feat(backend): add chat search tools and BM25 reranking (#11782)

This PR adds new chat tools for searching blocks and documentation,
along with BM25 reranking for improved search relevance.

### Changes 🏗️

**New Chat Tools:**
- `find_block` - Search for available blocks by name/description using
hybrid search
- `run_block` - Execute a block directly with provided inputs and
credentials
- `search_docs` - Search documentation with section-level granularity  
- `get_doc_page` - Retrieve full documentation page content

**Search Improvements:**
- Added BM25 reranking to hybrid search for better lexical relevance
- Documentation handler now chunks markdown by headings (##) for
finer-grained embeddings
- Section-based content IDs (`doc_path::section_index`) for precise doc
retrieval
- Startup embedding backfill in scheduler for immediate searchability

**Other Changes:**
- New response models for block and documentation search results
- Updated orphan cleanup to handle section-based doc embeddings
- Added `rank-bm25` dependency for BM25 scoring
- Removed max message limit check in chat service

### Checklist 📋

#### For code changes:
- [x] I have clearly listed my changes in the PR description
- [x] I have made a test plan
- [x] I have tested my changes according to the test plan:
  - [x] Run find_block tool to search for blocks (e.g., "current time")
  - [x] Run run_block tool to execute a found block
  - [x] Run search_docs tool to search documentation
  - [x] Run get_doc_page tool to retrieve full doc content
- [x] Verify BM25 reranking improves search relevance for exact term
matches
  - [x] Verify documentation sections are properly chunked and embedded

#### For configuration changes:
- [x] `.env.default` is updated or already compatible with my changes
- [x] `docker-compose.yml` is updated or already compatible with my
changes
- [x] I have included a list of my configuration changes in the PR
description (under **Changes**)

**Dependencies added:** `rank-bm25` for BM25 scoring algorithm

.dockerignore

@@ -1,6 +1,9 @@
 # Ignore everything by default, selectively add things to context
 *
 
+# Documentation (for embeddings/search)
+!docs/
+
 # Platform - Libs
 !autogpt_platform/autogpt_libs/autogpt_libs/
 !autogpt_platform/autogpt_libs/pyproject.toml

autogpt_platform/backend/Dockerfile

@@ -100,6 +100,7 @@ COPY autogpt_platform/backend/migrations /app/autogpt_platform/backend/migration
 FROM server_dependencies AS server
 
 COPY autogpt_platform/backend /app/autogpt_platform/backend
+COPY docs /app/docs
 RUN poetry install --no-ansi --only-root
 
 ENV PORT=8000

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

@@ -299,9 +299,6 @@ async def stream_chat_completion(
             f"new message_count={len(session.messages)}"
         )
 
-    if len(session.messages) > config.max_context_messages:
-        raise ValueError(f"Max messages exceeded: {config.max_context_messages}")
-
     logger.info(
         f"Upserting session: {session.session_id} with user id {session.user_id}, "
         f"message_count={len(session.messages)}"

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

@@ -8,8 +8,12 @@ from .add_understanding import AddUnderstandingTool
 from .agent_output import AgentOutputTool
 from .base import BaseTool
 from .find_agent import FindAgentTool
+from .find_block import FindBlockTool
 from .find_library_agent import FindLibraryAgentTool
+from .get_doc_page import GetDocPageTool
 from .run_agent import RunAgentTool
+from .run_block import RunBlockTool
+from .search_docs import SearchDocsTool
 
 if TYPE_CHECKING:
     from backend.api.features.chat.response_model import StreamToolOutputAvailable
@@ -18,9 +22,13 @@ if TYPE_CHECKING:
 TOOL_REGISTRY: dict[str, BaseTool] = {
     "add_understanding": AddUnderstandingTool(),
     "find_agent": FindAgentTool(),
+    "find_block": FindBlockTool(),
     "find_library_agent": FindLibraryAgentTool(),
     "run_agent": RunAgentTool(),
+    "run_block": RunBlockTool(),
     "agent_output": AgentOutputTool(),
+    "search_docs": SearchDocsTool(),
+    "get_doc_page": GetDocPageTool(),
 }
 
 # Export individual tool instances for backwards compatibility

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

@@ -0,0 +1,192 @@
+import logging
+from typing import Any
+
+from prisma.enums import ContentType
+
+from backend.api.features.chat.model import ChatSession
+from backend.api.features.chat.tools.base import BaseTool, ToolResponseBase
+from backend.api.features.chat.tools.models import (
+    BlockInfoSummary,
+    BlockInputFieldInfo,
+    BlockListResponse,
+    ErrorResponse,
+    NoResultsResponse,
+)
+from backend.api.features.store.hybrid_search import unified_hybrid_search
+from backend.data.block import get_block
+
+logger = logging.getLogger(__name__)
+
+
+class FindBlockTool(BaseTool):
+    """Tool for searching available blocks."""
+
+    @property
+    def name(self) -> str:
+        return "find_block"
+
+    @property
+    def description(self) -> str:
+        return (
+            "Search for available blocks by name or description. "
+            "Blocks are reusable components that perform specific tasks like "
+            "sending emails, making API calls, processing text, etc. "
+            "IMPORTANT: Use this tool FIRST to get the block's 'id' before calling run_block. "
+            "The response includes each block's id, required_inputs, and input_schema."
+        )
+
+    @property
+    def parameters(self) -> dict[str, Any]:
+        return {
+            "type": "object",
+            "properties": {
+                "query": {
+                    "type": "string",
+                    "description": (
+                        "Search query to find blocks by name or description. "
+                        "Use keywords like 'email', 'http', 'text', 'ai', etc."
+                    ),
+                },
+            },
+            "required": ["query"],
+        }
+
+    @property
+    def requires_auth(self) -> bool:
+        return True
+
+    async def _execute(
+        self,
+        user_id: str | None,
+        session: ChatSession,
+        **kwargs,
+    ) -> ToolResponseBase:
+        """Search for blocks matching the query.
+
+        Args:
+            user_id: User ID (required)
+            session: Chat session
+            query: Search query
+
+        Returns:
+            BlockListResponse: List of matching blocks
+            NoResultsResponse: No blocks found
+            ErrorResponse: Error message
+        """
+        query = kwargs.get("query", "").strip()
+        session_id = session.session_id
+
+        if not query:
+            return ErrorResponse(
+                message="Please provide a search query",
+                session_id=session_id,
+            )
+
+        try:
+            # Search for blocks using hybrid search
+            results, total = await unified_hybrid_search(
+                query=query,
+                content_types=[ContentType.BLOCK],
+                page=1,
+                page_size=10,
+            )
+
+            if not results:
+                return NoResultsResponse(
+                    message=f"No blocks found for '{query}'",
+                    suggestions=[
+                        "Try broader keywords like 'email', 'http', 'text', 'ai'",
+                        "Check spelling of technical terms",
+                    ],
+                    session_id=session_id,
+                )
+
+            # Enrich results with full block information
+            blocks: list[BlockInfoSummary] = []
+            for result in results:
+                block_id = result["content_id"]
+                block = get_block(block_id)
+
+                if block:
+                    # Get input/output schemas
+                    input_schema = {}
+                    output_schema = {}
+                    try:
+                        input_schema = block.input_schema.jsonschema()
+                    except Exception:
+                        pass
+                    try:
+                        output_schema = block.output_schema.jsonschema()
+                    except Exception:
+                        pass
+
+                    # Get categories from block instance
+                    categories = []
+                    if hasattr(block, "categories") and block.categories:
+                        categories = [cat.value for cat in block.categories]
+
+                    # Extract required inputs for easier use
+                    required_inputs: list[BlockInputFieldInfo] = []
+                    if input_schema:
+                        properties = input_schema.get("properties", {})
+                        required_fields = set(input_schema.get("required", []))
+                        # Get credential field names to exclude from required inputs
+                        credentials_fields = set(
+                            block.input_schema.get_credentials_fields().keys()
+                        )
+
+                        for field_name, field_schema in properties.items():
+                            # Skip credential fields - they're handled separately
+                            if field_name in credentials_fields:
+                                continue
+
+                            required_inputs.append(
+                                BlockInputFieldInfo(
+                                    name=field_name,
+                                    type=field_schema.get("type", "string"),
+                                    description=field_schema.get("description", ""),
+                                    required=field_name in required_fields,
+                                    default=field_schema.get("default"),
+                                )
+                            )
+
+                    blocks.append(
+                        BlockInfoSummary(
+                            id=block_id,
+                            name=block.name,
+                            description=block.description or "",
+                            categories=categories,
+                            input_schema=input_schema,
+                            output_schema=output_schema,
+                            required_inputs=required_inputs,
+                        )
+                    )
+
+            if not blocks:
+                return NoResultsResponse(
+                    message=f"No blocks found for '{query}'",
+                    suggestions=[
+                        "Try broader keywords like 'email', 'http', 'text', 'ai'",
+                    ],
+                    session_id=session_id,
+                )
+
+            return BlockListResponse(
+                message=(
+                    f"Found {len(blocks)} block(s) matching '{query}'. "
+                    "To execute a block, use run_block with the block's 'id' field "
+                    "and provide 'input_data' matching the block's input_schema."
+                ),
+                blocks=blocks,
+                count=len(blocks),
+                query=query,
+                session_id=session_id,
+            )
+
+        except Exception as e:
+            logger.error(f"Error searching blocks: {e}", exc_info=True)
+            return ErrorResponse(
+                message="Failed to search blocks",
+                error=str(e),
+                session_id=session_id,
+            )

