feat(backend): implement unified content embeddings with userId support

- Replace StoreListingEmbedding with UnifiedContentEmbedding table
- Add ContentType enum (STORE_AGENT, BLOCK, INTEGRATION, DOCUMENTATION, LIBRARY_AGENT)
- Support user-specific content with optional userId field for access control
- Maintain backward compatibility with wrapper functions for existing store APIs
- Update hybrid search to use unified embedding table with proper ContentType filtering
- Add comprehensive tests for new embedding service functionality
- Use proper Prisma ContentType enum instead of strings for type safety

The unified architecture enables future expansion to semantic search for blocks,
documentation, and library agents while maintaining existing store functionality.

.branchlet.json

@@ -1,37 +0,0 @@
-{
-  "worktreeCopyPatterns": [
-    ".env*",
-    ".vscode/**",
-    ".auth/**",
-    ".claude/**",
-    "autogpt_platform/.env*",
-    "autogpt_platform/backend/.env*",
-    "autogpt_platform/frontend/.env*",
-    "autogpt_platform/frontend/.auth/**",
-    "autogpt_platform/db/docker/.env*"
-  ],
-  "worktreeCopyIgnores": [
-    "**/node_modules/**",
-    "**/dist/**",
-    "**/.git/**",
-    "**/Thumbs.db",
-    "**/.DS_Store",
-    "**/.next/**",
-    "**/__pycache__/**",
-    "**/.ruff_cache/**",
-    "**/.pytest_cache/**",
-    "**/*.pyc",
-    "**/playwright-report/**",
-    "**/logs/**",
-    "**/site/**"
-  ],
-  "worktreePathTemplate": "$BASE_PATH.worktree",
-  "postCreateCmd": [
-    "cd autogpt_platform/autogpt_libs && poetry install",
-    "cd autogpt_platform/backend && poetry install && poetry run prisma generate",
-    "cd autogpt_platform/frontend && pnpm install",
-    "cd docs && pip install -r requirements.txt"
-  ],
-  "terminalCommand": "code .",
-  "deleteBranchWithWorktree": false
-}

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

@@ -401,11 +401,28 @@ async def add_generated_agent_image(
     )
 
 
+def _initialize_graph_settings(graph: graph_db.GraphModel) -> GraphSettings:
+    """
+    Initialize GraphSettings based on graph content.
+
+    Args:
+        graph: The graph to analyze
+
+    Returns:
+        GraphSettings with appropriate human_in_the_loop_safe_mode value
+    """
+    if graph.has_human_in_the_loop:
+        # Graph has HITL blocks - set safe mode to True by default
+        return GraphSettings(human_in_the_loop_safe_mode=True)
+    else:
+        # Graph has no HITL blocks - keep None
+        return GraphSettings(human_in_the_loop_safe_mode=None)
+
+
 async def create_library_agent(
     graph: graph_db.GraphModel,
     user_id: str,
     create_library_agents_for_sub_graphs: bool = True,
-    is_ai_generated: bool = False,
 ) -> list[library_model.LibraryAgent]:
     """
     Adds an agent to the user's library (LibraryAgent table).
@@ -414,7 +431,6 @@ async def create_library_agent(
         agent: The agent/Graph to add to the library.
         user_id: The user to whom the agent will be added.
         create_library_agents_for_sub_graphs: If True, creates LibraryAgent records for sub-graphs as well.
-        is_ai_generated: Whether this graph was AI-generated.
 
     Returns:
         The newly created LibraryAgent records.
@@ -449,9 +465,7 @@ async def create_library_agent(
                             }
                         },
                         settings=SafeJson(
-                            GraphSettings.from_graph(
-                                graph_entry, is_ai_generated=is_ai_generated
-                            ).model_dump()
+                            _initialize_graph_settings(graph_entry).model_dump()
                         ),
                     ),
                     include=library_agent_include(
@@ -475,7 +489,7 @@ async def update_agent_version_in_library(
     agent_graph_version: int,
 ) -> library_model.LibraryAgent:
     """
-    Updates the agent version in the library for any agent owned by the user.
+    Updates the agent version in the library if useGraphIsActiveVersion is True.
 
     Args:
         user_id: Owner of the LibraryAgent.
@@ -484,31 +498,20 @@ async def update_agent_version_in_library(
 
     Raises:
         DatabaseError: If there's an error with the update.
-        NotFoundError: If no library agent is found for this user and agent.
     """
     logger.debug(
         f"Updating agent version in library for user #{user_id}, "
         f"agent #{agent_graph_id} v{agent_graph_version}"
     )
-    async with transaction() as tx:
-        library_agent = await prisma.models.LibraryAgent.prisma(tx).find_first_or_raise(
+    try:
+        library_agent = await prisma.models.LibraryAgent.prisma().find_first_or_raise(
             where={
                 "userId": user_id,
                 "agentGraphId": agent_graph_id,
+                "useGraphIsActiveVersion": True,
             },
         )
-
-        # Delete any conflicting LibraryAgent for the target version
-        await prisma.models.LibraryAgent.prisma(tx).delete_many(
-            where={
-                "userId": user_id,
-                "agentGraphId": agent_graph_id,
-                "agentGraphVersion": agent_graph_version,
-                "id": {"not": library_agent.id},
-            }
-        )
-
-        lib = await prisma.models.LibraryAgent.prisma(tx).update(
+        lib = await prisma.models.LibraryAgent.prisma().update(
             where={"id": library_agent.id},
             data={
                 "AgentGraph": {
@@ -522,13 +525,13 @@ async def update_agent_version_in_library(
             },
             include={"AgentGraph": True},
         )
+        if lib is None:
+            raise NotFoundError(f"Library agent {library_agent.id} not found")
 
-    if lib is None:
-        raise NotFoundError(
-            f"Failed to update library agent for {agent_graph_id} v{agent_graph_version}"
-        )
-
-    return library_model.LibraryAgent.from_db(lib)
+        return library_model.LibraryAgent.from_db(lib)
+    except prisma.errors.PrismaError as e:
+        logger.error(f"Database error updating agent version in library: {e}")
+        raise DatabaseError("Failed to update agent version in library") from e
 
 
 async def update_library_agent(
@@ -613,6 +616,33 @@ async def update_library_agent(
         raise DatabaseError("Failed to update library agent") from e
 
 
+async def update_library_agent_settings(
+    user_id: str,
+    agent_id: str,
+    settings: GraphSettings,
+) -> library_model.LibraryAgent:
+    """
+    Updates the settings for a specific LibraryAgent.
+
+    Args:
+        user_id: The owner of the LibraryAgent.
+        agent_id: The ID of the LibraryAgent to update.
+        settings: New GraphSettings to apply.
+
+    Returns:
+        The updated LibraryAgent.
+
+    Raises:
+        NotFoundError: If the specified LibraryAgent does not exist.
+        DatabaseError: If there's an error in the update operation.
+    """
+    return await update_library_agent(
+        library_agent_id=agent_id,
+        user_id=user_id,
+        settings=settings,
+    )
+
+
 async def delete_library_agent(
     library_agent_id: str, user_id: str, soft_delete: bool = True
 ) -> None:
@@ -795,11 +825,8 @@ async def add_store_agent_to_library(
                     }
                 },
                 "isCreatedByUser": False,
-                "useGraphIsActiveVersion": False,
                 "settings": SafeJson(
-                    GraphSettings.from_graph(
-                        graph_model, is_ai_generated=False
-                    ).model_dump()
+                    _initialize_graph_settings(graph_model).model_dump()
                 ),
             },
             include=library_agent_include(

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

@@ -0,0 +1,72 @@
+#!/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

@@ -1,6 +1,5 @@
 import asyncio
 import logging
-import typing
 from datetime import datetime, timezone
 from typing import Literal
 
@@ -10,7 +9,7 @@ import prisma.errors
 import prisma.models
 import prisma.types
 
-from backend.data.db import query_raw_with_schema, transaction
+from backend.data.db import transaction
 from backend.data.graph import (
     GraphMeta,
     GraphModel,
@@ -30,6 +29,8 @@ 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()
@@ -56,122 +57,62 @@ 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, use full-text search
+        # If search_query is provided, try hybrid search (embeddings + tsvector)
         if search_query:
-            offset = (page - 1) * page_size
+            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
 
-            # Whitelist allowed order_by columns
-            ALLOWED_ORDER_BY = {
-                "rating": "rating DESC, rank DESC",
-                "runs": "runs DESC, rank DESC",
-                "name": "agent_name ASC, rank ASC",
-                "updated_at": "updated_at DESC, rank DESC",
-            }
+                # Convert 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
 
-            # 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"
+            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
 
-            # 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)
+        if not search_used_hybrid:
+            # Fallback path - use basic search or no search
             where_clause: prisma.types.StoreAgentWhereInput = {"is_available": True}
             if featured:
                 where_clause["featured"] = featured
@@ -180,6 +121,14 @@ 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"})
@@ -1600,6 +1549,22 @@ 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

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

@@ -0,0 +1,533 @@
+"""
+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

@@ -0,0 +1,359 @@
+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

@@ -0,0 +1,377 @@
+"""
+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/v1.py

@@ -762,10 +762,10 @@ async def create_new_graph(
     graph.reassign_ids(user_id=user_id, reassign_graph_id=True)
     graph.validate_graph(for_run=False)
 
+    # The return value of the create graph & library function is intentionally not used here,
+    # as the graph already valid and no sub-graphs are returned back.
     await graph_db.create_graph(graph, user_id=user_id)
-    await library_db.create_library_agent(
-        graph, user_id=user_id, is_ai_generated=create_graph.is_ai_generated
-    )
+    await library_db.create_library_agent(graph, user_id=user_id)
     activated_graph = await on_graph_activate(graph, user_id=user_id)
 
     if create_graph.source == "builder":
@@ -889,17 +889,21 @@ async def set_graph_active_version(
 async def _update_library_agent_version_and_settings(
     user_id: str, agent_graph: graph_db.GraphModel
 ) -> library_model.LibraryAgent:
+    # Keep the library agent up to date with the new active version
     library = await library_db.update_agent_version_in_library(
         user_id, agent_graph.id, agent_graph.version
     )
-    updated_settings = GraphSettings.from_graph(
-        agent_graph, is_ai_generated=library.settings.is_ai_generated_graph
-    )
-    if updated_settings != library.settings:
-        library = await library_db.update_library_agent(
-            library_agent_id=library.id,
+    # If the graph has HITL node, initialize the setting if it's not already set.
+    if (
+        agent_graph.has_human_in_the_loop
+        and library.settings.human_in_the_loop_safe_mode is None
+    ):
+        await library_db.update_library_agent_settings(
             user_id=user_id,
-            settings=updated_settings,
+            agent_id=library.id,
+            settings=library.settings.model_copy(
+                update={"human_in_the_loop_safe_mode": True}
+            ),
         )
     return library
 
@@ -916,18 +920,21 @@ async def update_graph_settings(
     user_id: Annotated[str, Security(get_user_id)],
 ) -> GraphSettings:
     """Update graph settings for the user's library agent."""
+    # Get the library agent for this graph
     library_agent = await library_db.get_library_agent_by_graph_id(
         graph_id=graph_id, user_id=user_id
     )
     if not library_agent:
         raise HTTPException(404, f"Graph #{graph_id} not found in user's library")
 
-    updated_agent = await library_db.update_library_agent(
-        library_agent_id=library_agent.id,
+    # Update the library agent settings
+    updated_agent = await library_db.update_library_agent_settings(
         user_id=user_id,
+        agent_id=library_agent.id,
         settings=settings,
     )
 
+    # Return the updated settings
     return GraphSettings.model_validate(updated_agent.settings)
 
 

autogpt_platform/backend/backend/api/model.py

@@ -43,7 +43,6 @@ GraphExecutionSource = Literal["builder", "library", "onboarding"]
 class CreateGraph(pydantic.BaseModel):
     graph: Graph
     source: GraphCreationSource | None = None
-    is_ai_generated: bool = False
 
 
 class CreateAPIKeyRequest(pydantic.BaseModel):

