-blue-200",
+                "bg-gradient-to-r from-pink-200 to-purple-200",
+                "bg-gradient-to-r from-yellow-200 to-orange-200",
+                "bg-gradient-to-r from-blue-200 to-cyan-200",
+                "bg-gradient-to-r from-indigo-200 to-purple-200",
+              ][parseInt(id.slice(0, 8), 16) % 5]
+            }`}
+            style={{
+              backgroundSize: "200% 200%",
+              animation: "gradient 15s ease infinite",
+            }}
+          />
+        ) : (
+          <Image
+            src={image_url}
+            alt={`${name} preview image`}
+            fill
+            className="object-cover"
+          />
+        )}
+        {isAgentFavoritingEnabled && (
+          <button
+            onClick={handleToggleFavorite}
+            className={cn(
+              "absolute right-4 top-4 rounded-full bg-white/90 p-2 backdrop-blur-sm transition-all duration-200",
+              "hover:scale-110 hover:bg-white",
+              "focus:outline-none focus:ring-2 focus:ring-red-500 focus:ring-offset-2",
+              isUpdating && "cursor-not-allowed opacity-50",
+              !isFavorite && "opacity-0 group-hover:opacity-100",
+            )}
+            disabled={isUpdating}
+            aria-label={
+              isFavorite ? "Remove from favorites" : "Add to favorites"
+            }
+          >
+            <Heart
+              size={20}
+              weight={isFavorite ? "fill" : "regular"}
+              className={cn(
+                "transition-colors duration-200",
+                isFavorite
+                  ? "text-red-500"
+                  : "text-gray-600 hover:text-red-500",
+              )}
+            />
+          </button>
+        )}
+        <div className="absolute bottom-4 left-4">
+          <Avatar className="h-16 w-16">
             <AvatarImage
               src={
-                isFromMarketplace
-                  ? creator_image_url || "/avatar-placeholder.png"
-                  : profile?.avatar_url || "/avatar-placeholder.png"
+                creator_image_url
+                  ? creator_image_url
+                  : "/avatar-placeholder.png"
               }
               alt={`${name} creator avatar`}
             />
-            <AvatarFallback size={48}>{name.charAt(0)}</AvatarFallback>
+            <AvatarFallback size={64}>{name.charAt(0)}</AvatarFallback>
           </Avatar>
-          <Text
-            variant="small-medium"
-            className="uppercase tracking-wide text-zinc-400"
-          >
-            {isFromMarketplace ? "FROM MARKETPLACE" : "Built by you"}
-          </Text>
         </div>
-      </NextLink>
-      <FavoriteButton
-        isFavorite={isFavorite}
-        onClick={handleToggleFavorite}
-        className="absolute right-10 top-0"
-      />
-      <AgentCardMenu agent={agent} />
+      </Link>
 
-      <div className="flex w-full flex-1 flex-col px-4 pb-2">
-        <Link
-          href={`/library/agents/${id}`}
-          className="flex w-full items-start justify-between gap-2 no-underline hover:no-underline"
-        >
-          <Text
-            variant="h5"
-            data-testid="library-agent-card-name"
-            className="line-clamp-3 hyphens-auto break-words no-underline hover:no-underline"
-          >
+      <div className="flex w-full flex-1 flex-col px-4 py-4">
+        <Link href={`/library/agents/${id}`}>
+          <h3 className="mb-2 line-clamp-2 font-poppins text-2xl font-semibold leading-tight text-[#272727] dark:text-neutral-100">
             {name}
-          </Text>
+          </h3>
 
-          {!image_url ? (
-            <div
-              className={`h-[3.64rem] w-[6.70rem] flex-shrink-0 rounded-small ${
-                [
-                  "bg-gradient-to-r from-green-200 to-blue-200",
-                  "bg-gradient-to-r from-pink-200 to-purple-200",
-                  "bg-gradient-to-r from-yellow-200 to-orange-200",
-                  "bg-gradient-to-r from-blue-200 to-cyan-200",
-                  "bg-gradient-to-r from-indigo-200 to-purple-200",
-                ][parseInt(id.slice(0, 8), 16) % 5]
-              }`}
-              style={{
-                backgroundSize: "200% 200%",
-                animation: "gradient 15s ease infinite",
-              }}
-            />
-          ) : (
-            <Image
-              src={image_url}
-              alt={`${name} preview image`}
-              width={107}
-              height={58}
-              className="flex-shrink-0 rounded-small object-cover"
-            />
-          )}
+          <p className="line-clamp-3 flex-1 text-sm text-gray-600 dark:text-gray-400">
+            {description}
+          </p>
         </Link>
 