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

@@ -0,0 +1,148 @@
+"""GetDocPageTool - Fetch full content of a documentation page."""
+
+import logging
+from pathlib import Path
+from typing import Any
+
+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 (
+    DocPageResponse,
+    ErrorResponse,
+    ToolResponseBase,
+)
+
+logger = logging.getLogger(__name__)
+
+# Base URL for documentation (can be configured)
+DOCS_BASE_URL = "https://docs.agpt.co"
+
+
+class GetDocPageTool(BaseTool):
+    """Tool for fetching full content of a documentation page."""
+
+    @property
+    def name(self) -> str:
+        return "get_doc_page"
+
+    @property
+    def description(self) -> str:
+        return (
+            "Get the full content of a documentation page by its path. "
+            "Use this after search_docs to read the complete content of a relevant page."
+        )
+
+    @property
+    def parameters(self) -> dict[str, Any]:
+        return {
+            "type": "object",
+            "properties": {
+                "path": {
+                    "type": "string",
+                    "description": (
+                        "The path to the documentation file, as returned by search_docs. "
+                        "Example: 'platform/block-sdk-guide.md'"
+                    ),
+                },
+            },
+            "required": ["path"],
+        }
+
+    @property
+    def requires_auth(self) -> bool:
+        return False  # Documentation is public
+
+    def _get_docs_root(self) -> Path:
+        """Get the documentation root directory."""
+        this_file = Path(__file__)
+        project_root = this_file.parent.parent.parent.parent.parent.parent.parent.parent
+        return project_root / "docs"
+
+    def _extract_title(self, content: str, fallback: str) -> str:
+        """Extract title from markdown content."""
+        lines = content.split("\n")
+        for line in lines:
+            if line.startswith("# "):
+                return line[2:].strip()
+        return fallback
+
+    def _make_doc_url(self, path: str) -> str:
+        """Create a URL for a documentation page."""
+        url_path = path.rsplit(".", 1)[0] if "." in path else path
+        return f"{DOCS_BASE_URL}/{url_path}"
+
+    async def _execute(
+        self,
+        user_id: str | None,
+        session: ChatSession,
+        **kwargs,
+    ) -> ToolResponseBase:
+        """Fetch full content of a documentation page.
+
+        Args:
+            user_id: User ID (not required for docs)
+            session: Chat session
+            path: Path to the documentation file
+
+        Returns:
+            DocPageResponse: Full document content
+            ErrorResponse: Error message
+        """
+        path = kwargs.get("path", "").strip()
+        session_id = session.session_id if session else None
+
+        if not path:
+            return ErrorResponse(
+                message="Please provide a documentation path.",
+                error="Missing path parameter",
+                session_id=session_id,
+            )
+
+        # Sanitize path to prevent directory traversal
+        if ".." in path or path.startswith("/"):
+            return ErrorResponse(
+                message="Invalid documentation path.",
+                error="invalid_path",
+                session_id=session_id,
+            )
+
+        docs_root = self._get_docs_root()
+        full_path = docs_root / path
+
+        if not full_path.exists():
+            return ErrorResponse(
+                message=f"Documentation page not found: {path}",
+                error="not_found",
+                session_id=session_id,
+            )
+
+        # Ensure the path is within docs root
+        try:
+            full_path.resolve().relative_to(docs_root.resolve())
+        except ValueError:
+            return ErrorResponse(
+                message="Invalid documentation path.",
+                error="invalid_path",
+                session_id=session_id,
+            )
+
+        try:
+            content = full_path.read_text(encoding="utf-8")
+            title = self._extract_title(content, path)
+
+            return DocPageResponse(
+                message=f"Retrieved documentation page: {title}",
+                title=title,
+                path=path,
+                content=content,
+                doc_url=self._make_doc_url(path),
+                session_id=session_id,
+            )
+
+        except Exception as e:
+            logger.error(f"Failed to read documentation page {path}: {e}")
+            return ErrorResponse(
+                message=f"Failed to read documentation page: {str(e)}",
+                error="read_failed",
+                session_id=session_id,
+            )

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

@@ -21,6 +21,10 @@ class ResponseType(str, Enum):
     NO_RESULTS = "no_results"
     AGENT_OUTPUT = "agent_output"
     UNDERSTANDING_UPDATED = "understanding_updated"
+    BLOCK_LIST = "block_list"
+    BLOCK_OUTPUT = "block_output"
+    DOC_SEARCH_RESULTS = "doc_search_results"
+    DOC_PAGE = "doc_page"
 
 
 # Base response model
@@ -209,3 +213,83 @@ class UnderstandingUpdatedResponse(ToolResponseBase):
     type: ResponseType = ResponseType.UNDERSTANDING_UPDATED
     updated_fields: list[str] = Field(default_factory=list)
     current_understanding: dict[str, Any] = Field(default_factory=dict)
+
+
+# Documentation search models
+class DocSearchResult(BaseModel):
+    """A single documentation search result."""
+
+    title: str
+    path: str
+    section: str
+    snippet: str  # Short excerpt for UI display
+    score: float
+    doc_url: str | None = None
+
+
+class DocSearchResultsResponse(ToolResponseBase):
+    """Response for search_docs tool."""
+
+    type: ResponseType = ResponseType.DOC_SEARCH_RESULTS
+    results: list[DocSearchResult]
+    count: int
+    query: str
+
+
+class DocPageResponse(ToolResponseBase):
+    """Response for get_doc_page tool."""
+
+    type: ResponseType = ResponseType.DOC_PAGE
+    title: str
+    path: str
+    content: str  # Full document content
+    doc_url: str | None = None
+
+
+# Block models
+class BlockInputFieldInfo(BaseModel):
+    """Information about a block input field."""
+
+    name: str
+    type: str
+    description: str = ""
+    required: bool = False
+    default: Any | None = None
+
+
+class BlockInfoSummary(BaseModel):
+    """Summary of a block for search results."""
+
+    id: str
+    name: str
+    description: str
+    categories: list[str]
+    input_schema: dict[str, Any]
+    output_schema: dict[str, Any]
+    required_inputs: list[BlockInputFieldInfo] = Field(
+        default_factory=list,
+        description="List of required input fields for this block",
+    )
+
+
+class BlockListResponse(ToolResponseBase):
+    """Response for find_block tool."""
+
+    type: ResponseType = ResponseType.BLOCK_LIST
+    blocks: list[BlockInfoSummary]
+    count: int
+    query: str
+    usage_hint: str = Field(
+        default="To execute a block, call run_block with block_id set to the block's "
+        "'id' field and input_data containing the required fields from input_schema."
+    )
+
+
+class BlockOutputResponse(ToolResponseBase):
+    """Response for run_block tool."""
+
+    type: ResponseType = ResponseType.BLOCK_OUTPUT
+    block_id: str
+    block_name: str
+    outputs: dict[str, list[Any]]
+    success: bool = True