autogpt_platform/backend/backend/blocks/helpers/review.py

@@ -1,184 +0,0 @@
-"""
-Shared helpers for Human-In-The-Loop (HITL) review functionality.
-Used by both the dedicated HumanInTheLoopBlock and blocks that require human review.
-"""
-
-import logging
-from typing import Any, Optional
-
-from prisma.enums import ReviewStatus
-from pydantic import BaseModel
-
-from backend.data.execution import ExecutionContext, ExecutionStatus
-from backend.data.human_review import ReviewResult
-from backend.executor.manager import async_update_node_execution_status
-from backend.util.clients import get_database_manager_async_client
-
-logger = logging.getLogger(__name__)
-
-
-class ReviewDecision(BaseModel):
-    """Result of a review decision."""
-
-    should_proceed: bool
-    message: str
-    review_result: ReviewResult
-
-
-class HITLReviewHelper:
-    """Helper class for Human-In-The-Loop review operations."""
-
-    @staticmethod
-    async def get_or_create_human_review(**kwargs) -> Optional[ReviewResult]:
-        """Create or retrieve a human review from the database."""
-        return await get_database_manager_async_client().get_or_create_human_review(
-            **kwargs
-        )
-
-    @staticmethod
-    async def update_node_execution_status(**kwargs) -> None:
-        """Update the execution status of a node."""
-        await async_update_node_execution_status(
-            db_client=get_database_manager_async_client(), **kwargs
-        )
-
-    @staticmethod
-    async def update_review_processed_status(
-        node_exec_id: str, processed: bool
-    ) -> None:
-        """Update the processed status of a review."""
-        return await get_database_manager_async_client().update_review_processed_status(
-            node_exec_id, processed
-        )
-
-    @staticmethod
-    async def _handle_review_request(
-        input_data: Any,
-        user_id: str,
-        node_exec_id: str,
-        graph_exec_id: str,
-        graph_id: str,
-        graph_version: int,
-        execution_context: ExecutionContext,
-        block_name: str = "Block",
-        editable: bool = False,
-    ) -> Optional[ReviewResult]:
-        """
-        Handle a review request for a block that requires human review.
-
-        Args:
-            input_data: The input data to be reviewed
-            user_id: ID of the user requesting the review
-            node_exec_id: ID of the node execution
-            graph_exec_id: ID of the graph execution
-            graph_id: ID of the graph
-            graph_version: Version of the graph
-            execution_context: Current execution context
-            block_name: Name of the block requesting review
-            editable: Whether the reviewer can edit the data
-
-        Returns:
-            ReviewResult if review is complete, None if waiting for human input
-
-        Raises:
-            Exception: If review creation or status update fails
-        """
-        # Skip review if safe mode is disabled - return auto-approved result
-        if not execution_context.safe_mode:
-            logger.info(
-                f"Block {block_name} skipping review for node {node_exec_id} - safe mode disabled"
-            )
-            return ReviewResult(
-                data=input_data,
-                status=ReviewStatus.APPROVED,
-                message="Auto-approved (safe mode disabled)",
-                processed=True,
-                node_exec_id=node_exec_id,
-            )
-
-        result = await HITLReviewHelper.get_or_create_human_review(
-            user_id=user_id,
-            node_exec_id=node_exec_id,
-            graph_exec_id=graph_exec_id,
-            graph_id=graph_id,
-            graph_version=graph_version,
-            input_data=input_data,
-            message=f"Review required for {block_name} execution",
-            editable=editable,
-        )
-
-        if result is None:
-            logger.info(
-                f"Block {block_name} pausing execution for node {node_exec_id} - awaiting human review"
-            )
-            await HITLReviewHelper.update_node_execution_status(
-                exec_id=node_exec_id,
-                status=ExecutionStatus.REVIEW,
-            )
-            return None  # Signal that execution should pause
-
-        # Mark review as processed if not already done
-        if not result.processed:
-            await HITLReviewHelper.update_review_processed_status(
-                node_exec_id=node_exec_id, processed=True
-            )
-
-        return result
-
-    @staticmethod
-    async def handle_review_decision(
-        input_data: Any,
-        user_id: str,
-        node_exec_id: str,
-        graph_exec_id: str,
-        graph_id: str,
-        graph_version: int,
-        execution_context: ExecutionContext,
-        block_name: str = "Block",
-        editable: bool = False,
-    ) -> Optional[ReviewDecision]:
-        """
-        Handle a review request and return the decision in a single call.
-
-        Args:
-            input_data: The input data to be reviewed
-            user_id: ID of the user requesting the review
-            node_exec_id: ID of the node execution
-            graph_exec_id: ID of the graph execution
-            graph_id: ID of the graph
-            graph_version: Version of the graph
-            execution_context: Current execution context
-            block_name: Name of the block requesting review
-            editable: Whether the reviewer can edit the data
-
-        Returns:
-            ReviewDecision if review is complete (approved/rejected),
-            None if execution should pause (awaiting review)
-        """
-        review_result = await HITLReviewHelper._handle_review_request(
-            input_data=input_data,
-            user_id=user_id,
-            node_exec_id=node_exec_id,
-            graph_exec_id=graph_exec_id,
-            graph_id=graph_id,
-            graph_version=graph_version,
-            execution_context=execution_context,
-            block_name=block_name,
-            editable=editable,
-        )
-
-        if review_result is None:
-            # Still awaiting review - return None to pause execution
-            return None
-
-        # Review is complete, determine outcome
-        should_proceed = review_result.status == ReviewStatus.APPROVED
-        message = review_result.message or (
-            "Execution approved by reviewer"
-            if should_proceed
-            else "Execution rejected by reviewer"
-        )
-
-        return ReviewDecision(
-            should_proceed=should_proceed, message=message, review_result=review_result
-        )

autogpt_platform/backend/backend/blocks/human_in_the_loop.py

@@ -3,7 +3,6 @@ from typing import Any
 
 from prisma.enums import ReviewStatus
 
-from backend.blocks.helpers.review import HITLReviewHelper
 from backend.data.block import (
     Block,
     BlockCategory,
@@ -12,9 +11,11 @@ from backend.data.block import (
     BlockSchemaOutput,
     BlockType,
 )
-from backend.data.execution import ExecutionContext
+from backend.data.execution import ExecutionContext, ExecutionStatus
 from backend.data.human_review import ReviewResult
 from backend.data.model import SchemaField
+from backend.executor.manager import async_update_node_execution_status
+from backend.util.clients import get_database_manager_async_client
 
 logger = logging.getLogger(__name__)
 
@@ -71,26 +72,32 @@ class HumanInTheLoopBlock(Block):
                 ("approved_data", {"name": "John Doe", "age": 30}),
             ],
             test_mock={
-                "handle_review_decision": lambda **kwargs: type(
-                    "ReviewDecision",
-                    (),
-                    {
-                        "should_proceed": True,
-                        "message": "Test approval message",
-                        "review_result": ReviewResult(
-                            data={"name": "John Doe", "age": 30},
-                            status=ReviewStatus.APPROVED,
-                            message="",
-                            processed=False,
-                            node_exec_id="test-node-exec-id",
-                        ),
-                    },
-                )(),
+                "get_or_create_human_review": lambda *_args, **_kwargs: ReviewResult(
+                    data={"name": "John Doe", "age": 30},
+                    status=ReviewStatus.APPROVED,
+                    message="",
+                    processed=False,
+                    node_exec_id="test-node-exec-id",
+                ),
+                "update_node_execution_status": lambda *_args, **_kwargs: None,
+                "update_review_processed_status": lambda *_args, **_kwargs: None,
             },
         )
 
-    async def handle_review_decision(self, **kwargs):
-        return await HITLReviewHelper.handle_review_decision(**kwargs)
+    async def get_or_create_human_review(self, **kwargs):
+        return await get_database_manager_async_client().get_or_create_human_review(
+            **kwargs
+        )
+
+    async def update_node_execution_status(self, **kwargs):
+        return await async_update_node_execution_status(
+            db_client=get_database_manager_async_client(), **kwargs
+        )
+
+    async def update_review_processed_status(self, node_exec_id: str, processed: bool):
+        return await get_database_manager_async_client().update_review_processed_status(
+            node_exec_id, processed
+        )
 
     async def run(
         self,
@@ -102,7 +109,7 @@ class HumanInTheLoopBlock(Block):
         graph_id: str,
         graph_version: int,
         execution_context: ExecutionContext,
-        **_kwargs,
+        **kwargs,
     ) -> BlockOutput:
         if not execution_context.safe_mode:
             logger.info(
@@ -112,28 +119,48 @@ class HumanInTheLoopBlock(Block):
             yield "review_message", "Auto-approved (safe mode disabled)"
             return
 
-        decision = await self.handle_review_decision(
-            input_data=input_data.data,
-            user_id=user_id,
-            node_exec_id=node_exec_id,
-            graph_exec_id=graph_exec_id,
-            graph_id=graph_id,
-            graph_version=graph_version,
-            execution_context=execution_context,
-            block_name=self.name,
-            editable=input_data.editable,
-        )
+        try:
+            result = await self.get_or_create_human_review(
+                user_id=user_id,
+                node_exec_id=node_exec_id,
+                graph_exec_id=graph_exec_id,
+                graph_id=graph_id,
+                graph_version=graph_version,
+                input_data=input_data.data,
+                message=input_data.name,
+                editable=input_data.editable,
+            )
+        except Exception as e:
+            logger.error(f"Error in HITL block for node {node_exec_id}: {str(e)}")
+            raise
 
-        if decision is None:
-            return
+        if result is None:
+            logger.info(
+                f"HITL block pausing execution for node {node_exec_id} - awaiting human review"
+            )
+            try:
+                await self.update_node_execution_status(
+                    exec_id=node_exec_id,
+                    status=ExecutionStatus.REVIEW,
+                )
+                return
+            except Exception as e:
+                logger.error(
+                    f"Failed to update node status for HITL block {node_exec_id}: {str(e)}"
+                )
+                raise
 
-        status = decision.review_result.status
-        if status == ReviewStatus.APPROVED:
-            yield "approved_data", decision.review_result.data
-        elif status == ReviewStatus.REJECTED:
-            yield "rejected_data", decision.review_result.data
-        else:
-            raise RuntimeError(f"Unexpected review status: {status}")
+        if not result.processed:
+            await self.update_review_processed_status(
+                node_exec_id=node_exec_id, processed=True
+            )
 
-        if decision.message:
-            yield "review_message", decision.message
+            if result.status == ReviewStatus.APPROVED:
+                yield "approved_data", result.data
+                if result.message:
+                    yield "review_message", result.message
+
+            elif result.status == ReviewStatus.REJECTED:
+                yield "rejected_data", result.data
+                if result.message:
+                    yield "review_message", result.message

autogpt_platform/backend/backend/blocks/smart_decision_maker.py

@@ -391,12 +391,8 @@ class SmartDecisionMakerBlock(Block):
         """
         block = sink_node.block
 
-        # Use custom name from node metadata if set, otherwise fall back to block.name
-        custom_name = sink_node.metadata.get("customized_name")
-        tool_name = custom_name if custom_name else block.name
-
         tool_function: dict[str, Any] = {
-            "name": SmartDecisionMakerBlock.cleanup(tool_name),
+            "name": SmartDecisionMakerBlock.cleanup(block.name),
             "description": block.description,
         }
         sink_block_input_schema = block.input_schema
@@ -493,24 +489,14 @@ class SmartDecisionMakerBlock(Block):
                 f"Sink graph metadata not found: {graph_id} {graph_version}"
             )
 
-        # Use custom name from node metadata if set, otherwise fall back to graph name
-        custom_name = sink_node.metadata.get("customized_name")
-        tool_name = custom_name if custom_name else sink_graph_meta.name
-
         tool_function: dict[str, Any] = {
-            "name": SmartDecisionMakerBlock.cleanup(tool_name),
+            "name": SmartDecisionMakerBlock.cleanup(sink_graph_meta.name),
             "description": sink_graph_meta.description,
         }
 
         properties = {}
-        field_mapping = {}
 
         for link in links:
-            field_name = link.sink_name
-
-            clean_field_name = SmartDecisionMakerBlock.cleanup(field_name)
-            field_mapping[clean_field_name] = field_name
-
             sink_block_input_schema = sink_node.input_default["input_schema"]
             sink_block_properties = sink_block_input_schema.get("properties", {}).get(
                 link.sink_name, {}
@@ -520,7 +506,7 @@ class SmartDecisionMakerBlock(Block):
                 if "description" in sink_block_properties
                 else f"The {link.sink_name} of the tool"
             )
-            properties[clean_field_name] = {
+            properties[link.sink_name] = {
                 "type": "string",
                 "description": description,
                 "default": json.dumps(sink_block_properties.get("default", None)),
@@ -533,7 +519,7 @@ class SmartDecisionMakerBlock(Block):
             "strict": True,
         }
 
-        tool_function["_field_mapping"] = field_mapping
+        # Store node info for later use in output processing
         tool_function["_sink_node_id"] = sink_node.id
 
         return {"type": "function", "function": tool_function}
@@ -1161,9 +1147,8 @@ class SmartDecisionMakerBlock(Block):
                 original_field_name = field_mapping.get(clean_arg_name, clean_arg_name)
                 arg_value = tool_args.get(clean_arg_name)
 
-                # Use original_field_name directly (not sanitized) to match link sink_name
-                # The field_mapping already translates from LLM's cleaned names to original names
-                emit_key = f"tools_^_{sink_node_id}_~_{original_field_name}"
+                sanitized_arg_name = self.cleanup(original_field_name)
+                emit_key = f"tools_^_{sink_node_id}_~_{sanitized_arg_name}"
 
                 logger.debug(
                     "[SmartDecisionMakerBlock|geid:%s|neid:%s] emit %s",

