Merge branch 'dev' into feat/ai-generated-mode

- Removed default value from is_ai_generated parameter
- Forces all callers to explicitly specify whether graph is AI-generated
- Explicitly set is_ai_generated=False for store installations (curated user-published agents)
- Makes the code more explicit and prevents accidental defaults

.branchlet.json

@@ -0,0 +1,37 @@
+{
+  "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

@@ -401,28 +401,11 @@ 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).
@@ -431,6 +414,7 @@ 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.
@@ -465,7 +449,9 @@ async def create_library_agent(
                             }
                         },
                         settings=SafeJson(
-                            _initialize_graph_settings(graph_entry).model_dump()
+                            GraphSettings.from_graph(
+                                graph_entry, is_ai_generated=is_ai_generated
+                            ).model_dump()
                         ),
                     ),
                     include=library_agent_include(
@@ -489,7 +475,7 @@ async def update_agent_version_in_library(
     agent_graph_version: int,
 ) -> library_model.LibraryAgent:
     """
-    Updates the agent version in the library if useGraphIsActiveVersion is True.
+    Updates the agent version in the library for any agent owned by the user.
 
     Args:
         user_id: Owner of the LibraryAgent.
@@ -498,20 +484,31 @@ 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}"
     )
-    try:
-        library_agent = await prisma.models.LibraryAgent.prisma().find_first_or_raise(
+    async with transaction() as tx:
+        library_agent = await prisma.models.LibraryAgent.prisma(tx).find_first_or_raise(
             where={
                 "userId": user_id,
                 "agentGraphId": agent_graph_id,
-                "useGraphIsActiveVersion": True,
             },
         )
-        lib = await prisma.models.LibraryAgent.prisma().update(
+
+        # 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(
             where={"id": library_agent.id},
             data={
                 "AgentGraph": {
@@ -525,13 +522,13 @@ async def update_agent_version_in_library(
             },
             include={"AgentGraph": True},
         )
-        if lib is None:
-            raise NotFoundError(f"Library agent {library_agent.id} not found")
 
-        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
+    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)
 
 
 async def update_library_agent(
@@ -616,33 +613,6 @@ 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:
@@ -825,8 +795,11 @@ async def add_store_agent_to_library(
                     }
                 },
                 "isCreatedByUser": False,
+                "useGraphIsActiveVersion": False,
                 "settings": SafeJson(
-                    _initialize_graph_settings(graph_model).model_dump()
+                    GraphSettings.from_graph(
+                        graph_model, is_ai_generated=False
+                    ).model_dump()
                 ),
             },
             include=library_agent_include(

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

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

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

@@ -1,5 +1,6 @@
 import asyncio
 import logging
+import typing
 from datetime import datetime, timezone
 from typing import Literal
 
@@ -9,7 +10,7 @@ import prisma.errors
 import prisma.models
 import prisma.types
 
-from backend.data.db import transaction
+from backend.data.db import query_raw_with_schema, transaction
 from backend.data.graph import (
     GraphMeta,
     GraphModel,
@@ -29,8 +30,6 @@ from backend.util.settings import Settings
 
 from . import exceptions as store_exceptions
 from . import model as store_model
-from .embeddings import ensure_embedding
-from .hybrid_search import hybrid_search
 
 logger = logging.getLogger(__name__)
 settings = Settings()
@@ -57,62 +56,122 @@ async def get_store_agents(
         f"Getting store agents. featured={featured}, creators={creators}, sorted_by={sorted_by}, search={search_query}, category={category}, page={page}"
     )
 
-    search_used_hybrid = False
-    store_agents: list[store_model.StoreAgent] = []
-    total = 0
-    total_pages = 0
-
     try:
-        # If search_query is provided, try hybrid search (embeddings + tsvector)
+        # If search_query is provided, use full-text search
         if search_query:
-            try:
-                # Use hybrid search combining semantic and lexical signals
-                agents, total = await hybrid_search(
-                    query=search_query,
-                    featured=featured,
-                    creators=creators,
-                    category=category,
-                    sorted_by="relevance",  # Use hybrid scoring for relevance
-                    page=page,
-                    page_size=page_size,
-                )
-                search_used_hybrid = True
+            offset = (page - 1) * page_size
 
