Update builder search

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

@@ -1,16 +1,17 @@
 import logging
 from dataclasses import dataclass
-from datetime import datetime, timedelta, timezone
 from difflib import SequenceMatcher
 from typing import Sequence
 
 import prisma
+from prisma.enums import ContentType
 
 import backend.api.features.library.db as library_db
 import backend.api.features.library.model as library_model
 import backend.api.features.store.db as store_db
 import backend.api.features.store.model as store_model
 import backend.data.block
+from backend.api.features.store.hybrid_search import unified_hybrid_search
 from backend.blocks import load_all_blocks
 from backend.blocks.llm import LlmModel
 from backend.data.block import AnyBlockSchema, BlockCategory, BlockInfo, BlockSchema
@@ -37,6 +38,14 @@ MAX_LIBRARY_AGENT_RESULTS = 100
 MAX_MARKETPLACE_AGENT_RESULTS = 100
 MIN_SCORE_FOR_FILTERED_RESULTS = 10.0
 
+# Boost blocks over marketplace agents in search results
+BLOCK_SCORE_BOOST = 50.0
+
+# Block IDs to exclude from search results
+EXCLUDED_BLOCK_IDS = frozenset({
+    "e189baac-8c20-45a1-94a7-55177ea42565",  # AgentExecutorBlock
+})
+
 SearchResultItem = BlockInfo | library_model.LibraryAgent | store_model.StoreAgent
 
 
@@ -250,14 +259,25 @@ async def _build_cached_search_results(
         "my_agents": 0,
     }
 
-    block_results, block_total, integration_total = _collect_block_results(
-        normalized_query=normalized_query,
-        include_blocks=include_blocks,
-        include_integrations=include_integrations,
-    )
-    scored_items.extend(block_results)
-    total_items["blocks"] = block_total
-    total_items["integrations"] = integration_total
+    # Use hybrid search when query is present, otherwise list all blocks
+    if (include_blocks or include_integrations) and normalized_query:
+        block_results, block_total, integration_total = await _hybrid_search_blocks(
+            query=search_query,
+            include_blocks=include_blocks,
+            include_integrations=include_integrations,
+        )
+        scored_items.extend(block_results)
+        total_items["blocks"] = block_total
+        total_items["integrations"] = integration_total
+    elif include_blocks or include_integrations:
+        # No query - list all blocks using in-memory approach
+        block_results, block_total, integration_total = _collect_block_results(
+            include_blocks=include_blocks,
+            include_integrations=include_integrations,
+        )
+        scored_items.extend(block_results)
+        total_items["blocks"] = block_total
+        total_items["integrations"] = integration_total
 
     if include_library_agents:
         library_response = await library_db.list_library_agents(
@@ -302,10 +322,14 @@ async def _build_cached_search_results(
 
 def _collect_block_results(
     *,
-    normalized_query: str,
     include_blocks: bool,
     include_integrations: bool,
 ) -> tuple[list[_ScoredItem], int, int]:
+    """
+    Collect all blocks for listing (no search query).
+
+    All blocks get BLOCK_SCORE_BOOST to prioritize them over marketplace agents.
+    """
     results: list[_ScoredItem] = []
     block_count = 0
     integration_count = 0
@@ -318,6 +342,10 @@ def _collect_block_results(
         if block.disabled:
             continue
 
+        # Skip excluded blocks
+        if block.id in EXCLUDED_BLOCK_IDS:
+            continue
+
         block_info = block.get_info()
         credentials = list(block.input_schema.get_credentials_fields().values())
         is_integration = len(credentials) > 0
@@ -327,10 +355,6 @@ def _collect_block_results(
         if not is_integration and not include_blocks:
             continue
 
-        score = _score_block(block, block_info, normalized_query)
-        if not _should_include_item(score, normalized_query):
-            continue
-
         filter_type: FilterType = "integrations" if is_integration else "blocks"
         if is_integration:
             integration_count += 1
@@ -341,8 +365,116 @@ def _collect_block_results(
             _ScoredItem(
                 item=block_info,
                 filter_type=filter_type,
-                score=score,
-                sort_key=_get_item_name(block_info),
+                score=BLOCK_SCORE_BOOST,
+                sort_key=block_info.name.lower(),
+            )
+        )
+
+    return results, block_count, integration_count
+
+
+async def _hybrid_search_blocks(
+    *,
+    query: str,
+    include_blocks: bool,
+    include_integrations: bool,
+) -> tuple[list[_ScoredItem], int, int]:
+    """
+    Search blocks using hybrid search with builder-specific filtering.
+
+    Uses unified_hybrid_search for semantic + lexical search, then applies
+    post-filtering for block/integration types and LLM model bonus scoring.
+
+    Args:
+        query: The search query string
+        include_blocks: Whether to include regular blocks
+        include_integrations: Whether to include integration blocks
+
+    Returns:
+        Tuple of (scored_items, block_count, integration_count)
+    """
+    results: list[_ScoredItem] = []
+    block_count = 0
+    integration_count = 0
+
+    if not include_blocks and not include_integrations:
+        return results, block_count, integration_count
+
+    normalized_query = query.strip().lower()
+
+    # Fetch more results to account for post-filtering
+    search_results, _ = await unified_hybrid_search(
+        query=query,
+        content_types=[ContentType.BLOCK],
+        page=1,
+        page_size=150,
+        min_score=0.10,
+    )
+
+    # Load all blocks for getting BlockInfo
+    all_blocks = load_all_blocks()
+
+    for result in search_results:
+        block_id = result["content_id"]
+
+        # Skip excluded blocks
+        if block_id in EXCLUDED_BLOCK_IDS:
+            continue
+
+        metadata = result.get("metadata", {})
+        hybrid_score = result.get("relevance", 0.0)
+
+        # Get the actual block class
+        if block_id not in all_blocks:
+            continue
+
+        block_cls = all_blocks[block_id]
+        block: AnyBlockSchema = block_cls()
+
+        if block.disabled:
+            continue
+
+        # Check block/integration filter using metadata
+        is_integration = metadata.get("is_integration", False)
+
+        if is_integration and not include_integrations:
+            continue
+        if not is_integration and not include_blocks:
+            continue
+
+        # Get block info
+        block_info = block.get_info()
+
+        # Calculate final score: scale hybrid score and add builder-specific bonuses
+        # Hybrid scores are 0-1, builder scores were 0-200+
+        # Add BLOCK_SCORE_BOOST to prioritize blocks over marketplace agents
+        final_score = hybrid_score * 100 + BLOCK_SCORE_BOOST
+
+        # Add LLM model match bonus
+        has_llm_field = metadata.get("has_llm_model_field", False)
+        if has_llm_field and _matches_llm_model(block.input_schema, normalized_query):
+            final_score += 20
+
+        # Add exact/prefix match bonus for deterministic tie-breaking
+        name = block_info.name.lower()
+        if name == normalized_query:
+            final_score += 30
+        elif name.startswith(normalized_query):
+            final_score += 15
+
+        # Track counts
+        filter_type: FilterType = "integrations" if is_integration else "blocks"
+        if is_integration:
+            integration_count += 1
+        else:
+            block_count += 1
+
+        results.append(
+            _ScoredItem(
+                item=block_info,
+                filter_type=filter_type,
+                score=final_score,
+                sort_key=name,
             )
         )
 
@@ -502,38 +634,6 @@ def _matches_llm_model(schema_cls: type[BlockSchema], query: str) -> bool:
     return False
 
 
-def _score_block(
-    block: AnyBlockSchema,
-    block_info: BlockInfo,
-    normalized_query: str,
-) -> float:
-    if not normalized_query:
-        return 0.0
-
-    name = block_info.name.lower()
-    description = block_info.description.lower()
-    score = _score_primary_fields(name, description, normalized_query)
-
-    category_text = " ".join(
-        category.get("category", "").lower() for category in block_info.categories
-    )
-    score += _score_additional_field(category_text, normalized_query, 12, 6)
-
-    credentials_info = block.input_schema.get_credentials_fields_info().values()
-    provider_names = [
-        provider.value.lower()
-        for info in credentials_info
-        for provider in info.provider
-    ]
-    provider_text = " ".join(provider_names)
-    score += _score_additional_field(provider_text, normalized_query, 15, 6)
-
-    if _matches_llm_model(block.input_schema, normalized_query):
-        score += 20
-
-    return score
-
-
 def _score_library_agent(
     agent: library_model.LibraryAgent,
     normalized_query: str,
@@ -640,26 +740,15 @@ def _get_all_providers() -> dict[ProviderName, Provider]:
     return providers
 
 
-@cached(ttl_seconds=3600)
+@cached(ttl_seconds=3600, shared_cache=True)
 async def get_suggested_blocks(count: int = 5) -> list[BlockInfo]:
-    suggested_blocks = []
-    # Sum the number of executions for each block type
-    # Prisma cannot group by nested relations, so we do a raw query
-    # Calculate the cutoff timestamp
-    timestamp_threshold = datetime.now(timezone.utc) - timedelta(days=30)
-
+    # Query the materialized view for execution counts per block
+    # The view aggregates executions from the last 14 days and is refreshed hourly
     results = await query_raw_with_schema(
         """
-        SELECT
-            agent_node."agentBlockId" AS block_id,
-            COUNT(execution.id) AS execution_count
-        FROM {schema_prefix}"AgentNodeExecution" execution
-        JOIN {schema_prefix}"AgentNode" agent_node ON execution."agentNodeId" = agent_node.id
-        WHERE execution."endedTime" >= $1::timestamp
-        GROUP BY agent_node."agentBlockId"
-        ORDER BY execution_count DESC;
-        """,
-        timestamp_threshold,
+        SELECT block_id, execution_count
+        FROM {schema_prefix}"mv_suggested_blocks";
+        """
     )
 
     # Get the top blocks based on execution count

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

@@ -27,7 +27,6 @@ class SearchEntry(BaseModel):
 
 # Suggestions
 class SuggestionsResponse(BaseModel):
-    otto_suggestions: list[str]
     recent_searches: list[SearchEntry]
     providers: list[ProviderName]
     top_blocks: list[BlockInfo]

autogpt_platform/backend/backend/api/features/builder/routes.py

@@ -1,5 +1,5 @@
 import logging
-from typing import Annotated, Sequence
+from typing import Annotated, Sequence, cast, get_args
 
 import fastapi
 from autogpt_libs.auth.dependencies import get_user_id, requires_user
@@ -10,6 +10,8 @@ from backend.util.models import Pagination
 from . import db as builder_db
 from . import model as builder_model
 
+VALID_FILTER_VALUES = get_args(builder_model.FilterType)
+
 logger = logging.getLogger(__name__)
 
 router = fastapi.APIRouter(
@@ -49,11 +51,6 @@ async def get_suggestions(
     Get all suggestions for the Blocks Menu.
     """
     return builder_model.SuggestionsResponse(
-        otto_suggestions=[
-            "What blocks do I need to get started?",
-            "Help me create a list",
-            "Help me feed my data to Google Maps",
-        ],
         recent_searches=await builder_db.get_recent_searches(user_id),
         providers=[
             ProviderName.TWITTER,
@@ -151,7 +148,7 @@ async def get_providers(
 async def search(
     user_id: Annotated[str, fastapi.Security(get_user_id)],
     search_query: Annotated[str | None, fastapi.Query()] = None,
-    filter: Annotated[list[builder_model.FilterType] | None, fastapi.Query()] = None,
+    filter: Annotated[str | None, fastapi.Query()] = None,
     search_id: Annotated[str | None, fastapi.Query()] = None,
     by_creator: Annotated[list[str] | None, fastapi.Query()] = None,
     page: Annotated[int, fastapi.Query()] = 1,
@@ -160,9 +157,20 @@ async def search(
     """
     Search for blocks (including integrations), marketplace agents, and user library agents.
     """
-    # If no filters are provided, then we will return all types
-    if not filter:
-        filter = [
+    # Parse and validate filter parameter
+    filters: list[builder_model.FilterType]
+    if filter:
+        filter_values = [f.strip() for f in filter.split(",")]
+        invalid_filters = [f for f in filter_values if f not in VALID_FILTER_VALUES]
+        if invalid_filters:
+            raise fastapi.HTTPException(
+                status_code=400,
+                detail=f"Invalid filter value(s): {', '.join(invalid_filters)}. "
+                f"Valid values are: {', '.join(VALID_FILTER_VALUES)}",
+            )
+        filters = cast(list[builder_model.FilterType], filter_values)
+    else:
+        filters = [
             "blocks",
             "integrations",
             "marketplace_agents",
@@ -174,7 +182,7 @@ async def search(
     cached_results = await builder_db.get_sorted_search_results(
         user_id=user_id,
         search_query=search_query,
-        filters=filter,
+        filters=filters,
         by_creator=by_creator,
     )
 
@@ -196,7 +204,7 @@ async def search(
         user_id,
         builder_model.SearchEntry(
             search_query=search_query,
-            filter=filter,
+            filter=filters,
             by_creator=by_creator,
             search_id=search_id,
         ),

autogpt_platform/backend/backend/api/features/chat/service.py

@@ -4,9 +4,14 @@ from collections.abc import AsyncGenerator
 from typing import Any
 
 import orjson
-from langfuse import get_client, propagate_attributes
-from langfuse.openai import openai  # type: ignore
-from openai import APIConnectionError, APIError, APIStatusError, RateLimitError
+from langfuse import Langfuse
+from openai import (
+    APIConnectionError,
+    APIError,
+    APIStatusError,
+    AsyncOpenAI,
+    RateLimitError,
+)
 from openai.types.chat import ChatCompletionChunk, ChatCompletionToolParam
 
 from backend.data.understanding import (
@@ -16,6 +21,7 @@ from backend.data.understanding import (
 from backend.util.exceptions import NotFoundError
 from backend.util.settings import Settings
 
+from . import db as chat_db
 from .config import ChatConfig
 from .model import (
     ChatMessage,
@@ -44,10 +50,10 @@ logger = logging.getLogger(__name__)
 
 config = ChatConfig()
 settings = Settings()
-client = openai.AsyncOpenAI(api_key=config.api_key, base_url=config.base_url)
+client = AsyncOpenAI(api_key=config.api_key, base_url=config.base_url)
 
-
-langfuse = get_client()
+# Langfuse client (lazy initialization)
+_langfuse_client: Langfuse | None = None
 
 
 class LangfuseNotConfiguredError(Exception):
@@ -63,6 +69,65 @@ def _is_langfuse_configured() -> bool:
     )
 
 
+def _get_langfuse_client() -> Langfuse:
+    """Get or create the Langfuse client for prompt management and tracing."""
+    global _langfuse_client
+    if _langfuse_client is None:
+        if not _is_langfuse_configured():
+            raise LangfuseNotConfiguredError(
+                "Langfuse is not configured. The chat feature requires Langfuse for prompt management. "
+                "Please set the LANGFUSE_PUBLIC_KEY and LANGFUSE_SECRET_KEY environment variables."
+            )
+        _langfuse_client = Langfuse(
+            public_key=settings.secrets.langfuse_public_key,
+            secret_key=settings.secrets.langfuse_secret_key,
+            host=settings.secrets.langfuse_host or "https://cloud.langfuse.com",
+        )
+    return _langfuse_client
+
+
+def _get_environment() -> str:
+    """Get the current environment name for Langfuse tagging."""
+    return settings.config.app_env.value
+
+
+def _get_langfuse_prompt() -> str:
+    """Fetch the latest production prompt from Langfuse.
+
+    Returns:
+        The compiled prompt text from Langfuse.
+
+    Raises:
+        Exception: If Langfuse is unavailable or prompt fetch fails.
+    """
+    try:
+        langfuse = _get_langfuse_client()
+        # cache_ttl_seconds=0 disables SDK caching to always get the latest prompt
+        prompt = langfuse.get_prompt(config.langfuse_prompt_name, cache_ttl_seconds=0)
+        compiled = prompt.compile()
+        logger.info(
+            f"Fetched prompt '{config.langfuse_prompt_name}' from Langfuse "
+            f"(version: {prompt.version})"
+        )
+        return compiled
+    except Exception as e:
+        logger.error(f"Failed to fetch prompt from Langfuse: {e}")
+        raise
+
+
+async def _is_first_session(user_id: str) -> bool:
+    """Check if this is the user's first chat session.
+
+    Returns True if the user has 1 or fewer sessions (meaning this is their first).
+    """
+    try:
+        session_count = await chat_db.get_user_session_count(user_id)
+        return session_count <= 1
+    except Exception as e:
+        logger.warning(f"Failed to check session count for user {user_id}: {e}")
+        return False  # Default to non-onboarding if we can't check
+
+
 async def _build_system_prompt(user_id: str | None) -> tuple[str, Any]:
     """Build the full system prompt including business understanding if available.
 