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

@@ -1057,153 +1057,3 @@ async def test_smart_decision_maker_traditional_mode_default():
         )  # Should yield individual tool parameters
         assert "tools_^_test-sink-node-id_~_max_keyword_difficulty" in outputs
         assert "conversations" in outputs
-
-
-@pytest.mark.asyncio
-async def test_smart_decision_maker_uses_customized_name_for_blocks():
-    """Test that SmartDecisionMakerBlock uses customized_name from node metadata for tool names."""
-    from unittest.mock import MagicMock
-
-    from backend.blocks.basic import StoreValueBlock
-    from backend.blocks.smart_decision_maker import SmartDecisionMakerBlock
-    from backend.data.graph import Link, Node
-
-    # Create a mock node with customized_name in metadata
-    mock_node = MagicMock(spec=Node)
-    mock_node.id = "test-node-id"
-    mock_node.block_id = StoreValueBlock().id
-    mock_node.metadata = {"customized_name": "My Custom Tool Name"}
-    mock_node.block = StoreValueBlock()
-
-    # Create a mock link
-    mock_link = MagicMock(spec=Link)
-    mock_link.sink_name = "input"
-
-    # Call the function directly
-    result = await SmartDecisionMakerBlock._create_block_function_signature(
-        mock_node, [mock_link]
-    )
-
-    # Verify the tool name uses the customized name (cleaned up)
-    assert result["type"] == "function"
-    assert result["function"]["name"] == "my_custom_tool_name"  # Cleaned version
-    assert result["function"]["_sink_node_id"] == "test-node-id"
-
-
-@pytest.mark.asyncio
-async def test_smart_decision_maker_falls_back_to_block_name():
-    """Test that SmartDecisionMakerBlock falls back to block.name when no customized_name."""
-    from unittest.mock import MagicMock
-
-    from backend.blocks.basic import StoreValueBlock
-    from backend.blocks.smart_decision_maker import SmartDecisionMakerBlock
-    from backend.data.graph import Link, Node
-
-    # Create a mock node without customized_name
-    mock_node = MagicMock(spec=Node)
-    mock_node.id = "test-node-id"
-    mock_node.block_id = StoreValueBlock().id
-    mock_node.metadata = {}  # No customized_name
-    mock_node.block = StoreValueBlock()
-
-    # Create a mock link
-    mock_link = MagicMock(spec=Link)
-    mock_link.sink_name = "input"
-
-    # Call the function directly
-    result = await SmartDecisionMakerBlock._create_block_function_signature(
-        mock_node, [mock_link]
-    )
-
-    # Verify the tool name uses the block's default name
-    assert result["type"] == "function"
-    assert result["function"]["name"] == "storevalueblock"  # Default block name cleaned
-    assert result["function"]["_sink_node_id"] == "test-node-id"
-
-
-@pytest.mark.asyncio
-async def test_smart_decision_maker_uses_customized_name_for_agents():
-    """Test that SmartDecisionMakerBlock uses customized_name from metadata for agent nodes."""
-    from unittest.mock import AsyncMock, MagicMock, patch
-
-    from backend.blocks.smart_decision_maker import SmartDecisionMakerBlock
-    from backend.data.graph import Link, Node
-
-    # Create a mock node with customized_name in metadata
-    mock_node = MagicMock(spec=Node)
-    mock_node.id = "test-agent-node-id"
-    mock_node.metadata = {"customized_name": "My Custom Agent"}
-    mock_node.input_default = {
-        "graph_id": "test-graph-id",
-        "graph_version": 1,
-        "input_schema": {"properties": {"test_input": {"description": "Test input"}}},
-    }
-
-    # Create a mock link
-    mock_link = MagicMock(spec=Link)
-    mock_link.sink_name = "test_input"
-
-    # Mock the database client
-    mock_graph_meta = MagicMock()
-    mock_graph_meta.name = "Original Agent Name"
-    mock_graph_meta.description = "Agent description"
-
-    mock_db_client = AsyncMock()
-    mock_db_client.get_graph_metadata.return_value = mock_graph_meta
-
-    with patch(
-        "backend.blocks.smart_decision_maker.get_database_manager_async_client",
-        return_value=mock_db_client,
-    ):
-        result = await SmartDecisionMakerBlock._create_agent_function_signature(
-            mock_node, [mock_link]
-        )
-
-    # Verify the tool name uses the customized name (cleaned up)
-    assert result["type"] == "function"
-    assert result["function"]["name"] == "my_custom_agent"  # Cleaned version
-    assert result["function"]["_sink_node_id"] == "test-agent-node-id"
-
-
-@pytest.mark.asyncio
-async def test_smart_decision_maker_agent_falls_back_to_graph_name():
-    """Test that agent node falls back to graph name when no customized_name."""
-    from unittest.mock import AsyncMock, MagicMock, patch
-
-    from backend.blocks.smart_decision_maker import SmartDecisionMakerBlock
-    from backend.data.graph import Link, Node
-
-    # Create a mock node without customized_name
-    mock_node = MagicMock(spec=Node)
-    mock_node.id = "test-agent-node-id"
-    mock_node.metadata = {}  # No customized_name
-    mock_node.input_default = {
-        "graph_id": "test-graph-id",
-        "graph_version": 1,
-        "input_schema": {"properties": {"test_input": {"description": "Test input"}}},
-    }
-
-    # Create a mock link
-    mock_link = MagicMock(spec=Link)
-    mock_link.sink_name = "test_input"
-
-    # Mock the database client
-    mock_graph_meta = MagicMock()
-    mock_graph_meta.name = "Original Agent Name"
-    mock_graph_meta.description = "Agent description"
-
-    mock_db_client = AsyncMock()
-    mock_db_client.get_graph_metadata.return_value = mock_graph_meta
-
-    with patch(
-        "backend.blocks.smart_decision_maker.get_database_manager_async_client",
-        return_value=mock_db_client,
-    ):
-        result = await SmartDecisionMakerBlock._create_agent_function_signature(
-            mock_node, [mock_link]
-        )
-
-    # Verify the tool name uses the graph's default name
-    assert result["type"] == "function"
-    assert result["function"]["name"] == "original_agent_name"  # Graph name cleaned
-    assert result["function"]["_sink_node_id"] == "test-agent-node-id"

autogpt_platform/backend/backend/blocks/test/test_smart_decision_maker_dict.py

@@ -15,7 +15,6 @@ async def test_smart_decision_maker_handles_dynamic_dict_fields():
     mock_node.block = CreateDictionaryBlock()
     mock_node.block_id = CreateDictionaryBlock().id
     mock_node.input_default = {}
-    mock_node.metadata = {}
 
     # Create mock links with dynamic dictionary fields
     mock_links = [
@@ -78,7 +77,6 @@ async def test_smart_decision_maker_handles_dynamic_list_fields():
     mock_node.block = AddToListBlock()
     mock_node.block_id = AddToListBlock().id
     mock_node.input_default = {}
-    mock_node.metadata = {}
 
     # Create mock links with dynamic list fields
     mock_links = [

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

@@ -44,7 +44,6 @@ async def test_create_block_function_signature_with_dict_fields():
     mock_node.block = CreateDictionaryBlock()
     mock_node.block_id = CreateDictionaryBlock().id
     mock_node.input_default = {}
-    mock_node.metadata = {}
 
     # Create mock links with dynamic dictionary fields (source sanitized, sink original)
     mock_links = [
@@ -107,7 +106,6 @@ async def test_create_block_function_signature_with_list_fields():
     mock_node.block = AddToListBlock()
     mock_node.block_id = AddToListBlock().id
     mock_node.input_default = {}
-    mock_node.metadata = {}
 
     # Create mock links with dynamic list fields
     mock_links = [
@@ -161,7 +159,6 @@ async def test_create_block_function_signature_with_object_fields():
     mock_node.block = MatchTextPatternBlock()
     mock_node.block_id = MatchTextPatternBlock().id
     mock_node.input_default = {}
-    mock_node.metadata = {}
 
     # Create mock links with dynamic object fields
     mock_links = [
@@ -211,13 +208,11 @@ async def test_create_tool_node_signatures():
         mock_dict_node.block = CreateDictionaryBlock()
         mock_dict_node.block_id = CreateDictionaryBlock().id
         mock_dict_node.input_default = {}
-        mock_dict_node.metadata = {}
 
         mock_list_node = Mock()
         mock_list_node.block = AddToListBlock()
         mock_list_node.block_id = AddToListBlock().id
         mock_list_node.input_default = {}
-        mock_list_node.metadata = {}
 
         # Mock links with dynamic fields
         dict_link1 = Mock(
@@ -428,7 +423,6 @@ async def test_mixed_regular_and_dynamic_fields():
     mock_node.block.name = "TestBlock"
     mock_node.block.description = "A test block"
     mock_node.block.input_schema = Mock()
-    mock_node.metadata = {}
 
     # Mock the get_field_schema to return a proper schema for regular fields
     def get_field_schema(field_name):

autogpt_platform/backend/backend/blocks/wordpress/__init__.py

@@ -1,3 +1,3 @@
-from .blog import WordPressCreatePostBlock, WordPressGetAllPostsBlock
+from .blog import WordPressCreatePostBlock
 
-__all__ = ["WordPressCreatePostBlock", "WordPressGetAllPostsBlock"]
+__all__ = ["WordPressCreatePostBlock"]

autogpt_platform/backend/backend/blocks/wordpress/_api.py

@@ -161,7 +161,7 @@ async def oauth_exchange_code_for_tokens(
         grant_type="authorization_code",
     ).model_dump(exclude_none=True)
 
-    response = await Requests(raise_for_status=False).post(
+    response = await Requests().post(
         f"{WORDPRESS_BASE_URL}oauth2/token",
         headers=headers,
         data=data,
@@ -205,7 +205,7 @@ async def oauth_refresh_tokens(
         grant_type="refresh_token",
     ).model_dump(exclude_none=True)
 
-    response = await Requests(raise_for_status=False).post(
+    response = await Requests().post(
         f"{WORDPRESS_BASE_URL}oauth2/token",
         headers=headers,
         data=data,
@@ -252,7 +252,7 @@ async def validate_token(
         "token": token,
     }
 
-    response = await Requests(raise_for_status=False).get(
+    response = await Requests().get(
         f"{WORDPRESS_BASE_URL}oauth2/token-info",
         params=params,
     )
@@ -296,7 +296,7 @@ async def make_api_request(
 
     url = f"{WORDPRESS_BASE_URL.rstrip('/')}{endpoint}"
 
-    request_method = getattr(Requests(raise_for_status=False), method.lower())
+    request_method = getattr(Requests(), method.lower())
     response = await request_method(
         url,
         headers=headers,
@@ -476,7 +476,6 @@ async def create_post(
         data["tags"] = ",".join(str(t) for t in data["tags"])
 
     # Make the API request
-    site = normalize_site(site)
     endpoint = f"/rest/v1.1/sites/{site}/posts/new"
 
     headers = {
@@ -484,7 +483,7 @@ async def create_post(
         "Content-Type": "application/x-www-form-urlencoded",
     }
 
-    response = await Requests(raise_for_status=False).post(
+    response = await Requests().post(
         f"{WORDPRESS_BASE_URL.rstrip('/')}{endpoint}",
         headers=headers,
         data=data,
@@ -500,132 +499,3 @@ async def create_post(
     )
     error_message = error_data.get("message", response.text)
     raise ValueError(f"Failed to create post: {response.status} - {error_message}")
-
-
-class Post(BaseModel):
-    """Response model for individual posts in a posts list response.
-
-    This is a simplified version compared to PostResponse, as the list endpoint
-    returns less detailed information than the create/get single post endpoints.
-    """
-
-    ID: int
-    site_ID: int
-    author: PostAuthor
-    date: datetime
-    modified: datetime
-    title: str
-    URL: str
-    short_URL: str
-    content: str | None = None
-    excerpt: str | None = None
-    slug: str
-    guid: str
-    status: str
-    sticky: bool
-    password: str | None = ""
-    parent: Union[Dict[str, Any], bool, None] = None
-    type: str
-    discussion: Dict[str, Union[str, bool, int]] | None = None
-    likes_enabled: bool | None = None
-    sharing_enabled: bool | None = None
-    like_count: int | None = None
-    i_like: bool | None = None
-    is_reblogged: bool | None = None
-    is_following: bool | None = None
-    global_ID: str | None = None
-    featured_image: str | None = None
-    post_thumbnail: Dict[str, Any] | None = None
-    format: str | None = None
-    geo: Union[Dict[str, Any], bool, None] = None
-    menu_order: int | None = None
-    page_template: str | None = None
-    publicize_URLs: List[str] | None = None
-    terms: Dict[str, Dict[str, Any]] | None = None
-    tags: Dict[str, Dict[str, Any]] | None = None
-    categories: Dict[str, Dict[str, Any]] | None = None
-    attachments: Dict[str, Dict[str, Any]] | None = None
-    attachment_count: int | None = None
-    metadata: List[Dict[str, Any]] | None = None
-    meta: Dict[str, Any] | None = None
-    capabilities: Dict[str, bool] | None = None
-    revisions: List[int] | None = None
-    other_URLs: Dict[str, Any] | None = None
-
-
-class PostsResponse(BaseModel):
-    """Response model for WordPress posts list."""
-
-    found: int
-    posts: List[Post]
-    meta: Dict[str, Any]
-
-
-def normalize_site(site: str) -> str:
-    """
-    Normalize a site identifier by stripping protocol and trailing slashes.
-
-    Args:
-        site: Site URL, domain, or ID (e.g., "https://myblog.wordpress.com/", "myblog.wordpress.com", "123456789")
-
-    Returns:
-        Normalized site identifier (domain or ID only)
-    """
-    site = site.strip()
-    if site.startswith("https://"):
-        site = site[8:]
-    elif site.startswith("http://"):
-        site = site[7:]
-    return site.rstrip("/")
-
-
-async def get_posts(
-    credentials: Credentials,
-    site: str,
-    status: PostStatus | None = None,
-    number: int = 100,
-    offset: int = 0,
-) -> PostsResponse:
-    """
-    Get posts from a WordPress site.
-
-    Args:
-        credentials: OAuth credentials
-        site: Site ID or domain (e.g., "myblog.wordpress.com" or "123456789")
-        status: Filter by post status using PostStatus enum, or None for all
-        number: Number of posts to retrieve (max 100)
-        offset: Number of posts to skip (for pagination)
-
-    Returns:
-        PostsResponse with the list of posts
-    """
-    site = normalize_site(site)
-    endpoint = f"/rest/v1.1/sites/{site}/posts"
-
-    headers = {
-        "Authorization": credentials.auth_header(),
-    }
-
-    params: Dict[str, Any] = {
-        "number": max(1, min(number, 100)),  # 1–100 posts per request
-        "offset": offset,
-    }
-
-    if status:
-        params["status"] = status.value
-    response = await Requests(raise_for_status=False).get(
-        f"{WORDPRESS_BASE_URL.rstrip('/')}{endpoint}",
-        headers=headers,
-        params=params,
-    )
-
-    if response.ok:
-        return PostsResponse.model_validate(response.json())
-
-    error_data = (
-        response.json()
-        if response.headers.get("content-type", "").startswith("application/json")
-        else {}
-    )
-    error_message = error_data.get("message", response.text)
-    raise ValueError(f"Failed to get posts: {response.status} - {error_message}")

autogpt_platform/backend/backend/blocks/wordpress/blog.py

@@ -9,15 +9,7 @@ from backend.sdk import (
     SchemaField,
 )
 
-from ._api import (
-    CreatePostRequest,
-    Post,
-    PostResponse,
-    PostsResponse,
-    PostStatus,
-    create_post,
-    get_posts,
-)
+from ._api import CreatePostRequest, PostResponse, PostStatus, create_post
 from ._config import wordpress
 
 
@@ -57,15 +49,8 @@ class WordPressCreatePostBlock(Block):
         media_urls: list[str] = SchemaField(
             description="URLs of images to sideload and attach to the post", default=[]
         )
-        publish_as_draft: bool = SchemaField(
-            description="If True, publishes the post as a draft. If False, publishes it publicly.",
-            default=False,
-        )
 
     class Output(BlockSchemaOutput):
-        site: str = SchemaField(
-            description="The site ID or domain (pass-through for chaining with other blocks)"
-        )
         post_id: int = SchemaField(description="The ID of the created post")
         post_url: str = SchemaField(description="The full URL of the created post")
         short_url: str = SchemaField(description="The shortened wp.me URL")
@@ -93,9 +78,7 @@ class WordPressCreatePostBlock(Block):
             tags=input_data.tags,
             featured_image=input_data.featured_image,
             media_urls=input_data.media_urls,
-            status=(
-                PostStatus.DRAFT if input_data.publish_as_draft else PostStatus.PUBLISH
-            ),
+            status=PostStatus.PUBLISH,
         )
 
         post_response: PostResponse = await create_post(
@@ -104,69 +87,7 @@ class WordPressCreatePostBlock(Block):
             post_data=post_request,
         )
 