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

@@ -0,0 +1,297 @@
+"""Tool for executing blocks directly."""
+
+import logging
+from collections import defaultdict
+from typing import Any
+
+from backend.api.features.chat.model import ChatSession
+from backend.data.block import get_block
+from backend.data.execution import ExecutionContext
+from backend.data.model import CredentialsMetaInput
+from backend.integrations.creds_manager import IntegrationCredentialsManager
+from backend.util.exceptions import BlockError
+
+from .base import BaseTool
+from .models import (
+    BlockOutputResponse,
+    ErrorResponse,
+    SetupInfo,
+    SetupRequirementsResponse,
+    ToolResponseBase,
+    UserReadiness,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class RunBlockTool(BaseTool):
+    """Tool for executing a block and returning its outputs."""
+
+    @property
+    def name(self) -> str:
+        return "run_block"
+
+    @property
+    def description(self) -> str:
+        return (
+            "Execute a specific block with the provided input data. "
+            "IMPORTANT: You MUST call find_block first to get the block's 'id' - "
+            "do NOT guess or make up block IDs. "
+            "Use the 'id' from find_block results and provide input_data "
+            "matching the block's required_inputs."
+        )
+
+    @property
+    def parameters(self) -> dict[str, Any]:
+        return {
+            "type": "object",
+            "properties": {
+                "block_id": {
+                    "type": "string",
+                    "description": (
+                        "The block's 'id' field from find_block results. "
+                        "NEVER guess this - always get it from find_block first."
+                    ),
+                },
+                "input_data": {
+                    "type": "object",
+                    "description": (
+                        "Input values for the block. Use the 'required_inputs' field "
+                        "from find_block to see what fields are needed."
+                    ),
+                },
+            },
+            "required": ["block_id", "input_data"],
+        }
+
+    @property
+    def requires_auth(self) -> bool:
+        return True
+
+    async def _check_block_credentials(
+        self,
+        user_id: str,
+        block: Any,
+    ) -> tuple[dict[str, CredentialsMetaInput], list[CredentialsMetaInput]]:
+        """
+        Check if user has required credentials for a block.
+
+        Returns:
+            tuple[matched_credentials, missing_credentials]
+        """
+        matched_credentials: dict[str, CredentialsMetaInput] = {}
+        missing_credentials: list[CredentialsMetaInput] = []
+
+        # Get credential field info from block's input schema
+        credentials_fields_info = block.input_schema.get_credentials_fields_info()
+
+        if not credentials_fields_info:
+            return matched_credentials, missing_credentials
+
+        # Get user's available credentials
+        creds_manager = IntegrationCredentialsManager()
+        available_creds = await creds_manager.store.get_all_creds(user_id)
+
+        for field_name, field_info in credentials_fields_info.items():
+            # field_info.provider is a frozenset of acceptable providers
+            # field_info.supported_types is a frozenset of acceptable types
+            matching_cred = next(
+                (
+                    cred
+                    for cred in available_creds
+                    if cred.provider in field_info.provider
+                    and cred.type in field_info.supported_types
+                ),
+                None,
+            )
+
+            if matching_cred:
+                matched_credentials[field_name] = CredentialsMetaInput(
+                    id=matching_cred.id,
+                    provider=matching_cred.provider,  # type: ignore
+                    type=matching_cred.type,
+                    title=matching_cred.title,
+                )
+            else:
+                # Create a placeholder for the missing credential
+                provider = next(iter(field_info.provider), "unknown")
+                cred_type = next(iter(field_info.supported_types), "api_key")
+                missing_credentials.append(
+                    CredentialsMetaInput(
+                        id=field_name,
+                        provider=provider,  # type: ignore
+                        type=cred_type,  # type: ignore
+                        title=field_name.replace("_", " ").title(),
+                    )
+                )
+
+        return matched_credentials, missing_credentials
+
+    async def _execute(
+        self,
+        user_id: str | None,
+        session: ChatSession,
+        **kwargs,
+    ) -> ToolResponseBase:
+        """Execute a block with the given input data.
+
+        Args:
+            user_id: User ID (required)
+            session: Chat session
+            block_id: Block UUID to execute
+            input_data: Input values for the block
+
+        Returns:
+            BlockOutputResponse: Block execution outputs
+            SetupRequirementsResponse: Missing credentials
+            ErrorResponse: Error message
+        """
+        block_id = kwargs.get("block_id", "").strip()
+        input_data = kwargs.get("input_data", {})
+        session_id = session.session_id
+
+        if not block_id:
+            return ErrorResponse(
+                message="Please provide a block_id",
+                session_id=session_id,
+            )
+
+        if not isinstance(input_data, dict):
+            return ErrorResponse(
+                message="input_data must be an object",
+                session_id=session_id,
+            )
+
+        if not user_id:
+            return ErrorResponse(
+                message="Authentication required",
+                session_id=session_id,
+            )
+
+        # Get the block
+        block = get_block(block_id)
+        if not block:
+            return ErrorResponse(
+                message=f"Block '{block_id}' not found",
+                session_id=session_id,
+            )
+
+        logger.info(f"Executing block {block.name} ({block_id}) for user {user_id}")
+
+        # Check credentials
+        creds_manager = IntegrationCredentialsManager()
+        matched_credentials, missing_credentials = await self._check_block_credentials(
+            user_id, block
+        )
+
+        if missing_credentials:
+            # Return setup requirements response with missing credentials
+            missing_creds_dict = {c.id: c.model_dump() for c in missing_credentials}
+
+            return SetupRequirementsResponse(
+                message=(
+                    f"Block '{block.name}' requires credentials that are not configured. "
+                    "Please set up the required credentials before running this block."
+                ),
+                session_id=session_id,
+                setup_info=SetupInfo(
+                    agent_id=block_id,
+                    agent_name=block.name,
+                    user_readiness=UserReadiness(
+                        has_all_credentials=False,
+                        missing_credentials=missing_creds_dict,
+                        ready_to_run=False,
+                    ),
+                    requirements={
+                        "credentials": [c.model_dump() for c in missing_credentials],
+                        "inputs": self._get_inputs_list(block),
+                        "execution_modes": ["immediate"],
+                    },
+                ),
+                graph_id=None,
+                graph_version=None,
+            )
+
+        try:
+            # Fetch actual credentials and prepare kwargs for block execution
+            # Create execution context with defaults (blocks may require it)
+            exec_kwargs: dict[str, Any] = {
+                "user_id": user_id,
+                "execution_context": ExecutionContext(),
+            }
+
+            for field_name, cred_meta in matched_credentials.items():
+                # Inject metadata into input_data (for validation)
+                if field_name not in input_data:
+                    input_data[field_name] = cred_meta.model_dump()
+
+                # Fetch actual credentials and pass as kwargs (for execution)
+                actual_credentials = await creds_manager.get(
+                    user_id, cred_meta.id, lock=False
+                )
+                if actual_credentials:
+                    exec_kwargs[field_name] = actual_credentials
+                else:
+                    return ErrorResponse(
+                        message=f"Failed to retrieve credentials for {field_name}",
+                        session_id=session_id,
+                    )
+
+            # Execute the block and collect outputs
+            outputs: dict[str, list[Any]] = defaultdict(list)
+            async for output_name, output_data in block.execute(
+                input_data,
+                **exec_kwargs,
+            ):
+                outputs[output_name].append(output_data)
+
+            return BlockOutputResponse(
+                message=f"Block '{block.name}' executed successfully",
+                block_id=block_id,
+                block_name=block.name,
+                outputs=dict(outputs),
+                success=True,
+                session_id=session_id,
+            )
+
+        except BlockError as e:
+            logger.warning(f"Block execution failed: {e}")
+            return ErrorResponse(
+                message=f"Block execution failed: {e}",
+                error=str(e),
+                session_id=session_id,
+            )
+        except Exception as e:
+            logger.error(f"Unexpected error executing block: {e}", exc_info=True)
+            return ErrorResponse(
+                message=f"Failed to execute block: {str(e)}",
+                error=str(e),
+                session_id=session_id,
+            )
+
+    def _get_inputs_list(self, block: Any) -> list[dict[str, Any]]:
+        """Extract non-credential inputs from block schema."""
+        inputs_list = []
+        schema = block.input_schema.jsonschema()
+        properties = schema.get("properties", {})
+        required_fields = set(schema.get("required", []))
+
+        # Get credential field names to exclude
+        credentials_fields = set(block.input_schema.get_credentials_fields().keys())
+
+        for field_name, field_schema in properties.items():
+            # Skip credential fields
+            if field_name in credentials_fields:
+                continue
+
+            inputs_list.append(
+                {
+                    "name": field_name,
+                    "title": field_schema.get("title", field_name),
+                    "type": field_schema.get("type", "string"),
+                    "description": field_schema.get("description", ""),
+                    "required": field_name in required_fields,
+                }
+            )
+
+        return inputs_list