-                # Convert hybrid search results (dict format)
-                total_pages = (total + page_size - 1) // page_size
-                store_agents: list[store_model.StoreAgent] = []
-                for agent in agents:
-                    try:
-                        store_agent = store_model.StoreAgent(
-                            slug=agent["slug"],
-                            agent_name=agent["agent_name"],
-                            agent_image=(
-                                agent["agent_image"][0] if agent["agent_image"] else ""
-                            ),
-                            creator=agent["creator_username"] or "Needs Profile",
-                            creator_avatar=agent["creator_avatar"] or "",
-                            sub_heading=agent["sub_heading"],
-                            description=agent["description"],
-                            runs=agent["runs"],
-                            rating=agent["rating"],
-                        )
-                        store_agents.append(store_agent)
-                    except Exception as e:
-                        logger.error(
-                            f"Error parsing Store agent from hybrid search results: {e}"
-                        )
-                        continue
+            # Whitelist allowed order_by columns
+            ALLOWED_ORDER_BY = {
+                "rating": "rating DESC, rank DESC",
+                "runs": "runs DESC, rank DESC",
+                "name": "agent_name ASC, rank ASC",
+                "updated_at": "updated_at DESC, rank DESC",
+            }
 
-            except Exception as hybrid_error:
-                # If hybrid search fails (e.g., missing embeddings table),
-                # fallback to basic search logic below
-                logger.warning(
-                    f"Hybrid search failed, falling back to basic search: {hybrid_error}"
-                )
-                search_used_hybrid = False
+            # Validate and get order clause
+            if sorted_by and sorted_by in ALLOWED_ORDER_BY:
+                order_by_clause = ALLOWED_ORDER_BY[sorted_by]
+            else:
+                order_by_clause = "updated_at DESC, rank DESC"
 
-        if not search_used_hybrid:
-            # Fallback path - use basic search or no search
+            # Build WHERE conditions and parameters list
+            where_parts: list[str] = []
+            params: list[typing.Any] = [search_query]  # $1 - search term
+            param_index = 2  # Start at $2 for next parameter
+
+            # Always filter for available agents
+            where_parts.append("is_available = true")
+
+            if featured:
+                where_parts.append("featured = true")
+
+            if creators and creators:
+                # Use ANY with array parameter
+                where_parts.append(f"creator_username = ANY(${param_index})")
+                params.append(creators)
+                param_index += 1
+
+            if category and category:
+                where_parts.append(f"${param_index} = ANY(categories)")
+                params.append(category)
+                param_index += 1
+
+            sql_where_clause: str = " AND ".join(where_parts) if where_parts else "1=1"
+
+            # Add pagination params
+            params.extend([page_size, offset])
+            limit_param = f"${param_index}"
+            offset_param = f"${param_index + 1}"
+
+            # Execute full-text search query with parameterized values
+            sql_query = f"""
+                SELECT
+                    slug,
+                    agent_name,
+                    agent_image,
+                    creator_username,
+                    creator_avatar,
+                    sub_heading,
+                    description,
+                    runs,
+                    rating,
+                    categories,
+                    featured,
+                    is_available,
+                    updated_at,
+                    ts_rank_cd(search, query) AS rank
+                FROM {{schema_prefix}}"StoreAgent",
+                    plainto_tsquery('english', $1) AS query
+                WHERE {sql_where_clause}
+                    AND search @@ query
+                ORDER BY {order_by_clause}
+                LIMIT {limit_param} OFFSET {offset_param}
+            """
+
+            # Count query for pagination - only uses search term parameter
+            count_query = f"""
+                SELECT COUNT(*) as count
+                FROM {{schema_prefix}}"StoreAgent",
+                    plainto_tsquery('english', $1) AS query
+                WHERE {sql_where_clause}
+                    AND search @@ query
+            """
+
+            # Execute both queries with parameters
+            agents = await query_raw_with_schema(sql_query, *params)
+
+            # For count, use params without pagination (last 2 params)
+            count_params = params[:-2]
+            count_result = await query_raw_with_schema(count_query, *count_params)
+
+            total = count_result[0]["count"] if count_result else 0
+            total_pages = (total + page_size - 1) // page_size
+
+            # Convert raw results to StoreAgent models
+            store_agents: list[store_model.StoreAgent] = []
+            for agent in agents:
+                try:
+                    store_agent = store_model.StoreAgent(
+                        slug=agent["slug"],
+                        agent_name=agent["agent_name"],
+                        agent_image=(
+                            agent["agent_image"][0] if agent["agent_image"] else ""
+                        ),
+                        creator=agent["creator_username"] or "Needs Profile",
+                        creator_avatar=agent["creator_avatar"] or "",
+                        sub_heading=agent["sub_heading"],
+                        description=agent["description"],
+                        runs=agent["runs"],
+                        rating=agent["rating"],
+                    )
+                    store_agents.append(store_agent)
+                except Exception as e:
+                    logger.error(f"Error parsing Store agent from search results: {e}")
+                    continue
+
+        else:
+            # Non-search query path (original logic)
             where_clause: prisma.types.StoreAgentWhereInput = {"is_available": True}
             if featured:
                 where_clause["featured"] = featured
@@ -121,14 +180,6 @@ async def get_store_agents(
             if category:
                 where_clause["categories"] = {"has": category}
 