-        yield "site", input_data.site
         yield "post_id", post_response.ID
         yield "post_url", post_response.URL
         yield "short_url", post_response.short_URL
         yield "post_data", post_response.model_dump()
-
-
-class WordPressGetAllPostsBlock(Block):
-    """
-    Fetches all posts from a WordPress.com site or Jetpack-enabled site.
-    Supports filtering by status and pagination.
-    """
-
-    class Input(BlockSchemaInput):
-        credentials: CredentialsMetaInput = wordpress.credentials_field()
-        site: str = SchemaField(
-            description="Site ID or domain (e.g., 'myblog.wordpress.com' or '123456789')"
-        )
-        status: PostStatus | None = SchemaField(
-            description="Filter by post status, or None for all",
-            default=None,
-        )
-        number: int = SchemaField(
-            description="Number of posts to retrieve (max 100 per request)", default=20
-        )
-        offset: int = SchemaField(
-            description="Number of posts to skip (for pagination)", default=0
-        )
-
-    class Output(BlockSchemaOutput):
-        site: str = SchemaField(
-            description="The site ID or domain (pass-through for chaining with other blocks)"
-        )
-        found: int = SchemaField(description="Total number of posts found")
-        posts: list[Post] = SchemaField(
-            description="List of post objects with their details"
-        )
-        post: Post = SchemaField(
-            description="Individual post object (yielded for each post)"
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="97728fa7-7f6f-4789-ba0c-f2c114119536",
-            description="Fetch all posts from WordPress.com or Jetpack sites",
-            categories={BlockCategory.SOCIAL},
-            input_schema=self.Input,
-            output_schema=self.Output,
-        )
-
-    async def run(
-        self, input_data: Input, *, credentials: Credentials, **kwargs
-    ) -> BlockOutput:
-        posts_response: PostsResponse = await get_posts(
-            credentials=credentials,
-            site=input_data.site,
-            status=input_data.status,
-            number=input_data.number,
-            offset=input_data.offset,
-        )
-
-        yield "site", input_data.site
-        yield "found", posts_response.found
-        yield "posts", posts_response.posts
-        for post in posts_response.posts:
-            yield "post", post

autogpt_platform/backend/backend/data/block.py

@@ -50,8 +50,6 @@ from .model import (
 logger = logging.getLogger(__name__)
 
 if TYPE_CHECKING:
-    from backend.data.execution import ExecutionContext
-
     from .graph import Link
 
 app_config = Config()
@@ -474,7 +472,6 @@ class Block(ABC, Generic[BlockSchemaInputType, BlockSchemaOutputType]):
         self.block_type = block_type
         self.webhook_config = webhook_config
         self.execution_stats: NodeExecutionStats = NodeExecutionStats()
-        self.requires_human_review: bool = False
 
         if self.webhook_config:
             if isinstance(self.webhook_config, BlockWebhookConfig):
@@ -617,80 +614,7 @@ class Block(ABC, Generic[BlockSchemaInputType, BlockSchemaOutputType]):
                     block_id=self.id,
                 ) from ex
 
-    async def is_block_exec_need_review(
-        self,
-        input_data: BlockInput,
-        *,
-        user_id: str,
-        node_exec_id: str,
-        graph_exec_id: str,
-        graph_id: str,
-        graph_version: int,
-        execution_context: "ExecutionContext",
-        **kwargs,
-    ) -> tuple[bool, BlockInput]:
-        """
-        Check if this block execution needs human review and handle the review process.
-
-        Returns:
-            Tuple of (should_pause, input_data_to_use)
-            - should_pause: True if execution should be paused for review
-            - input_data_to_use: The input data to use (may be modified by reviewer)
-        """
-        if not (
-            self.requires_human_review
-            and execution_context.safe_mode
-            and execution_context.is_ai_generated_graph
-        ):
-            return False, input_data
-
-        from backend.blocks.helpers.review import HITLReviewHelper
-
-        # Handle the review request and get decision
-        decision = await HITLReviewHelper.handle_review_decision(
-            input_data=input_data,
-            user_id=user_id,
-            node_exec_id=node_exec_id,
-            graph_exec_id=graph_exec_id,
-            graph_id=graph_id,
-            graph_version=graph_version,
-            execution_context=execution_context,
-            block_name=self.name,
-            editable=True,
-        )
-
-        if decision is None:
-            # We're awaiting review - pause execution
-            return True, input_data
-
-        if not decision.should_proceed:
-            # Review was rejected, raise an error to stop execution
-            raise BlockExecutionError(
-                message=f"Block execution rejected by reviewer: {decision.message}",
-                block_name=self.name,
-                block_id=self.id,
-            )
-
-        # Review was approved - use the potentially modified data
-        # ReviewResult.data must be a dict for block inputs
-        reviewed_data = decision.review_result.data
-        if not isinstance(reviewed_data, dict):
-            raise BlockExecutionError(
-                message=f"Review data must be a dict for block input, got {type(reviewed_data).__name__}",
-                block_name=self.name,
-                block_id=self.id,
-            )
-        return False, reviewed_data
-
     async def _execute(self, input_data: BlockInput, **kwargs) -> BlockOutput:
-        # Check for review requirement and get potentially modified input data
-        should_pause, input_data = await self.is_block_exec_need_review(
-            input_data, **kwargs
-        )
-        if should_pause:
-            return
-
-        # Validate the input data (original or reviewer-modified) once
         if error := self.input_schema.validate_data(input_data):
             raise BlockInputError(
                 message=f"Unable to execute block with invalid input data: {error}",
@@ -698,7 +622,6 @@ class Block(ABC, Generic[BlockSchemaInputType, BlockSchemaOutputType]):
                 block_id=self.id,
             )
 
-        # Use the validated input data
         async for output_name, output_data in self.run(
             self.input_schema(**{k: v for k, v in input_data.items() if v is not None}),
             **kwargs,

autogpt_platform/backend/backend/data/execution.py

@@ -82,7 +82,6 @@ class ExecutionContext(BaseModel):
     """
 
     safe_mode: bool = True
-    is_ai_generated_graph: bool = False
     user_timezone: str = "UTC"
     root_execution_id: Optional[str] = None
     parent_execution_id: Optional[str] = None

autogpt_platform/backend/backend/data/graph.py

@@ -63,14 +63,6 @@ logger = logging.getLogger(__name__)
 
 class GraphSettings(BaseModel):
     human_in_the_loop_safe_mode: bool | None = None
-    is_ai_generated_graph: bool = False
-
-    @classmethod
-    def from_graph(cls, graph: "GraphModel", is_ai_generated: bool) -> "GraphSettings":
-        return cls(
-            human_in_the_loop_safe_mode=(True if graph.has_human_in_the_loop else None),
-            is_ai_generated_graph=is_ai_generated,
-        )
 
 
 class Link(BaseDbModel):
@@ -252,10 +244,7 @@ class BaseGraph(BaseDbModel):
         return any(
             node.block_id
             for node in self.nodes
-            if (
-                node.block.block_type == BlockType.HUMAN_IN_THE_LOOP
-                or node.block.requires_human_review
-            )
+            if node.block.block_type == BlockType.HUMAN_IN_THE_LOOP
         )
 
     @property

autogpt_platform/backend/backend/executor/utils.py

@@ -877,7 +877,6 @@ async def add_graph_execution(
                 if settings.human_in_the_loop_safe_mode is not None
                 else True
             ),
-            is_ai_generated_graph=settings.is_ai_generated_graph,
             user_timezone=(
                 user.timezone if user.timezone != USER_TIMEZONE_NOT_SET else "UTC"
             ),

autogpt_platform/backend/backend/integrations/oauth/__init__.py

@@ -8,7 +8,6 @@ from .discord import DiscordOAuthHandler
 from .github import GitHubOAuthHandler
 from .google import GoogleOAuthHandler
 from .notion import NotionOAuthHandler
-from .reddit import RedditOAuthHandler
 from .twitter import TwitterOAuthHandler
 
 if TYPE_CHECKING:
@@ -21,7 +20,6 @@ _ORIGINAL_HANDLERS = [
     GitHubOAuthHandler,
     GoogleOAuthHandler,
     NotionOAuthHandler,
-    RedditOAuthHandler,
     TwitterOAuthHandler,
     TodoistOAuthHandler,
 ]

autogpt_platform/backend/backend/integrations/oauth/reddit.py

@@ -1,208 +0,0 @@
-import time
-import urllib.parse
-from typing import ClassVar, Optional
-
-from pydantic import SecretStr
-
-from backend.data.model import OAuth2Credentials
-from backend.integrations.oauth.base import BaseOAuthHandler
-from backend.integrations.providers import ProviderName
-from backend.util.request import Requests
-from backend.util.settings import Settings
-
-settings = Settings()
-
-
-class RedditOAuthHandler(BaseOAuthHandler):
-    """
-    Reddit OAuth 2.0 handler.
-
-    Based on the documentation at:
-    - https://github.com/reddit-archive/reddit/wiki/OAuth2
-
-    Notes:
-    - Reddit requires `duration=permanent` to get refresh tokens
-    - Access tokens expire after 1 hour (3600 seconds)
-    - Reddit requires HTTP Basic Auth for token requests
-    - Reddit requires a unique User-Agent header
-    """
-
-    PROVIDER_NAME = ProviderName.REDDIT
-    DEFAULT_SCOPES: ClassVar[list[str]] = [
-        "identity",  # Get username, verify auth
-        "read",  # Access posts and comments
-        "submit",  # Submit new posts and comments
-        "edit",  # Edit own posts and comments
-        "history",  # Access user's post history
-        "privatemessages",  # Access inbox and send private messages
-        "flair",  # Access and set flair on posts/subreddits
-    ]
-
-    AUTHORIZE_URL = "https://www.reddit.com/api/v1/authorize"
-    TOKEN_URL = "https://www.reddit.com/api/v1/access_token"
-    USERNAME_URL = "https://oauth.reddit.com/api/v1/me"
-    REVOKE_URL = "https://www.reddit.com/api/v1/revoke_token"
-
-    def __init__(self, client_id: str, client_secret: str, redirect_uri: str):
-        self.client_id = client_id
-        self.client_secret = client_secret
-        self.redirect_uri = redirect_uri
-
-    def get_login_url(
-        self, scopes: list[str], state: str, code_challenge: Optional[str]
-    ) -> str:
-        """Generate Reddit OAuth 2.0 authorization URL"""
-        scopes = self.handle_default_scopes(scopes)
-
-        params = {
-            "response_type": "code",
-            "client_id": self.client_id,
-            "redirect_uri": self.redirect_uri,
-            "scope": " ".join(scopes),
-            "state": state,
-            "duration": "permanent",  # Required for refresh tokens
-        }
-
-        return f"{self.AUTHORIZE_URL}?{urllib.parse.urlencode(params)}"
-
-    async def exchange_code_for_tokens(
-        self, code: str, scopes: list[str], code_verifier: Optional[str]
-    ) -> OAuth2Credentials:
-        """Exchange authorization code for access tokens"""
-        scopes = self.handle_default_scopes(scopes)
-
-        headers = {
-            "Content-Type": "application/x-www-form-urlencoded",
-            "User-Agent": settings.config.reddit_user_agent,
-        }
-
-        data = {
-            "grant_type": "authorization_code",
-            "code": code,
-            "redirect_uri": self.redirect_uri,
-        }
-
-        # Reddit requires HTTP Basic Auth for token requests
-        auth = (self.client_id, self.client_secret)
-
-        response = await Requests().post(
-            self.TOKEN_URL, headers=headers, data=data, auth=auth
-        )
-
-        if not response.ok:
-            error_text = response.text()
-            raise ValueError(
-                f"Reddit token exchange failed: {response.status} - {error_text}"
-            )
-
-        tokens = response.json()
-
-        if "error" in tokens:
-            raise ValueError(f"Reddit OAuth error: {tokens.get('error')}")
-
-        username = await self._get_username(tokens["access_token"])
-
-        return OAuth2Credentials(
-            provider=self.PROVIDER_NAME,
-            title=None,
-            username=username,
-            access_token=tokens["access_token"],
-            refresh_token=tokens.get("refresh_token"),
-            access_token_expires_at=int(time.time()) + tokens.get("expires_in", 3600),
-            refresh_token_expires_at=None,  # Reddit refresh tokens don't expire
-            scopes=scopes,
-        )
-
-    async def _get_username(self, access_token: str) -> str:
-        """Get the username from the access token"""
-        headers = {
-            "Authorization": f"Bearer {access_token}",
-            "User-Agent": settings.config.reddit_user_agent,
-        }
-
-        response = await Requests().get(self.USERNAME_URL, headers=headers)
-
-        if not response.ok:
-            raise ValueError(f"Failed to get Reddit username: {response.status}")
-
-        data = response.json()
-        return data.get("name", "unknown")
-
-    async def _refresh_tokens(
-        self, credentials: OAuth2Credentials
-    ) -> OAuth2Credentials:
-        """Refresh access tokens using refresh token"""
-        if not credentials.refresh_token:
-            raise ValueError("No refresh token available")
-
-        headers = {
-            "Content-Type": "application/x-www-form-urlencoded",
-            "User-Agent": settings.config.reddit_user_agent,
-        }
-
-        data = {
-            "grant_type": "refresh_token",
-            "refresh_token": credentials.refresh_token.get_secret_value(),
-        }
-
-        auth = (self.client_id, self.client_secret)
-
-        response = await Requests().post(
-            self.TOKEN_URL, headers=headers, data=data, auth=auth
-        )
-
-        if not response.ok:
-            error_text = response.text()
-            raise ValueError(
-                f"Reddit token refresh failed: {response.status} - {error_text}"
-            )
-
-        tokens = response.json()
-
-        if "error" in tokens:
-            raise ValueError(f"Reddit OAuth error: {tokens.get('error')}")
-
-        username = await self._get_username(tokens["access_token"])
-
-        # Reddit may or may not return a new refresh token
-        new_refresh_token = tokens.get("refresh_token")
-        if new_refresh_token:
-            refresh_token: SecretStr | None = SecretStr(new_refresh_token)
-        elif credentials.refresh_token:
-            # Keep the existing refresh token
-            refresh_token = credentials.refresh_token
-        else:
-            refresh_token = None
-
-        return OAuth2Credentials(
-            id=credentials.id,
-            provider=self.PROVIDER_NAME,
-            title=credentials.title,
-            username=username,
-            access_token=tokens["access_token"],
-            refresh_token=refresh_token,
-            access_token_expires_at=int(time.time()) + tokens.get("expires_in", 3600),
-            refresh_token_expires_at=None,
-            scopes=credentials.scopes,
-        )
-
-    async def revoke_tokens(self, credentials: OAuth2Credentials) -> bool:
-        """Revoke the access token"""
-        headers = {
-            "Content-Type": "application/x-www-form-urlencoded",
-            "User-Agent": settings.config.reddit_user_agent,
-        }
-
-        data = {
-            "token": credentials.access_token.get_secret_value(),
-            "token_type_hint": "access_token",
-        }
-
-        auth = (self.client_id, self.client_secret)
-
-        response = await Requests().post(
-            self.REVOKE_URL, headers=headers, data=data, auth=auth
-        )
-
-        # Reddit returns 204 No Content on successful revocation
-        return response.ok

autogpt_platform/backend/backend/util/settings.py

@@ -264,7 +264,7 @@ class Config(UpdateTrackingModel["Config"], BaseSettings):
     )
 
     reddit_user_agent: str = Field(
-        default="web:AutoGPT:v0.6.0 (by /u/autogpt)",
+        default="AutoGPT:1.0 (by /u/autogpt)",
         description="The user agent for the Reddit API",
     )
 