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

@@ -0,0 +1,208 @@
+"""SearchDocsTool - Search documentation using hybrid search."""
+
+import logging
+from typing import Any
+
+from prisma.enums import ContentType
+
+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 (
+    DocSearchResult,
+    DocSearchResultsResponse,
+    ErrorResponse,
+    NoResultsResponse,
+    ToolResponseBase,
+)
+from backend.api.features.store.hybrid_search import unified_hybrid_search
+
+logger = logging.getLogger(__name__)
+
+# Base URL for documentation (can be configured)
+DOCS_BASE_URL = "https://docs.agpt.co"
+
+# Maximum number of results to return
+MAX_RESULTS = 5
+
+# Snippet length for preview
+SNIPPET_LENGTH = 200
+
+
+class SearchDocsTool(BaseTool):
+    """Tool for searching AutoGPT platform documentation."""
+
+    @property
+    def name(self) -> str:
+        return "search_docs"
+
+    @property
+    def description(self) -> str:
+        return (
+            "Search the AutoGPT platform documentation for information about "
+            "how to use the platform, build agents, configure blocks, and more. "
+            "Returns relevant documentation sections. Use get_doc_page to read full content."
+        )
+
+    @property
+    def parameters(self) -> dict[str, Any]:
+        return {
+            "type": "object",
+            "properties": {
+                "query": {
+                    "type": "string",
+                    "description": (
+                        "Search query to find relevant documentation. "
+                        "Use natural language to describe what you're looking for."
+                    ),
+                },
+            },
+            "required": ["query"],
+        }
+
+    @property
+    def requires_auth(self) -> bool:
+        return False  # Documentation is public
+
+    def _create_snippet(self, content: str, max_length: int = SNIPPET_LENGTH) -> str:
+        """Create a short snippet from content for preview."""
+        # Remove markdown formatting for cleaner snippet
+        clean_content = content.replace("#", "").replace("*", "").replace("`", "")
+        # Remove extra whitespace
+        clean_content = " ".join(clean_content.split())
+
+        if len(clean_content) <= max_length:
+            return clean_content
+
+        # Truncate at word boundary
+        truncated = clean_content[:max_length]
+        last_space = truncated.rfind(" ")
+        if last_space > max_length // 2:
+            truncated = truncated[:last_space]
+
+        return truncated + "..."
+
+    def _make_doc_url(self, path: str) -> str:
+        """Create a URL for a documentation page."""
+        # Remove file extension for URL
+        url_path = path.rsplit(".", 1)[0] if "." in path else path
+        return f"{DOCS_BASE_URL}/{url_path}"
+
+    async def _execute(
+        self,
+        user_id: str | None,
+        session: ChatSession,
+        **kwargs,
+    ) -> ToolResponseBase:
+        """Search documentation and return relevant sections.
+
+        Args:
+            user_id: User ID (not required for docs)
+            session: Chat session
+            query: Search query
+
+        Returns:
+            DocSearchResultsResponse: List of matching documentation sections
+            NoResultsResponse: No results found
+            ErrorResponse: Error message
+        """
+        query = kwargs.get("query", "").strip()
+        session_id = session.session_id if session else None
+
+        if not query:
+            return ErrorResponse(
+                message="Please provide a search query.",
+                error="Missing query parameter",
+                session_id=session_id,
+            )
+
+        try:
+            # Search using hybrid search for DOCUMENTATION content type only
+            results, total = await unified_hybrid_search(
+                query=query,
+                content_types=[ContentType.DOCUMENTATION],
+                page=1,
+                page_size=MAX_RESULTS * 2,  # Fetch extra for deduplication
+                min_score=0.1,  # Lower threshold for docs
+            )
+
+            if not results:
+                return NoResultsResponse(
+                    message=f"No documentation found for '{query}'.",
+                    suggestions=[
+                        "Try different keywords",
+                        "Use more general terms",
+                        "Check for typos in your query",
+                    ],
+                    session_id=session_id,
+                )
+
+            # Deduplicate by document path (keep highest scoring section per doc)
+            seen_docs: dict[str, dict[str, Any]] = {}
+            for result in results:
+                metadata = result.get("metadata", {})
+                doc_path = metadata.get("path", "")
+
+                if not doc_path:
+                    continue
+
+                # Keep the highest scoring result for each document
+                if doc_path not in seen_docs:
+                    seen_docs[doc_path] = result
+                elif result.get("combined_score", 0) > seen_docs[doc_path].get(
+                    "combined_score", 0
+                ):
+                    seen_docs[doc_path] = result
+
+            # Sort by score and take top MAX_RESULTS
+            deduplicated = sorted(
+                seen_docs.values(),
+                key=lambda x: x.get("combined_score", 0),
+                reverse=True,
+            )[:MAX_RESULTS]
+
+            if not deduplicated:
+                return NoResultsResponse(
+                    message=f"No documentation found for '{query}'.",
+                    suggestions=[
+                        "Try different keywords",
+                        "Use more general terms",
+                    ],
+                    session_id=session_id,
+                )
+
+            # Build response
+            doc_results: list[DocSearchResult] = []
+            for result in deduplicated:
+                metadata = result.get("metadata", {})
+                doc_path = metadata.get("path", "")
+                doc_title = metadata.get("doc_title", "")
+                section_title = metadata.get("section_title", "")
+                searchable_text = result.get("searchable_text", "")
+                score = result.get("combined_score", 0)
+
+                doc_results.append(
+                    DocSearchResult(
+                        title=doc_title or section_title or doc_path,
+                        path=doc_path,
+                        section=section_title,
+                        snippet=self._create_snippet(searchable_text),
+                        score=round(score, 3),
+                        doc_url=self._make_doc_url(doc_path),
+                    )
+                )
+
+            return DocSearchResultsResponse(
+                message=f"Found {len(doc_results)} relevant documentation sections.",
+                results=doc_results,
+                count=len(doc_results),
+                query=query,
+                session_id=session_id,
+            )
+
+        except Exception as e:
+            logger.error(f"Documentation search failed: {e}")
+            return ErrorResponse(
+                message=f"Failed to search documentation: {str(e)}",
+                error="search_failed",
+                session_id=session_id,
+            )