-            # Add basic text search if search_query provided but hybrid failed
-            if search_query:
-                where_clause["OR"] = [
-                    {"agent_name": {"contains": search_query, "mode": "insensitive"}},
-                    {"sub_heading": {"contains": search_query, "mode": "insensitive"}},
-                    {"description": {"contains": search_query, "mode": "insensitive"}},
-                ]
-
             order_by = []
             if sorted_by == "rating":
                 order_by.append({"rating": "desc"})
@@ -1549,22 +1600,6 @@ async def review_store_submission(
                     },
                 )
 
-            # Generate embedding for approved listing (non-blocking)
-            try:
-                await ensure_embedding(
-                    version_id=store_listing_version_id,
-                    name=store_listing_version.name,
-                    description=store_listing_version.description,
-                    sub_heading=store_listing_version.subHeading,
-                    categories=store_listing_version.categories or [],
-                )
-            except Exception as e:
-                # Don't fail approval if embedding generation fails
-                logger.warning(
-                    f"Failed to generate embedding for approved listing "
-                    f"{store_listing_version_id}: {e}"
-                )
-
         # If rejecting an approved agent, update the StoreListing accordingly
         if is_rejecting_approved:
             # Check if there are other approved versions

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

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

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

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

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

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

autogpt_platform/backend/backend/api/features/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)
+    await library_db.create_library_agent(
+        graph, user_id=user_id, is_ai_generated=create_graph.is_ai_generated
+    )
     activated_graph = await on_graph_activate(graph, user_id=user_id)
 
     if create_graph.source == "builder":
@@ -889,21 +889,17 @@ 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
     )
-    # 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(
+    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,
             user_id=user_id,
-            agent_id=library.id,
-            settings=library.settings.model_copy(
-                update={"human_in_the_loop_safe_mode": True}
-            ),
+            settings=updated_settings,
         )
     return library
 
@@ -920,21 +916,18 @@ 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")
 
-    # Update the library agent settings
-    updated_agent = await library_db.update_library_agent_settings(
+    updated_agent = await library_db.update_library_agent(
+        library_agent_id=library_agent.id,
         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

@@ -43,6 +43,7 @@ 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

@@ -0,0 +1,184 @@
+"""
+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

@@ -3,6 +3,7 @@ from typing import Any
 
 from prisma.enums import ReviewStatus
 
+from backend.blocks.helpers.review import HITLReviewHelper
 from backend.data.block import (
     Block,
     BlockCategory,
@@ -11,11 +12,9 @@ from backend.data.block import (
     BlockSchemaOutput,
     BlockType,
 )
-from backend.data.execution import ExecutionContext, ExecutionStatus
+from backend.data.execution import ExecutionContext
 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__)
 
@@ -72,32 +71,26 @@ class HumanInTheLoopBlock(Block):
                 ("approved_data", {"name": "John Doe", "age": 30}),
             ],
             test_mock={
-                "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,
+                "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",
+                        ),
+                    },
+                )(),
             },
         )
 
-    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 handle_review_decision(self, **kwargs):
+        return await HITLReviewHelper.handle_review_decision(**kwargs)
 
     async def run(
         self,
@@ -109,7 +102,7 @@ class HumanInTheLoopBlock(Block):
         graph_id: str,
         graph_version: int,
         execution_context: ExecutionContext,
-        **kwargs,
+        **_kwargs,
     ) -> BlockOutput:
         if not execution_context.safe_mode:
             logger.info(
@@ -119,48 +112,28 @@ class HumanInTheLoopBlock(Block):
             yield "review_message", "Auto-approved (safe mode disabled)"
             return
 
-        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
+        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,
+        )
 
-        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
+        if decision is None:
+            return
 
-        if not result.processed:
-            await self.update_review_processed_status(
-                node_exec_id=node_exec_id, processed=True
-            )
+        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 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
+        if decision.message:
+            yield "review_message", decision.message

autogpt_platform/backend/backend/blocks/smart_decision_maker.py

@@ -391,8 +391,12 @@ 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(block.name),
+            "name": SmartDecisionMakerBlock.cleanup(tool_name),
             "description": block.description,
         }
         sink_block_input_schema = block.input_schema
@@ -489,14 +493,24 @@ 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(sink_graph_meta.name),
+            "name": SmartDecisionMakerBlock.cleanup(tool_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, {}
@@ -506,7 +520,7 @@ class SmartDecisionMakerBlock(Block):
                 if "description" in sink_block_properties
                 else f"The {link.sink_name} of the tool"
             )
-            properties[link.sink_name] = {
+            properties[clean_field_name] = {
                 "type": "string",
                 "description": description,
                 "default": json.dumps(sink_block_properties.get("default", None)),
@@ -519,7 +533,7 @@ class SmartDecisionMakerBlock(Block):
             "strict": True,
         }
 