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

@@ -0,0 +1,35 @@
+-- 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/schema.prisma

@@ -1,14 +1,15 @@
 datasource db {
-  provider  = "postgresql"
-  url       = env("DATABASE_URL")
-  directUrl = env("DIRECT_URL")
+  provider   = "postgresql"
+  url        = env("DATABASE_URL")
+  directUrl  = env("DIRECT_URL")
+  extensions = [pgvector(map: "vector")]
 }
 
 generator client {
   provider               = "prisma-client-py"
   recursive_type_depth   = -1
   interface              = "asyncio"
-  previewFeatures        = ["views", "fullTextSearch"]
+  previewFeatures        = ["views", "fullTextSearch", "postgresqlExtensions"]
   partial_type_generator = "backend/data/partial_types.py"
 }
 
@@ -127,8 +128,8 @@ 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)
@@ -721,26 +722,25 @@ 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[]
-  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)
+  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)
 
   // 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)
 
@@ -899,6 +899,9 @@ 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])
@@ -906,6 +909,42 @@ 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())
@@ -998,16 +1037,16 @@ 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/frontend/src/app/(platform)/build/components/FlowEditor/nodes/CustomNode/components/NodeHeader.tsx

@@ -68,10 +68,7 @@ export const NodeHeader = ({ data, nodeId }: Props) => {
                 <Tooltip>
                   <TooltipTrigger asChild>
                     <div>
-                      <Text
-                        variant="large-semibold"
-                        className="line-clamp-1 hover:cursor-text"
-                      >
+                      <Text variant="large-semibold" className="line-clamp-1">
                         {beautifyString(title).replace("Block", "").trim()}
                       </Text>
                     </div>

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

@@ -89,18 +89,6 @@ export function extractOptions(
 
 // get display type and color for schema types [need for type display next to field name]
 export const getTypeDisplayInfo = (schema: any) => {
-  if (
-    schema?.type === "array" &&
-    "format" in schema &&
-    schema.format === "table"
-  ) {
-    return {
-      displayType: "table",
-      colorClass: "!text-indigo-500",
-      hexColor: "#6366f1",
-    };
-  }
-
   if (schema?.type === "string" && schema?.format) {
     const formatMap: Record<
       string,

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

@@ -36,7 +36,6 @@ type Props = {
   readOnly?: boolean;
   isOptional?: boolean;
   showTitle?: boolean;
-  variant?: "default" | "node";
 };
 
 export function CredentialsInput({
@@ -49,7 +48,6 @@ export function CredentialsInput({
   readOnly = false,
   isOptional = false,
   showTitle = true,
-  variant = "default",
 }: Props) {
   const hookData = useCredentialsInput({
     schema,
@@ -125,7 +123,6 @@ export function CredentialsInput({
               onClearCredential={() => onSelectCredential(undefined)}
               readOnly={readOnly}
               allowNone={isOptional}
-              variant={variant}
             />
           ) : (
             <div className="mb-4 space-y-2">

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

@@ -30,8 +30,6 @@ type CredentialRowProps = {
   readOnly?: boolean;
   showCaret?: boolean;
   asSelectTrigger?: boolean;
-  /** When "node", applies compact styling for node context */
-  variant?: "default" | "node";
 };
 
 export function CredentialRow({
@@ -43,22 +41,14 @@ export function CredentialRow({
   readOnly = false,
   showCaret = false,
   asSelectTrigger = false,
-  variant = "default",
 }: CredentialRowProps) {
   const ProviderIcon = providerIcons[provider] || fallbackIcon;
-  const isNodeVariant = variant === "node";
 
   return (
     <div
       className={cn(
         "flex items-center gap-3 rounded-medium border border-zinc-200 bg-white p-3 transition-colors",
-        asSelectTrigger && isNodeVariant
-          ? "min-w-0 flex-1 overflow-hidden border-0 bg-transparent"
-          : asSelectTrigger
-            ? "border-0 bg-transparent"
-            : readOnly
-              ? "w-fit"
-              : "",
+        asSelectTrigger ? "border-0 bg-transparent" : readOnly ? "w-fit" : "",
       )}
       onClick={readOnly || showCaret || asSelectTrigger ? undefined : onSelect}
       style={
@@ -71,31 +61,19 @@ export function CredentialRow({
         <ProviderIcon className="h-3 w-3 text-white" />
       </div>
       <IconKey className="h-5 w-5 shrink-0 text-zinc-800" />
-      <div
-        className={cn(
-          "flex min-w-0 flex-1 flex-nowrap items-center gap-4",
-          isNodeVariant && "overflow-hidden",
-        )}
-      >
+      <div className="flex min-w-0 flex-1 flex-nowrap items-center gap-4">
         <Text
           variant="body"
-          className={cn(
-            "tracking-tight",
-            isNodeVariant
-              ? "truncate"
-              : "line-clamp-1 flex-[0_0_50%] text-ellipsis",
-          )}
+          className="line-clamp-1 flex-[0_0_50%] text-ellipsis tracking-tight"
         >
           {getCredentialDisplayName(credential, displayName)}
         </Text>
-        {!(asSelectTrigger && isNodeVariant) && (
-          <Text
-            variant="large"
-            className="relative top-1 hidden overflow-hidden whitespace-nowrap font-mono tracking-tight md:block"
-          >
-            {"*".repeat(MASKED_KEY_LENGTH)}
-          </Text>
-        )}
+        <Text
+          variant="large"
+          className="lex-[0_0_40%] relative top-1 hidden overflow-hidden whitespace-nowrap font-mono tracking-tight md:block"
+        >
+          {"*".repeat(MASKED_KEY_LENGTH)}
+        </Text>
       </div>
       {showCaret && !asSelectTrigger && (
         <CaretDown className="h-4 w-4 shrink-0 text-gray-400" />

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

@@ -7,7 +7,6 @@ import {
 } from "@/components/__legacy__/ui/select";
 import { Text } from "@/components/atoms/Text/Text";
 import { CredentialsMetaInput } from "@/lib/autogpt-server-api/types";
-import { cn } from "@/lib/utils";
 import { useEffect } from "react";
 import { getCredentialDisplayName } from "../../helpers";
 import { CredentialRow } from "../CredentialRow/CredentialRow";
@@ -27,8 +26,6 @@ interface Props {
   onClearCredential?: () => void;
   readOnly?: boolean;
   allowNone?: boolean;
-  /** When "node", applies compact styling for node context */
-  variant?: "default" | "node";
 }
 
 export function CredentialsSelect({
@@ -40,7 +37,6 @@ export function CredentialsSelect({
   onClearCredential,
   readOnly = false,
   allowNone = true,
-  variant = "default",
 }: Props) {
   // Auto-select first credential if none is selected (only if allowNone is false)
   useEffect(() => {
@@ -63,12 +59,7 @@ export function CredentialsSelect({
         value={selectedCredentials?.id || (allowNone ? "__none__" : "")}
         onValueChange={handleValueChange}
       >
-        <SelectTrigger
-          className={cn(
-            "h-auto min-h-12 w-full rounded-medium border-zinc-200 p-0 pr-4 shadow-none",
-            variant === "node" && "overflow-hidden",
-          )}
-        >
+        <SelectTrigger className="h-auto min-h-12 w-full rounded-medium border-zinc-200 p-0 pr-4 shadow-none">
           {selectedCredentials ? (
             <SelectValue key={selectedCredentials.id} asChild>
               <CredentialRow
@@ -84,7 +75,6 @@ export function CredentialsSelect({
                 onDelete={() => {}}
                 readOnly={readOnly}
                 asSelectTrigger={true}
-                variant={variant}
               />
             </SelectValue>
           ) : (

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

@@ -92,33 +92,29 @@ export function useMarketplaceUpdate({ agent }: UseMarketplaceUpdateProps) {
 
     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 agentSubmissions =
-      submissionsResponse?.submissions?.filter(
-        (submission: StoreSubmission) => submission.agent_id === agent.graph_id,
-      ) || [];
-
-    const highestSubmittedVersion =
-      agentSubmissions.length > 0
-        ? Math.max(
-            ...agentSubmissions.map(
-              (submission: StoreSubmission) => submission.agent_version,
-            ),
-          )
-        : 0;
-
-    const hasUnpublishedChanges =
-      isUserCreator && agent.graph_version > highestSubmittedVersion;
+    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: agentSubmissions.length > 0 && hasUnpublishedChanges,
+        hasPublishUpdate:
+          isUserCreator && !hasPendingSubmissionForCurrentVersion,
       };
     }
 
+    // Get the latest version from the marketplace
+    // agentGraphVersions array contains graph version numbers as strings, get the highest one
     const latestMarketplaceVersion =
       storeAgent.agentGraphVersions?.length > 0
         ? Math.max(
@@ -128,11 +124,18 @@ 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
     const hasPublishUpdate =
       isUserCreator &&
-      agent.graph_version >
-        Math.max(latestMarketplaceVersion || 0, highestSubmittedVersion);
+      !hasPendingSubmissionForCurrentVersion &&
+      (latestMarketplaceVersion === undefined || // Not published yet
+        agent.graph_version > latestMarketplaceVersion); // Or local version is newer
 
+    // If marketplace version is newer than user's version, show update banner
+    // This applies to both creators and non-creators
     const hasMarketplaceUpdate =
       latestMarketplaceVersion !== undefined &&
       latestMarketplaceVersion > agent.graph_version;

autogpt_platform/frontend/src/components/contextual/EditAgentModal/components/EditAgentForm.tsx

@@ -133,7 +133,7 @@ export function EditAgentForm({
               <Input
                 id={field.name}
                 label="Changes Summary"
-                type="textarea"
+                type="text"
                 placeholder="Briefly describe what you changed"
                 error={form.formState.errors.changes_summary?.message}
                 {...field}

autogpt_platform/frontend/src/components/contextual/EditAgentModal/components/useEditAgentForm.ts

@@ -47,7 +47,7 @@ export const useEditAgentForm = ({
     changes_summary: z
       .string()
       .min(1, "Changes summary is required")
-      .max(500, "Changes summary must be less than 500 characters"),
+      .max(200, "Changes summary must be less than 200 characters"),
     agentOutputDemo: z
       .string()
       .refine(validateYouTubeUrl, "Please enter a valid YouTube URL"),

autogpt_platform/frontend/src/components/contextual/PublishAgentModal/components/AgentInfoStep/useAgentInfoStep.ts

@@ -53,24 +53,21 @@ export function useAgentInfoStep({
   useEffect(() => {
     if (initialData?.agent_id) {
       setAgentId(initialData.agent_id);
-      setImages(
-        Array.from(
-          new Set([
-            ...(initialData?.thumbnailSrc ? [initialData.thumbnailSrc] : []),
-            ...(initialData.additionalImages || []),
-          ]),
-        ),
-      );
+      const initialImages = [
+        ...(initialData?.thumbnailSrc ? [initialData.thumbnailSrc] : []),
+        ...(initialData.additionalImages || []),
+      ];
+      setImages(initialImages);
+
+      // Update form with initial data
       form.reset({
-        changesSummary: isMarketplaceUpdate
-          ? ""
-          : initialData.changesSummary || "",
+        changesSummary: initialData.changesSummary || "",
         title: initialData.title,
         subheader: initialData.subheader,
         slug: initialData.slug.toLocaleLowerCase().trim(),
         youtubeLink: initialData.youtubeLink,
         category: initialData.category,
-        description: isMarketplaceUpdate ? "" : initialData.description,
+        description: initialData.description,
         recommendedScheduleCron: initialData.recommendedScheduleCron || "",
         instructions: initialData.instructions || "",
         agentOutputDemo: initialData.agentOutputDemo || "",
@@ -152,7 +149,12 @@ export function useAgentInfoStep({
     agentId,
     images,
     isSubmitting,
-    initialImages: images,
+    initialImages: initialData
+      ? [
+          ...(initialData?.thumbnailSrc ? [initialData.thumbnailSrc] : []),
+          ...(initialData.additionalImages || []),
+        ]
+      : [],
     initialSelectedImage: initialData?.thumbnailSrc || null,
     handleImagesChange,
     handleSubmit: form.handleSubmit(handleFormSubmit),