@@ -74,6 +139,8 @@ async def _build_system_prompt(user_id: str | None) -> tuple[str, Any]:
         Tuple of (compiled prompt string, Langfuse prompt object for tracing)
     """
 
+    langfuse = _get_langfuse_client()
+
     # cache_ttl_seconds=0 disables SDK caching to always get the latest prompt
     prompt = langfuse.get_prompt(config.langfuse_prompt_name, cache_ttl_seconds=0)
 
@@ -91,7 +158,7 @@ async def _build_system_prompt(user_id: str | None) -> tuple[str, Any]:
         context = "This is the first time you are meeting the user. Greet them and introduce them to the platform"
 
     compiled = prompt.compile(users_information=context)
-    return compiled, understanding
+    return compiled, prompt
 
 
 async def _generate_session_title(message: str) -> str | None:
@@ -150,7 +217,6 @@ async def assign_user_to_session(
 async def stream_chat_completion(
     session_id: str,
     message: str | None = None,
-    tool_call_response: str | None = None,
     is_user_message: bool = True,
     user_id: str | None = None,
     retry_count: int = 0,
@@ -190,6 +256,11 @@ async def stream_chat_completion(
         yield StreamFinish()
         return
 
+    # Langfuse observations will be created after session is loaded (need messages for input)
+    # Initialize to None so finally block can safely check and end them
+    trace = None
+    generation = None
+
     # Only fetch from Redis if session not provided (initial call)
     if session is None:
         session = await get_chat_session(session_id, user_id)
@@ -265,259 +336,297 @@ async def stream_chat_completion(
             asyncio.create_task(_update_title())
 
     # Build system prompt with business understanding
-    system_prompt, understanding = await _build_system_prompt(user_id)
+    system_prompt, langfuse_prompt = await _build_system_prompt(user_id)
+
+    # Build input messages including system prompt for complete Langfuse logging
+    trace_input_messages = [{"role": "system", "content": system_prompt}] + [
+        m.model_dump() for m in session.messages
+    ]
 
     # Create Langfuse trace for this LLM call (each call gets its own trace, grouped by session_id)
     # Using v3 SDK: start_observation creates a root span, update_trace sets trace-level attributes
-    input = message
-    if not message and tool_call_response:
-        input = tool_call_response
-
-    langfuse = get_client()
-    with langfuse.start_as_current_observation(
-        as_type="span",
-        name="user-copilot-request",
-        input=input,
-    ) as span:
-        with propagate_attributes(
+    try:
+        langfuse = _get_langfuse_client()
+        env = _get_environment()
+        trace = langfuse.start_observation(
+            name="chat_completion",
+            input={"messages": trace_input_messages},
+            metadata={
+                "environment": env,
+                "model": config.model,
+                "message_count": len(session.messages),
+                "prompt_name": langfuse_prompt.name if langfuse_prompt else None,
+                "prompt_version": langfuse_prompt.version if langfuse_prompt else None,
+            },
+        )
+        # Set trace-level attributes (session_id, user_id, tags)
+        trace.update_trace(
             session_id=session_id,
             user_id=user_id,
-            tags=["copilot"],
-            metadata={
-                "users_information": format_understanding_for_prompt(understanding)[
-                    :200
-                ]  # langfuse only accepts upto to 200 chars
-            },
-        ):
+            tags=[env, "copilot"],
+        )
+    except Exception as e:
+        logger.warning(f"Failed to create Langfuse trace: {e}")
 
-            # Initialize variables that will be used in finally block (must be defined before try)
-            assistant_response = ChatMessage(
-                role="assistant",
-                content="",
+    # Initialize variables that will be used in finally block (must be defined before try)
+    assistant_response = ChatMessage(
+        role="assistant",
+        content="",
+    )
+    accumulated_tool_calls: list[dict[str, Any]] = []
+
+    # Wrap main logic in try/finally to ensure Langfuse observations are always ended
+    try:
+        has_yielded_end = False
+        has_yielded_error = False
+        has_done_tool_call = False
+        has_received_text = False
+        text_streaming_ended = False
+        tool_response_messages: list[ChatMessage] = []
+        should_retry = False
+
+        # Generate unique IDs for AI SDK protocol
+        import uuid as uuid_module
+
+        message_id = str(uuid_module.uuid4())
+        text_block_id = str(uuid_module.uuid4())
+
+        # Yield message start
+        yield StreamStart(messageId=message_id)
+
+        # Create Langfuse generation for each LLM call, linked to the prompt
+        # Using v3 SDK: start_observation with as_type="generation"
+        generation = (
+            trace.start_observation(
+                as_type="generation",
+                name="llm_call",
+                model=config.model,
+                input={"messages": trace_input_messages},
+                prompt=langfuse_prompt,
             )
-            accumulated_tool_calls: list[dict[str, Any]] = []
+            if trace
+            else None
+        )
 
-            # Wrap main logic in try/finally to ensure Langfuse observations are always ended
-            has_yielded_end = False
-            has_yielded_error = False
-            has_done_tool_call = False
-            has_received_text = False
-            text_streaming_ended = False
-            tool_response_messages: list[ChatMessage] = []
-            should_retry = False
+        try:
+            async for chunk in _stream_chat_chunks(
+                session=session,
+                tools=tools,
+                system_prompt=system_prompt,
+                text_block_id=text_block_id,
+            ):
 
-            # Generate unique IDs for AI SDK protocol
-            import uuid as uuid_module
-
-            message_id = str(uuid_module.uuid4())
-            text_block_id = str(uuid_module.uuid4())
-
-            # Yield message start
-            yield StreamStart(messageId=message_id)
-
-            try:
-                async for chunk in _stream_chat_chunks(
-                    session=session,
-                    tools=tools,
-                    system_prompt=system_prompt,
-                    text_block_id=text_block_id,
-                ):
-
-                    if isinstance(chunk, StreamTextStart):
-                        # Emit text-start before first text delta
-                        if not has_received_text:
-                            yield chunk
-                    elif isinstance(chunk, StreamTextDelta):
-                        delta = chunk.delta or ""
-                        assert assistant_response.content is not None
-                        assistant_response.content += delta
-                        has_received_text = True
+                if isinstance(chunk, StreamTextStart):
+                    # Emit text-start before first text delta
+                    if not has_received_text:
                         yield chunk
-                    elif isinstance(chunk, StreamTextEnd):
-                        # Emit text-end after text completes
-                        if has_received_text and not text_streaming_ended:
-                            text_streaming_ended = True
-                            if assistant_response.content:
-                                logger.warn(
-                                    f"StreamTextEnd: Attempting to set output {assistant_response.content}"
-                                )
-                                span.update_trace(output=assistant_response.content)
-                                span.update(output=assistant_response.content)
-                            yield chunk
-                    elif isinstance(chunk, StreamToolInputStart):
-                        # Emit text-end before first tool call, but only if we've received text
+                elif isinstance(chunk, StreamTextDelta):
+                    delta = chunk.delta or ""
+                    assert assistant_response.content is not None
+                    assistant_response.content += delta
+                    has_received_text = True
+                    yield chunk
+                elif isinstance(chunk, StreamTextEnd):
+                    # Emit text-end after text completes
+                    if has_received_text and not text_streaming_ended:
+                        text_streaming_ended = True
+                        yield chunk
+                elif isinstance(chunk, StreamToolInputStart):
+                    # Emit text-end before first tool call, but only if we've received text
+                    if has_received_text and not text_streaming_ended:
+                        yield StreamTextEnd(id=text_block_id)
+                        text_streaming_ended = True
+                    yield chunk
+                elif isinstance(chunk, StreamToolInputAvailable):
+                    # Accumulate tool calls in OpenAI format
+                    accumulated_tool_calls.append(
+                        {
+                            "id": chunk.toolCallId,
+                            "type": "function",
+                            "function": {
+                                "name": chunk.toolName,
+                                "arguments": orjson.dumps(chunk.input).decode("utf-8"),
+                            },
+                        }
+                    )
+                elif isinstance(chunk, StreamToolOutputAvailable):
+                    result_content = (
+                        chunk.output
+                        if isinstance(chunk.output, str)
+                        else orjson.dumps(chunk.output).decode("utf-8")
+                    )
+                    tool_response_messages.append(
+                        ChatMessage(
+                            role="tool",
+                            content=result_content,
+                            tool_call_id=chunk.toolCallId,
+                        )
+                    )
+                    has_done_tool_call = True
+                    # Track if any tool execution failed
+                    if not chunk.success:
+                        logger.warning(
+                            f"Tool {chunk.toolName} (ID: {chunk.toolCallId}) execution failed"
+                        )
+                    yield chunk
+                elif isinstance(chunk, StreamFinish):
+                    if not has_done_tool_call:
+                        # Emit text-end before finish if we received text but haven't closed it
                         if has_received_text and not text_streaming_ended:
                             yield StreamTextEnd(id=text_block_id)
                             text_streaming_ended = True
+                        has_yielded_end = True
                         yield chunk
-                    elif isinstance(chunk, StreamToolInputAvailable):
-                        # Accumulate tool calls in OpenAI format
-                        accumulated_tool_calls.append(
-                            {
-                                "id": chunk.toolCallId,
-                                "type": "function",
-                                "function": {
-                                    "name": chunk.toolName,
-                                    "arguments": orjson.dumps(chunk.input).decode(
-                                        "utf-8"
-                                    ),
-                                },
-                            }
+                elif isinstance(chunk, StreamError):
+                    has_yielded_error = True
+                elif isinstance(chunk, StreamUsage):
+                    session.usage.append(
+                        Usage(
+                            prompt_tokens=chunk.promptTokens,
+                            completion_tokens=chunk.completionTokens,
+                            total_tokens=chunk.totalTokens,
                         )
-                    elif isinstance(chunk, StreamToolOutputAvailable):
-                        result_content = (
-                            chunk.output
-                            if isinstance(chunk.output, str)
-                            else orjson.dumps(chunk.output).decode("utf-8")
-                        )
-                        tool_response_messages.append(
-                            ChatMessage(
-                                role="tool",
-                                content=result_content,
-                                tool_call_id=chunk.toolCallId,
-                            )
-                        )
-                        has_done_tool_call = True
-                        # Track if any tool execution failed
-                        if not chunk.success:
-                            logger.warning(
-                                f"Tool {chunk.toolName} (ID: {chunk.toolCallId}) execution failed"
-                            )
-                        yield chunk
-                    elif isinstance(chunk, StreamFinish):
-                        if not has_done_tool_call:
-                            # Emit text-end before finish if we received text but haven't closed it
-                            if has_received_text and not text_streaming_ended:
-                                yield StreamTextEnd(id=text_block_id)
-                                text_streaming_ended = True
-                            has_yielded_end = True
-                            yield chunk
-                    elif isinstance(chunk, StreamError):
-                        has_yielded_error = True
-                    elif isinstance(chunk, StreamUsage):
-                        session.usage.append(
-                            Usage(
-                                prompt_tokens=chunk.promptTokens,
-                                completion_tokens=chunk.completionTokens,
-                                total_tokens=chunk.totalTokens,
-                            )
-                        )
-                    else:
-                        logger.error(
-                            f"Unknown chunk type: {type(chunk)}", exc_info=True
-                        )
-                if assistant_response.content:
-                    langfuse.update_current_trace(output=assistant_response.content)
-                    langfuse.update_current_span(output=assistant_response.content)
-                elif tool_response_messages:
-                    langfuse.update_current_trace(output=str(tool_response_messages))
-                    langfuse.update_current_span(output=str(tool_response_messages))
-
-            except Exception as e:
-                logger.error(f"Error during stream: {e!s}", exc_info=True)
-
-                # Check if this is a retryable error (JSON parsing, incomplete tool calls, etc.)
-                is_retryable = isinstance(
-                    e, (orjson.JSONDecodeError, KeyError, TypeError)
-                )
-
-                if is_retryable and retry_count < config.max_retries:
-                    logger.info(
-                        f"Retryable error encountered. Attempt {retry_count + 1}/{config.max_retries}"
                     )
-                    should_retry = True
                 else:
-                    # Non-retryable error or max retries exceeded
-                    # Save any partial progress before reporting error
-                    messages_to_save: list[ChatMessage] = []
+                    logger.error(f"Unknown chunk type: {type(chunk)}", exc_info=True)
+        except Exception as e:
+            logger.error(f"Error during stream: {e!s}", exc_info=True)
 
-                    # Add assistant message if it has content or tool calls
-                    if accumulated_tool_calls:
-                        assistant_response.tool_calls = accumulated_tool_calls
-                    if assistant_response.content or assistant_response.tool_calls:
-                        messages_to_save.append(assistant_response)
+            # Check if this is a retryable error (JSON parsing, incomplete tool calls, etc.)
+            is_retryable = isinstance(e, (orjson.JSONDecodeError, KeyError, TypeError))
 
-                    # Add tool response messages after assistant message
-                    messages_to_save.extend(tool_response_messages)
-
-                    session.messages.extend(messages_to_save)
-                    await upsert_chat_session(session)
-
-                    if not has_yielded_error:
-                        error_message = str(e)
-                        if not is_retryable:
-                            error_message = f"Non-retryable error: {error_message}"
-                        elif retry_count >= config.max_retries:
-                            error_message = f"Max retries ({config.max_retries}) exceeded: {error_message}"
-
-                        error_response = StreamError(errorText=error_message)
-                        yield error_response
-                    if not has_yielded_end:
-                        yield StreamFinish()
-                    return
-
-            # Handle retry outside of exception handler to avoid nesting
-            if should_retry and retry_count < config.max_retries:
+            if is_retryable and retry_count < config.max_retries:
                 logger.info(
-                    f"Retrying stream_chat_completion for session {session_id}, attempt {retry_count + 1}"
+                    f"Retryable error encountered. Attempt {retry_count + 1}/{config.max_retries}"
                 )
-                async for chunk in stream_chat_completion(
-                    session_id=session.session_id,
-                    user_id=user_id,
-                    retry_count=retry_count + 1,
-                    session=session,
-                    context=context,
-                ):
-                    yield chunk
-                return  # Exit after retry to avoid double-saving in finally block
+                should_retry = True
+            else:
+                # Non-retryable error or max retries exceeded
+                # Save any partial progress before reporting error
+                messages_to_save: list[ChatMessage] = []
 