-        # Store node info for later use in output processing
+        tool_function["_field_mapping"] = field_mapping
         tool_function["_sink_node_id"] = sink_node.id
 
         return {"type": "function", "function": tool_function}
@@ -1147,8 +1161,9 @@ class SmartDecisionMakerBlock(Block):
                 original_field_name = field_mapping.get(clean_arg_name, clean_arg_name)
                 arg_value = tool_args.get(clean_arg_name)
 
-                sanitized_arg_name = self.cleanup(original_field_name)
-                emit_key = f"tools_^_{sink_node_id}_~_{sanitized_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}"
 
                 logger.debug(
                     "[SmartDecisionMakerBlock|geid:%s|neid:%s] emit %s",

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

@@ -1057,3 +1057,153 @@ 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

@@ -15,6 +15,7 @@ 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 = [
@@ -77,6 +78,7 @@ 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

@@ -44,6 +44,7 @@ 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 = [
@@ -106,6 +107,7 @@ 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 = [
@@ -159,6 +161,7 @@ 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 = [
@@ -208,11 +211,13 @@ 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(
@@ -423,6 +428,7 @@ 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
+from .blog import WordPressCreatePostBlock, WordPressGetAllPostsBlock
 
-__all__ = ["WordPressCreatePostBlock"]
+__all__ = ["WordPressCreatePostBlock", "WordPressGetAllPostsBlock"]

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().post(
+    response = await Requests(raise_for_status=False).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().post(
+    response = await Requests(raise_for_status=False).post(
         f"{WORDPRESS_BASE_URL}oauth2/token",
         headers=headers,
         data=data,
@@ -252,7 +252,7 @@ async def validate_token(
         "token": token,
     }
 
-    response = await Requests().get(
+    response = await Requests(raise_for_status=False).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(), method.lower())
+    request_method = getattr(Requests(raise_for_status=False), method.lower())
     response = await request_method(
         url,
         headers=headers,
@@ -476,6 +476,7 @@ 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 = {
@@ -483,7 +484,7 @@ async def create_post(
         "Content-Type": "application/x-www-form-urlencoded",
     }
 
-    response = await Requests().post(
+    response = await Requests(raise_for_status=False).post(
         f"{WORDPRESS_BASE_URL.rstrip('/')}{endpoint}",
         headers=headers,
         data=data,
@@ -499,3 +500,132 @@ 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

@@ -9,7 +9,15 @@ from backend.sdk import (
     SchemaField,
 )
 
-from ._api import CreatePostRequest, PostResponse, PostStatus, create_post
+from ._api import (
+    CreatePostRequest,
+    Post,
+    PostResponse,
+    PostsResponse,
+    PostStatus,
+    create_post,
+    get_posts,
+)
 from ._config import wordpress
 
 
@@ -49,8 +57,15 @@ 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")
@@ -78,7 +93,9 @@ class WordPressCreatePostBlock(Block):
             tags=input_data.tags,
             featured_image=input_data.featured_image,
             media_urls=input_data.media_urls,
-            status=PostStatus.PUBLISH,
+            status=(
+                PostStatus.DRAFT if input_data.publish_as_draft else PostStatus.PUBLISH
+            ),
         )
 
         post_response: PostResponse = await create_post(
@@ -87,7 +104,69 @@ 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

@@ -50,6 +50,8 @@ from .model import (
 logger = logging.getLogger(__name__)
 
 if TYPE_CHECKING:
+    from backend.data.execution import ExecutionContext
+
     from .graph import Link
 
 app_config = Config()
@@ -472,6 +474,7 @@ 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):
@@ -614,7 +617,80 @@ 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}",
@@ -622,6 +698,7 @@ 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

@@ -82,6 +82,7 @@ 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

@@ -63,6 +63,14 @@ 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):
@@ -244,7 +252,10 @@ class BaseGraph(BaseDbModel):
         return any(
             node.block_id
             for node in self.nodes
-            if node.block.block_type == BlockType.HUMAN_IN_THE_LOOP
+            if (
+                node.block.block_type == BlockType.HUMAN_IN_THE_LOOP
+                or node.block.requires_human_review
+            )
         )
 
     @property

autogpt_platform/backend/backend/executor/utils.py

@@ -877,6 +877,7 @@ 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

@@ -8,6 +8,7 @@ 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:
@@ -20,6 +21,7 @@ _ORIGINAL_HANDLERS = [
     GitHubOAuthHandler,
     GoogleOAuthHandler,
     NotionOAuthHandler,
+    RedditOAuthHandler,
     TwitterOAuthHandler,
     TodoistOAuthHandler,
 ]