-        <div className="mt-auto flex w-full justify-start gap-6 border-t border-zinc-100 pb-1 pt-3">
+        <div className="flex-grow" />
+        {/* Spacer */}
+
+        <div className="items-between mt-4 flex w-full justify-between gap-3">
           <Link
             href={`/library/agents/${id}`}
-            data-testid="library-agent-card-see-runs-link"
-            className="flex items-center gap-1 text-[13px]"
+            className="text-lg font-semibold text-neutral-800 hover:underline dark:text-neutral-200"
           >
-            See runs <CaretCircleRightIcon size={20} />
+            See runs
           </Link>
 
           {can_access_graph && (
             <Link
               href={`/build?flowID=${graph_id}`}
-              data-testid="library-agent-card-open-in-builder-link"
-              className="flex items-center gap-1 text-[13px]"
-              isExternal
+              className="text-lg font-semibold text-neutral-800 hover:underline dark:text-neutral-200"
             >
-              Open in builder <CaretCircleRightIcon size={20} />
+              Open in builder
             </Link>
           )}
         </div>

autogpt_platform/frontend/src/app/(platform)/library/components/LibraryAgentCard/components/AgentCardMenu.tsx

@@ -1,188 +0,0 @@
-"use client";
-
-import {
-  getGetV2ListLibraryAgentsQueryKey,
-  useDeleteV2DeleteLibraryAgent,
-  usePostV2ForkLibraryAgent,
-} from "@/app/api/__generated__/endpoints/library/library";
-import { LibraryAgent } from "@/app/api/__generated__/models/libraryAgent";
-import { Button } from "@/components/atoms/Button/Button";
-import { Text } from "@/components/atoms/Text/Text";
-import { Dialog } from "@/components/molecules/Dialog/Dialog";
-import {
-  DropdownMenu,
-  DropdownMenuContent,
-  DropdownMenuItem,
-  DropdownMenuSeparator,
-  DropdownMenuTrigger,
-} from "@/components/molecules/DropdownMenu/DropdownMenu";
-import { useToast } from "@/components/molecules/Toast/use-toast";
-import { DotsThree } from "@phosphor-icons/react";
-import { useQueryClient } from "@tanstack/react-query";
-import Link from "next/link";
-import { useRouter } from "next/navigation";
-import { useState } from "react";
-
-interface AgentCardMenuProps {
-  agent: LibraryAgent;
-}
-
-export function AgentCardMenu({ agent }: AgentCardMenuProps) {
-  const { toast } = useToast();
-  const queryClient = useQueryClient();
-  const router = useRouter();
-  const [showDeleteDialog, setShowDeleteDialog] = useState(false);
-  const [isDeletingAgent, setIsDeletingAgent] = useState(false);
-  const [isDuplicatingAgent, setIsDuplicatingAgent] = useState(false);
-
-  const { mutateAsync: deleteAgent } = useDeleteV2DeleteLibraryAgent();
-  const { mutateAsync: forkAgent } = usePostV2ForkLibraryAgent();
-
-  async function handleDuplicateAgent() {
-    if (!agent.id) return;
-
-    setIsDuplicatingAgent(true);
-
-    try {
-      const result = await forkAgent({ libraryAgentId: agent.id });
-
-      if (result.status === 200) {
-        await queryClient.refetchQueries({
-          queryKey: getGetV2ListLibraryAgentsQueryKey(),
-        });
-
-        toast({
-          title: "Agent duplicated",
-          description: `${result.data.name} has been created.`,
-        });
-      }
-    } catch (error: unknown) {
-      toast({
-        title: "Failed to duplicate agent",
-        description:
-          error instanceof Error
-            ? error.message
-            : "An unexpected error occurred.",
-        variant: "destructive",
-      });
-    } finally {
-      setIsDuplicatingAgent(false);