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

@@ -0,0 +1,610 @@
+"""
+Content Type Handlers for Unified Embeddings
+
+Pluggable system for different content sources (store agents, blocks, docs).
+Each handler knows how to fetch and process its content type for embedding.
+"""
+
+import logging
+from abc import ABC, abstractmethod
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any
+
+from prisma.enums import ContentType
+
+from backend.data.db import query_raw_with_schema
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class ContentItem:
+    """Represents a piece of content to be embedded."""
+
+    content_id: str  # Unique identifier (DB ID or file path)
+    content_type: ContentType
+    searchable_text: str  # Combined text for embedding
+    metadata: dict[str, Any]  # Content-specific metadata
+    user_id: str | None = None  # For user-scoped content
+
+
+class ContentHandler(ABC):
+    """Base handler for fetching and processing content for embeddings."""
+
+    @property
+    @abstractmethod
+    def content_type(self) -> ContentType:
+        """The ContentType this handler manages."""
+        pass
+
+    @abstractmethod
+    async def get_missing_items(self, batch_size: int) -> list[ContentItem]:
+        """
+        Fetch items that don't have embeddings yet.
+
+        Args:
+            batch_size: Maximum number of items to return
+
+        Returns:
+            List of ContentItem objects ready for embedding
+        """
+        pass
+
+    @abstractmethod
+    async def get_stats(self) -> dict[str, int]:
+        """
+        Get statistics about embedding coverage.
+
+        Returns:
+            Dict with keys: total, with_embeddings, without_embeddings
+        """
+        pass
+
+
+class StoreAgentHandler(ContentHandler):
+    """Handler for marketplace store agent listings."""
+
+    @property
+    def content_type(self) -> ContentType:
+        return ContentType.STORE_AGENT
+
+    async def get_missing_items(self, batch_size: int) -> list[ContentItem]:
+        """Fetch approved store listings without embeddings."""
+        from backend.api.features.store.embeddings import build_searchable_text
+
+        missing = await query_raw_with_schema(
+            """
+            SELECT
+                slv.id,
+                slv.name,
+                slv.description,
+                slv."subHeading",
+                slv.categories
+            FROM {schema_prefix}"StoreListingVersion" slv
+            LEFT JOIN {schema_prefix}"UnifiedContentEmbedding" uce
+                ON slv.id = uce."contentId" AND uce."contentType" = 'STORE_AGENT'::{schema_prefix}"ContentType"
+            WHERE slv."submissionStatus" = 'APPROVED'
+            AND slv."isDeleted" = false
+            AND uce."contentId" IS NULL
+            LIMIT $1
+            """,
+            batch_size,
+        )
+
+        return [
+            ContentItem(
+                content_id=row["id"],
+                content_type=ContentType.STORE_AGENT,
+                searchable_text=build_searchable_text(
+                    name=row["name"],
+                    description=row["description"],
+                    sub_heading=row["subHeading"],
+                    categories=row["categories"] or [],
+                ),
+                metadata={
+                    "name": row["name"],
+                    "categories": row["categories"] or [],
+                },
+                user_id=None,  # Store agents are public
+            )
+            for row in missing
+        ]
+
+    async def get_stats(self) -> dict[str, int]:
+        """Get statistics about store agent embedding coverage."""
+        # Count approved versions
+        approved_result = await query_raw_with_schema(
+            """
+            SELECT COUNT(*) as count
+            FROM {schema_prefix}"StoreListingVersion"
+            WHERE "submissionStatus" = 'APPROVED'
+            AND "isDeleted" = false
+            """
+        )
+        total_approved = approved_result[0]["count"] if approved_result else 0
+
+        # Count versions with embeddings
+        embedded_result = await query_raw_with_schema(
+            """
+            SELECT COUNT(*) as count
+            FROM {schema_prefix}"StoreListingVersion" slv
+            JOIN {schema_prefix}"UnifiedContentEmbedding" uce ON slv.id = uce."contentId" AND uce."contentType" = 'STORE_AGENT'::{schema_prefix}"ContentType"
+            WHERE slv."submissionStatus" = 'APPROVED'
+            AND slv."isDeleted" = false
+            """
+        )
+        with_embeddings = embedded_result[0]["count"] if embedded_result else 0
+
+        return {
+            "total": total_approved,
+            "with_embeddings": with_embeddings,
+            "without_embeddings": total_approved - with_embeddings,
+        }
+
+
+class BlockHandler(ContentHandler):
+    """Handler for block definitions (Python classes)."""
+
+    @property
+    def content_type(self) -> ContentType:
+        return ContentType.BLOCK
+
+    async def get_missing_items(self, batch_size: int) -> list[ContentItem]:
+        """Fetch blocks without embeddings."""
+        from backend.data.block import get_blocks
+
+        # Get all available blocks
+        all_blocks = get_blocks()
+
+        # Check which ones have embeddings
+        if not all_blocks:
+            return []
+
+        block_ids = list(all_blocks.keys())
+
+        # Query for existing embeddings
+        placeholders = ",".join([f"${i+1}" for i in range(len(block_ids))])
+        existing_result = await query_raw_with_schema(
+            f"""
+            SELECT "contentId"
+            FROM {{schema_prefix}}"UnifiedContentEmbedding"
+            WHERE "contentType" = 'BLOCK'::{{schema_prefix}}"ContentType"
+            AND "contentId" = ANY(ARRAY[{placeholders}])
+            """,
+            *block_ids,
+        )
+
+        existing_ids = {row["contentId"] for row in existing_result}
+        missing_blocks = [
+            (block_id, block_cls)
+            for block_id, block_cls in all_blocks.items()
+            if block_id not in existing_ids
+        ]
+
+        # Convert to ContentItem
+        items = []
+        for block_id, block_cls in missing_blocks[:batch_size]:
+            try:
+                block_instance = block_cls()
+
+                # Build searchable text from block metadata
+                parts = []
+                if hasattr(block_instance, "name") and block_instance.name:
+                    parts.append(block_instance.name)
+                if (
+                    hasattr(block_instance, "description")
+                    and block_instance.description
+                ):
+                    parts.append(block_instance.description)
+                if hasattr(block_instance, "categories") and block_instance.categories:
+                    # Convert BlockCategory enum to strings
+                    parts.append(
+                        " ".join(str(cat.value) for cat in block_instance.categories)
+                    )
+
+                # Add input/output schema info
+                if hasattr(block_instance, "input_schema"):
+                    schema = block_instance.input_schema
+                    if hasattr(schema, "model_json_schema"):
+                        schema_dict = schema.model_json_schema()
+                        if "properties" in schema_dict:
+                            for prop_name, prop_info in schema_dict[
+                                "properties"
+                            ].items():
+                                if "description" in prop_info:
+                                    parts.append(
+                                        f"{prop_name}: {prop_info['description']}"
+                                    )
+
+                searchable_text = " ".join(parts)
+
+                # Convert categories set of enums to list of strings for JSON serialization
+                categories = getattr(block_instance, "categories", set())
+                categories_list = (
+                    [cat.value for cat in categories] if categories else []
+                )
+
+                items.append(
+                    ContentItem(
+                        content_id=block_id,
+                        content_type=ContentType.BLOCK,
+                        searchable_text=searchable_text,
+                        metadata={
+                            "name": getattr(block_instance, "name", ""),
+                            "categories": categories_list,
+                        },
+                        user_id=None,  # Blocks are public
+                    )
+                )
+            except Exception as e:
+                logger.warning(f"Failed to process block {block_id}: {e}")
+                continue
+
+        return items
+
+    async def get_stats(self) -> dict[str, int]:
+        """Get statistics about block embedding coverage."""
+        from backend.data.block import get_blocks
+
+        all_blocks = get_blocks()
+        total_blocks = len(all_blocks)
+
+        if total_blocks == 0:
+            return {"total": 0, "with_embeddings": 0, "without_embeddings": 0}
+
+        block_ids = list(all_blocks.keys())
+        placeholders = ",".join([f"${i+1}" for i in range(len(block_ids))])
+
+        embedded_result = await query_raw_with_schema(
+            f"""
+            SELECT COUNT(*) as count
+            FROM {{schema_prefix}}"UnifiedContentEmbedding"
+            WHERE "contentType" = 'BLOCK'::{{schema_prefix}}"ContentType"
+            AND "contentId" = ANY(ARRAY[{placeholders}])
+            """,
+            *block_ids,
+        )
+
+        with_embeddings = embedded_result[0]["count"] if embedded_result else 0
+
+        return {
+            "total": total_blocks,
+            "with_embeddings": with_embeddings,
+            "without_embeddings": total_blocks - with_embeddings,
+        }
+
+
+@dataclass
+class MarkdownSection:
+    """Represents a section of a markdown document."""
+
+    title: str  # Section heading text
+    content: str  # Section content (including the heading line)
+    level: int  # Heading level (1 for #, 2 for ##, etc.)
+    index: int  # Section index within the document
+
+
+class DocumentationHandler(ContentHandler):
+    """Handler for documentation files (.md/.mdx).
+
+    Chunks documents by markdown headings to create multiple embeddings per file.
+    Each section (## heading) becomes a separate embedding for better retrieval.
+    """
+
+    @property
+    def content_type(self) -> ContentType:
+        return ContentType.DOCUMENTATION
+
+    def _get_docs_root(self) -> Path:
+        """Get the documentation root directory."""
+        # content_handlers.py is at: backend/backend/api/features/store/content_handlers.py
+        # Need to go up to project root then into docs/
+        # In container: /app/autogpt_platform/backend/backend/api/features/store -> /app/docs
+        # In development: /repo/autogpt_platform/backend/backend/api/features/store -> /repo/docs
+        this_file = Path(
+            __file__
+        )  # .../backend/backend/api/features/store/content_handlers.py
+        project_root = (
+            this_file.parent.parent.parent.parent.parent.parent.parent
+        )  # -> /app or /repo
+        docs_root = project_root / "docs"
+        return docs_root
+
+    def _extract_doc_title(self, file_path: Path) -> str:
+        """Extract the document title from a markdown file."""
+        try:
+            content = file_path.read_text(encoding="utf-8")