-            # Normal completion path - save session and handle tool call continuation
+                # Add assistant message if it has content or tool calls
+                if accumulated_tool_calls:
+                    assistant_response.tool_calls = accumulated_tool_calls
+                if assistant_response.content or assistant_response.tool_calls:
+                    messages_to_save.append(assistant_response)
+
+                # Add tool response messages after assistant message
+                messages_to_save.extend(tool_response_messages)
+
+                session.messages.extend(messages_to_save)
+                await upsert_chat_session(session)
+
+                if not has_yielded_error:
+                    error_message = str(e)
+                    if not is_retryable:
+                        error_message = f"Non-retryable error: {error_message}"
+                    elif retry_count >= config.max_retries:
+                        error_message = f"Max retries ({config.max_retries}) exceeded: {error_message}"
+
+                    error_response = StreamError(errorText=error_message)
+                    yield error_response
+                if not has_yielded_end:
+                    yield StreamFinish()
+                return
+
+        # Handle retry outside of exception handler to avoid nesting
+        if should_retry and retry_count < config.max_retries:
             logger.info(
-                f"Normal completion path: session={session.session_id}, "
-                f"current message_count={len(session.messages)}"
+                f"Retrying stream_chat_completion for session {session_id}, attempt {retry_count + 1}"
+            )
+            async for chunk in stream_chat_completion(
+                session_id=session.session_id,
+                user_id=user_id,
+                retry_count=retry_count + 1,
+                session=session,
+                context=context,
+            ):
+                yield chunk
+            return  # Exit after retry to avoid double-saving in finally block
+
+        # Normal completion path - save session and handle tool call continuation
+        logger.info(
+            f"Normal completion path: session={session.session_id}, "
+            f"current message_count={len(session.messages)}"
+        )
+
+        # Build the messages list in the correct order
+        messages_to_save: list[ChatMessage] = []
+
+        # Add assistant message with tool_calls if any
+        if accumulated_tool_calls:
+            assistant_response.tool_calls = accumulated_tool_calls
+            logger.info(
+                f"Added {len(accumulated_tool_calls)} tool calls to assistant message"
+            )
+        if assistant_response.content or assistant_response.tool_calls:
+            messages_to_save.append(assistant_response)
+            logger.info(
+                f"Saving assistant message with content_len={len(assistant_response.content or '')}, tool_calls={len(assistant_response.tool_calls or [])}"
             )
 
-            # Build the messages list in the correct order
-            messages_to_save: list[ChatMessage] = []
+        # Add tool response messages after assistant message
+        messages_to_save.extend(tool_response_messages)
+        logger.info(
+            f"Saving {len(tool_response_messages)} tool response messages, "
+            f"total_to_save={len(messages_to_save)}"
+        )
 
-            # Add assistant message with tool_calls if any
-            if accumulated_tool_calls:
-                assistant_response.tool_calls = accumulated_tool_calls
-                logger.info(
-                    f"Added {len(accumulated_tool_calls)} tool calls to assistant message"
-                )
-            if assistant_response.content or assistant_response.tool_calls:
-                messages_to_save.append(assistant_response)
-                logger.info(
-                    f"Saving assistant message with content_len={len(assistant_response.content or '')}, tool_calls={len(assistant_response.tool_calls or [])}"
-                )
+        session.messages.extend(messages_to_save)
+        logger.info(
+            f"Extended session messages, new message_count={len(session.messages)}"
+        )
+        await upsert_chat_session(session)
 
-            # Add tool response messages after assistant message
-            messages_to_save.extend(tool_response_messages)
+        # If we did a tool call, stream the chat completion again to get the next response
+        if has_done_tool_call:
             logger.info(
-                f"Saving {len(tool_response_messages)} tool response messages, "
-                f"total_to_save={len(messages_to_save)}"
+                "Tool call executed, streaming chat completion again to get assistant response"
             )
+            async for chunk in stream_chat_completion(
+                session_id=session.session_id,
+                user_id=user_id,
+                session=session,  # Pass session object to avoid Redis refetch
+                context=context,
+            ):
+                yield chunk
 
-            session.messages.extend(messages_to_save)
-            logger.info(
-                f"Extended session messages, new message_count={len(session.messages)}"
-            )
-            await upsert_chat_session(session)
-
-            # If we did a tool call, stream the chat completion again to get the next response
-            if has_done_tool_call:
-                logger.info(
-                    "Tool call executed, streaming chat completion again to get assistant response"
+    finally:
+        # Always end Langfuse observations to prevent resource leaks
+        # Guard against None and catch errors to avoid masking original exceptions
+        if generation is not None:
+            try:
+                latest_usage = session.usage[-1] if session.usage else None
+                generation.update(
+                    model=config.model,
+                    output={
+                        "content": assistant_response.content,
+                        "tool_calls": accumulated_tool_calls or None,
+                    },
+                    usage_details=(
+                        {
+                            "input": latest_usage.prompt_tokens,
+                            "output": latest_usage.completion_tokens,
+                            "total": latest_usage.total_tokens,
+                        }
+                        if latest_usage
+                        else None
+                    ),
                 )
-                async for chunk in stream_chat_completion(
-                    session_id=session.session_id,
-                    user_id=user_id,
-                    session=session,  # Pass session object to avoid Redis refetch
-                    context=context,
-                    tool_call_response=str(tool_response_messages),
-                ):
-                    yield chunk
+                generation.end()
+            except Exception as e:
+                logger.warning(f"Failed to end Langfuse generation: {e}")
+
+        if trace is not None:
+            try:
+                if accumulated_tool_calls:
+                    trace.update_trace(output={"tool_calls": accumulated_tool_calls})
+                else:
+                    trace.update_trace(output={"response": assistant_response.content})
+                trace.end()
+            except Exception as e:
+                logger.warning(f"Failed to end Langfuse trace: {e}")
 
 
 # Retry configuration for OpenAI API calls
@@ -791,4 +900,5 @@ async def _yield_tool_call(
         session=session,
     )
 
+    logger.info(f"Yielding Tool execution response: {tool_execution_response}")
     yield tool_execution_response

autogpt_platform/backend/backend/api/features/chat/tools/__init__.py

@@ -30,7 +30,7 @@ TOOL_REGISTRY: dict[str, BaseTool] = {
     "find_library_agent": FindLibraryAgentTool(),
     "run_agent": RunAgentTool(),
     "run_block": RunBlockTool(),
-    "view_agent_output": AgentOutputTool(),
+    "agent_output": AgentOutputTool(),
     "search_docs": SearchDocsTool(),
     "get_doc_page": GetDocPageTool(),
 }

autogpt_platform/backend/backend/api/features/chat/tools/add_understanding.py

@@ -3,8 +3,6 @@
 import logging
 from typing import Any
 
-from langfuse import observe
-
 from backend.api.features.chat.model import ChatSession
 from backend.data.understanding import (
     BusinessUnderstandingInput,
@@ -61,7 +59,6 @@ and automations for the user's specific needs."""
         """Requires authentication to store user-specific data."""
         return True
 
-    @observe(as_type="tool", name="add_understanding")
     async def _execute(
         self,
         user_id: str | None,

autogpt_platform/backend/backend/api/features/chat/tools/agent_output.py

@@ -5,7 +5,6 @@ import re
 from datetime import datetime, timedelta, timezone
 from typing import Any
 
-from langfuse import observe
 from pydantic import BaseModel, field_validator
 
 from backend.api.features.chat.model import ChatSession
@@ -104,7 +103,7 @@ class AgentOutputTool(BaseTool):
 
     @property
     def name(self) -> str:
-        return "view_agent_output"
+        return "agent_output"
 
     @property
     def description(self) -> str:
@@ -329,7 +328,6 @@ class AgentOutputTool(BaseTool):
             total_executions=len(available_executions) if available_executions else 1,
         )
 
-    @observe(as_type="tool", name="view_agent_output")
     async def _execute(
         self,
         user_id: str | None,

autogpt_platform/backend/backend/api/features/chat/tools/create_agent.py

@@ -3,8 +3,6 @@
 import logging
 from typing import Any
 
-from langfuse import observe
-
 from backend.api.features.chat.model import ChatSession
 
 from .agent_generator import (
@@ -80,7 +78,6 @@ class CreateAgentTool(BaseTool):
             "required": ["description"],
         }
 
-    @observe(as_type="tool", name="create_agent")
     async def _execute(
         self,
         user_id: str | None,

autogpt_platform/backend/backend/api/features/chat/tools/edit_agent.py

@@ -3,8 +3,6 @@
 import logging
 from typing import Any
 
-from langfuse import observe
-
 from backend.api.features.chat.model import ChatSession
 
 from .agent_generator import (
@@ -87,7 +85,6 @@ class EditAgentTool(BaseTool):
             "required": ["agent_id", "changes"],
         }
 
-    @observe(as_type="tool", name="edit_agent")
     async def _execute(
         self,
         user_id: str | None,

autogpt_platform/backend/backend/api/features/chat/tools/find_agent.py

@@ -2,8 +2,6 @@
 
 from typing import Any
 
-from langfuse import observe
-
 from backend.api.features.chat.model import ChatSession
 
 from .agent_search import search_agents
@@ -37,7 +35,6 @@ class FindAgentTool(BaseTool):
             "required": ["query"],
         }
 
-    @observe(as_type="tool", name="find_agent")
     async def _execute(
         self, user_id: str | None, session: ChatSession, **kwargs
     ) -> ToolResponseBase:

autogpt_platform/backend/backend/api/features/chat/tools/find_block.py

@@ -1,7 +1,6 @@
 import logging
 from typing import Any
 
-from langfuse import observe
 from prisma.enums import ContentType
 
 from backend.api.features.chat.model import ChatSession
@@ -56,7 +55,6 @@ class FindBlockTool(BaseTool):
     def requires_auth(self) -> bool:
         return True
 
-    @observe(as_type="tool", name="find_block")
     async def _execute(
         self,
         user_id: str | None,

autogpt_platform/backend/backend/api/features/chat/tools/find_library_agent.py

@@ -2,8 +2,6 @@
 
 from typing import Any
 
-from langfuse import observe
-
 from backend.api.features.chat.model import ChatSession
 
 from .agent_search import search_agents
@@ -43,7 +41,6 @@ class FindLibraryAgentTool(BaseTool):
     def requires_auth(self) -> bool:
         return True
 
-    @observe(as_type="tool", name="find_library_agent")
     async def _execute(
         self, user_id: str | None, session: ChatSession, **kwargs
     ) -> ToolResponseBase:

autogpt_platform/backend/backend/api/features/chat/tools/get_doc_page.py

@@ -4,8 +4,6 @@ import logging
 from pathlib import Path
 from typing import Any
 
-from langfuse import observe
-
 from backend.api.features.chat.model import ChatSession
 from backend.api.features.chat.tools.base import BaseTool
 from backend.api.features.chat.tools.models import (
@@ -73,7 +71,6 @@ class GetDocPageTool(BaseTool):
         url_path = path.rsplit(".", 1)[0] if "." in path else path
         return f"{DOCS_BASE_URL}/{url_path}"
 
-    @observe(as_type="tool", name="get_doc_page")
     async def _execute(
         self,
         user_id: str | None,

autogpt_platform/backend/backend/api/features/chat/tools/run_agent.py

@@ -3,7 +3,6 @@
 import logging
 from typing import Any
 
-from langfuse import observe
 from pydantic import BaseModel, Field, field_validator
 
 from backend.api.features.chat.config import ChatConfig
@@ -155,7 +154,6 @@ class RunAgentTool(BaseTool):
         """All operations require authentication."""
         return True
 
-    @observe(as_type="tool", name="run_agent")
     async def _execute(
         self,
         user_id: str | None,

autogpt_platform/backend/backend/api/features/chat/tools/run_block.py

@@ -4,8 +4,6 @@ import logging
 from collections import defaultdict
 from typing import Any
 
-from langfuse import observe
-
 from backend.api.features.chat.model import ChatSession
 from backend.data.block import get_block
 from backend.data.execution import ExecutionContext
@@ -129,7 +127,6 @@ class RunBlockTool(BaseTool):
 
         return matched_credentials, missing_credentials
 
-    @observe(as_type="tool", name="run_block")
     async def _execute(
         self,
         user_id: str | None,

autogpt_platform/backend/backend/api/features/chat/tools/search_docs.py

@@ -3,7 +3,6 @@
 import logging
 from typing import Any
 
-from langfuse import observe
 from prisma.enums import ContentType
 
 from backend.api.features.chat.model import ChatSession
@@ -88,7 +87,6 @@ class SearchDocsTool(BaseTool):
         url_path = path.rsplit(".", 1)[0] if "." in path else path
         return f"{DOCS_BASE_URL}/{url_path}"
 
-    @observe(as_type="tool", name="search_docs")
     async def _execute(
         self,
         user_id: str | None,

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

@@ -225,6 +225,28 @@ class BlockHandler(ContentHandler):
                     [cat.value for cat in categories] if categories else []
                 )
 
+                # Extract provider names from credentials fields
+                provider_names: list[str] = []
+                is_integration = False
+                if hasattr(block_instance, "input_schema"):
+                    credentials_info = (
+                        block_instance.input_schema.get_credentials_fields_info()
+                    )
+                    is_integration = len(credentials_info) > 0
+                    for info in credentials_info.values():
+                        for provider in info.provider:
+                            provider_names.append(provider.value.lower())
+
+                # Check if block has LlmModel field in input schema
+                has_llm_model_field = False
+                if hasattr(block_instance, "input_schema"):
+                    from backend.blocks.llm import LlmModel
+
+                    for field in block_instance.input_schema.model_fields.values():
+                        if field.annotation == LlmModel:
+                            has_llm_model_field = True
+                            break
+
                 items.append(
                     ContentItem(
                         content_id=block_id,
@@ -233,6 +255,9 @@ class BlockHandler(ContentHandler):
                         metadata={
                             "name": getattr(block_instance, "name", ""),
                             "categories": categories_list,
+                            "providers": provider_names,
+                            "has_llm_model_field": has_llm_model_field,
+                            "is_integration": is_integration,
                         },
                         user_id=None,  # Blocks are public
                     )

autogpt_platform/backend/backend/data/understanding.py

@@ -328,8 +328,6 @@ async def clear_business_understanding(user_id: str) -> bool:
 
 def format_understanding_for_prompt(understanding: BusinessUnderstanding) -> str:
     """Format business understanding as text for system prompt injection."""
-    if not understanding:
-        return ""
     sections = []
 