-    }
-  }
-
-  async function handleDeleteAgent() {
-    if (!agent.id) return;
-
-    setIsDeletingAgent(true);
-
-    try {
-      await deleteAgent({ libraryAgentId: agent.id });
-
-      await queryClient.refetchQueries({
-        queryKey: getGetV2ListLibraryAgentsQueryKey(),
-      });
-
-      toast({ title: "Agent deleted" });
-      setShowDeleteDialog(false);
-      router.push("/library");
-    } catch (error: unknown) {
-      toast({
-        title: "Failed to delete agent",
-        description:
-          error instanceof Error
-            ? error.message
-            : "An unexpected error occurred.",
-        variant: "destructive",
-      });
-    } finally {
-      setIsDeletingAgent(false);
-    }
-  }
-
-  return (
-    <>
-      <DropdownMenu>
-        <DropdownMenuTrigger asChild>
-          <button
-            className="absolute right-2 top-1 rounded p-1.5 transition-opacity hover:bg-neutral-100"
-            onClick={(e) => e.stopPropagation()}
-            aria-label="More actions"
-          >
-            <DotsThree className="h-5 w-5 text-neutral-600" />
-          </button>
-        </DropdownMenuTrigger>
-        <DropdownMenuContent align="end">
-          {agent.can_access_graph && (
-            <>
-              <DropdownMenuItem asChild>
-                <Link
-                  href={`/build?flowID=${agent.graph_id}&flowVersion=${agent.graph_version}`}
-                  target="_blank"
-                  className="flex items-center gap-2"
-                  onClick={(e) => e.stopPropagation()}
-                >
-                  Edit agent
-                </Link>
-              </DropdownMenuItem>
-              <DropdownMenuSeparator />
-            </>
-          )}
-          <DropdownMenuItem
-            onClick={(e) => {
-              e.stopPropagation();
-              handleDuplicateAgent();
-            }}
-            disabled={isDuplicatingAgent}
-            className="flex items-center gap-2"
-          >
-            Duplicate agent
-          </DropdownMenuItem>
-          <DropdownMenuSeparator />
-          <DropdownMenuItem
-            onClick={(e) => {
-              e.stopPropagation();
-              setShowDeleteDialog(true);
-            }}
-            className="flex items-center gap-2 text-red-600 focus:bg-red-50 focus:text-red-600"
-          >
-            Delete agent
-          </DropdownMenuItem>
-        </DropdownMenuContent>
-      </DropdownMenu>
-
-      <Dialog
-        controlled={{
-          isOpen: showDeleteDialog,
-          set: setShowDeleteDialog,
-        }}
-        styling={{ maxWidth: "32rem" }}
-        title="Delete agent"
-      >
-        <Dialog.Content>
-          <div>
-            <Text variant="large">
-              Are you sure you want to delete this agent? This action cannot be
-              undone.
-            </Text>
-            <Dialog.Footer>
-              <Button
-                variant="secondary"
-                disabled={isDeletingAgent}
-                onClick={() => setShowDeleteDialog(false)}
-              >
-                Cancel
-              </Button>
-              <Button
-                variant="destructive"
-                onClick={handleDeleteAgent}
-                loading={isDeletingAgent}
-              >
-                Delete Agent
-              </Button>
-            </Dialog.Footer>
-          </div>
-        </Dialog.Content>
-      </Dialog>
-    </>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/library/components/LibraryAgentCard/components/FavoriteButton.tsx