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

@@ -1,116 +0,0 @@
-import type { Meta, StoryObj } from "@storybook/nextjs";
-import { TooltipProvider } from "@/components/atoms/Tooltip/BaseTooltip";
-import { Table } from "./Table";
-
-const meta = {
-  title: "Molecules/Table",
-  component: Table,
-  decorators: [
-    (Story) => (
-      <TooltipProvider>
-        <Story />
-      </TooltipProvider>
-    ),
-  ],
-  parameters: {
-    layout: "centered",
-  },
-  tags: ["autodocs"],
-  argTypes: {
-    allowAddRow: {
-      control: "boolean",
-      description: "Whether to show the Add row button",
-    },
-    allowDeleteRow: {
-      control: "boolean",
-      description: "Whether to show delete buttons for each row",
-    },
-    readOnly: {
-      control: "boolean",
-      description:
-        "Whether the table is read-only (renders text instead of inputs)",
-    },
-    addRowLabel: {
-      control: "text",
-      description: "Label for the Add row button",
-    },
-  },
-} satisfies Meta<typeof Table>;
-
-export default meta;
-type Story = StoryObj<typeof meta>;
-
-export const Default: Story = {
-  args: {
-    columns: ["name", "email", "role"],
-    allowAddRow: true,
-    allowDeleteRow: true,
-  },
-};
-
-export const WithDefaultValues: Story = {
-  args: {
-    columns: ["name", "email", "role"],
-    defaultValues: [
-      { name: "John Doe", email: "john@example.com", role: "Admin" },
-      { name: "Jane Smith", email: "jane@example.com", role: "User" },
-      { name: "Bob Wilson", email: "bob@example.com", role: "Editor" },
-    ],
-    allowAddRow: true,
-    allowDeleteRow: true,
-  },
-};
-
-export const ReadOnly: Story = {
-  args: {
-    columns: ["name", "email"],
-    defaultValues: [
-      { name: "John Doe", email: "john@example.com" },
-      { name: "Jane Smith", email: "jane@example.com" },
-    ],
-    readOnly: true,
-  },
-};
-
-export const NoAddOrDelete: Story = {
-  args: {
-    columns: ["name", "email"],
-    defaultValues: [
-      { name: "John Doe", email: "john@example.com" },
-      { name: "Jane Smith", email: "jane@example.com" },
-    ],
-    allowAddRow: false,
-    allowDeleteRow: false,
-  },
-};
-
-export const SingleColumn: Story = {
-  args: {
-    columns: ["item"],
-    allowAddRow: true,
-    allowDeleteRow: true,
-    addRowLabel: "Add item",
-  },
-};
-
-export const CustomAddLabel: Story = {
-  args: {
-    columns: ["key", "value"],
-    allowAddRow: true,
-    allowDeleteRow: true,
-    addRowLabel: "Add new entry",
-  },
-};
-
-export const KeyValuePairs: Story = {
-  args: {
-    columns: ["key", "value"],
-    defaultValues: [
-      { key: "API_KEY", value: "sk-..." },
-      { key: "DATABASE_URL", value: "postgres://..." },
-    ],
-    allowAddRow: true,
-    allowDeleteRow: true,
-    addRowLabel: "Add variable",
-  },
-};

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

@@ -1,133 +0,0 @@
-import * as React from "react";
-import {
-  Table as BaseTable,
-  TableBody,
-  TableCell,
-  TableHead,
-  TableHeader,
-  TableRow,
-} from "@/components/__legacy__/ui/table";
-import { Button } from "@/components/atoms/Button/Button";
-import { Input } from "@/components/atoms/Input/Input";
-import { Text } from "@/components/atoms/Text/Text";
-import { Plus, Trash2 } from "lucide-react";
-import { cn } from "@/lib/utils";
-import { useTable, RowData } from "./useTable";
-import { formatColumnTitle, formatPlaceholder } from "./helpers";
-
-export interface TableProps {
-  columns: string[];
-  defaultValues?: RowData[];
-  onChange?: (rows: RowData[]) => void;
-  allowAddRow?: boolean;
-  allowDeleteRow?: boolean;
-  addRowLabel?: string;
-  className?: string;
-  readOnly?: boolean;
-}
-
-export function Table({
-  columns,
-  defaultValues,
-  onChange,
-  allowAddRow = true,
-  allowDeleteRow = true,
-  addRowLabel = "Add row",
-  className,
-  readOnly = false,
-}: TableProps) {
-  const { rows, handleAddRow, handleDeleteRow, handleCellChange } = useTable({
-    columns,
-    defaultValues,
-    onChange,
-  });
-
-  const showDeleteColumn = allowDeleteRow && !readOnly;
-  const showAddButton = allowAddRow && !readOnly;
-
-  return (
-    <div className={cn("flex flex-col gap-3", className)}>
-      <div className="overflow-hidden rounded-xl border border-zinc-200 bg-white">
-        <BaseTable>
-          <TableHeader>
-            <TableRow className="border-b border-zinc-100 bg-zinc-50/50">
-              {columns.map((column) => (
-                <TableHead
-                  key={column}
-                  className="h-10 px-3 text-sm font-medium text-zinc-600"
-                >
-                  {formatColumnTitle(column)}
-                </TableHead>
-              ))}
-              {showDeleteColumn && <TableHead className="w-[50px]" />}
-            </TableRow>
-          </TableHeader>
-          <TableBody>
-            {rows.map((row, rowIndex) => (
-              <TableRow key={rowIndex} className="border-none">
-                {columns.map((column) => (
-                  <TableCell key={`${rowIndex}-${column}`} className="p-2">
-                    {readOnly ? (
-                      <Text
-                        variant="body"
-                        className="px-3 py-2 text-sm text-zinc-800"
-                      >
-                        {row[column] || "-"}
-                      </Text>
-                    ) : (
-                      <Input
-                        id={`table-${rowIndex}-${column}`}
-                        label={formatColumnTitle(column)}
-                        hideLabel
-                        value={row[column] ?? ""}
-                        onChange={(e) =>
-                          handleCellChange(rowIndex, column, e.target.value)
-                        }
-                        placeholder={formatPlaceholder(column)}
-                        size="small"
-                        wrapperClassName="mb-0"
-                      />
-                    )}
-                  </TableCell>
-                ))}
-                {showDeleteColumn && (
-                  <TableCell className="p-2">
-                    <Button
-                      variant="icon"
-                      size="icon"
-                      onClick={() => handleDeleteRow(rowIndex)}
-                      aria-label="Delete row"
-                      className="text-zinc-400 transition-colors hover:text-red-500"
-                    >
-                      <Trash2 className="h-4 w-4" />
-                    </Button>
-                  </TableCell>
-                )}
-              </TableRow>
-            ))}
-            {showAddButton && (
-              <TableRow>
-                <TableCell
-                  colSpan={columns.length + (showDeleteColumn ? 1 : 0)}
-                  className="p-2"
-                >
-                  <Button
-                    variant="outline"
-                    size="small"
-                    onClick={handleAddRow}
-                    leftIcon={<Plus className="h-4 w-4" />}
-                    className="w-fit"
-                  >
-                    {addRowLabel}
-                  </Button>
-                </TableCell>
-              </TableRow>
-            )}
-          </TableBody>
-        </BaseTable>
-      </div>
-    </div>
-  );
-}
-
-export { type RowData } from "./useTable";

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

@@ -1,7 +0,0 @@
-export const formatColumnTitle = (key: string): string => {
-  return key.charAt(0).toUpperCase() + key.slice(1);
-};
-
-export const formatPlaceholder = (key: string): string => {
-  return `Enter ${key.toLowerCase()}`;
-};

autogpt_platform/frontend/src/components/molecules/Table/useTable.ts

@@ -1,81 +0,0 @@
-import { useState, useEffect } from "react";
-
-export type RowData = Record<string, string>;
-
-interface UseTableOptions {
-  columns: string[];
-  defaultValues?: RowData[];
-  onChange?: (rows: RowData[]) => void;
-}
-
-export function useTable({
-  columns,
-  defaultValues,
-  onChange,
-}: UseTableOptions) {
-  const createEmptyRow = (): RowData => {
-    const emptyRow: RowData = {};
-    columns.forEach((column) => {
-      emptyRow[column] = "";
-    });
-    return emptyRow;
-  };
-
-  const [rows, setRows] = useState<RowData[]>(() => {
-    if (defaultValues && defaultValues.length > 0) {
-      return defaultValues;
-    }
-    return [];
-  });
-
-  useEffect(() => {
-    if (defaultValues !== undefined) {
-      setRows(defaultValues);
-    }
-  }, [defaultValues]);
-
-  const updateRows = (newRows: RowData[]) => {
-    setRows(newRows);
-    onChange?.(newRows);
-  };
-
-  const handleAddRow = () => {
-    const newRows = [...rows, createEmptyRow()];
-    updateRows(newRows);
-  };
-
-  const handleDeleteRow = (rowIndex: number) => {
-    const newRows = rows.filter((_, index) => index !== rowIndex);
-    updateRows(newRows);
-  };
-
-  const handleCellChange = (
-    rowIndex: number,
-    columnKey: string,
-    value: string,
-  ) => {
-    const newRows = rows.map((row, index) => {
-      if (index === rowIndex) {
-        return {
-          ...row,
-          [columnKey]: value,
-        };
-      }
-      return row;
-    });
-    updateRows(newRows);
-  };
-
-  const clearAll = () => {
-    updateRows([]);
-  };
-
-  return {
-    rows,
-    handleAddRow,
-    handleDeleteRow,
-    handleCellChange,
-    clearAll,
-    createEmptyRow,
-  };
-}

autogpt_platform/frontend/src/components/renderers/InputRenderer/FormRenderer.tsx

@@ -30,8 +30,6 @@ export const FormRenderer = ({
     return generateUiSchemaForCustomFields(preprocessedSchema, uiSchema);
   }, [preprocessedSchema, uiSchema]);
 
-  console.log("preprocessedSchema", preprocessedSchema);
-
   return (
     <div className={"mb-6 mt-4"}>
       <Form

autogpt_platform/frontend/src/components/renderers/InputRenderer/base/anyof/AnyOfField.tsx

@@ -5,14 +5,19 @@ import { useAnyOfField } from "./useAnyOfField";
 import { getHandleId, updateUiOption } from "../../helpers";
 import { useEdgeStore } from "@/app/(platform)/build/stores/edgeStore";
 import { ANY_OF_FLAG } from "../../constants";
-import { findCustomFieldId } from "../../registry";
 
 export const AnyOfField = (props: FieldProps) => {
   const { registry, schema } = props;
   const { fields } = registry;
   const { SchemaField: _SchemaField } = fields;
   const { nodeId } = registry.formContext;
+
   const { isInputConnected } = useEdgeStore();
+
+  const uiOptions = getUiOptions(props.uiSchema, props.globalUiOptions);
+
+  const Widget = getWidget({ type: "string" }, "select", registry.widgets);
+
   const {
     handleOptionChange,
     enumOptions,
@@ -21,15 +26,6 @@ export const AnyOfField = (props: FieldProps) => {
     field_id,
   } = useAnyOfField(props);
 
-  const parentCustomFieldId = findCustomFieldId(schema);
-  if (parentCustomFieldId) {
-    return null;
-  }
-
-  const uiOptions = getUiOptions(props.uiSchema, props.globalUiOptions);
-
-  const Widget = getWidget({ type: "string" }, "select", registry.widgets);
-
   const handleId = getHandleId({
     uiOptions,
     id: field_id + ANY_OF_FLAG,
@@ -44,21 +40,12 @@ export const AnyOfField = (props: FieldProps) => {
 
   const isHandleConnected = isInputConnected(nodeId, handleId);
 
-  // Now anyOf can render - custom fields if the option schema matches a custom field
-  const optionCustomFieldId = optionSchema
-    ? findCustomFieldId(optionSchema)
-    : null;
-
-  const optionUiSchema = optionCustomFieldId
-    ? { ...updatedUiSchema, "ui:field": optionCustomFieldId }
-    : updatedUiSchema;
-
   const optionsSchemaField =
     (optionSchema && optionSchema.type !== "null" && (
       <_SchemaField
         {...props}
         schema={optionSchema}
-        uiSchema={optionUiSchema}
+        uiSchema={updatedUiSchema}
       />
     )) ||
     null;

autogpt_platform/frontend/src/components/renderers/InputRenderer/base/standard/widgets/TextInput/TextInputExpanderModal.tsx

@@ -17,7 +17,6 @@ interface InputExpanderModalProps {
   defaultValue: string;
   description?: string;
   placeholder?: string;
-  inputType?: "text" | "json";
 }
 
 export const InputExpanderModal: FC<InputExpanderModalProps> = ({
@@ -28,7 +27,6 @@ export const InputExpanderModal: FC<InputExpanderModalProps> = ({
   defaultValue,
   description,
   placeholder,
-  inputType = "text",
 }) => {
   const [tempValue, setTempValue] = useState(defaultValue);
   const [isCopied, setIsCopied] = useState(false);
@@ -80,10 +78,7 @@ export const InputExpanderModal: FC<InputExpanderModalProps> = ({
             hideLabel
             id="input-expander-modal"
             value={tempValue}
-            className={cn(
-              "!min-h-[300px] rounded-2xlarge",
-              inputType === "json" && "font-mono text-sm",
-            )}
+            className="!min-h-[300px] rounded-2xlarge"
             onChange={(e) => setTempValue(e.target.value)}
             placeholder={placeholder || "Enter text..."}
             autoFocus

autogpt_platform/frontend/src/components/renderers/InputRenderer/custom/CredentialField/CredentialField.tsx

@@ -88,8 +88,6 @@ export const CredentialsField = (props: FieldProps) => {
         showTitle={false}
         readOnly={formContext?.readOnly}
         isOptional={!isRequired}
-        className="w-full"
-        variant="node"
       />
 
       {/* Optional credentials toggle - only show in builder canvas, not run dialogs */}

autogpt_platform/frontend/src/components/renderers/InputRenderer/custom/JsonTextField/JsonTextField.tsx