+            lines = content.split("\n")
+
+            # Try to extract title from first # heading
+            for line in lines:
+                if line.startswith("# "):
+                    return line[2:].strip()
+
+            # If no title found, use filename
+            return file_path.stem.replace("-", " ").replace("_", " ").title()
+        except Exception as e:
+            logger.warning(f"Failed to read title from {file_path}: {e}")
+            return file_path.stem.replace("-", " ").replace("_", " ").title()
+
+    def _chunk_markdown_by_headings(
+        self, file_path: Path, min_heading_level: int = 2
+    ) -> list[MarkdownSection]:
+        """
+        Split a markdown file into sections based on headings.
+
+        Args:
+            file_path: Path to the markdown file
+            min_heading_level: Minimum heading level to split on (default: 2 for ##)
+
+        Returns:
+            List of MarkdownSection objects, one per section.
+            If no headings found, returns a single section with all content.
+        """
+        try:
+            content = file_path.read_text(encoding="utf-8")
+        except Exception as e:
+            logger.warning(f"Failed to read {file_path}: {e}")
+            return []
+
+        lines = content.split("\n")
+        sections: list[MarkdownSection] = []
+        current_section_lines: list[str] = []
+        current_title = ""
+        current_level = 0
+        section_index = 0
+        doc_title = ""
+
+        for line in lines:
+            # Check if line is a heading
+            if line.startswith("#"):
+                # Count heading level
+                level = 0
+                for char in line:
+                    if char == "#":
+                        level += 1
+                    else:
+                        break
+
+                heading_text = line[level:].strip()
+
+                # Track document title (level 1 heading)
+                if level == 1 and not doc_title:
+                    doc_title = heading_text
+                    # Don't create a section for just the title - add it to first section
+                    current_section_lines.append(line)
+                    continue
+
+                # Check if this heading should start a new section
+                if level >= min_heading_level:
+                    # Save previous section if it has content
+                    if current_section_lines:
+                        section_content = "\n".join(current_section_lines).strip()
+                        if section_content:
+                            # Use doc title for first section if no specific title
+                            title = current_title if current_title else doc_title
+                            if not title:
+                                title = file_path.stem.replace("-", " ").replace(
+                                    "_", " "
+                                )
+                            sections.append(
+                                MarkdownSection(
+                                    title=title,
+                                    content=section_content,
+                                    level=current_level if current_level else 1,
+                                    index=section_index,
+                                )
+                            )
+                            section_index += 1
+
+                    # Start new section
+                    current_section_lines = [line]
+                    current_title = heading_text
+                    current_level = level
+                else:
+                    # Lower level heading (e.g., # when splitting on ##)
+                    current_section_lines.append(line)
+            else:
+                current_section_lines.append(line)
+
+        # Don't forget the last section
+        if current_section_lines:
+            section_content = "\n".join(current_section_lines).strip()
+            if section_content:
+                title = current_title if current_title else doc_title
+                if not title:
+                    title = file_path.stem.replace("-", " ").replace("_", " ")
+                sections.append(
+                    MarkdownSection(
+                        title=title,
+                        content=section_content,
+                        level=current_level if current_level else 1,
+                        index=section_index,
+                    )
+                )
+
+        # If no sections were created (no headings found), create one section with all content
+        if not sections and content.strip():
+            title = (
+                doc_title
+                if doc_title
+                else file_path.stem.replace("-", " ").replace("_", " ")
+            )
+            sections.append(
+                MarkdownSection(
+                    title=title,
+                    content=content.strip(),
+                    level=1,
+                    index=0,
+                )
+            )
+
+        return sections
+
+    def _make_section_content_id(self, doc_path: str, section_index: int) -> str:
+        """Create a unique content ID for a document section.
+
+        Format: doc_path::section_index
+        Example: 'platform/getting-started.md::0'
+        """
+        return f"{doc_path}::{section_index}"
+
+    def _parse_section_content_id(self, content_id: str) -> tuple[str, int]:
+        """Parse a section content ID back into doc_path and section_index.
+
+        Returns: (doc_path, section_index)
+        """
+        if "::" in content_id:
+            parts = content_id.rsplit("::", 1)
+            return parts[0], int(parts[1])
+        # Legacy format (whole document)
+        return content_id, 0
+
+    async def get_missing_items(self, batch_size: int) -> list[ContentItem]:
+        """Fetch documentation sections without embeddings.
+
+        Chunks each document by markdown headings and creates embeddings for each section.
+        Content IDs use the format: 'path/to/doc.md::section_index'
+        """
+        docs_root = self._get_docs_root()
+
+        if not docs_root.exists():
+            logger.warning(f"Documentation root not found: {docs_root}")
+            return []
+
+        # Find all .md and .mdx files
+        all_docs = list(docs_root.rglob("*.md")) + list(docs_root.rglob("*.mdx"))
+
+        if not all_docs:
+            return []
+
+        # Build list of all sections from all documents
+        all_sections: list[tuple[str, Path, MarkdownSection]] = []
+        for doc_file in all_docs:
+            doc_path = str(doc_file.relative_to(docs_root))
+            sections = self._chunk_markdown_by_headings(doc_file)
+            for section in sections:
+                all_sections.append((doc_path, doc_file, section))
+
+        if not all_sections:
+            return []
+
+        # Generate content IDs for all sections
+        section_content_ids = [
+            self._make_section_content_id(doc_path, section.index)
+            for doc_path, _, section in all_sections
+        ]
+
+        # Check which ones have embeddings
+        placeholders = ",".join([f"${i+1}" for i in range(len(section_content_ids))])
+        existing_result = await query_raw_with_schema(
+            f"""
+            SELECT "contentId"
+            FROM {{schema_prefix}}"UnifiedContentEmbedding"
+            WHERE "contentType" = 'DOCUMENTATION'::{{schema_prefix}}"ContentType"
+            AND "contentId" = ANY(ARRAY[{placeholders}])
+            """,
+            *section_content_ids,
+        )
+
+        existing_ids = {row["contentId"] for row in existing_result}
+
+        # Filter to missing sections
+        missing_sections = [
+            (doc_path, doc_file, section, content_id)
+            for (doc_path, doc_file, section), content_id in zip(
+                all_sections, section_content_ids
+            )
+            if content_id not in existing_ids
+        ]
+
+        # Convert to ContentItem (up to batch_size)
+        items = []
+        for doc_path, doc_file, section, content_id in missing_sections[:batch_size]:
+            try:
+                # Get document title for context
+                doc_title = self._extract_doc_title(doc_file)
+
+                # Build searchable text with context
+                # Include doc title and section title for better search relevance
+                searchable_text = f"{doc_title} - {section.title}\n\n{section.content}"
+
+                items.append(
+                    ContentItem(
+                        content_id=content_id,
+                        content_type=ContentType.DOCUMENTATION,
+                        searchable_text=searchable_text,
+                        metadata={
+                            "doc_title": doc_title,
+                            "section_title": section.title,
+                            "section_index": section.index,
+                            "heading_level": section.level,
+                            "path": doc_path,
+                        },
+                        user_id=None,  # Documentation is public
+                    )
+                )
+            except Exception as e:
+                logger.warning(f"Failed to process section {content_id}: {e}")
+                continue
+
+        return items
+
+    def _get_all_section_content_ids(self, docs_root: Path) -> set[str]:
+        """Get all current section content IDs from the docs directory.
+
+        Used for stats and cleanup to know what sections should exist.
+        """
+        all_docs = list(docs_root.rglob("*.md")) + list(docs_root.rglob("*.mdx"))
+        content_ids = set()
+
+        for doc_file in all_docs:
+            doc_path = str(doc_file.relative_to(docs_root))
+            sections = self._chunk_markdown_by_headings(doc_file)
+            for section in sections:
+                content_ids.add(self._make_section_content_id(doc_path, section.index))
+
+        return content_ids
+
+    async def get_stats(self) -> dict[str, int]:
+        """Get statistics about documentation embedding coverage.
+
+        Counts sections (not documents) since each section gets its own embedding.
+        """
+        docs_root = self._get_docs_root()
+
+        if not docs_root.exists():
+            return {"total": 0, "with_embeddings": 0, "without_embeddings": 0}
+
+        # Get all section content IDs
+        all_section_ids = self._get_all_section_content_ids(docs_root)
+        total_sections = len(all_section_ids)
+
+        if total_sections == 0:
+            return {"total": 0, "with_embeddings": 0, "without_embeddings": 0}
+
+        # Count embeddings in database for DOCUMENTATION type
+        embedded_result = await query_raw_with_schema(
+            """
+            SELECT COUNT(*) as count
+            FROM {schema_prefix}"UnifiedContentEmbedding"
+            WHERE "contentType" = 'DOCUMENTATION'::{schema_prefix}"ContentType"
+            """
+        )
+
+        with_embeddings = embedded_result[0]["count"] if embedded_result else 0
+
+        return {
+            "total": total_sections,
+            "with_embeddings": with_embeddings,
+            "without_embeddings": total_sections - with_embeddings,
+        }
+
+
+# Content handler registry
+CONTENT_HANDLERS: dict[ContentType, ContentHandler] = {
+    ContentType.STORE_AGENT: StoreAgentHandler(),
+    ContentType.BLOCK: BlockHandler(),
+    ContentType.DOCUMENTATION: DocumentationHandler(),
+}