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

@@ -0,0 +1,208 @@
+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="AutoGPT:1.0 (by /u/autogpt)",
+        default="web:AutoGPT:v0.6.0 (by /u/autogpt)",
         description="The user agent for the Reddit API",
     )
 

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

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

autogpt_platform/backend/schema.prisma

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

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

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

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

@@ -89,6 +89,18 @@ 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

@@ -36,6 +36,7 @@ type Props = {
   readOnly?: boolean;
   isOptional?: boolean;
   showTitle?: boolean;
+  variant?: "default" | "node";
 };
 
 export function CredentialsInput({
@@ -48,6 +49,7 @@ export function CredentialsInput({
   readOnly = false,
   isOptional = false,
   showTitle = true,
+  variant = "default",
 }: Props) {
   const hookData = useCredentialsInput({
     schema,
@@ -123,6 +125,7 @@ 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

@@ -30,6 +30,8 @@ type CredentialRowProps = {
   readOnly?: boolean;
   showCaret?: boolean;
   asSelectTrigger?: boolean;
+  /** When "node", applies compact styling for node context */
+  variant?: "default" | "node";
 };
 
 export function CredentialRow({
@@ -41,14 +43,22 @@ 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 ? "border-0 bg-transparent" : readOnly ? "w-fit" : "",
+        asSelectTrigger && isNodeVariant
+          ? "min-w-0 flex-1 overflow-hidden border-0 bg-transparent"
+          : asSelectTrigger
+            ? "border-0 bg-transparent"
+            : readOnly
+              ? "w-fit"
+              : "",
       )}
       onClick={readOnly || showCaret || asSelectTrigger ? undefined : onSelect}
       style={
@@ -61,19 +71,31 @@ 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="flex min-w-0 flex-1 flex-nowrap items-center gap-4">
+      <div
+        className={cn(
+          "flex min-w-0 flex-1 flex-nowrap items-center gap-4",
+          isNodeVariant && "overflow-hidden",
+        )}
+      >
         <Text
           variant="body"
-          className="line-clamp-1 flex-[0_0_50%] text-ellipsis tracking-tight"
+          className={cn(
+            "tracking-tight",
+            isNodeVariant
+              ? "truncate"
+              : "line-clamp-1 flex-[0_0_50%] text-ellipsis",
+          )}
         >
           {getCredentialDisplayName(credential, displayName)}
         </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>
+        {!(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>
+        )}
       </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

@@ -7,6 +7,7 @@ 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";
@@ -26,6 +27,8 @@ interface Props {
   onClearCredential?: () => void;
   readOnly?: boolean;
   allowNone?: boolean;
+  /** When "node", applies compact styling for node context */
+  variant?: "default" | "node";
 }
 
 export function CredentialsSelect({
@@ -37,6 +40,7 @@ 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(() => {
@@ -59,7 +63,12 @@ export function CredentialsSelect({
         value={selectedCredentials?.id || (allowNone ? "__none__" : "")}
         onValueChange={handleValueChange}
       >
-        <SelectTrigger className="h-auto min-h-12 w-full rounded-medium border-zinc-200 p-0 pr-4 shadow-none">
+        <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",
+          )}
+        >
           {selectedCredentials ? (
             <SelectValue key={selectedCredentials.id} asChild>
               <CredentialRow
@@ -75,6 +84,7 @@ 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

@@ -92,29 +92,33 @@ 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 hasPendingSubmissionForCurrentVersion =
-      isUserCreator &&
-      submissionsResponse?.submissions?.some(
-        (submission: StoreSubmission) =>
-          submission.agent_id === agent.graph_id &&
-          submission.agent_version === agent.graph_version &&
-          submission.status === "PENDING",
-      );
+    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;
 
     if (!storeAgent) {
       return {
         hasUpdate: false,
         latestVersion: undefined,
         isUserCreator,
-        hasPublishUpdate:
-          isUserCreator && !hasPendingSubmissionForCurrentVersion,
+        hasPublishUpdate: agentSubmissions.length > 0 && hasUnpublishedChanges,
       };
     }
 
-    // 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(
@@ -124,18 +128,11 @@ 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 &&
-      !hasPendingSubmissionForCurrentVersion &&
-      (latestMarketplaceVersion === undefined || // Not published yet
-        agent.graph_version > latestMarketplaceVersion); // Or local version is newer
+      agent.graph_version >
+        Math.max(latestMarketplaceVersion || 0, highestSubmittedVersion);
 
-    // 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="text"
+                type="textarea"
                 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(200, "Changes summary must be less than 200 characters"),
+      .max(500, "Changes summary must be less than 500 characters"),
     agentOutputDemo: z
       .string()
       .refine(validateYouTubeUrl, "Please enter a valid YouTube URL"),