     # User info section

autogpt_platform/backend/migrations/20250120120000_add_suggested_blocks_materialized_view/migration.sql

@@ -0,0 +1,102 @@
+-- This migration creates a materialized view for suggested blocks based on execution counts
+-- The view aggregates execution counts per block for the last 14 days
+--
+-- IMPORTANT: For production environments, pg_cron is REQUIRED for automatic refresh
+-- Prerequisites for production:
+--   1. pg_cron extension must be installed: CREATE EXTENSION pg_cron;
+--   2. pg_cron must be configured in postgresql.conf:
+--      shared_preload_libraries = 'pg_cron'
+--      cron.database_name = 'your_database_name'
+--
+-- For development environments without pg_cron:
+--   The migration will succeed but you must manually refresh views with:
+--   SELECT refresh_suggested_blocks_view();
+
+-- Check if pg_cron extension is installed and set a flag
+DO $$
+DECLARE
+    has_pg_cron BOOLEAN;
+BEGIN
+    SELECT EXISTS (SELECT 1 FROM pg_extension WHERE extname = 'pg_cron') INTO has_pg_cron;
+
+    IF NOT has_pg_cron THEN
+        RAISE WARNING 'pg_cron extension is not installed!';
+        RAISE WARNING 'Materialized view will be created but WILL NOT refresh automatically.';
+        RAISE WARNING 'For production use, install pg_cron with: CREATE EXTENSION pg_cron;';
+        RAISE WARNING 'For development, manually refresh with: SELECT refresh_suggested_blocks_view();';
+    END IF;
+
+    -- Store the flag for later use in the migration
+    PERFORM set_config('migration.has_pg_cron', has_pg_cron::text, false);
+END
+$$;
+
+-- Create materialized view for suggested blocks based on execution counts in last 14 days
+-- The 14-day threshold is hardcoded to ensure consistent behavior
+CREATE MATERIALIZED VIEW IF NOT EXISTS "mv_suggested_blocks" AS
+SELECT
+    agent_node."agentBlockId" AS block_id,
+    COUNT(execution.id) AS execution_count
+FROM "AgentNodeExecution" execution
+JOIN "AgentNode" agent_node ON execution."agentNodeId" = agent_node.id
+WHERE execution."endedTime" >= (NOW() - INTERVAL '14 days')
+GROUP BY agent_node."agentBlockId"
+ORDER BY execution_count DESC;
+
+-- Create unique index for concurrent refresh support
+CREATE UNIQUE INDEX IF NOT EXISTS "idx_mv_suggested_blocks_block_id" ON "mv_suggested_blocks"("block_id");
+
+-- Create refresh function
+CREATE OR REPLACE FUNCTION refresh_suggested_blocks_view()
+RETURNS void
+LANGUAGE plpgsql
+AS $$
+DECLARE
+    target_schema text := current_schema();
+BEGIN
+    -- Use CONCURRENTLY for better performance during refresh
+    REFRESH MATERIALIZED VIEW CONCURRENTLY "mv_suggested_blocks";
+    RAISE NOTICE 'Suggested blocks materialized view refreshed in schema % at %', target_schema, NOW();
+EXCEPTION
+    WHEN OTHERS THEN
+        -- Fallback to non-concurrent refresh if concurrent fails
+        REFRESH MATERIALIZED VIEW "mv_suggested_blocks";
+        RAISE NOTICE 'Suggested blocks materialized view refreshed (non-concurrent) in schema % at %. Concurrent refresh failed due to: %', target_schema, NOW(), SQLERRM;
+END;
+$$;
+
+-- Schedule automatic refresh every hour (only if pg_cron is available)
+DO $$
+DECLARE
+    has_pg_cron BOOLEAN;
+    current_schema_name text := current_schema();
+    old_job_name text;
+    job_name text;
+BEGIN
+    -- Check if pg_cron extension exists
+    SELECT EXISTS (SELECT 1 FROM pg_extension WHERE extname = 'pg_cron') INTO has_pg_cron;
+
+    IF has_pg_cron THEN
+        job_name := format('refresh-suggested-blocks_%s', current_schema_name);
+
+        -- Try to unschedule existing job (ignore errors if it doesn't exist)
+        BEGIN
+            PERFORM cron.unschedule(job_name);
+        EXCEPTION WHEN OTHERS THEN
+            NULL;
+        END;
+
+        -- Schedule the new job to run every hour
+        PERFORM cron.schedule(
+            job_name,
+            '0 * * * *',  -- Every hour at minute 0
+            format('SET search_path TO %I; SELECT refresh_suggested_blocks_view();', current_schema_name)
+        );
+        RAISE NOTICE 'Scheduled job %; runs every hour for schema %', job_name, current_schema_name;
+    ELSE
+        RAISE WARNING 'Automatic refresh NOT configured - pg_cron is not available';
+        RAISE WARNING 'You must manually refresh the view with: SELECT refresh_suggested_blocks_view();';
+        RAISE WARNING 'Or install pg_cron for automatic refresh in production';
+    END IF;
+END;
+$$;

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

@@ -10,7 +10,6 @@ import { useRunInputDialog } from "./useRunInputDialog";
 import { CronSchedulerDialog } from "../CronSchedulerDialog/CronSchedulerDialog";
 import { useTutorialStore } from "@/app/(platform)/build/stores/tutorialStore";
 import { useEffect } from "react";
-import { CredentialsGroupedView } from "@/components/contextual/CredentialsInput/components/CredentialsGroupedView/CredentialsGroupedView";
 
 export const RunInputDialog = ({
   isOpen,
@@ -24,17 +23,19 @@ export const RunInputDialog = ({
   const hasInputs = useGraphStore((state) => state.hasInputs);
   const hasCredentials = useGraphStore((state) => state.hasCredentials);
   const inputSchema = useGraphStore((state) => state.inputSchema);
+  const credentialsSchema = useGraphStore(
+    (state) => state.credentialsInputSchema,
+  );
 
   const {
-    credentialFields,
-    requiredCredentials,
+    credentialsUiSchema,
     handleManualRun,
     handleInputChange,
     openCronSchedulerDialog,
     setOpenCronSchedulerDialog,
     inputValues,
     credentialValues,
-    handleCredentialFieldChange,
+    handleCredentialChange,
     isExecutingGraph,
   } = useRunInputDialog({ setIsOpen });
 
@@ -66,7 +67,7 @@ export const RunInputDialog = ({
         <Dialog.Content>
           <div className="space-y-6 p-1" data-id="run-input-dialog-content">
             {/* Credentials Section */}
-            {hasCredentials() && credentialFields.length > 0 && (
+            {hasCredentials() && (
               <div data-id="run-input-credentials-section">
                 <div className="mb-4">
                   <Text variant="h4" className="text-gray-900">
@@ -74,12 +75,16 @@ export const RunInputDialog = ({
                   </Text>
                 </div>
                 <div className="px-2" data-id="run-input-credentials-form">
-                  <CredentialsGroupedView
-                    credentialFields={credentialFields}
-                    requiredCredentials={requiredCredentials}
-                    inputCredentials={credentialValues}
-                    inputValues={inputValues}
-                    onCredentialChange={handleCredentialFieldChange}
+                  <FormRenderer
+                    jsonSchema={credentialsSchema as RJSFSchema}
+                    handleChange={(v) => handleCredentialChange(v.formData)}
+                    uiSchema={credentialsUiSchema}
+                    initialValues={{}}
+                    formContext={{
+                      showHandles: false,
+                      size: "large",
+                      showOptionalToggle: false,
+                    }}
                   />
                 </div>
               </div>

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

@@ -7,11 +7,12 @@ import {
   GraphExecutionMeta,
 } from "@/lib/autogpt-server-api";
 import { parseAsInteger, parseAsString, useQueryStates } from "nuqs";
-import { useCallback, useMemo, useState } from "react";
+import { useMemo, useState } from "react";
+import { uiSchema } from "../../../FlowEditor/nodes/uiSchema";
+import { isCredentialFieldSchema } from "@/components/renderers/InputRenderer/custom/CredentialField/helpers";
 import { useNodeStore } from "@/app/(platform)/build/stores/nodeStore";
 import { useToast } from "@/components/molecules/Toast/use-toast";
 import { useReactFlow } from "@xyflow/react";
-import type { CredentialField } from "@/components/contextual/CredentialsInput/components/CredentialsGroupedView/helpers";
 
 export const useRunInputDialog = ({
   setIsOpen,
@@ -119,32 +120,27 @@ export const useRunInputDialog = ({
       },
     });
 
-  // Convert credentials schema to credential fields array for CredentialsGroupedView
-  const credentialFields: CredentialField[] = useMemo(() => {
-    if (!credentialsSchema?.properties) return [];
-    return Object.entries(credentialsSchema.properties);
-  }, [credentialsSchema]);
+  // We are rendering the credentials field differently compared to other fields.
+  // In the node, we have the field name as "credential" - so our library catches it and renders it differently.
+  // But here we have a different name, something like `Firecrawl credentials`, so here we are telling the library that this field is a credential field type.
 
-  // Get required credentials as a Set
-  const requiredCredentials = useMemo(() => {
-    return new Set<string>(credentialsSchema?.required || []);
-  }, [credentialsSchema]);
+  const credentialsUiSchema = useMemo(() => {
+    const dynamicUiSchema: any = { ...uiSchema };
 
-  // Handler for individual credential changes
-  const handleCredentialFieldChange = useCallback(
-    (key: string, value?: CredentialsMetaInput) => {
-      setCredentialValues((prev) => {
-        if (value) {
-          return { ...prev, [key]: value };
-        } else {
-          const next = { ...prev };
-          delete next[key];
-          return next;
+    if (credentialsSchema?.properties) {
+      Object.keys(credentialsSchema.properties).forEach((fieldName) => {
+        const fieldSchema = credentialsSchema.properties[fieldName];
+        if (isCredentialFieldSchema(fieldSchema)) {
+          dynamicUiSchema[fieldName] = {
+            ...dynamicUiSchema[fieldName],
+            "ui:field": "custom/credential_field",
+          };
         }
       });
-    },
-    [],
-  );
+    }
+
+    return dynamicUiSchema;
+  }, [credentialsSchema]);
 
   const handleManualRun = async () => {
     // Filter out incomplete credentials (those without a valid id)
@@ -177,14 +173,12 @@ export const useRunInputDialog = ({
   };
 
   return {
-    credentialFields,
-    requiredCredentials,
+    credentialsUiSchema,
     inputValues,
     credentialValues,
     isExecutingGraph,
     handleInputChange,
     handleCredentialChange,
-    handleCredentialFieldChange,
     handleManualRun,
     openCronSchedulerDialog,
     setOpenCronSchedulerDialog,

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

@@ -139,6 +139,14 @@ export const useFlow = () => {
       useNodeStore.getState().setNodes([]);
       useNodeStore.getState().clearResolutionState();
       addNodes(customNodes);
+
+      // Sync hardcoded values with handle IDs.
+      // If a key–value field has a key without a value, the backend omits it from hardcoded values.
+      // But if a handleId exists for that key, it causes inconsistency.
+      // This ensures hardcoded values stay in sync with handle IDs.
+      customNodes.forEach((node) => {
+        useNodeStore.getState().syncHardcodedValuesWithHandleIds(node.id);
+      });
     }
   }, [customNodes, addNodes]);
 
@@ -150,14 +158,6 @@ export const useFlow = () => {
     }
   }, [graph?.links, addLinks]);
 
-  useEffect(() => {
-    if (customNodes.length > 0 && graph?.links) {
-      customNodes.forEach((node) => {
-        useNodeStore.getState().syncHardcodedValuesWithHandleIds(node.id);
-      });
-    }
-  }, [customNodes, graph?.links]);
-
   // update node execution status in nodes
   useEffect(() => {
     if (

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

@@ -1,21 +1,22 @@
 import { Button } from "@/components/atoms/Button/Button";
 import { Text } from "@/components/atoms/Text/Text";
-import {
-  Accordion,
-  AccordionContent,
-  AccordionItem,
-  AccordionTrigger,
-} from "@/components/molecules/Accordion/Accordion";
 import { beautifyString, cn } from "@/lib/utils";
-import { CopyIcon, CheckIcon } from "@phosphor-icons/react";
+import { CaretDownIcon, CopyIcon, CheckIcon } from "@phosphor-icons/react";
 import { NodeDataViewer } from "./components/NodeDataViewer/NodeDataViewer";
 import { ContentRenderer } from "./components/ContentRenderer";
 import { useNodeOutput } from "./useNodeOutput";
 import { ViewMoreData } from "./components/ViewMoreData";
 
 export const NodeDataRenderer = ({ nodeId }: { nodeId: string }) => {
-  const { outputData, copiedKey, handleCopy, executionResultId, inputData } =
-    useNodeOutput(nodeId);
+  const {
+    outputData,
+    isExpanded,
+    setIsExpanded,
+    copiedKey,
+    handleCopy,
+    executionResultId,
+    inputData,
+  } = useNodeOutput(nodeId);
 
   if (Object.keys(outputData).length === 0) {
     return null;
@@ -24,117 +25,122 @@ export const NodeDataRenderer = ({ nodeId }: { nodeId: string }) => {
   return (
     <div
       data-tutorial-id={`node-output`}
-      className="rounded-b-xl border-t border-zinc-200 px-4 py-2"
+      className="flex flex-col gap-3 rounded-b-xl border-t border-zinc-200 px-4 py-4"
     >
-      <Accordion type="single" collapsible defaultValue="node-output">
-        <AccordionItem value="node-output" className="border-none">
-          <AccordionTrigger className="py-2 hover:no-underline">
-            <Text
-              variant="body-medium"
-              className="!font-semibold text-slate-700"
-            >
-              Node Output
-            </Text>
-          </AccordionTrigger>
-          <AccordionContent className="pt-2">
-            <div className="flex max-w-[350px] flex-col gap-4">
-              <div className="space-y-2">
-                <Text variant="small-medium">Input</Text>
+      <div className="flex items-center justify-between">
+        <Text variant="body-medium" className="!font-semibold text-slate-700">
+          Node Output
+        </Text>
+        <Button
+          variant="ghost"
+          size="small"
+          onClick={() => setIsExpanded(!isExpanded)}
+          className="h-fit min-w-0 p-1 text-slate-600 hover:text-slate-900"
+        >
+          <CaretDownIcon
+            size={16}
+            weight="bold"
+            className={`transition-transform ${isExpanded ? "rotate-180" : ""}`}
+          />
+        </Button>
+      </div>
 
-                <ContentRenderer value={inputData} shortContent={false} />
+      {isExpanded && (
+        <>
+          <div className="flex max-w-[350px] flex-col gap-4">
+            <div className="space-y-2">
+              <Text variant="small-medium">Input</Text>
 