@@ -1,124 +0,0 @@
-"use client";
-
-import { FieldProps, getTemplate, getUiOptions } from "@rjsf/utils";
-import { Input } from "@/components/atoms/Input/Input";
-import { Button } from "@/components/atoms/Button/Button";
-import {
-  Tooltip,
-  TooltipContent,
-  TooltipTrigger,
-} from "@/components/atoms/Tooltip/BaseTooltip";
-import { ArrowsOutIcon } from "@phosphor-icons/react";
-import { InputExpanderModal } from "../../base/standard/widgets/TextInput/TextInputExpanderModal";
-import { getHandleId, updateUiOption } from "../../helpers";
-import { useJsonTextField } from "./useJsonTextField";
-import { getPlaceholder } from "./helpers";
-
-export const JsonTextField = (props: FieldProps) => {
-  const {
-    formData,
-    onChange,
-    schema,
-    registry,
-    uiSchema,
-    required,
-    name,
-    fieldPathId,
-  } = props;
-
-  const uiOptions = getUiOptions(uiSchema);
-
-  const TitleFieldTemplate = getTemplate(
-    "TitleFieldTemplate",
-    registry,
-    uiOptions,
-  );
-
-  const fieldId = fieldPathId?.$id ?? props.id ?? "json-field";
-
-  const handleId = getHandleId({
-    uiOptions,
-    id: fieldId,
-    schema: schema,
-  });
-
-  const updatedUiSchema = updateUiOption(uiSchema, {
-    handleId: handleId,
-  });
-
-  const {
-    textValue,
-    isModalOpen,
-    handleChange,
-    handleModalOpen,
-    handleModalClose,
-    handleModalSave,
-  } = useJsonTextField({
-    formData,
-    onChange,
-    path: fieldPathId?.path,
-  });
-
-  const placeholder = getPlaceholder(schema);
-  const title = schema.title || name || "JSON Value";
-
-  return (
-    <div className="flex flex-col gap-2">
-      <TitleFieldTemplate
-        id={fieldId}
-        title={title}
-        required={required}
-        schema={schema}
-        uiSchema={updatedUiSchema}
-        registry={registry}
-      />
-      <div className="nodrag relative flex items-center gap-2">
-        <Input
-          id={fieldId}
-          hideLabel={true}
-          type="textarea"
-          label=""
-          size="small"
-          wrapperClassName="mb-0 flex-1 "
-          value={textValue}
-          onChange={handleChange}
-          placeholder={placeholder}
-          required={required}
-          disabled={props.disabled}
-          className="min-h-[60px] pr-8 font-mono text-xs"
-        />
-
-        <Tooltip delayDuration={0}>
-          <TooltipTrigger asChild>
-            <Button
-              variant="ghost"
-              size="icon"
-              onClick={handleModalOpen}
-              type="button"
-              className="p-1"
-            >
-              <ArrowsOutIcon className="size-4" />
-            </Button>
-          </TooltipTrigger>
-          <TooltipContent>Expand input</TooltipContent>
-        </Tooltip>
-      </div>
-      {schema.description && (
-        <span className="text-xs text-gray-500">{schema.description}</span>
-      )}
-
-      <InputExpanderModal
-        isOpen={isModalOpen}
-        onClose={handleModalClose}
-        onSave={handleModalSave}
-        title={`Edit ${title}`}
-        description={schema.description || "Enter valid JSON"}
-        defaultValue={textValue}
-        placeholder={placeholder}
-        inputType="json"
-      />
-    </div>
-  );
-};
-
-export default JsonTextField;

autogpt_platform/frontend/src/components/renderers/InputRenderer/custom/JsonTextField/helpers.ts

@@ -1,67 +0,0 @@
-import { RJSFSchema } from "@rjsf/utils";
-
-/**
- * Converts form data to a JSON string for display
- * @param formData - The data to stringify
- * @returns JSON string or empty string if data is null/undefined
- */
-export function stringifyFormData(formData: unknown): string {
-  if (formData === undefined || formData === null) {
-    return "";
-  }
-  try {
-    return JSON.stringify(formData, null, 2);
-  } catch {
-    return "";
-  }
-}
-
-/**
- * Parses a JSON string into an object/array
- * @param value - The JSON string to parse
- * @returns Parsed value or undefined if parsing fails or empty
- */
-export function parseJsonValue(value: string): unknown | undefined {
-  const trimmed = value.trim();
-  if (trimmed === "") {
-    return undefined;
-  }
-
-  try {
-    return JSON.parse(trimmed);
-  } catch {
-    return undefined;
-  }
-}
-
-/**
- * Gets the appropriate placeholder text based on schema type
- * @param schema - The JSON schema
- * @returns Placeholder string
- */
-export function getPlaceholder(schema: RJSFSchema): string {
-  if (schema.type === "array") {
-    return '["item1", "item2"] or [{"key": "value"}]';
-  }
-  if (schema.type === "object") {
-    return '{"key": "value"}';
-  }
-  return "Enter JSON value...";
-}
-
-/**
- * Checks if a JSON string is valid
- * @param value - The JSON string to validate
- * @returns true if valid JSON, false otherwise
- */
-export function isValidJson(value: string): boolean {
-  if (value.trim() === "") {
-    return true; // Empty is considered valid (will be undefined)
-  }
-  try {
-    JSON.parse(value);
-    return true;
-  } catch {
-    return false;
-  }
-}

autogpt_platform/frontend/src/components/renderers/InputRenderer/custom/JsonTextField/useJsonTextField.ts

@@ -1,107 +0,0 @@
-import { useState, useEffect, useCallback } from "react";
-import { FieldProps } from "@rjsf/utils";
-import { stringifyFormData, parseJsonValue, isValidJson } from "./helpers";
-
-type FieldOnChange = FieldProps["onChange"];
-type FieldPathId = FieldProps["fieldPathId"];
-
-interface UseJsonTextFieldOptions {
-  formData: unknown;
-  onChange: FieldOnChange;
-  path?: FieldPathId["path"];
-}
-
-interface UseJsonTextFieldReturn {
-  textValue: string;
-  isModalOpen: boolean;
-  hasError: boolean;
-  handleChange: (
-    e: React.ChangeEvent<HTMLInputElement | HTMLTextAreaElement>,
-  ) => void;
-  handleModalOpen: () => void;
-  handleModalClose: () => void;
-  handleModalSave: (value: string) => void;
-}
-
-/**
- * Custom hook for managing JSON text field state and handlers
- */
-export function useJsonTextField({
-  formData,
-  onChange,
-  path,
-}: UseJsonTextFieldOptions): UseJsonTextFieldReturn {
-  const [textValue, setTextValue] = useState(() => stringifyFormData(formData));
-  const [isModalOpen, setIsModalOpen] = useState(false);
-  const [hasError, setHasError] = useState(false);
-
-  // Update text value when formData changes externally
-  useEffect(() => {
-    const newValue = stringifyFormData(formData);
-    setTextValue(newValue);
-    setHasError(false);
-  }, [formData]);
-
-  const handleChange = useCallback(
-    (e: React.ChangeEvent<HTMLInputElement | HTMLTextAreaElement>) => {
-      const value = e.target.value;
-      setTextValue(value);
-
-      // Validate JSON and update error state
-      const valid = isValidJson(value);
-      setHasError(!valid);
-
-      // Try to parse and update formData
-      if (value.trim() === "") {
-        onChange(undefined, path ?? []);
-        return;
-      }
-
-      const parsed = parseJsonValue(value);
-      if (parsed !== undefined) {
-        onChange(parsed, path ?? []);
-      }
-    },
-    [onChange, path],
-  );
-
-  const handleModalOpen = useCallback(() => {
-    setIsModalOpen(true);
-  }, []);
-
-  const handleModalClose = useCallback(() => {
-    setIsModalOpen(false);
-  }, []);
-
-  const handleModalSave = useCallback(
-    (value: string) => {
-      setTextValue(value);
-      setIsModalOpen(false);
-
-      // Validate and update
-      const valid = isValidJson(value);
-      setHasError(!valid);
-
-      if (value.trim() === "") {
-        onChange(undefined, path ?? []);
-        return;
-      }
-
-      const parsed = parseJsonValue(value);
-      if (parsed !== undefined) {
-        onChange(parsed, path ?? []);
-      }
-    },
-    [onChange, path],
-  );
-
-  return {
-    textValue,
-    isModalOpen,
-    hasError,
-    handleChange,
-    handleModalOpen,
-    handleModalClose,
-    handleModalSave,
-  };
-}

autogpt_platform/frontend/src/components/renderers/InputRenderer/custom/MultiSelectField/MultiSelectField.tsx

@@ -1,57 +0,0 @@
-import React from "react";
-import { FieldProps, getUiOptions } from "@rjsf/utils";
-import { BlockIOObjectSubSchema } from "@/lib/autogpt-server-api/types";
-import {
-  MultiSelector,
-  MultiSelectorContent,
-  MultiSelectorInput,
-  MultiSelectorItem,
-  MultiSelectorList,
-  MultiSelectorTrigger,
-} from "@/components/__legacy__/ui/multiselect";
-import { cn } from "@/lib/utils";
-import { useMultiSelectField } from "./useMultiSelectField";
-
-export const MultiSelectField = (props: FieldProps) => {
-  const { schema, formData, onChange, fieldPathId } = props;
-  const uiOptions = getUiOptions(props.uiSchema);
-
-  const { optionSchema, options, selection, createChangeHandler } =
-    useMultiSelectField({
-      schema: schema as BlockIOObjectSubSchema,
-      formData,
-    });
-
-  const handleValuesChange = createChangeHandler(onChange, fieldPathId);
-
-  const displayName = schema.title || "options";
-
-  return (
-    <div className={cn("flex flex-col", uiOptions.className)}>
-      <MultiSelector
-        className="nodrag"
-        values={selection}
-        onValuesChange={handleValuesChange}
-      >
-        <MultiSelectorTrigger className="rounded-3xl border border-zinc-200 bg-white px-2 shadow-none">
-          <MultiSelectorInput
-            placeholder={
-              (schema as any).placeholder ?? `Select ${displayName}...`
-            }
-          />
-        </MultiSelectorTrigger>
-        <MultiSelectorContent className="nowheel">
-          <MultiSelectorList>
-            {options
-              .map((key) => ({ ...optionSchema[key], key }))
-              .map(({ key, title, description }) => (
-                <MultiSelectorItem key={key} value={key} title={description}>
-                  {title ?? key}
-                </MultiSelectorItem>
-              ))}
-          </MultiSelectorList>
-        </MultiSelectorContent>
-      </MultiSelector>
-    </div>
-  );
-};

autogpt_platform/frontend/src/components/renderers/InputRenderer/custom/MultiSelectField/index.ts

@@ -1 +0,0 @@
-export { MultiSelectField } from "./MultiSelectField";

autogpt_platform/frontend/src/components/renderers/InputRenderer/custom/MultiSelectField/useMultiSelectField.ts

@@ -1,65 +0,0 @@
-import { FieldProps } from "@rjsf/utils";
-import { BlockIOObjectSubSchema } from "@/lib/autogpt-server-api/types";
-
-type FormData = Record<string, boolean> | null | undefined;
-
-interface UseMultiSelectFieldOptions {
-  schema: BlockIOObjectSubSchema;
-  formData: FormData;
-}
-
-export function useMultiSelectField({
-  schema,
-  formData,
-}: UseMultiSelectFieldOptions) {
-  const getOptionSchema = (): Record<string, BlockIOObjectSubSchema> => {
-    if (schema.properties) {
-      return schema.properties as Record<string, BlockIOObjectSubSchema>;
-    }
-    if (
-      "anyOf" in schema &&
-      Array.isArray(schema.anyOf) &&
-      schema.anyOf.length > 0 &&
-      "properties" in schema.anyOf[0]
-    ) {
-      return (schema.anyOf[0] as BlockIOObjectSubSchema).properties as Record<
-        string,
-        BlockIOObjectSubSchema
-      >;
-    }
-    return {};
-  };
-
-  const optionSchema = getOptionSchema();
-  const options = Object.keys(optionSchema);
-
-  const getSelection = (): string[] => {
-    if (!formData || typeof formData !== "object") {
-      return [];
-    }
-    return Object.entries(formData)
-      .filter(([_, value]) => value === true)
-      .map(([key]) => key);
-  };
-
-  const selection = getSelection();
-
-  const createChangeHandler =
-    (
-      onChange: FieldProps["onChange"],
-      fieldPathId: FieldProps["fieldPathId"],
-    ) =>
-    (values: string[]) => {
-      const newValue = Object.fromEntries(
-        options.map((opt) => [opt, values.includes(opt)]),
-      );
-      onChange(newValue, fieldPathId?.path);
-    };
-
-  return {
-    optionSchema,
-    options,
-    selection,
-    createChangeHandler,
-  };
-}

autogpt_platform/frontend/src/components/renderers/InputRenderer/custom/TableField/TableField.tsx

@@ -1,52 +0,0 @@
-import { descriptionId, FieldProps, getTemplate, titleId } from "@rjsf/utils";
-import { Table, RowData } from "@/components/molecules/Table/Table";
-import { useMemo } from "react";
-
-export const TableField = (props: FieldProps) => {
-  const { schema, formData, onChange, fieldPathId, registry, uiSchema } = props;
-
-  const itemSchema = schema.items as any;
-  const properties = itemSchema?.properties || {};
-
-  const columns: string[] = useMemo(() => {
-    return Object.keys(properties);
-  }, [properties]);
-
-  const handleChange = (rows: RowData[]) => {
-    onChange(rows, fieldPathId?.path.slice(0, -1));
-  };
-
-  const TitleFieldTemplate = getTemplate("TitleFieldTemplate", registry);
-  const DescriptionFieldTemplate = getTemplate(
-    "DescriptionFieldTemplate",
-    registry,
-  );
-
-  return (
-    <div className="flex flex-col gap-2">
-      <TitleFieldTemplate
-        id={titleId(fieldPathId)}
-        title={schema.title || ""}
-        required={true}
-        schema={schema}
-        uiSchema={uiSchema}
-        registry={registry}
-      />
-      <DescriptionFieldTemplate
-        id={descriptionId(fieldPathId)}
-        description={schema.description || ""}
-        schema={schema}
-        registry={registry}
-      />
-
-      <Table
-        columns={columns}
-        defaultValues={formData}
-        onChange={handleChange}
-        allowAddRow={true}
-        allowDeleteRow={true}
-        addRowLabel="Add row"
-      />
-    </div>
-  );
-};

autogpt_platform/frontend/src/components/renderers/InputRenderer/custom/custom-registry.ts