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

@@ -0,0 +1,215 @@
+"""
+Integration tests for content handlers using real DB.
+
+Run with: poetry run pytest backend/api/features/store/content_handlers_integration_test.py -xvs
+
+These tests use the real database but mock OpenAI calls.
+"""
+
+from unittest.mock import patch
+
+import pytest
+
+from backend.api.features.store.content_handlers import (
+    CONTENT_HANDLERS,
+    BlockHandler,
+    DocumentationHandler,
+    StoreAgentHandler,
+)
+from backend.api.features.store.embeddings import (
+    EMBEDDING_DIM,
+    backfill_all_content_types,
+    ensure_content_embedding,
+    get_embedding_stats,
+)
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_store_agent_handler_real_db():
+    """Test StoreAgentHandler with real database queries."""
+    handler = StoreAgentHandler()
+
+    # Get stats from real DB
+    stats = await handler.get_stats()
+
+    # Stats should have correct structure
+    assert "total" in stats
+    assert "with_embeddings" in stats
+    assert "without_embeddings" in stats
+    assert stats["total"] >= 0
+    assert stats["with_embeddings"] >= 0
+    assert stats["without_embeddings"] >= 0
+
+    # Get missing items (max 1 to keep test fast)
+    items = await handler.get_missing_items(batch_size=1)
+
+    # Items should be list (may be empty if all have embeddings)
+    assert isinstance(items, list)
+
+    if items:
+        item = items[0]
+        assert item.content_id is not None
+        assert item.content_type.value == "STORE_AGENT"
+        assert item.searchable_text != ""
+        assert item.user_id is None
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_block_handler_real_db():
+    """Test BlockHandler with real database queries."""
+    handler = BlockHandler()
+
+    # Get stats from real DB
+    stats = await handler.get_stats()
+
+    # Stats should have correct structure
+    assert "total" in stats
+    assert "with_embeddings" in stats
+    assert "without_embeddings" in stats
+    assert stats["total"] >= 0  # Should have at least some blocks
+    assert stats["with_embeddings"] >= 0
+    assert stats["without_embeddings"] >= 0
+
+    # Get missing items (max 1 to keep test fast)
+    items = await handler.get_missing_items(batch_size=1)
+
+    # Items should be list
+    assert isinstance(items, list)
+
+    if items:
+        item = items[0]
+        assert item.content_id is not None  # Should be block UUID
+        assert item.content_type.value == "BLOCK"
+        assert item.searchable_text != ""
+        assert item.user_id is None
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_documentation_handler_real_fs():
+    """Test DocumentationHandler with real filesystem."""
+    handler = DocumentationHandler()
+
+    # Get stats from real filesystem
+    stats = await handler.get_stats()
+
+    # Stats should have correct structure
+    assert "total" in stats
+    assert "with_embeddings" in stats
+    assert "without_embeddings" in stats
+    assert stats["total"] >= 0
+    assert stats["with_embeddings"] >= 0
+    assert stats["without_embeddings"] >= 0
+
+    # Get missing items (max 1 to keep test fast)
+    items = await handler.get_missing_items(batch_size=1)
+
+    # Items should be list
+    assert isinstance(items, list)
+
+    if items:
+        item = items[0]
+        assert item.content_id is not None  # Should be relative path
+        assert item.content_type.value == "DOCUMENTATION"
+        assert item.searchable_text != ""
+        assert item.user_id is None
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_get_embedding_stats_all_types():
+    """Test get_embedding_stats aggregates all content types."""
+    stats = await get_embedding_stats()
+
+    # Should have structure with by_type and totals
+    assert "by_type" in stats
+    assert "totals" in stats
+
+    # Check each content type is present
+    by_type = stats["by_type"]
+    assert "STORE_AGENT" in by_type
+    assert "BLOCK" in by_type
+    assert "DOCUMENTATION" in by_type
+
+    # Check totals are aggregated
+    totals = stats["totals"]
+    assert totals["total"] >= 0
+    assert totals["with_embeddings"] >= 0
+    assert totals["without_embeddings"] >= 0
+    assert "coverage_percent" in totals
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@patch("backend.api.features.store.embeddings.generate_embedding")
+async def test_ensure_content_embedding_blocks(mock_generate):
+    """Test creating embeddings for blocks (mocked OpenAI)."""
+    # Mock OpenAI to return fake embedding
+    mock_generate.return_value = [0.1] * EMBEDDING_DIM
+
+    # Get one block without embedding
+    handler = BlockHandler()
+    items = await handler.get_missing_items(batch_size=1)
+
+    if not items:
+        pytest.skip("No blocks without embeddings")
+
+    item = items[0]
+
+    # Try to create embedding (OpenAI mocked)
+    result = await ensure_content_embedding(
+        content_type=item.content_type,
+        content_id=item.content_id,
+        searchable_text=item.searchable_text,
+        metadata=item.metadata,
+        user_id=item.user_id,
+    )
+
+    # Should succeed with mocked OpenAI
+    assert result is True
+    mock_generate.assert_called_once()
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@patch("backend.api.features.store.embeddings.generate_embedding")
+async def test_backfill_all_content_types_dry_run(mock_generate):
+    """Test backfill_all_content_types processes all handlers in order."""
+    # Mock OpenAI to return fake embedding
+    mock_generate.return_value = [0.1] * EMBEDDING_DIM
+
+    # Run backfill with batch_size=1 to process max 1 per type
+    result = await backfill_all_content_types(batch_size=1)
+
+    # Should have results for all content types
+    assert "by_type" in result
+    assert "totals" in result
+
+    by_type = result["by_type"]
+    assert "BLOCK" in by_type
+    assert "STORE_AGENT" in by_type
+    assert "DOCUMENTATION" in by_type
+
+    # Each type should have correct structure
+    for content_type, type_result in by_type.items():
+        assert "processed" in type_result
+        assert "success" in type_result
+        assert "failed" in type_result
+
+    # Totals should aggregate
+    totals = result["totals"]
+    assert totals["processed"] >= 0
+    assert totals["success"] >= 0
+    assert totals["failed"] >= 0
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_content_handler_registry():
+    """Test all handlers are registered in correct order."""
+    from prisma.enums import ContentType
+
+    # All three types should be registered
+    assert ContentType.STORE_AGENT in CONTENT_HANDLERS
+    assert ContentType.BLOCK in CONTENT_HANDLERS
+    assert ContentType.DOCUMENTATION in CONTENT_HANDLERS
+
+    # Check handler types
+    assert isinstance(CONTENT_HANDLERS[ContentType.STORE_AGENT], StoreAgentHandler)
+    assert isinstance(CONTENT_HANDLERS[ContentType.BLOCK], BlockHandler)
+    assert isinstance(CONTENT_HANDLERS[ContentType.DOCUMENTATION], DocumentationHandler)