-                <div className="mt-1 flex justify-end gap-1">
-                  <NodeDataViewer
-                    data={inputData}
-                    pinName="Input"
-                    execId={executionResultId}
-                  />
-                  <Button
-                    variant="secondary"
-                    size="small"
-                    onClick={() => handleCopy("input", inputData)}
-                    className={cn(
-                      "h-fit min-w-0 gap-1.5 border border-zinc-200 p-2 text-black hover:text-slate-900",
-                      copiedKey === "input" &&
-                        "border-green-400 bg-green-100 hover:border-green-400 hover:bg-green-200",
-                    )}
-                  >
-                    {copiedKey === "input" ? (
-                      <CheckIcon size={12} className="text-green-600" />
-                    ) : (
-                      <CopyIcon size={12} />
-                    )}
-                  </Button>
-                </div>
+              <ContentRenderer value={inputData} shortContent={false} />
+
+              <div className="mt-1 flex justify-end gap-1">
+                <NodeDataViewer
+                  data={inputData}
+                  pinName="Input"
+                  execId={executionResultId}
+                />
+                <Button
+                  variant="secondary"
+                  size="small"
+                  onClick={() => handleCopy("input", inputData)}
+                  className={cn(
+                    "h-fit min-w-0 gap-1.5 border border-zinc-200 p-2 text-black hover:text-slate-900",
+                    copiedKey === "input" &&
+                      "border-green-400 bg-green-100 hover:border-green-400 hover:bg-green-200",
+                  )}
+                >
+                  {copiedKey === "input" ? (
+                    <CheckIcon size={12} className="text-green-600" />
+                  ) : (
+                    <CopyIcon size={12} />
+                  )}
+                </Button>
               </div>
+            </div>
 
-              {Object.entries(outputData)
-                .slice(0, 2)
-                .map(([key, value]) => (
-                  <div key={key} className="flex flex-col gap-2">
-                    <div className="flex items-center gap-2">
-                      <Text
-                        variant="small-medium"
-                        className="!font-semibold text-slate-600"
-                      >
-                        Pin:
-                      </Text>
-                      <Text variant="small" className="text-slate-700">
-                        {beautifyString(key)}
-                      </Text>
-                    </div>
-                    <div className="w-full space-y-2">
-                      <Text
-                        variant="small"
-                        className="!font-semibold text-slate-600"
-                      >
-                        Data:
-                      </Text>
-                      <div className="relative space-y-2">
-                        {value.map((item, index) => (
-                          <div key={index}>
-                            <ContentRenderer value={item} shortContent={true} />
-                          </div>
-                        ))}
-
-                        <div className="mt-1 flex justify-end gap-1">
-                          <NodeDataViewer
-                            data={value}
-                            pinName={key}
-                            execId={executionResultId}
-                          />
-                          <Button
-                            variant="secondary"
-                            size="small"
-                            onClick={() => handleCopy(key, value)}
-                            className={cn(
-                              "h-fit min-w-0 gap-1.5 border border-zinc-200 p-2 text-black hover:text-slate-900",
-                              copiedKey === key &&
-                                "border-green-400 bg-green-100 hover:border-green-400 hover:bg-green-200",
-                            )}
-                          >
-                            {copiedKey === key ? (
-                              <CheckIcon size={12} className="text-green-600" />
-                            ) : (
-                              <CopyIcon size={12} />
-                            )}
-                          </Button>
+            {Object.entries(outputData)
+              .slice(0, 2)
+              .map(([key, value]) => (
+                <div key={key} className="flex flex-col gap-2">
+                  <div className="flex items-center gap-2">
+                    <Text
+                      variant="small-medium"
+                      className="!font-semibold text-slate-600"
+                    >
+                      Pin:
+                    </Text>
+                    <Text variant="small" className="text-slate-700">
+                      {beautifyString(key)}
+                    </Text>
+                  </div>
+                  <div className="w-full space-y-2">
+                    <Text
+                      variant="small"
+                      className="!font-semibold text-slate-600"
+                    >
+                      Data:
+                    </Text>
+                    <div className="relative space-y-2">
+                      {value.map((item, index) => (
+                        <div key={index}>
+                          <ContentRenderer value={item} shortContent={true} />
                         </div>
+                      ))}
+
+                      <div className="mt-1 flex justify-end gap-1">
+                        <NodeDataViewer
+                          data={value}
+                          pinName={key}
+                          execId={executionResultId}
+                        />
+                        <Button
+                          variant="secondary"
+                          size="small"
+                          onClick={() => handleCopy(key, value)}
+                          className={cn(
+                            "h-fit min-w-0 gap-1.5 border border-zinc-200 p-2 text-black hover:text-slate-900",
+                            copiedKey === key &&
+                              "border-green-400 bg-green-100 hover:border-green-400 hover:bg-green-200",
+                          )}
+                        >
+                          {copiedKey === key ? (
+                            <CheckIcon size={12} className="text-green-600" />
+                          ) : (
+                            <CopyIcon size={12} />
+                          )}
+                        </Button>
                       </div>
                     </div>
                   </div>
-                ))}
-            </div>
+                </div>
+              ))}
+          </div>
 
-            {Object.keys(outputData).length > 2 && (
-              <ViewMoreData
-                outputData={outputData}
-                execId={executionResultId}
-              />
-            )}
-          </AccordionContent>
-        </AccordionItem>
-      </Accordion>
+          {Object.keys(outputData).length > 2 && (
+            <ViewMoreData outputData={outputData} execId={executionResultId} />
+          )}
+        </>
+      )}
     </div>
   );
 };

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

@@ -4,6 +4,7 @@ import { useShallow } from "zustand/react/shallow";
 import { useState } from "react";
 
 export const useNodeOutput = (nodeId: string) => {
+  const [isExpanded, setIsExpanded] = useState(true);
   const [copiedKey, setCopiedKey] = useState<string | null>(null);
   const { toast } = useToast();
 
@@ -36,10 +37,13 @@ export const useNodeOutput = (nodeId: string) => {
     }
   };
   return {
-    outputData,
-    inputData,
-    copiedKey,
-    handleCopy,
+    outputData: outputData,
+    inputData: inputData,
+    isExpanded: isExpanded,
+    setIsExpanded: setIsExpanded,
+    copiedKey: copiedKey,
+    setCopiedKey: setCopiedKey,
+    handleCopy: handleCopy,
     executionResultId: nodeExecutionResult?.node_exec_id,
   };
 };

autogpt_platform/frontend/src/app/(platform)/build/components/helper.ts

@@ -61,18 +61,12 @@ export const convertNodesPlusBlockInfoIntoCustomNodes = (
   return customNode;
 };
 
-const isToolSourceName = (sourceName: string): boolean =>
-  sourceName.startsWith("tools_^_");
-
-const cleanupSourceName = (sourceName: string): string =>
-  isToolSourceName(sourceName) ? "tools" : sourceName;
-
 export const linkToCustomEdge = (link: Link): CustomEdge => ({
   id: link.id ?? "",
   type: "custom" as const,
   source: link.source_id,
   target: link.sink_id,
-  sourceHandle: cleanupSourceName(link.source_name),
+  sourceHandle: link.source_name,
   targetHandle: link.sink_name,
   data: {
     isStatic: link.is_static,

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

@@ -5,37 +5,30 @@ import {
   AccordionItem,
   AccordionTrigger,
 } from "@/components/molecules/Accordion/Accordion";
-import {
-  CredentialsMetaInput,
-  CredentialsType,
-} from "@/lib/autogpt-server-api/types";
 import { CredentialsProvidersContext } from "@/providers/agent-credentials/credentials-provider";
-import { SlidersHorizontalIcon } from "@phosphor-icons/react";
+import { SlidersHorizontal } from "@phosphor-icons/react";
 import { useContext, useEffect, useMemo, useRef } from "react";
+import { useRunAgentModalContext } from "../../context";
 import {
   areSystemCredentialProvidersLoading,
   CredentialField,
   findSavedCredentialByProviderAndType,
   hasMissingRequiredSystemCredentials,
   splitCredentialFieldsBySystem,
-} from "./helpers";
+} from "../helpers";
 
 type Props = {
   credentialFields: CredentialField[];
   requiredCredentials: Set<string>;
-  inputCredentials: Record<string, CredentialsMetaInput | undefined>;
-  inputValues: Record<string, any>;
-  onCredentialChange: (key: string, value?: CredentialsMetaInput) => void;
 };
 
 export function CredentialsGroupedView({
   credentialFields,
   requiredCredentials,
-  inputCredentials,
-  inputValues,
-  onCredentialChange,
 }: Props) {
   const allProviders = useContext(CredentialsProvidersContext);
+  const { inputCredentials, setInputCredentialsValue, inputValues } =
+    useRunAgentModalContext();
 
   const { userCredentialFields, systemCredentialFields } = useMemo(
     () =>
@@ -94,11 +87,11 @@ export function CredentialsGroupedView({
       );
 
       if (savedCredential) {
-        onCredentialChange(key, {
+        setInputCredentialsValue(key, {
           id: savedCredential.id,
           provider: savedCredential.provider,
-          type: savedCredential.type as CredentialsType,
-          title: savedCredential.title,
+          type: savedCredential.type,
+          title: (savedCredential as { title?: string }).title,
         });
       }
     }
@@ -110,7 +103,7 @@ export function CredentialsGroupedView({
     systemCredentialFields,
     requiredCredentials,
     inputCredentials,
-    onCredentialChange,
+    setInputCredentialsValue,
     isLoadingProviders,
   ]);
 
@@ -130,7 +123,7 @@ export function CredentialsGroupedView({
                   }
                   selectedCredentials={selectedCred}
                   onSelectCredentials={(value) => {
-                    onCredentialChange(key, value);
+                    setInputCredentialsValue(key, value);
                   }}
                   siblingInputs={inputValues}
                   isOptional={!requiredCredentials.has(key)}
@@ -150,8 +143,7 @@ export function CredentialsGroupedView({
           <AccordionItem value="system-credentials" className="border-none">
             <AccordionTrigger className="py-2 text-sm text-muted-foreground hover:no-underline">
               <div className="flex items-center gap-1">
-                <SlidersHorizontalIcon size={16} weight="bold" /> System
-                credentials
+                <SlidersHorizontal size={16} weight="bold" /> System credentials
                 {hasMissingSystemCredentials && (
                   <span className="text-destructive">(missing)</span>
                 )}
@@ -171,7 +163,7 @@ export function CredentialsGroupedView({
                         }
                         selectedCredentials={selectedCred}
                         onSelectCredentials={(value) => {
-                          onCredentialChange(key, value);
+                          setInputCredentialsValue(key, value);
                         }}
                         siblingInputs={inputValues}
                         isOptional={!requiredCredentials.has(key)}

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

@@ -1,9 +1,9 @@
 import { Input } from "@/components/atoms/Input/Input";
-import { CredentialsGroupedView } from "@/components/contextual/CredentialsInput/components/CredentialsGroupedView/CredentialsGroupedView";
 import { InformationTooltip } from "@/components/molecules/InformationTooltip/InformationTooltip";
 import { useMemo } from "react";
 import { RunAgentInputs } from "../../../RunAgentInputs/RunAgentInputs";
 import { useRunAgentModalContext } from "../../context";
+import { CredentialsGroupedView } from "../CredentialsGroupedView/CredentialsGroupedView";
 import { ModalSection } from "../ModalSection/ModalSection";
 import { WebhookTriggerBanner } from "../WebhookTriggerBanner/WebhookTriggerBanner";
 
@@ -19,8 +19,6 @@ export function ModalRunSection() {
     setInputValue,
     agentInputFields,
     agentCredentialsInputFields,
-    inputCredentials,
-    setInputCredentialsValue,
   } = useRunAgentModalContext();
 
   const inputFields = Object.entries(agentInputFields || {});
@@ -104,9 +102,6 @@ export function ModalRunSection() {
           <CredentialsGroupedView
             credentialFields={credentialFields}
             requiredCredentials={requiredCredentials}
-            inputCredentials={inputCredentials}
-            inputValues={inputValues}
-            onCredentialChange={setInputCredentialsValue}
           />
         </ModalSection>
       ) : null}

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

@@ -1,5 +1,5 @@
 import { CredentialsProvidersContextType } from "@/providers/agent-credentials/credentials-provider";
-import { getSystemCredentials } from "../../helpers";
+import { getSystemCredentials } from "../../../../../../../../../../../components/contextual/CredentialsInput/helpers";
 
 export type CredentialField = [string, any];
 

autogpt_platform/frontend/src/app/(platform)/marketplace/components/AgentsSection/AgentsSection.tsx

@@ -5,8 +5,6 @@ import {
   CarouselContent,
   CarouselItem,
 } from "@/components/__legacy__/ui/carousel";
-import { FadeIn } from "@/components/molecules/FadeIn/FadeIn";
-import { StaggeredList } from "@/components/molecules/StaggeredList/StaggeredList";
 import { useAgentsSection } from "./useAgentsSection";
 import { StoreAgent } from "@/app/api/__generated__/models/storeAgent";
 import { StoreCard } from "../StoreCard/StoreCard";
@@ -43,14 +41,12 @@ export const AgentsSection = ({
   return (
     <div className="flex flex-col items-center justify-center">
       <div className="w-full max-w-[1360px]">
-        <FadeIn direction="left" duration={0.5}>
-          <h2
-            style={{ marginBottom: margin }}
-            className="font-poppins text-lg font-semibold text-neutral-800 dark:text-neutral-200"
-          >
-            {sectionTitle}
-          </h2>
-        </FadeIn>
+        <h2
+          style={{ marginBottom: margin }}
+          className="font-poppins text-lg font-semibold text-[#282828] dark:text-neutral-200"
+        >
+          {sectionTitle}
+        </h2>
         {!displayedAgents || displayedAgents.length === 0 ? (
           <div className="text-center text-gray-500 dark:text-gray-400">
             No agents found
@@ -58,38 +54,32 @@ export const AgentsSection = ({
         ) : (
           <>
             {/* Mobile Carousel View */}
-            <FadeIn direction="up" className="md:hidden">
-              <Carousel
-                opts={{
-                  loop: true,
-                }}
-              >
-                <CarouselContent>
-                  {displayedAgents.map((agent, index) => (
-                    <CarouselItem key={index} className="min-w-64 max-w-71">
-                      <StoreCard
-                        agentName={agent.agent_name}
-                        agentImage={agent.agent_image}
-                        description={agent.description}
-                        runs={agent.runs}
-                        rating={agent.rating}
-                        avatarSrc={agent.creator_avatar}
-                        creatorName={agent.creator}
-                        hideAvatar={hideAvatars}
-                        onClick={() => handleCardClick(agent.creator, agent.slug)}
-                      />
-                    </CarouselItem>
-                  ))}
-                </CarouselContent>
-              </Carousel>
-            </FadeIn>
-
-            {/* Desktop Grid View with Staggered Animation */}
-            <StaggeredList
-              direction="up"
-              staggerDelay={0.08}
-              className="hidden grid-cols-1 place-items-center gap-6 md:grid md:grid-cols-2 lg:grid-cols-3 2xl:grid-cols-4"
+            <Carousel
+              className="md:hidden"
+              opts={{
+                loop: true,
+              }}
             >
+              <CarouselContent>
+                {displayedAgents.map((agent, index) => (
+                  <CarouselItem key={index} className="min-w-64 max-w-71">
+                    <StoreCard
+                      agentName={agent.agent_name}
+                      agentImage={agent.agent_image}
+                      description={agent.description}
+                      runs={agent.runs}
+                      rating={agent.rating}
+                      avatarSrc={agent.creator_avatar}
+                      creatorName={agent.creator}
+                      hideAvatar={hideAvatars}
+                      onClick={() => handleCardClick(agent.creator, agent.slug)}
+                    />
+                  </CarouselItem>
+                ))}
+              </CarouselContent>
+            </Carousel>
+
+            <div className="hidden grid-cols-1 place-items-center gap-6 md:grid md:grid-cols-2 lg:grid-cols-3 2xl:grid-cols-4">
               {displayedAgents.map((agent, index) => (
                 <StoreCard
                   key={index}
@@ -104,7 +94,7 @@ export const AgentsSection = ({
                   onClick={() => handleCardClick(agent.creator, agent.slug)}
                 />
               ))}
-            </StaggeredList>
+            </div>
           </>
         )}
       </div>

autogpt_platform/frontend/src/app/(platform)/marketplace/components/BecomeACreator/BecomeACreator.tsx