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

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

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

@@ -0,0 +1,116 @@
+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

@@ -0,0 +1,133 @@
+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

@@ -0,0 +1,7 @@
+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

@@ -0,0 +1,81 @@
+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

@@ -30,6 +30,8 @@ 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

@@ -5,19 +5,14 @@ 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,
@@ -26,6 +21,15 @@ 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,
@@ -40,12 +44,21 @@ 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={updatedUiSchema}
+        uiSchema={optionUiSchema}
       />
     )) ||
     null;

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

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

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

@@ -88,6 +88,8 @@ 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

@@ -0,0 +1,124 @@
+"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

@@ -0,0 +1,67 @@
+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

@@ -0,0 +1,107 @@
+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

@@ -0,0 +1,57 @@
+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

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

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

@@ -0,0 +1,65 @@
+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

@@ -0,0 +1,52 @@
+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

@@ -1,6 +1,10 @@
 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;
@@ -8,6 +12,9 @@ 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",
@@ -30,6 +37,28 @@ 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

@@ -1,19 +1,46 @@
 import { RJSFSchema, UiSchema } from "@rjsf/utils";
-import { findCustomFieldId } from "../custom/custom-registry";
+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"),
+  );
+}
 
 /**
  * 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) {
@@ -21,8 +48,33 @@ 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 &&
@@ -31,6 +83,7 @@ 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),
@@ -38,9 +91,11 @@ 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] = {
@@ -49,10 +104,28 @@ 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] = {
@@ -63,6 +136,61 @@ 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

@@ -1,7 +1,11 @@
 import { getUiOptions, RJSFSchema, UiSchema } from "@rjsf/utils";
 
 export function isAnyOfSchema(schema: RJSFSchema | undefined): boolean {
-  return Array.isArray(schema?.anyOf) && schema!.anyOf.length > 0;
+  return (
+    Array.isArray(schema?.anyOf) &&
+    schema!.anyOf.length > 0 &&
+    schema?.enum === undefined
+  );
 }
 
 export const isAnyOfChild = (
@@ -33,3 +37,21 @@ 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

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

docs/_javascript/tablesort.js

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

docs/content/contribute/index.md

@@ -1,48 +1,35 @@
 # Contributing to the Docs
 
-We welcome contributions to our documentation! If you would like to contribute, please follow the steps below.
+We welcome contributions to our documentation! Our docs are hosted on GitBook and synced with GitHub.
 
-## Setting up the Docs
+## How It Works
 
-1. Clone the repository:
+- 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:
 
     ```shell
-    git clone github.com/Significant-Gravitas/AutoGPT.git
+    git clone https://github.com/Significant-Gravitas/AutoGPT.git
+    cd AutoGPT
+    git checkout gitbook
     ```
 
-1. Install the dependencies:
+2. Make your changes to markdown files in `docs/`
 
-    ```shell
-    python -m pip install -r docs/requirements.txt
-    ```
+3. Preview changes:
+   - Push to a branch and create a PR - GitBook will generate a preview
+   - Or use any markdown preview tool locally
 