@@ -1,39 +0,0 @@
-"use client";
-
-import { cn } from "@/lib/utils";
-import { HeartIcon } from "@phosphor-icons/react";
-import type { MouseEvent } from "react";
-
-interface FavoriteButtonProps {
-  isFavorite: boolean;
-  onClick: (e: MouseEvent<HTMLButtonElement>) => void;
-  className?: string;
-}
-
-export function FavoriteButton({
-  isFavorite,
-  onClick,
-  className,
-}: FavoriteButtonProps) {
-  return (
-    <button
-      onClick={onClick}
-      className={cn(
-        "rounded-full p-2 transition-all duration-200",
-        "hover:scale-110",
-        !isFavorite && "opacity-0 group-hover:opacity-100",
-        className,
-      )}
-      aria-label={isFavorite ? "Remove from favorites" : "Add to favorites"}
-    >
-      <HeartIcon
-        size={20}
-        weight={isFavorite ? "fill" : "regular"}
-        className={cn(
-          "transition-colors duration-200",
-          isFavorite ? "text-red-500" : "text-gray-600 hover:text-red-500",
-        )}
-      />
-    </button>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/library/components/LibraryAgentCard/helpers.ts

@@ -1,150 +0,0 @@
-import { InfiniteData, QueryClient } from "@tanstack/react-query";
-
-import {
-  getV2ListFavoriteLibraryAgentsResponse,
-  getV2ListLibraryAgentsResponse,
-} from "@/app/api/__generated__/endpoints/library/library";
-import { LibraryAgent } from "@/app/api/__generated__/models/libraryAgent";
-
-interface UpdateFavoriteInQueriesParams {
-  queryClient: QueryClient;
-  agentId: string;
-  agent: LibraryAgent;
-  newIsFavorite: boolean;
-}
-
-export function updateFavoriteInQueries({
-  queryClient,
-  agentId,
-  agent,
-  newIsFavorite,
-}: UpdateFavoriteInQueriesParams) {
-  queryClient.setQueriesData(
-    { queryKey: ["/api/library/agents"] },
-    (
-      oldData:
-        | InfiniteData<getV2ListLibraryAgentsResponse, number | undefined>
-        | undefined,
-    ) => {
-      if (!oldData?.pages) return oldData;
-
-      return {
-        ...oldData,
-        pages: oldData.pages.map((page) => {
-          if (page.status !== 200) return page;
-
-          return {
-            ...page,
-            data: {
-              ...page.data,
-              agents: page.data.agents.map((currentAgent: LibraryAgent) =>
-                currentAgent.id === agentId
-                  ? { ...currentAgent, is_favorite: newIsFavorite }
-                  : currentAgent,
-              ),
-            },
-          };
-        }),
-      };
-    },
-  );
-
-  queryClient.setQueriesData(
-    { queryKey: ["/api/library/agents/favorites"] },
-    (
-      oldData:
-        | InfiniteData<
-            getV2ListFavoriteLibraryAgentsResponse,
-            number | undefined
-          >
-        | undefined,
-    ) => {
-      if (!oldData?.pages) return oldData;
-
-      if (newIsFavorite) {
-        const exists = oldData.pages.some(
-          (page) =>
-            page.status === 200 &&
-            page.data.agents.some(
-              (currentAgent: LibraryAgent) => currentAgent.id === agentId,
-            ),
-        );
-
-        if (!exists) {
-          const firstPage = oldData.pages[0];
-          if (firstPage?.status === 200) {
-            const updatedAgent = {
-              id: agent.id,
-              name: agent.name,
-              description: agent.description,
-              graph_id: agent.graph_id,
-              can_access_graph: agent.can_access_graph,
-              creator_image_url: agent.creator_image_url,
-              image_url: agent.image_url,
-              is_favorite: true,
-            };
-
-            return {
-              ...oldData,
-              pages: [
-                {
-                  ...firstPage,
-                  data: {
-                    ...firstPage.data,
-                    agents: [updatedAgent, ...firstPage.data.agents],
-                    pagination: {
-                      ...firstPage.data.pagination,
-                      total_items: firstPage.data.pagination.total_items + 1,
-                    },
-                  },
-                },
-                ...oldData.pages.slice(1).map((page) =>
-                  page.status === 200
-                    ? {
-                        ...page,
-                        data: {
-                          ...page.data,
-                          pagination: {
-                            ...page.data.pagination,
-                            total_items: page.data.pagination.total_items + 1,
-                          },
-                        },
-                      }
-                    : page,
-                ),
-              ],
-            };
-          }
-        }
-      } else {
-        return {
-          ...oldData,
-          pages: oldData.pages.map((page) => {
-            if (page.status !== 200) return page;
-
-            const filteredAgents = page.data.agents.filter(
-              (currentAgent: LibraryAgent) => currentAgent.id !== agentId,
-            );
-
-            const removedCount =
-              filteredAgents.length < page.data.agents.length ? 1 : 0;
-
-            return {
-              ...page,
-              data: {
-                ...page.data,
-                agents: filteredAgents,
-                pagination: {
-                  ...page.data.pagination,
-                  total_items: page.data.pagination.total_items - removedCount,
-                },
-              },
-            };
-          }),
-        };
-      }
-
-      return oldData;
-    },
-  );
-}

autogpt_platform/frontend/src/app/(platform)/library/components/LibraryAgentCard/useLibraryAgentCard.ts