@@ -38,7 +38,7 @@ export function BecomeACreator({
 
         <PublishAgentModal
           trigger={
-            <button className="inline-flex h-[48px] cursor-pointer items-center justify-center rounded-[38px] bg-neutral-800 px-8 py-3 transition-colors hover:bg-neutral-700 focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-neutral-950 focus-visible:ring-offset-2 dark:bg-neutral-700 dark:hover:bg-neutral-600 dark:focus-visible:ring-neutral-50 md:h-[56px] md:px-10 md:py-4 lg:h-[68px] lg:px-12 lg:py-5">
+            <button className="inline-flex h-[48px] cursor-pointer items-center justify-center rounded-[38px] bg-neutral-800 px-8 py-3 transition-colors hover:bg-neutral-700 dark:bg-neutral-700 dark:hover:bg-neutral-600 md:h-[56px] md:px-10 md:py-4 lg:h-[68px] lg:px-12 lg:py-5">
               <span className="whitespace-nowrap font-poppins text-base font-medium leading-normal text-neutral-50 md:text-lg md:leading-relaxed lg:text-xl lg:leading-7">
                 {buttonText}
               </span>

autogpt_platform/frontend/src/app/(platform)/marketplace/components/CreatorCard/CreatorCard.tsx

@@ -20,18 +20,9 @@ export const CreatorCard = ({
 }: CreatorCardProps) => {
   return (
     <div
-      className={`h-[264px] w-full px-[18px] pb-5 pt-6 ${backgroundColor(index)} inline-flex cursor-pointer flex-col items-start justify-start gap-3.5 rounded-[26px] transition-[filter] duration-200 hover:brightness-95 focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-neutral-950 focus-visible:ring-offset-2 dark:focus-visible:ring-neutral-50`}
+      className={`h-[264px] w-full px-[18px] pb-5 pt-6 ${backgroundColor(index)} inline-flex cursor-pointer flex-col items-start justify-start gap-3.5 rounded-[26px] transition-all duration-200 hover:brightness-95`}
       onClick={onClick}
-      onKeyDown={(e) => {
-        if (e.key === "Enter" || e.key === " ") {
-          e.preventDefault();
-          onClick();
-        }
-      }}
       data-testid="creator-card"
-      role="button"
-      tabIndex={0}
-      aria-label={`View ${creatorName}'s profile - ${agentsUploaded} agents`}
     >
       <div className="relative h-[64px] w-[64px]">
         <div className="absolute inset-0 overflow-hidden rounded-full">

autogpt_platform/frontend/src/app/(platform)/marketplace/components/FeaturedCreators/FeaturedCreators.tsx

@@ -1,7 +1,5 @@
 "use client";
 
-import { FadeIn } from "@/components/molecules/FadeIn/FadeIn";
-import { StaggeredList } from "@/components/molecules/StaggeredList/StaggeredList";
 import { CreatorCard } from "../CreatorCard/CreatorCard";
 import { useFeaturedCreators } from "./useFeaturedCreators";
 import { Creator } from "@/app/api/__generated__/models/creator";
@@ -21,17 +19,11 @@ export const FeaturedCreators = ({
   return (
     <div className="flex w-full flex-col items-center justify-center">
       <div className="w-full max-w-[1360px]">
-        <FadeIn direction="left" duration={0.5}>
-          <h2 className="mb-9 font-poppins text-lg font-semibold text-neutral-800 dark:text-neutral-200">
-            {title}
-          </h2>
-        </FadeIn>
+        <h2 className="mb-9 font-poppins text-lg font-semibold text-neutral-800 dark:text-neutral-200">
+          {title}
+        </h2>
 
-        <StaggeredList
-          direction="up"
-          staggerDelay={0.1}
-          className="grid grid-cols-1 gap-6 md:grid-cols-2 lg:grid-cols-4"
-        >
+        <div className="grid grid-cols-1 gap-6 md:grid-cols-2 lg:grid-cols-4">
           {displayedCreators.map((creator, index) => (
             <CreatorCard
               key={index}
@@ -43,7 +35,7 @@ export const FeaturedCreators = ({
               index={index}
             />
           ))}
-        </StaggeredList>
+        </div>
       </div>
     </div>
   );

autogpt_platform/frontend/src/app/(platform)/marketplace/components/FeaturedSection/FeaturedSection.tsx

@@ -8,7 +8,6 @@ import {
   CarouselNext,
   CarouselIndicator,
 } from "@/components/__legacy__/ui/carousel";
-import { FadeIn } from "@/components/molecules/FadeIn/FadeIn";
 import Link from "next/link";
 import { useFeaturedSection } from "./useFeaturedSection";
 import { StoreAgent } from "@/app/api/__generated__/models/storeAgent";
@@ -26,44 +25,40 @@ export const FeaturedSection = ({ featuredAgents }: FeaturedSectionProps) => {
 
   return (
     <section className="w-full">
-      <FadeIn direction="left" duration={0.5}>
-        <h2 className="mb-8 font-poppins text-2xl font-semibold leading-7 text-neutral-800 dark:text-neutral-200">
-          Featured agents
-        </h2>
-      </FadeIn>
+      <h2 className="mb-8 font-poppins text-2xl font-semibold leading-7 text-neutral-800 dark:text-neutral-200">
+        Featured agents
+      </h2>
 
-      <FadeIn direction="up" duration={0.6} delay={0.1}>
-        <Carousel
-          opts={{
-            align: "center",
-            containScroll: "trimSnaps",
-          }}
-        >
-          <CarouselContent>
-            {featuredAgents.map((agent, index) => (
-              <CarouselItem
-                key={index}
-                className="h-[480px] md:basis-1/2 lg:basis-1/3"
+      <Carousel
+        opts={{
+          align: "center",
+          containScroll: "trimSnaps",
+        }}
+      >
+        <CarouselContent>
+          {featuredAgents.map((agent, index) => (
+            <CarouselItem
+              key={index}
+              className="h-[480px] md:basis-1/2 lg:basis-1/3"
+            >
+              <Link
+                href={`/marketplace/agent/${encodeURIComponent(agent.creator)}/${encodeURIComponent(agent.slug)}`}
+                className="block h-full"
               >
-                <Link
-                  href={`/marketplace/agent/${encodeURIComponent(agent.creator)}/${encodeURIComponent(agent.slug)}`}
-                  className="block h-full"
-                >
-                  <FeaturedAgentCard
-                    agent={agent}
-                    backgroundColor={getBackgroundColor(index)}
-                  />
-                </Link>
-              </CarouselItem>
-            ))}
-          </CarouselContent>
-          <div className="relative mt-4">
-            <CarouselIndicator />
-            <CarouselPrevious afterClick={handlePrevSlide} />
-            <CarouselNext afterClick={handleNextSlide} />
-          </div>
-        </Carousel>
-      </FadeIn>
+                <FeaturedAgentCard
+                  agent={agent}
+                  backgroundColor={getBackgroundColor(index)}
+                />
+              </Link>
+            </CarouselItem>
+          ))}
+        </CarouselContent>
+        <div className="relative mt-4">
+          <CarouselIndicator />
+          <CarouselPrevious afterClick={handlePrevSlide} />
+          <CarouselNext afterClick={handleNextSlide} />
+        </div>
+      </Carousel>
     </section>
   );
 };

autogpt_platform/frontend/src/app/(platform)/marketplace/components/FilterChips/FilterChips.tsx

@@ -1,6 +1,6 @@
 "use client";
 
-import { FilterChip } from "@/components/atoms/FilterChip/FilterChip";
+import { Badge } from "@/components/__legacy__/ui/badge";
 import { useFilterChips } from "./useFilterChips";
 
 interface FilterChipsProps {
@@ -9,6 +9,8 @@ interface FilterChipsProps {
   multiSelect?: boolean;
 }
 
+// Some flaws in its logic
+// FRONTEND-TODO : This needs to be fixed
 export const FilterChips = ({
   badges,
   onFilterChange,
@@ -20,20 +22,18 @@ export const FilterChips = ({
   });
 
   return (
-    <div
-      className="flex h-auto min-h-8 flex-wrap items-center justify-center gap-3 lg:min-h-14 lg:justify-start lg:gap-5"
-      role="group"
-      aria-label="Filter options"
-    >
+    <div className="flex h-auto min-h-8 flex-wrap items-center justify-center gap-3 lg:min-h-14 lg:justify-start lg:gap-5">
       {badges.map((badge) => (
-        <FilterChip
+        <Badge
           key={badge}
-          label={badge}
-          selected={selectedFilters.includes(badge)}
+          variant={selectedFilters.includes(badge) ? "secondary" : "outline"}
+          className="mb-2 flex cursor-pointer items-center justify-center gap-2 rounded-full border border-black/50 px-3 py-1 dark:border-white/50 lg:mb-3 lg:gap-2.5 lg:px-6 lg:py-2"
           onClick={() => handleBadgeClick(badge)}
-          size="lg"
-          className="mb-2 lg:mb-3"
-        />
+        >
+          <div className="text-sm font-light tracking-tight text-[#474747] dark:text-[#e0e0e0] lg:text-xl lg:font-medium lg:leading-9">
+            {badge}
+          </div>
+        </Badge>
       ))}
     </div>
   );

autogpt_platform/frontend/src/app/(platform)/marketplace/components/HeroSection/HeroSection.tsx

@@ -1,6 +1,5 @@
 "use client";
 
-import { FadeIn } from "@/components/molecules/FadeIn/FadeIn";
 import { FilterChips } from "../FilterChips/FilterChips";
 import { SearchBar } from "../SearchBar/SearchBar";
 import { useHeroSection } from "./useHeroSection";
@@ -10,36 +9,30 @@ export const HeroSection = () => {
   return (
     <div className="mb-2 mt-8 flex flex-col items-center justify-center px-4 sm:mb-4 sm:mt-12 sm:px-6 md:mb-6 md:mt-16 lg:my-24 lg:px-8 xl:my-16">
       <div className="w-full max-w-3xl lg:max-w-4xl xl:max-w-5xl">
-        <FadeIn direction="down" duration={0.6} delay={0}>
-          <div className="mb-4 text-center md:mb-8">
-            <h1 className="text-center">
-              <span className="font-poppins text-[48px] font-semibold leading-[54px] text-neutral-950 dark:text-neutral-50">
-                Explore AI agents built for{" "}
-              </span>
-              <span className="font-poppins text-[48px] font-semibold leading-[54px] text-violet-600">
-                you
-              </span>
-              <br />
-              <span className="font-poppins text-[48px] font-semibold leading-[54px] text-neutral-950 dark:text-neutral-50">
-                by the{" "}
-              </span>
-              <span className="font-poppins text-[48px] font-semibold leading-[54px] text-blue-500">
-                community
-              </span>
-            </h1>
-          </div>
-        </FadeIn>
-        <FadeIn direction="up" duration={0.6} delay={0.15}>
-          <h3 className="mb:text-2xl mb-6 text-center font-sans text-xl font-normal leading-loose text-neutral-700 dark:text-neutral-300 md:mb-12">
-            Bringing you AI agents designed by thinkers from around the world
-          </h3>
-        </FadeIn>
-        <FadeIn direction="up" duration={0.5} delay={0.3}>
-          <div className="mb-4 flex justify-center sm:mb-5">
-            <SearchBar height="h-[74px]" />
-          </div>
-        </FadeIn>
-        <FadeIn direction="up" duration={0.5} delay={0.4}>
+        <div className="mb-4 text-center md:mb-8">
+          <h1 className="text-center">
+            <span className="font-poppins text-[48px] font-semibold leading-[54px] text-neutral-950 dark:text-neutral-50">
+              Explore AI agents built for{" "}
+            </span>
+            <span className="font-poppins text-[48px] font-semibold leading-[54px] text-violet-600">
+              you
+            </span>
+            <br />
+            <span className="font-poppins text-[48px] font-semibold leading-[54px] text-neutral-950 dark:text-neutral-50">
+              by the{" "}
+            </span>
+            <span className="font-poppins text-[48px] font-semibold leading-[54px] text-blue-500">
+              community
+            </span>
+          </h1>
+        </div>
+        <h3 className="mb:text-2xl mb-6 text-center font-sans text-xl font-normal leading-loose text-neutral-700 dark:text-neutral-300 md:mb-12">
+          Bringing you AI agents designed by thinkers from around the world
+        </h3>
+        <div className="mb-4 flex justify-center sm:mb-5">
+          <SearchBar height="h-[74px]" />
+        </div>
+        <div>
           <div className="flex justify-center">
             <FilterChips
               badges={searchTerms}
@@ -47,7 +40,7 @@ export const HeroSection = () => {
               multiSelect={false}
             />
           </div>
-        </FadeIn>
+        </div>
       </div>
     </div>
   );

autogpt_platform/frontend/src/app/(platform)/marketplace/components/MainMarketplacePage/MainMarketplacePage.tsx

@@ -1,6 +1,5 @@
 "use client";
-import { Separator } from "@/components/atoms/Separator/Separator";
-import { FadeIn } from "@/components/molecules/FadeIn/FadeIn";
+import { Separator } from "@/components/__legacy__/ui/separator";
 import { FeaturedSection } from "../FeaturedSection/FeaturedSection";
 import { BecomeACreator } from "../BecomeACreator/BecomeACreator";
 import { HeroSection } from "../HeroSection/HeroSection";
@@ -55,13 +54,11 @@ export const MainMarkeplacePage = () => {
           <FeaturedCreators featuredCreators={featuredCreators.creators} />
         )}
         <Separator className="mb-[25px] mt-[60px]" />
-        <FadeIn direction="up" duration={0.6}>
-          <BecomeACreator
-            title="Become a Creator"
-            description="Join our ever-growing community of hackers and tinkerers"
-            buttonText="Become a Creator"
-          />
-        </FadeIn>
+        <BecomeACreator
+          title="Become a Creator"
+          description="Join our ever-growing community of hackers and tinkerers"
+          buttonText="Become a Creator"
+        />
       </main>
     </div>
   );

autogpt_platform/frontend/src/app/(platform)/marketplace/components/SearchBar/SearchBar.tsx