-    or
+## Adding a New Page
 
-    ```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.
+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
 
 ## Submitting a Pull Request
 
-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.
+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.

docs/mkdocs.yml

@@ -1,194 +0,0 @@
-site_name: AutoGPT Documentation
-site_url: https://docs.agpt.co/
-repo_url: https://github.com/Significant-Gravitas/AutoGPT
-repo_name: AutoGPT
-edit_uri: edit/master/docs/content
-docs_dir: content
-nav:
-  - Home: index.md
-
-  - The AutoGPT Platform 🆕:
-    - Getting Started:
-        - Setup AutoGPT (Local-Host): platform/getting-started.md
-        - Edit an Agent: platform/edit-agent.md
-        - Delete an Agent: platform/delete-agent.md
-        - Download & Import and Agent: platform/download-agent-from-marketplace-local.md
-        - Create a Basic Agent: platform/create-basic-agent.md
-        - Submit an Agent to the Marketplace: platform/submit-agent-to-marketplace.md
-    - Advanced Setup: platform/advanced_setup.md
-    - Agent Blocks: platform/agent-blocks.md
-    - Build your own Blocks: platform/new_blocks.md
-    - Block SDK Guide: platform/block-sdk-guide.md
-    - Using Ollama: platform/ollama.md
-    - Using AI/ML API: platform/aimlapi.md
-    - Using D-ID: platform/d_id.md
-    - Blocks: platform/blocks/blocks.md
-    - API:
-      - Introduction: platform/integrating/api-guide.md
-      - OAuth & SSO: platform/integrating/oauth-guide.md
-    - Contributing:
-        - Tests: platform/contributing/tests.md
-        - OAuth Flows: platform/contributing/oauth-integration-flow.md
-  - AutoGPT Classic:
-      - Introduction: classic/index.md
-      - Setup:
-          - Setting up AutoGPT: classic/setup/index.md
-          - Set up with Docker: classic/setup/docker.md
-          - For Developers: classic/setup/for-developers.md
-          - Configuration:
-              - Options: classic/configuration/options.md
-              - Search: classic/configuration/search.md
-              - Voice: classic/configuration/voice.md
-      - Usage: classic/usage.md
-      - Help us improve AutoGPT:
-          - Share your debug logs with us: classic/share-your-logs.md
-          - Contribution guide: contributing.md
-          - Running tests: classic/testing.md
-          - Code of Conduct: code-of-conduct.md
-      - Benchmark:
-          - Readme: https://github.com/Significant-Gravitas/AutoGPT/blob/master/classic/benchmark/README.md
-      - Forge:
-         - Introduction: forge/get-started.md
-         - Components:
-             - Introduction: forge/components/introduction.md
-             - Agents: forge/components/agents.md
-             - Components: forge/components/components.md
-             - Protocols: forge/components/protocols.md
-             - Commands: forge/components/commands.md
-             - Built in Components: forge/components/built-in-components.md
-             - Creating Components: forge/components/creating-components.md
-      - Frontend:
-         - Readme: https://github.com/Significant-Gravitas/AutoGPT/blob/master/classic/frontend/README.md
-
-  - Contribute: 
-        - Introduction: contribute/index.md
-        - Testing: ../../autogpt_platform/backend/TESTING.md
-    
-  # - Challenges:
-  #     - Introduction: challenges/introduction.md
-  #     - List of Challenges:
-  #         - Memory:
-  #             - Introduction: challenges/memory/introduction.md
-  #             - Memory Challenge A: challenges/memory/challenge_a.md
-  #             - Memory Challenge B: challenges/memory/challenge_b.md
-  #             - Memory Challenge C: challenges/memory/challenge_c.md
-  #             - Memory Challenge D: challenges/memory/challenge_d.md
-  #         - Information retrieval:
-  #             - Introduction: challenges/information_retrieval/introduction.md
-  #             - Information Retrieval Challenge A: challenges/information_retrieval/challenge_a.md
-  #             - Information Retrieval Challenge B: challenges/information_retrieval/challenge_b.md
-  # - Submit a Challenge: challenges/submit.md
-  # - Beat a Challenge: challenges/beat.md
-
-  - License: https://github.com/Significant-Gravitas/AutoGPT/blob/master/LICENSE
-
-theme:
-  name: material
-  custom_dir: overrides
-  language: en
-  icon:
-    repo: fontawesome/brands/github
-    logo: material/book-open-variant
-    edit: material/pencil
-    view: material/eye
-  favicon: assets/favicon.png
-  features:
-    - navigation.sections
-    - navigation.footer
-    - navigation.top
-    - navigation.tracking
-    - navigation.tabs
-    # - navigation.path
-    - toc.follow
-    - toc.integrate
-    - content.action.edit
-    - content.action.view
-    - content.code.copy
-    - content.code.annotate
-    - content.tabs.link
-  palette:
-    # Palette toggle for light mode
-    - media: "(prefers-color-scheme: light)"
-      scheme: default
-      toggle:
-        icon: material/weather-night
-        name: Switch to dark mode
-
-    # Palette toggle for dark mode