@@ -1,10 +1,6 @@
 import { FieldProps, RJSFSchema, RegistryFieldsType } from "@rjsf/utils";
 import { CredentialsField } from "./CredentialField/CredentialField";
 import { GoogleDrivePickerField } from "./GoogleDrivePickerField/GoogleDrivePickerField";
-import { JsonTextField } from "./JsonTextField/JsonTextField";
-import { MultiSelectField } from "./MultiSelectField/MultiSelectField";
-import { isMultiSelectSchema } from "../utils/schema-utils";
-import { TableField } from "./TableField/TableField";
 
 export interface CustomFieldDefinition {
   id: string;
@@ -12,9 +8,6 @@ export interface CustomFieldDefinition {
   component: (props: FieldProps<any, RJSFSchema, any>) => JSX.Element | null;
 }
 
-/** Field ID for JsonTextField - used to render nested complex types as text input */
-export const JSON_TEXT_FIELD_ID = "custom/json_text_field";
-
 export const CUSTOM_FIELDS: CustomFieldDefinition[] = [
   {
     id: "custom/credential_field",
@@ -37,28 +30,6 @@ export const CUSTOM_FIELDS: CustomFieldDefinition[] = [
     },
     component: GoogleDrivePickerField,
   },
-  {
-    id: "custom/json_text_field",
-    // Not matched by schema - assigned via uiSchema for nested complex types
-    matcher: () => false,
-    component: JsonTextField,
-  },
-  {
-    id: "custom/multi_select_field",
-    matcher: isMultiSelectSchema,
-    component: MultiSelectField,
-  },
-  {
-    id: "custom/table_field",
-    matcher: (schema: any) => {
-      return (
-        schema.type === "array" &&
-        "format" in schema &&
-        schema.format === "table"
-      );
-    },
-    component: TableField,
-  },
 ];
 
 export function findCustomFieldId(schema: any): string | null {

autogpt_platform/frontend/src/components/renderers/InputRenderer/utils/generate-ui-schema.ts

@@ -1,46 +1,19 @@
 import { RJSFSchema, UiSchema } from "@rjsf/utils";
-import {
-  findCustomFieldId,
-  JSON_TEXT_FIELD_ID,
-} from "../custom/custom-registry";
-
-function isComplexType(schema: RJSFSchema): boolean {
-  return schema.type === "object" || schema.type === "array";
-}
-
-function hasComplexAnyOfOptions(schema: RJSFSchema): boolean {
-  const options = schema.anyOf || schema.oneOf;
-  if (!Array.isArray(options)) return false;
-  return options.some(
-    (opt: any) =>
-      opt &&
-      typeof opt === "object" &&
-      (opt.type === "object" || opt.type === "array"),
-  );
-}
+import { findCustomFieldId } from "../custom/custom-registry";
 
 /**
  * Generates uiSchema with ui:field settings for custom fields based on schema matchers.
  * This is the standard RJSF way to route fields to custom components.
- *
- * Nested complex types (arrays/objects inside arrays/objects) are rendered as JsonTextField
- * to avoid deeply nested form UIs. Users can enter raw JSON for these fields.
- *
- * @param schema - The JSON schema
- * @param existingUiSchema - Existing uiSchema to merge with
- * @param insideComplexType - Whether we're already inside a complex type (object/array)
  */
 export function generateUiSchemaForCustomFields(
   schema: RJSFSchema,
   existingUiSchema: UiSchema = {},
-  insideComplexType: boolean = false,
 ): UiSchema {
   const uiSchema: UiSchema = { ...existingUiSchema };
 
   if (schema.properties) {
     for (const [key, propSchema] of Object.entries(schema.properties)) {
       if (propSchema && typeof propSchema === "object") {
-        // First check for custom field matchers (credentials, google drive, etc.)
         const customFieldId = findCustomFieldId(propSchema);
 
         if (customFieldId) {
@@ -48,33 +21,8 @@ export function generateUiSchemaForCustomFields(
             ...(uiSchema[key] as object),
             "ui:field": customFieldId,
           };
-          // Skip further processing for custom fields
-          continue;
         }
 
-        // Handle nested complex types - render as JsonTextField
-        if (insideComplexType && isComplexType(propSchema as RJSFSchema)) {
-          uiSchema[key] = {
-            ...(uiSchema[key] as object),
-            "ui:field": JSON_TEXT_FIELD_ID,
-          };
-          // Don't recurse further - this field is now a text input
-          continue;
-        }
-
-        // Handle anyOf/oneOf inside complex types
-        if (
-          insideComplexType &&
-          hasComplexAnyOfOptions(propSchema as RJSFSchema)
-        ) {
-          uiSchema[key] = {
-            ...(uiSchema[key] as object),
-            "ui:field": JSON_TEXT_FIELD_ID,
-          };
-          continue;
-        }
-
-        // Recurse into object properties
         if (
           propSchema.type === "object" &&
           propSchema.properties &&
@@ -83,7 +31,6 @@ export function generateUiSchemaForCustomFields(
           const nestedUiSchema = generateUiSchemaForCustomFields(
             propSchema as RJSFSchema,
             (uiSchema[key] as UiSchema) || {},
-            true, // Now inside a complex type
           );
           uiSchema[key] = {
             ...(uiSchema[key] as object),
@@ -91,11 +38,9 @@ export function generateUiSchemaForCustomFields(
           };
         }
 
-        // Handle arrays
         if (propSchema.type === "array" && propSchema.items) {
           const itemsSchema = propSchema.items as RJSFSchema;
           if (itemsSchema && typeof itemsSchema === "object") {
-            // Check for custom field on array items
             const itemsCustomFieldId = findCustomFieldId(itemsSchema);
             if (itemsCustomFieldId) {
               uiSchema[key] = {
@@ -104,28 +49,10 @@ export function generateUiSchemaForCustomFields(
                   "ui:field": itemsCustomFieldId,
                 },
               };
-            } else if (isComplexType(itemsSchema)) {
-              // Array items that are complex types become JsonTextField
-              uiSchema[key] = {
-                ...(uiSchema[key] as object),
-                items: {
-                  "ui:field": JSON_TEXT_FIELD_ID,
-                },
-              };
-            } else if (hasComplexAnyOfOptions(itemsSchema)) {
-              // Array items with anyOf containing complex types become JsonTextField
-              uiSchema[key] = {
-                ...(uiSchema[key] as object),
-                items: {
-                  "ui:field": JSON_TEXT_FIELD_ID,
-                },
-              };
             } else if (itemsSchema.properties) {
-              // Recurse into object items (but they're now inside a complex type)
               const itemsUiSchema = generateUiSchemaForCustomFields(
                 itemsSchema,
                 ((uiSchema[key] as UiSchema)?.items as UiSchema) || {},
-                true, // Inside complex type (array)
               );
               if (Object.keys(itemsUiSchema).length > 0) {
                 uiSchema[key] = {
@@ -136,61 +63,6 @@ export function generateUiSchemaForCustomFields(
             }
           }
         }
-
-        // Handle anyOf/oneOf at root level - process complex options
-        if (!insideComplexType) {
-          const anyOfOptions = propSchema.anyOf || propSchema.oneOf;
-
-          if (Array.isArray(anyOfOptions)) {
-            for (let i = 0; i < anyOfOptions.length; i++) {
-              const option = anyOfOptions[i] as RJSFSchema;
-              if (option && typeof option === "object") {
-                // Handle anyOf array options with complex items
-                if (option.type === "array" && option.items) {
-                  const itemsSchema = option.items as RJSFSchema;
-                  if (itemsSchema && typeof itemsSchema === "object") {
-                    // Array items that are complex types become JsonTextField
-                    if (isComplexType(itemsSchema)) {
-                      uiSchema[key] = {
-                        ...(uiSchema[key] as object),
-                        items: {
-                          "ui:field": JSON_TEXT_FIELD_ID,
-                        },
-                      };
-                    } else if (hasComplexAnyOfOptions(itemsSchema)) {
-                      uiSchema[key] = {
-                        ...(uiSchema[key] as object),
-                        items: {
-                          "ui:field": JSON_TEXT_FIELD_ID,
-                        },
-                      };
-                    }
-                  }
-                }
-
-                // Recurse into anyOf object options with properties
-                if (
-                  option.type === "object" &&
-                  option.properties &&
-                  typeof option.properties === "object"
-                ) {
-                  const optionUiSchema = generateUiSchemaForCustomFields(
-                    option,
-                    {},
-                    true, // Inside complex type (anyOf object option)
-                  );
-                  if (Object.keys(optionUiSchema).length > 0) {
-                    // Store under the property key - RJSF will apply it
-                    uiSchema[key] = {
-                      ...(uiSchema[key] as object),
-                      ...optionUiSchema,
-                    };
-                  }
-                }
-              }
-            }
-          }
-        }
       }
     }
   }

autogpt_platform/frontend/src/components/renderers/InputRenderer/utils/schema-utils.ts

@@ -1,11 +1,7 @@
 import { getUiOptions, RJSFSchema, UiSchema } from "@rjsf/utils";
 
 export function isAnyOfSchema(schema: RJSFSchema | undefined): boolean {
-  return (
-    Array.isArray(schema?.anyOf) &&
-    schema!.anyOf.length > 0 &&
-    schema?.enum === undefined
-  );
+  return Array.isArray(schema?.anyOf) && schema!.anyOf.length > 0;
 }
 
 export const isAnyOfChild = (
@@ -37,21 +33,3 @@ export function isOptionalType(schema: RJSFSchema | undefined): {
 export function isAnyOfSelector(name: string) {
   return name.includes("anyof_select");
 }
-
-export function isMultiSelectSchema(schema: RJSFSchema | undefined): boolean {
-  if (typeof schema !== "object" || schema === null) {
-    return false;
-  }
-
-  if ("anyOf" in schema || "oneOf" in schema) {
-    return false;
-  }
-
-  return !!(
-    schema.type === "object" &&
-    schema.properties &&
-    Object.values(schema.properties).every(
-      (prop: any) => prop.type === "boolean",
-    )
-  );
-}

docs/_javascript/mathjax.js

@@ -0,0 +1,16 @@
+window.MathJax = {
+    tex: {
+        inlineMath: [["\\(", "\\)"]],
+        displayMath: [["\\[", "\\]"]],
+        processEscapes: true,
+        processEnvironments: true
+    },
+    options: {
+        ignoreHtmlClass: ".*|",
+        processHtmlClass: "arithmatex"
+    }
+};
+
+document$.subscribe(() => {
+    MathJax.typesetPromise()
+})

docs/_javascript/tablesort.js

@@ -0,0 +1,6 @@
+document$.subscribe(function () {
+    var tables = document.querySelectorAll("article table:not([class])")
+    tables.forEach(function (table) {
+        new Tablesort(table)
+    })
+})

docs/content/contribute/index.md

@@ -1,35 +1,48 @@
 # Contributing to the Docs
 
-We welcome contributions to our documentation! Our docs are hosted on GitBook and synced with GitHub.
+We welcome contributions to our documentation! If you would like to contribute, please follow the steps below.
 
-## How It Works
+## Setting up the Docs
 
-- Documentation lives in the `docs/` directory on the `gitbook` branch
-- GitBook automatically syncs changes from GitHub
-- You can edit docs directly on GitHub or locally
-
-## Editing Docs Locally
-
-1. Clone the repository and switch to the gitbook branch:
+1. Clone the repository:
 
     ```shell
-    git clone https://github.com/Significant-Gravitas/AutoGPT.git
-    cd AutoGPT
-    git checkout gitbook
+    git clone github.com/Significant-Gravitas/AutoGPT.git
     ```
 
-2. Make your changes to markdown files in `docs/`
+1. Install the dependencies:
 
-3. Preview changes:
-   - Push to a branch and create a PR - GitBook will generate a preview
-   - Or use any markdown preview tool locally
+    ```shell
+    python -m pip install -r docs/requirements.txt
+    ```
 
-## Adding a New Page
+    or
 
-1. Create a new markdown file in the appropriate `docs/` subdirectory
-2. Add the new page to the relevant `SUMMARY.md` file to include it in the navigation
-3. Submit a pull request to the `gitbook` branch
+    ```shell
+    python3 -m pip install -r docs/requirements.txt
+    ```
+
+1. Start iterating using mkdocs' live server:
+
+    ```shell
+    mkdocs serve
+    ```
+
+1. Open your browser and navigate to `http://127.0.0.1:8000`.
+
+1. The server will automatically reload the docs when you save your changes.
+
+## Adding a new page
+
+1. Create a new markdown file in the `docs/content` directory.
+1. Add the new page to the `nav` section in the `mkdocs.yml` file.
+1. Add the content to the new markdown file.
+1. Run `mkdocs serve` to see your changes.
+
+## Checking links
+
+To check for broken links in the documentation, run `mkdocs build` and look for warnings in the console output.
 
 ## Submitting a Pull Request
 
-When you're ready to submit your changes, create a pull request targeting the `gitbook` branch. We will review your changes and merge them if appropriate.
+When you're ready to submit your changes, please create a pull request. We will review your changes and merge them if they are appropriate.

docs/content/platform/block-sdk-guide.md

@@ -227,7 +227,7 @@ backend/blocks/my_provider/
 
 ## Best Practices
 
-1. **Error Handling**: Use `BlockInputError` for validation failures and `BlockExecutionError` for runtime errors (import from `backend.util.exceptions`). These inherit from `ValueError` so the executor treats them as user-fixable. See [Error Handling in new_blocks.md](new_blocks.md#error-handling) for details.
+1. **Error Handling**: Error output pin is already defined on BlockSchemaOutput
 2. **Credentials**: Use the provider's `credentials_field()` method
 3. **Validation**: Use SchemaField constraints (ge, le, min_length, etc.)
 4. **Categories**: Choose appropriate categories for discoverability