@@ -16,9 +16,9 @@ interface SearchBarProps {
 export const SearchBar = ({
   placeholder = 'Search for tasks like "optimise SEO"',
   backgroundColor = "bg-neutral-100 dark:bg-neutral-800",
-  iconColor = "text-neutral-500 dark:text-neutral-400",
-  textColor = "text-neutral-500 dark:text-neutral-200",
-  placeholderColor = "text-neutral-500 dark:text-neutral-400",
+  iconColor = "text-[#646464] dark:text-neutral-400",
+  textColor = "text-[#707070] dark:text-neutral-200",
+  placeholderColor = "text-[#707070] dark:text-neutral-400",
   width = "w-9/10 lg:w-[56.25rem]",
   height = "h-[60px]",
 }: SearchBarProps) => {
@@ -32,13 +32,10 @@ export const SearchBar = ({
     >
       <MagnifyingGlassIcon className={`h-5 w-5 md:h-7 md:w-7 ${iconColor}`} />
       <input
-        type="search"
-        name="search"
-        autoComplete="off"
+        type="text"
         value={searchQuery}
         onChange={(e) => setSearchQuery(e.target.value)}
         placeholder={placeholder}
-        aria-label="Search for AI agents"
         className={`flex-grow border-none bg-transparent ${textColor} font-sans text-lg font-normal leading-[2.25rem] tracking-tight md:text-xl placeholder:${placeholderColor} focus:outline-none`}
         data-testid="store-search-input"
       />

autogpt_platform/frontend/src/app/(platform)/marketplace/components/StoreCard/StoreCard.tsx

@@ -1,25 +1,10 @@
 import Image from "next/image";
-import { Star } from "@phosphor-icons/react";
+import { StarRatingIcons } from "@/components/__legacy__/ui/icons";
 import Avatar, {
   AvatarFallback,
   AvatarImage,
 } from "@/components/atoms/Avatar/Avatar";
 
-function StarRating({ rating }: { rating: number }) {
-  const stars = [];
-  const clampedRating = Math.max(0, Math.min(5, rating));
-  for (let i = 1; i <= 5; i++) {
-    stars.push(
-      <Star
-        key={i}
-        weight={i <= clampedRating ? "fill" : "regular"}
-        className="h-4 w-4 text-neutral-900 dark:text-yellow-500"
-      />,
-    );
-  }
-  return <>{stars}</>;
-}
-
 interface StoreCardProps {
   agentName: string;
   agentImage: string;
@@ -49,7 +34,7 @@ export const StoreCard: React.FC<StoreCardProps> = ({
 
   return (
     <div
-      className="flex h-[27rem] w-full max-w-md cursor-pointer flex-col items-start rounded-3xl bg-background transition-shadow duration-300 hover:shadow-lg focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-neutral-950 focus-visible:ring-offset-2 dark:hover:shadow-gray-700 dark:focus-visible:ring-neutral-50"
+      className="flex h-[27rem] w-full max-w-md cursor-pointer flex-col items-start rounded-3xl bg-background transition-all duration-300 hover:shadow-lg dark:hover:shadow-gray-700"
       onClick={handleClick}
       data-testid="store-card"
       role="button"
@@ -91,7 +76,7 @@ export const StoreCard: React.FC<StoreCardProps> = ({
       <div className="mt-3 flex w-full flex-1 flex-col px-4">
         {/* Second Section: Agent Name and Creator Name */}
         <div className="flex w-full flex-col">
-          <h3 className="line-clamp-2 font-poppins text-2xl font-semibold text-neutral-800 dark:text-neutral-100">
+          <h3 className="line-clamp-2 font-poppins text-2xl font-semibold text-[#272727] dark:text-neutral-100">
             {agentName}
           </h3>
           {!hideAvatar && creatorName && (
@@ -122,11 +107,11 @@ export const StoreCard: React.FC<StoreCardProps> = ({
                 {rating.toFixed(1)}
               </span>
               <div
-                className="inline-flex items-center gap-0.5"
+                className="inline-flex items-center"
                 role="img"
                 aria-label={`Rating: ${rating.toFixed(1)} out of 5 stars`}
               >
-                <StarRating rating={rating} />
+                {StarRatingIcons(rating)}
               </div>
             </div>
           </div>

autogpt_platform/frontend/src/components/atoms/FilterChip/FilterChip.stories.tsx

@@ -1,151 +0,0 @@
-import type { Meta, StoryObj } from "@storybook/react";
-import { useState } from "react";
-import { FilterChip } from "./FilterChip";
-
-const meta: Meta<typeof FilterChip> = {
-  title: "Atoms/FilterChip",
-  component: FilterChip,
-  tags: ["autodocs"],
-  parameters: {
-    layout: "centered",
-  },
-  argTypes: {
-    size: {
-      control: "select",
-      options: ["sm", "md", "lg"],
-    },
-  },
-};
-
-export default meta;
-type Story = StoryObj<typeof FilterChip>;
-
-export const Default: Story = {
-  args: {
-    label: "Marketing",
-  },
-};
-
-export const Selected: Story = {
-  args: {
-    label: "Marketing",
-    selected: true,
-  },
-};
-
-export const Dismissible: Story = {
-  args: {
-    label: "Marketing",
-    selected: true,
-    dismissible: true,
-  },
-};
-
-export const Sizes: Story = {
-  render: () => (
-    <div className="flex items-center gap-4">
-      <FilterChip label="Small" size="sm" />
-      <FilterChip label="Medium" size="md" />
-      <FilterChip label="Large" size="lg" />
-    </div>
-  ),
-};
-
-export const Disabled: Story = {
-  args: {
-    label: "Disabled",
-    disabled: true,
-  },
-};
-
-function FilterChipGroupDemo() {
-  const filters = [
-    "Marketing",
-    "Sales",
-    "Development",
-    "Design",
-    "Research",
-    "Analytics",
-  ];
-  const [selected, setSelected] = useState<string[]>(["Marketing"]);
-
-  function handleToggle(filter: string) {
-    setSelected((prev) =>
-      prev.includes(filter)
-        ? prev.filter((f) => f !== filter)
-        : [...prev, filter],
-    );
-  }
-
-  return (
-    <div className="flex flex-wrap gap-3">
-      {filters.map((filter) => (
-        <FilterChip
-          key={filter}
-          label={filter}
-          selected={selected.includes(filter)}
-          onClick={() => handleToggle(filter)}
-        />
-      ))}
-    </div>
-  );
-}
-
-export const FilterGroup: Story = {
-  render: () => <FilterChipGroupDemo />,
-};
-
-function SingleSelectDemo() {
-  const filters = ["All", "Featured", "Popular", "New"];
-  const [selected, setSelected] = useState("All");
-
-  return (
-    <div className="flex flex-wrap gap-3">
-      {filters.map((filter) => (
-        <FilterChip
-          key={filter}
-          label={filter}
-          selected={selected === filter}
-          onClick={() => setSelected(filter)}
-        />
-      ))}
-    </div>
-  );
-}
-
-export const SingleSelect: Story = {
-  render: () => <SingleSelectDemo />,
-};
-
-function DismissibleDemo() {
-  const [filters, setFilters] = useState([
-    "Marketing",
-    "Sales",
-    "Development",
-  ]);
-
-  function handleDismiss(filter: string) {
-    setFilters((prev) => prev.filter((f) => f !== filter));
-  }
-
-  return (
-    <div className="flex flex-wrap gap-3">
-      {filters.map((filter) => (
-        <FilterChip
-          key={filter}
-          label={filter}
-          selected
-          dismissible
-          onDismiss={() => handleDismiss(filter)}
-        />
-      ))}
-      {filters.length === 0 && (
-        <span className="text-neutral-500">No filters selected</span>
-      )}
-    </div>
-  );
-}
-
-export const DismissibleGroup: Story = {
-  render: () => <DismissibleDemo />,
-};

autogpt_platform/frontend/src/components/atoms/FilterChip/FilterChip.tsx

@@ -1,100 +0,0 @@
-"use client";
-
-import { cn } from "@/lib/utils";
-import { X } from "@phosphor-icons/react";
-
-type FilterChipSize = "sm" | "md" | "lg";
-
-interface FilterChipProps {
-  /** The label text displayed in the chip */
-  label: string;
-  /** Whether the chip is currently selected */
-  selected?: boolean;
-  /** Callback when the chip is clicked */
-  onClick?: () => void;
-  /** Whether to show a dismiss/remove button */
-  dismissible?: boolean;
-  /** Callback when the dismiss button is clicked */
-  onDismiss?: () => void;
-  /** Size variant of the chip */
-  size?: FilterChipSize;
-  /** Whether the chip is disabled */
-  disabled?: boolean;
-  /** Additional CSS classes */
-  className?: string;
-}
-
-const sizeStyles: Record<FilterChipSize, string> = {
-  sm: "px-3 py-1 text-sm gap-1.5",
-  md: "px-4 py-1.5 text-base gap-2",
-  lg: "px-6 py-2 text-lg gap-2.5 lg:text-xl lg:leading-9",
-};
-
-const iconSizes: Record<FilterChipSize, string> = {
-  sm: "h-3 w-3",
-  md: "h-4 w-4",
-  lg: "h-5 w-5",
-};
-
-/**
- * A filter chip component for selecting/deselecting filter options.
- * Supports single and multi-select patterns with proper accessibility.
- */
-export function FilterChip({
-  label,
-  selected = false,
-  onClick,
-  dismissible = false,
-  onDismiss,
-  size = "md",
-  disabled = false,
-  className,
-}: FilterChipProps) {
-  function handleDismiss(e: React.MouseEvent) {
-    e.stopPropagation();
-    onDismiss?.();
-  }
-
-  return (
-    <button
-      type="button"
-      onClick={onClick}
-      disabled={disabled}
-      aria-pressed={selected}
-      className={cn(
-        // Base styles
-        "inline-flex items-center justify-center rounded-full border font-medium transition-colors",
-        // Focus styles
-        "focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-neutral-950 focus-visible:ring-offset-2 dark:focus-visible:ring-neutral-50",
-        // Size styles
-        sizeStyles[size],
-        // State styles
-        selected
-          ? "border-neutral-900 bg-neutral-100 text-neutral-800 dark:border-neutral-100 dark:bg-neutral-800 dark:text-neutral-200"
-          : "border-neutral-400 bg-transparent text-neutral-600 hover:bg-neutral-50 dark:border-neutral-500 dark:text-neutral-300 dark:hover:bg-neutral-800",
-        // Disabled styles
-        disabled && "pointer-events-none opacity-50",
-        className,
-      )}
-    >
-      <span>{label}</span>
-      {dismissible && selected && (
-        <span
-          role="button"
-          tabIndex={0}
-          onClick={handleDismiss}
-          onKeyDown={(e) => {
-            if (e.key === "Enter" || e.key === " ") {
-              e.preventDefault();
-              handleDismiss(e as unknown as React.MouseEvent);
-            }
-          }}
-          className="rounded-full p-0.5 hover:bg-neutral-200 dark:hover:bg-neutral-700"
-          aria-label={`Remove ${label} filter`}
-        >
-          <X className={iconSizes[size]} weight="bold" />
-        </span>
-      )}
-    </button>
-  );
-}

autogpt_platform/frontend/src/components/atoms/Separator/Separator.stories.tsx

@@ -1,72 +0,0 @@
-import type { Meta, StoryObj } from "@storybook/react";
-import { Separator } from "./Separator";
-
-const meta: Meta<typeof Separator> = {
-  title: "Atoms/Separator",
-  component: Separator,
-  tags: ["autodocs"],
-  parameters: {
-    layout: "padded",
-  },
-};
-
-export default meta;
-type Story = StoryObj<typeof Separator>;
-
-export const Horizontal: Story = {
-  render: () => (
-    <div className="w-full max-w-md">
-      <p className="mb-4 text-neutral-700 dark:text-neutral-300">
-        Content above the separator
-      </p>
-      <Separator />
-      <p className="mt-4 text-neutral-700 dark:text-neutral-300">
-        Content below the separator
-      </p>
-    </div>
-  ),
-};
-
-export const Vertical: Story = {
-  render: () => (
-    <div className="flex h-16 items-center gap-4">
-      <span className="text-neutral-700 dark:text-neutral-300">Left</span>
-      <Separator orientation="vertical" />
-      <span className="text-neutral-700 dark:text-neutral-300">Right</span>
-    </div>
-  ),
-};
-
-export const WithCustomStyles: Story = {
-  render: () => (
-    <div className="w-full max-w-md space-y-4">
-      <Separator className="bg-violet-500" />
-      <Separator className="h-0.5 bg-gradient-to-r from-violet-500 to-blue-500" />
-      <Separator className="bg-neutral-400 dark:bg-neutral-600" />
-    </div>
-  ),
-};
-
-export const InSection: Story = {
-  render: () => (
-    <div className="w-full max-w-md space-y-6">
-      <section>
-        <h2 className="mb-2 text-lg font-semibold text-neutral-900 dark:text-neutral-100">
-          Featured Agents
-        </h2>
-        <p className="text-neutral-600 dark:text-neutral-400">
-          Browse our collection of featured AI agents.
-        </p>
-      </section>
-      <Separator className="my-6" />
-      <section>
-        <h2 className="mb-2 text-lg font-semibold text-neutral-900 dark:text-neutral-100">
-          Top Creators
-        </h2>
-        <p className="text-neutral-600 dark:text-neutral-400">
-          Meet the creators behind the most popular agents.
-        </p>
-      </section>
-    </div>
-  ),
-};

autogpt_platform/frontend/src/components/atoms/Separator/Separator.tsx

@@ -1,43 +0,0 @@
-import { cn } from "@/lib/utils";
-
-type SeparatorOrientation = "horizontal" | "vertical";
-
-interface SeparatorProps {
-  /** The orientation of the separator */
-  orientation?: SeparatorOrientation;
-  /** Whether the separator is purely decorative (true) or represents a semantic boundary (false) */
-  decorative?: boolean;
-  /** Additional CSS classes */
-  className?: string;
-}
-
-/**
- * A visual separator that divides content.
- * Uses semantic `<hr>` for horizontal separators and a styled `<div>` for vertical.
- */
-export function Separator({
-  orientation = "horizontal",
-  decorative = true,
-  className,
-}: SeparatorProps) {
-  const baseStyles = "shrink-0 bg-neutral-200 dark:bg-neutral-800";
-
-  if (orientation === "horizontal") {
-    return (
-      <hr
-        className={cn(baseStyles, "h-px w-full border-0", className)}
-        aria-hidden={decorative}
-        role={decorative ? "none" : "separator"}
-      />
-    );
-  }
-
-  return (
-    <div
-      className={cn(baseStyles, "h-full w-px", className)}
-      aria-hidden={decorative}
-      role={decorative ? "none" : "separator"}
-      aria-orientation="vertical"
-    />
-  );
-}