@@ -1,84 +0,0 @@
-"use client";
-
-import { getQueryClient } from "@/lib/react-query/queryClient";
-import { useEffect, useState } from "react";
-
-import { usePatchV2UpdateLibraryAgent } from "@/app/api/__generated__/endpoints/library/library";
-import { useGetV2GetUserProfile } from "@/app/api/__generated__/endpoints/store/store";
-import { LibraryAgent } from "@/app/api/__generated__/models/libraryAgent";
-import { okData } from "@/app/api/helpers";
-import { useToast } from "@/components/molecules/Toast/use-toast";
-import { updateFavoriteInQueries } from "./helpers";
-
-interface Props {
-  agent: LibraryAgent;
-}
-
-export function useLibraryAgentCard({ agent }: Props) {
-  const { id, name, is_favorite, creator_image_url, marketplace_listing } =
-    agent;
-
-  const isFromMarketplace = Boolean(marketplace_listing);
-  const [isFavorite, setIsFavorite] = useState(is_favorite);
-  const { toast } = useToast();
-  const queryClient = getQueryClient();
-  const { mutateAsync: updateLibraryAgent } = usePatchV2UpdateLibraryAgent();
-
-  const { data: profile } = useGetV2GetUserProfile({
-    query: {
-      select: okData,
-    },
-  });
-
-  useEffect(() => {
-    setIsFavorite(is_favorite);
-  }, [is_favorite]);
-
-  function updateQueryData(newIsFavorite: boolean) {
-    updateFavoriteInQueries({
-      queryClient,
-      agentId: id,
-      agent,
-      newIsFavorite,
-    });
-  }
-
-  async function handleToggleFavorite(e: React.MouseEvent) {
-    e.preventDefault();
-    e.stopPropagation();
-
-    const newIsFavorite = !isFavorite;
-
-    setIsFavorite(newIsFavorite);
-    updateQueryData(newIsFavorite);
-
-    try {
-      await updateLibraryAgent({
-        libraryAgentId: id,
-        data: { is_favorite: newIsFavorite },
-      });
-
-      toast({
-        title: newIsFavorite ? "Added to favorites" : "Removed from favorites",
-        description: `${name} has been ${newIsFavorite ? "added to" : "removed from"} your favorites.`,
-      });
-    } catch {
-      setIsFavorite(!newIsFavorite);
-      updateQueryData(!newIsFavorite);
-
-      toast({
-        title: "Error",
-        description: "Failed to update favorite status. Please try again.",
-        variant: "destructive",
-      });
-    }
-  }
-
-  return {
-    isFromMarketplace,
-    isFavorite,
-    profile,
-    creator_image_url,
-    handleToggleFavorite,
-  };
-}

autogpt_platform/frontend/src/app/(platform)/library/components/LibraryAgentList/LibraryAgentList.tsx

@@ -1,22 +1,10 @@
 "use client";
-import { LibraryAgentSort } from "@/app/api/__generated__/models/libraryAgentSort";
-import { LoadingSpinner } from "@/components/atoms/LoadingSpinner/LoadingSpinner";
+import LibraryActionSubHeader from "../LibraryActionSubHeader/LibraryActionSubHeader";
+import LibraryAgentCard from "../LibraryAgentCard/LibraryAgentCard";
 import { InfiniteScroll } from "@/components/contextual/InfiniteScroll/InfiniteScroll";
-import { LibraryActionSubHeader } from "../LibraryActionSubHeader/LibraryActionSubHeader";
-import { LibraryAgentCard } from "../LibraryAgentCard/LibraryAgentCard";
 import { useLibraryAgentList } from "./useLibraryAgentList";
 
-interface Props {
-  searchTerm: string;
-  librarySort: LibraryAgentSort;
-  setLibrarySort: (value: LibraryAgentSort) => void;
-}
-
-export function LibraryAgentList({
-  searchTerm,
-  librarySort,
-  setLibrarySort,
-}: Props) {
+export default function LibraryAgentList() {
   const {
     agentLoading,
     agentCount,
@@ -24,27 +12,28 @@ export function LibraryAgentList({
     hasNextPage,
     isFetchingNextPage,
     fetchNextPage,
-  } = useLibraryAgentList({ searchTerm, librarySort });
+  } = useLibraryAgentList();
+
+  const LoadingSpinner = () => (
+    <div className="h-8 w-8 animate-spin rounded-full border-b-2 border-t-2 border-neutral-800" />
+  );
 
   return (
     <>
-      <LibraryActionSubHeader
-        agentCount={agentCount}
-        setLibrarySort={setLibrarySort}
-      />
+      <LibraryActionSubHeader agentCount={agentCount} />
       <div className="px-2">
         {agentLoading ? (
           <div className="flex h-[200px] items-center justify-center">
-            <LoadingSpinner size="large" />
+            <LoadingSpinner />
           </div>
         ) : (
           <InfiniteScroll
             isFetchingNextPage={isFetchingNextPage}
             fetchNextPage={fetchNextPage}
             hasNextPage={hasNextPage}
-            loader={<LoadingSpinner size="medium" />}
+            loader={<LoadingSpinner />}
           >
-            <div className="grid grid-cols-1 gap-6 sm:grid-cols-2 md:grid-cols-2 lg:grid-cols-3 xl:grid-cols-4">
+            <div className="grid grid-cols-1 gap-3 sm:grid-cols-2 md:grid-cols-2 lg:grid-cols-3 xl:grid-cols-4">
               {agents.map((agent) => (
                 <LibraryAgentCard key={agent.id} agent={agent} />
               ))}