-    - media: "(prefers-color-scheme: dark)"
-      scheme: slate
-      toggle:
-        icon: material/weather-sunny
-        name: Switch to light mode
-
-markdown_extensions:
-  # Python Markdown
-  - abbr
-  - admonition
-  - attr_list
-  - def_list
-  - footnotes
-  - md_in_html
-  - toc:
-      permalink: true
-  - tables
-
-  # Python Markdown Extensions
-  - pymdownx.arithmatex:
-      generic: true
-  - pymdownx.betterem:
-      smart_enable: all
-  - pymdownx.critic
-  - pymdownx.caret
-  - pymdownx.details
-  - pymdownx.emoji:
-      emoji_index: !!python/name:material.extensions.emoji.twemoji
-      emoji_generator: !!python/name:material.extensions.emoji.to_svg
-  - pymdownx.highlight
-  - pymdownx.inlinehilite
-  - pymdownx.keys
-  - pymdownx.mark
-  - pymdownx.smartsymbols
-  - pymdownx.snippets:
-      base_path: ['.','../']
-      check_paths: true
-      dedent_subsections: true
-  - pymdownx.superfences:
-      custom_fences:
-        - name: mermaid
-          class: mermaid
-          format: !!python/name:pymdownx.superfences.fence_code_format
-  - pymdownx.tabbed:
-      alternate_style: true
-  - pymdownx.tasklist:
-      custom_checkbox: true
-  - pymdownx.tilde
-
-plugins:
-  - table-reader
-  - search
-  - git-revision-date-localized:
-      enable_creation_date: true
-
-extra:
-  social:
-    - icon: fontawesome/brands/github
-      link: https://github.com/Significant-Gravitas/AutoGPT
-    - icon: fontawesome/brands/x-twitter
-      link: https://x.com/Auto_GPT
-    - icon: fontawesome/brands/instagram
-      link: https://www.instagram.com/autogpt/
-    - icon: fontawesome/brands/discord
-      link: https://discord.gg/autogpt
-      
-extra:
-  analytics:
-    provider: google
-    property: G-XKPNKB9XG6
-
-extra_javascript:
-  - https://unpkg.com/tablesort@5.3.0/dist/tablesort.min.js
-  - _javascript/tablesort.js
-  - _javascript/mathjax.js
-  - https://cdnjs.cloudflare.com/polyfill/v3/polyfill.min.js?features=es6
-  - https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js

docs/netlify.toml

@@ -1,5 +0,0 @@
-# Netlify config for AutoGPT docs
-
-[build]
-  publish = "public/"
-  command = "mkdocs build -d public"

docs/overrides/main.html

@@ -1,61 +0,0 @@
-{% extends "base.html" %}
-
-
-{% block extrahead %}
-<style>
-  svg.ms-w-12 {
-    width: 2rem !important;
-  }
-  svg.ms-h-12 {
-    height: 2rem !important;
-  }
-  /* Temporary solution for the font sizes  */
-  #headlessui-dialog-panel-2 p{
-    font-size: 16px;
-  }
-  #headlessui-dialog-panel-2 textarea{
-    font-size: 16px;
-  }
-  #headlessui-dialog-panel-2 span{
-    font-size: 14px;
-
-  }
-  #headlessui-dialog-panel-2 div{
-    font-size: 16px;
-  }
-  #headlessui-dialog-panel-2 a{
-    font-size: 14px;
-    line-height: 1rem;
-  }
-  #headlessui-dialog-panel-2 button{
-    border: 1px solid #0000001d;
-    line-height: 1rem;
-
-}
-  /* Temporary solution for the font size  */
-  #headlessui-dialog-panel-2 > div > div > div > div.ms-w-full.ms-rounded-xl.ms-flex.ms-flex-col.ms-relative > div > div > div > div > div.ms-flex.ms-flex-row.ms-items-center.ms-justify-between.ms-gap-1 > p{
-    font-size: 14px;
-    line-height: 1rem;
-  }
-
-</style>
-
-<div id="my-component-root" class="mendable-search"></div>
-
-<script src="https://unpkg.com/@mendable/search@0.0.155/dist/umd/mendable-bundle.min.js"></script>
-{% raw %}
-<script>
-    Mendable.initialize({
-      anon_key: 'a0bd44db-eb3b-412f-8924-b31c58244a64',
-      type: 'floatingButton',
-      style : { accentColor: '#3F51B5', darkMode: false },
-      floatingButtonStyle:{
-        backgroundColor: '#3F51B5',
-        color: '#fff',
-      },
-      icon: "https://mendable-storage.s3.amazonaws.com/autoGPT.gif",
-      botIcon: "https://mendable-storage.s3.amazonaws.com/autoGPTbot.gif",
-    })
-  </script>
-{% endraw %}
-{% endblock %}

docs/platform/SUMMARY.md

@@ -0,0 +1,34 @@
+# Table of contents
+
+* [What is the AutoGPT Platform?](what-is-autogpt-platform.md)
+
+## Getting Started
+
+* [Setting Up Auto-GPT (Local Host)](getting-started.md)
+* [AutoGPT Platform Installer](installer.md)
+* [Edit an Agent](edit-agent.md)
+* [Delete an Agent](delete-agent.md)
+* [Download & Import an Agent](download-agent-from-marketplace-local.md)
+* [Create a Basic Agent](create-basic-agent.md)
+* [Submit an Agent to the Marketplace](submit-agent-to-marketplace.md)
+
+## Advanced Setup
+
+* [Advanced Setup](advanced_setup.md)
+
+## Building Blocks
+
+* [Agent Blocks Overview](agent-blocks.md)
+* [Build your own Blocks](new_blocks.md)
+* [Block SDK Guide](block-sdk-guide.md)
+
+## Using AI Services
+
+* [Using Ollama](ollama.md)
+* [Using AI/ML API](aimlapi.md)
+* [Using D-ID](d_id.md)
+
+## API & Integrations
+
+* [API Introduction](integrating/api-guide.md)
+* [OAuth & SSO](integrating/oauth-guide.md)

docs/platform/block-sdk-guide.md

@@ -227,7 +227,7 @@ backend/blocks/my_provider/
 
 ## Best Practices
 
-1. **Error Handling**: Error output pin is already defined on BlockSchemaOutput
+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.
 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