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

@@ -1,128 +0,0 @@
-import type { Meta, StoryObj } from "@storybook/react";
-import { FadeIn } from "./FadeIn";
-
-const meta: Meta<typeof FadeIn> = {
-  title: "Molecules/FadeIn",
-  component: FadeIn,
-  tags: ["autodocs"],
-  parameters: {
-    layout: "padded",
-  },
-  argTypes: {
-    direction: {
-      control: "select",
-      options: ["up", "down", "left", "right", "none"],
-    },
-  },
-};
-
-export default meta;
-type Story = StoryObj<typeof FadeIn>;
-
-const DemoCard = ({ title }: { title: string }) => (
-  <div className="rounded-xl bg-neutral-100 p-6 dark:bg-neutral-800">
-    <h3 className="mb-2 text-lg font-semibold text-neutral-900 dark:text-neutral-100">
-      {title}
-    </h3>
-    <p className="text-neutral-600 dark:text-neutral-400">
-      This card fades in with a smooth animation.
-    </p>
-  </div>
-);
-
-export const Default: Story = {
-  args: {
-    direction: "up",
-    children: <DemoCard title="Fade Up" />,
-  },
-};
-
-export const FadeDown: Story = {
-  args: {
-    direction: "down",
-    children: <DemoCard title="Fade Down" />,
-  },
-};
-
-export const FadeLeft: Story = {
-  args: {
-    direction: "left",
-    children: <DemoCard title="Fade Left" />,
-  },
-};
-
-export const FadeRight: Story = {
-  args: {
-    direction: "right",
-    children: <DemoCard title="Fade Right" />,
-  },
-};
-
-export const FadeOnly: Story = {
-  args: {
-    direction: "none",
-    children: <DemoCard title="Fade Only (No Direction)" />,
-  },
-};
-
-export const WithDelay: Story = {
-  args: {
-    direction: "up",
-    delay: 0.5,
-    children: <DemoCard title="Delayed Fade (0.5s)" />,
-  },
-};
-
-export const SlowAnimation: Story = {
-  args: {
-    direction: "up",
-    duration: 1.5,
-    children: <DemoCard title="Slow Animation (1.5s)" />,
-  },
-};
-
-export const LargeDistance: Story = {
-  args: {
-    direction: "up",
-    distance: 60,
-    children: <DemoCard title="Large Distance (60px)" />,
-  },
-};
-
-export const MultipleElements: Story = {
-  render: () => (
-    <div className="space-y-4">
-      <FadeIn direction="up" delay={0}>
-        <DemoCard title="First Card" />
-      </FadeIn>
-      <FadeIn direction="up" delay={0.1}>
-        <DemoCard title="Second Card" />
-      </FadeIn>
-      <FadeIn direction="up" delay={0.2}>
-        <DemoCard title="Third Card" />
-      </FadeIn>
-    </div>
-  ),
-};
-
-export const HeroExample: Story = {
-  render: () => (
-    <div className="text-center">
-      <FadeIn direction="down" delay={0}>
-        <h1 className="mb-4 text-4xl font-bold text-neutral-900 dark:text-neutral-100">
-          Welcome to the Marketplace
-        </h1>
-      </FadeIn>
-      <FadeIn direction="up" delay={0.2}>
-        <p className="mb-8 text-xl text-neutral-600 dark:text-neutral-400">
-          Discover AI agents built by the community
-        </p>
-      </FadeIn>
-      <FadeIn direction="up" delay={0.4}>
-        <button className="rounded-full bg-violet-600 px-8 py-3 text-white hover:bg-violet-700">
-          Get Started
-        </button>
-      </FadeIn>
-    </div>
-  ),
-};

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

@@ -1,109 +0,0 @@
-"use client";
-
-import { cn } from "@/lib/utils";
-import { motion, useReducedMotion, type Variants } from "framer-motion";
-import { ReactNode } from "react";
-
-type FadeDirection = "up" | "down" | "left" | "right" | "none";
-
-interface FadeInProps {
-  /** Content to animate */
-  children: ReactNode;
-  /** Direction the content fades in from */
-  direction?: FadeDirection;
-  /** Distance to travel in pixels (only applies when direction is not "none") */
-  distance?: number;
-  /** Animation duration in seconds */
-  duration?: number;
-  /** Delay before animation starts in seconds */
-  delay?: number;
-  /** Whether to trigger animation when element enters viewport */
-  viewport?: boolean;
-  /** How much of element must be visible to trigger (0-1) */
-  viewportAmount?: number;
-  /** Whether animation should only trigger once */
-  once?: boolean;
-  /** Additional CSS classes */
-  className?: string;
-  /** HTML element to render as */
-  as?: keyof JSX.IntrinsicElements;
-}
-
-function getDirectionOffset(
-  direction: FadeDirection,
-  distance: number,
-): { x: number; y: number } {
-  switch (direction) {
-    case "up":
-      return { x: 0, y: distance };
-    case "down":
-      return { x: 0, y: -distance };
-    case "left":
-      return { x: distance, y: 0 };
-    case "right":
-      return { x: -distance, y: 0 };
-    case "none":
-    default:
-      return { x: 0, y: 0 };
-  }
-}
-
-/**
- * A fade-in animation wrapper component.
- * Animates children with a fade effect and optional directional slide.
- * Respects user's reduced motion preferences.
- */
-export function FadeIn({
-  children,
-  direction = "up",
-  distance = 24,
-  duration = 0.5,
-  delay = 0,
-  viewport = true,
-  viewportAmount = 0.2,
-  once = true,
-  className,
-  as = "div",
-}: FadeInProps) {
-  const shouldReduceMotion = useReducedMotion();
-  const offset = getDirectionOffset(direction, distance);
-
-  // If user prefers reduced motion, render without animation
-  if (shouldReduceMotion) {
-    const Component = as as keyof JSX.IntrinsicElements;
-    return <Component className={className}>{children}</Component>;
-  }
-
-  const variants: Variants = {
-    hidden: {
-      opacity: 0,
-      x: offset.x,
-      y: offset.y,
-    },
-    visible: {
-      opacity: 1,
-      x: 0,
-      y: 0,
-      transition: {
-        duration,
-        delay,
-        ease: [0.25, 0.1, 0.25, 1], // Custom easing for smooth feel
-      },
-    },
-  };
-
-  const MotionComponent = motion[as as keyof typeof motion] as typeof motion.div;
-
-  return (
-    <MotionComponent
-      className={cn(className)}
-      initial="hidden"
-      animate={viewport ? undefined : "visible"}
-      whileInView={viewport ? "visible" : undefined}
-      viewport={viewport ? { once, amount: viewportAmount } : undefined}
-      variants={variants}
-    >
-      {children}
-    </MotionComponent>
-  );
-}

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

@@ -1,180 +0,0 @@
-import type { Meta, StoryObj } from "@storybook/react";
-import { StaggeredList } from "./StaggeredList";
-
-const meta: Meta<typeof StaggeredList> = {
-  title: "Molecules/StaggeredList",
-  component: StaggeredList,
-  tags: ["autodocs"],
-  parameters: {
-    layout: "padded",
-  },
-  argTypes: {
-    direction: {
-      control: "select",
-      options: ["up", "down", "left", "right", "none"],
-    },
-  },
-};
-
-export default meta;
-type Story = StoryObj<typeof StaggeredList>;
-
-const DemoCard = ({ title, index }: { title: string; index: number }) => (
-  <div className="rounded-xl bg-neutral-100 p-4 dark:bg-neutral-800">
-    <h3 className="mb-1 font-semibold text-neutral-900 dark:text-neutral-100">
-      {title}
-    </h3>
-    <p className="text-sm text-neutral-600 dark:text-neutral-400">
-      Card #{index + 1} with staggered animation
-    </p>
-  </div>
-);
-
-const items = ["First Item", "Second Item", "Third Item", "Fourth Item"];
-
-export const Default: Story = {
-  args: {
-    direction: "up",
-    className: "space-y-4",
-    children: items.map((item, i) => <DemoCard key={i} title={item} index={i} />),
-  },
-};
-
-export const FadeDown: Story = {
-  args: {
-    direction: "down",
-    className: "space-y-4",
-    children: items.map((item, i) => <DemoCard key={i} title={item} index={i} />),
-  },
-};
-
-export const FadeLeft: Story = {
-  args: {
-    direction: "left",
-    className: "flex gap-4",
-    children: items.map((item, i) => <DemoCard key={i} title={item} index={i} />),
-  },
-};
-
-export const FadeRight: Story = {
-  args: {
-    direction: "right",
-    className: "flex gap-4",
-    children: items.map((item, i) => <DemoCard key={i} title={item} index={i} />),
-  },
-};
-
-export const FastStagger: Story = {
-  args: {
-    direction: "up",
-    staggerDelay: 0.05,
-    className: "space-y-4",
-    children: items.map((item, i) => <DemoCard key={i} title={item} index={i} />),
-  },
-};
-
-export const SlowStagger: Story = {
-  args: {
-    direction: "up",
-    staggerDelay: 0.3,
-    className: "space-y-4",
-    children: items.map((item, i) => <DemoCard key={i} title={item} index={i} />),
-  },
-};
-
-export const WithInitialDelay: Story = {
-  args: {
-    direction: "up",
-    initialDelay: 0.5,
-    className: "space-y-4",
-    children: items.map((item, i) => <DemoCard key={i} title={item} index={i} />),
-  },
-};
-
-export const GridLayout: Story = {
-  args: {
-    direction: "up",
-    staggerDelay: 0.08,
-    className: "grid grid-cols-2 gap-4 md:grid-cols-4",
-    children: [
-      ...items,
-      "Fifth Item",
-      "Sixth Item",
-      "Seventh Item",
-      "Eighth Item",
-    ].map((item, i) => <DemoCard key={i} title={item} index={i} />),
-  },
-};
-
-export const AgentCardsExample: Story = {
-  render: () => {
-    const agents = [
-      { name: "SEO Optimizer", runs: 1234 },
-      { name: "Content Writer", runs: 987 },
-      { name: "Data Analyzer", runs: 756 },
-      { name: "Code Reviewer", runs: 543 },
-    ];
-
-    return (
-      <StaggeredList
-        direction="up"
-        staggerDelay={0.1}
-        className="grid grid-cols-2 gap-6 md:grid-cols-4"
-      >
-        {agents.map((agent, i) => (
-          <div
-            key={i}
-            className="rounded-2xl bg-white p-4 shadow-md dark:bg-neutral-900"
-          >
-            <div className="mb-3 aspect-video rounded-xl bg-gradient-to-br from-violet-500 to-blue-500" />
-            <h3 className="mb-1 font-semibold text-neutral-900 dark:text-neutral-100">
-              {agent.name}
-            </h3>
-            <p className="text-sm text-neutral-500">{agent.runs} runs</p>
-          </div>
-        ))}
-      </StaggeredList>
-    );
-  },
-};
-
-export const CreatorCardsExample: Story = {
-  render: () => {
-    const creators = [
-      { name: "Alice", agents: 12 },
-      { name: "Bob", agents: 8 },
-      { name: "Charlie", agents: 15 },
-      { name: "Diana", agents: 6 },
-    ];
-
-    const colors = [
-      "bg-violet-100 dark:bg-violet-900/30",
-      "bg-blue-100 dark:bg-blue-900/30",
-      "bg-green-100 dark:bg-green-900/30",
-      "bg-orange-100 dark:bg-orange-900/30",
-    ];
-
-    return (
-      <StaggeredList
-        direction="up"
-        staggerDelay={0.12}
-        className="grid grid-cols-2 gap-6 md:grid-cols-4"
-      >
-        {creators.map((creator, i) => (
-          <div
-            key={i}
-            className={`rounded-2xl p-5 ${colors[i % colors.length]}`}
-          >
-            <div className="mb-3 h-12 w-12 rounded-full bg-neutral-300 dark:bg-neutral-700" />
-            <h3 className="mb-1 font-semibold text-neutral-900 dark:text-neutral-100">
-              {creator.name}
-            </h3>
-            <p className="text-sm text-neutral-600 dark:text-neutral-400">
-              {creator.agents} agents
-            </p>
-          </div>
-        ))}
-      </StaggeredList>
-    );
-  },
-};

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

@@ -1,130 +0,0 @@
-"use client";
-
-import { cn } from "@/lib/utils";
-import { motion, useReducedMotion, type Variants } from "framer-motion";
-import { ReactNode } from "react";
-
-type StaggerDirection = "up" | "down" | "left" | "right" | "none";
-
-interface StaggeredListProps {
-  /** Array of items to render with staggered animation */
-  children: ReactNode[];
-  /** Direction items animate from */
-  direction?: StaggerDirection;
-  /** Distance to travel in pixels */
-  distance?: number;
-  /** Base duration for each item's animation */
-  duration?: number;
-  /** Delay between each item's animation start */
-  staggerDelay?: number;
-  /** Initial delay before first item animates */
-  initialDelay?: number;
-  /** Whether to trigger animation when element enters viewport */
-  viewport?: boolean;
-  /** How much of container must be visible to trigger */
-  viewportAmount?: number;
-  /** Whether animation should only trigger once */
-  once?: boolean;
-  /** Additional CSS classes for the container */
-  className?: string;
-  /** Additional CSS classes for each item wrapper */
-  itemClassName?: string;
-}
-
-function getDirectionOffset(
-  direction: StaggerDirection,
-  distance: number,
-): { x: number; y: number } {
-  switch (direction) {
-    case "up":
-      return { x: 0, y: distance };
-    case "down":
-      return { x: 0, y: -distance };
-    case "left":
-      return { x: distance, y: 0 };
-    case "right":
-      return { x: -distance, y: 0 };
-    case "none":
-    default:
-      return { x: 0, y: 0 };
-  }
-}
-
-/**
- * Animates a list of children with staggered fade-in effects.
- * Each child appears sequentially with a configurable delay.
- * Respects user's reduced motion preferences.
- */
-export function StaggeredList({
-  children,
-  direction = "up",
-  distance = 20,
-  duration = 0.4,
-  staggerDelay = 0.1,
-  initialDelay = 0,
-  viewport = true,
-  viewportAmount = 0.1,
-  once = true,
-  className,
-  itemClassName,
-}: StaggeredListProps) {
-  const shouldReduceMotion = useReducedMotion();
-  const offset = getDirectionOffset(direction, distance);
-
-  // If user prefers reduced motion, render without animation
-  if (shouldReduceMotion) {
-    return (
-      <div className={className}>
-        {children.map((child, index) => (
-          <div key={index} className={itemClassName}>
-            {child}
-          </div>
-        ))}
-      </div>
-    );
-  }
-
-  const containerVariants: Variants = {
-    hidden: {},
-    visible: {
-      transition: {
-        staggerChildren: staggerDelay,
-        delayChildren: initialDelay,
-      },
-    },
-  };
-
-  const itemVariants: Variants = {
-    hidden: {
-      opacity: 0,
-      x: offset.x,
-      y: offset.y,
-    },
-    visible: {
-      opacity: 1,
-      x: 0,
-      y: 0,
-      transition: {
-        duration,
-        ease: [0.25, 0.1, 0.25, 1],
-      },
-    },
-  };
-
-  return (
-    <motion.div
-      className={cn(className)}
-      initial="hidden"
-      animate={viewport ? undefined : "visible"}
-      whileInView={viewport ? "visible" : undefined}
-      viewport={viewport ? { once, amount: viewportAmount } : undefined}
-      variants={containerVariants}
-    >
-      {children.map((child, index) => (
-        <motion.div key={index} className={itemClassName} variants={itemVariants}>
-          {child}
-        </motion.div>
-      ))}
-    </motion.div>
-  );
-}