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

@@ -0,0 +1,381 @@
+"""
+E2E tests for content handlers (blocks, store agents, documentation).
+
+Tests the full flow: discovering content → generating embeddings → storing.
+"""
+
+from pathlib import Path
+from unittest.mock import MagicMock, patch
+
+import pytest
+from prisma.enums import ContentType
+
+from backend.api.features.store.content_handlers import (
+    CONTENT_HANDLERS,
+    BlockHandler,
+    DocumentationHandler,
+    StoreAgentHandler,
+)
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_store_agent_handler_get_missing_items(mocker):
+    """Test StoreAgentHandler fetches approved agents without embeddings."""
+    handler = StoreAgentHandler()
+
+    # Mock database query
+    mock_missing = [
+        {
+            "id": "agent-1",
+            "name": "Test Agent",
+            "description": "A test agent",
+            "subHeading": "Test heading",
+            "categories": ["AI", "Testing"],
+        }
+    ]
+
+    with patch(
+        "backend.api.features.store.content_handlers.query_raw_with_schema",
+        return_value=mock_missing,
+    ):
+        items = await handler.get_missing_items(batch_size=10)
+
+        assert len(items) == 1
+        assert items[0].content_id == "agent-1"
+        assert items[0].content_type == ContentType.STORE_AGENT
+        assert "Test Agent" in items[0].searchable_text
+        assert "A test agent" in items[0].searchable_text
+        assert items[0].metadata["name"] == "Test Agent"
+        assert items[0].user_id is None
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_store_agent_handler_get_stats(mocker):
+    """Test StoreAgentHandler returns correct stats."""
+    handler = StoreAgentHandler()
+
+    # Mock approved count query
+    mock_approved = [{"count": 50}]
+    # Mock embedded count query
+    mock_embedded = [{"count": 30}]
+
+    with patch(
+        "backend.api.features.store.content_handlers.query_raw_with_schema",
+        side_effect=[mock_approved, mock_embedded],
+    ):
+        stats = await handler.get_stats()
+
+        assert stats["total"] == 50
+        assert stats["with_embeddings"] == 30
+        assert stats["without_embeddings"] == 20
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_block_handler_get_missing_items(mocker):
+    """Test BlockHandler discovers blocks without embeddings."""
+    handler = BlockHandler()
+
+    # Mock get_blocks to return test blocks
+    mock_block_class = MagicMock()
+    mock_block_instance = MagicMock()
+    mock_block_instance.name = "Calculator Block"
+    mock_block_instance.description = "Performs calculations"
+    mock_block_instance.categories = [MagicMock(value="MATH")]
+    mock_block_instance.input_schema.model_json_schema.return_value = {
+        "properties": {"expression": {"description": "Math expression to evaluate"}}
+    }
+    mock_block_class.return_value = mock_block_instance
+
+    mock_blocks = {"block-uuid-1": mock_block_class}
+
+    # Mock existing embeddings query (no embeddings exist)
+    mock_existing = []
+
+    with patch(
+        "backend.data.block.get_blocks",
+        return_value=mock_blocks,
+    ):
+        with patch(
+            "backend.api.features.store.content_handlers.query_raw_with_schema",
+            return_value=mock_existing,
+        ):
+            items = await handler.get_missing_items(batch_size=10)
+
+            assert len(items) == 1
+            assert items[0].content_id == "block-uuid-1"
+            assert items[0].content_type == ContentType.BLOCK
+            assert "Calculator Block" in items[0].searchable_text
+            assert "Performs calculations" in items[0].searchable_text
+            assert "MATH" in items[0].searchable_text
+            assert "expression: Math expression" in items[0].searchable_text
+            assert items[0].user_id is None
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_block_handler_get_stats(mocker):
+    """Test BlockHandler returns correct stats."""
+    handler = BlockHandler()
+
+    # Mock get_blocks
+    mock_blocks = {
+        "block-1": MagicMock(),
+        "block-2": MagicMock(),
+        "block-3": MagicMock(),
+    }
+
+    # Mock embedded count query (2 blocks have embeddings)
+    mock_embedded = [{"count": 2}]
+
+    with patch(
+        "backend.data.block.get_blocks",
+        return_value=mock_blocks,
+    ):
+        with patch(
+            "backend.api.features.store.content_handlers.query_raw_with_schema",
+            return_value=mock_embedded,
+        ):
+            stats = await handler.get_stats()
+
+            assert stats["total"] == 3
+            assert stats["with_embeddings"] == 2
+            assert stats["without_embeddings"] == 1
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_documentation_handler_get_missing_items(tmp_path, mocker):
+    """Test DocumentationHandler discovers docs without embeddings."""
+    handler = DocumentationHandler()
+
+    # Create temporary docs directory with test files
+    docs_root = tmp_path / "docs"
+    docs_root.mkdir()
+
+    (docs_root / "guide.md").write_text("# Getting Started\n\nThis is a guide.")
+    (docs_root / "api.mdx").write_text("# API Reference\n\nAPI documentation.")
+
+    # Mock _get_docs_root to return temp dir
+    with patch.object(handler, "_get_docs_root", return_value=docs_root):
+        # Mock existing embeddings query (no embeddings exist)
+        with patch(
+            "backend.api.features.store.content_handlers.query_raw_with_schema",
+            return_value=[],
+        ):
+            items = await handler.get_missing_items(batch_size=10)
+
+            assert len(items) == 2
+
+            # Check guide.md (content_id format: doc_path::section_index)
+            guide_item = next(
+                (item for item in items if item.content_id == "guide.md::0"), None
+            )
+            assert guide_item is not None
+            assert guide_item.content_type == ContentType.DOCUMENTATION
+            assert "Getting Started" in guide_item.searchable_text
+            assert "This is a guide" in guide_item.searchable_text
+            assert guide_item.metadata["doc_title"] == "Getting Started"
+            assert guide_item.user_id is None
+
+            # Check api.mdx (content_id format: doc_path::section_index)
+            api_item = next(
+                (item for item in items if item.content_id == "api.mdx::0"), None
+            )
+            assert api_item is not None
+            assert "API Reference" in api_item.searchable_text
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_documentation_handler_get_stats(tmp_path, mocker):
+    """Test DocumentationHandler returns correct stats."""
+    handler = DocumentationHandler()
+
+    # Create temporary docs directory
+    docs_root = tmp_path / "docs"
+    docs_root.mkdir()
+    (docs_root / "doc1.md").write_text("# Doc 1")
+    (docs_root / "doc2.md").write_text("# Doc 2")
+    (docs_root / "doc3.mdx").write_text("# Doc 3")
+
+    # Mock embedded count query (1 doc has embedding)
+    mock_embedded = [{"count": 1}]
+
+    with patch.object(handler, "_get_docs_root", return_value=docs_root):
+        with patch(
+            "backend.api.features.store.content_handlers.query_raw_with_schema",
+            return_value=mock_embedded,
+        ):
+            stats = await handler.get_stats()
+
+            assert stats["total"] == 3
+            assert stats["with_embeddings"] == 1
+            assert stats["without_embeddings"] == 2
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_documentation_handler_title_extraction(tmp_path):
+    """Test DocumentationHandler extracts title from markdown heading."""
+    handler = DocumentationHandler()
+
+    # Test with heading
+    doc_with_heading = tmp_path / "with_heading.md"
+    doc_with_heading.write_text("# My Title\n\nContent here")
+    title = handler._extract_doc_title(doc_with_heading)
+    assert title == "My Title"
+
+    # Test without heading
+    doc_without_heading = tmp_path / "no-heading.md"
+    doc_without_heading.write_text("Just content, no heading")
+    title = handler._extract_doc_title(doc_without_heading)
+    assert title == "No Heading"  # Uses filename
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_documentation_handler_markdown_chunking(tmp_path):
+    """Test DocumentationHandler chunks markdown by headings."""
+    handler = DocumentationHandler()
+
+    # Test document with multiple sections
+    doc_with_sections = tmp_path / "sections.md"
+    doc_with_sections.write_text(
+        "# Document Title\n\n"
+        "Intro paragraph.\n\n"
+        "## Section One\n\n"
+        "Content for section one.\n\n"
+        "## Section Two\n\n"
+        "Content for section two.\n"
+    )
+    sections = handler._chunk_markdown_by_headings(doc_with_sections)
+
+    # Should have 3 sections: intro (with doc title), section one, section two
+    assert len(sections) == 3
+    assert sections[0].title == "Document Title"
+    assert sections[0].index == 0
+    assert "Intro paragraph" in sections[0].content
+
+    assert sections[1].title == "Section One"
+    assert sections[1].index == 1
+    assert "Content for section one" in sections[1].content
+
+    assert sections[2].title == "Section Two"
+    assert sections[2].index == 2
+    assert "Content for section two" in sections[2].content
+
+    # Test document without headings
+    doc_no_sections = tmp_path / "no-sections.md"
+    doc_no_sections.write_text("Just plain content without any headings.")
+    sections = handler._chunk_markdown_by_headings(doc_no_sections)
+    assert len(sections) == 1
+    assert sections[0].index == 0
+    assert "Just plain content" in sections[0].content
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_documentation_handler_section_content_ids():
+    """Test DocumentationHandler creates and parses section content IDs."""
+    handler = DocumentationHandler()
+
+    # Test making content ID
+    content_id = handler._make_section_content_id("docs/guide.md", 2)
+    assert content_id == "docs/guide.md::2"
+
+    # Test parsing content ID
+    doc_path, section_index = handler._parse_section_content_id("docs/guide.md::2")
+    assert doc_path == "docs/guide.md"
+    assert section_index == 2
+
+    # Test parsing legacy format (no section index)
+    doc_path, section_index = handler._parse_section_content_id("docs/old-format.md")
+    assert doc_path == "docs/old-format.md"
+    assert section_index == 0
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_content_handlers_registry():
+    """Test all content types are registered."""
+    assert ContentType.STORE_AGENT in CONTENT_HANDLERS
+    assert ContentType.BLOCK in CONTENT_HANDLERS
+    assert ContentType.DOCUMENTATION in CONTENT_HANDLERS
+
+    assert isinstance(CONTENT_HANDLERS[ContentType.STORE_AGENT], StoreAgentHandler)
+    assert isinstance(CONTENT_HANDLERS[ContentType.BLOCK], BlockHandler)
+    assert isinstance(CONTENT_HANDLERS[ContentType.DOCUMENTATION], DocumentationHandler)
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_block_handler_handles_missing_attributes():
+    """Test BlockHandler gracefully handles blocks with missing attributes."""
+    handler = BlockHandler()
+
+    # Mock block with minimal attributes
+    mock_block_class = MagicMock()
+    mock_block_instance = MagicMock()
+    mock_block_instance.name = "Minimal Block"
+    # No description, categories, or schema
+    del mock_block_instance.description
+    del mock_block_instance.categories
+    del mock_block_instance.input_schema
+    mock_block_class.return_value = mock_block_instance
+
+    mock_blocks = {"block-minimal": mock_block_class}
+
+    with patch(
+        "backend.data.block.get_blocks",
+        return_value=mock_blocks,
+    ):
+        with patch(
+            "backend.api.features.store.content_handlers.query_raw_with_schema",
+            return_value=[],
+        ):
+            items = await handler.get_missing_items(batch_size=10)
+
+            assert len(items) == 1
+            assert items[0].searchable_text == "Minimal Block"
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_block_handler_skips_failed_blocks():
+    """Test BlockHandler skips blocks that fail to instantiate."""
+    handler = BlockHandler()
+
+    # Mock one good block and one bad block
+    good_block = MagicMock()
+    good_instance = MagicMock()
+    good_instance.name = "Good Block"
+    good_instance.description = "Works fine"
+    good_instance.categories = []
+    good_block.return_value = good_instance
+
+    bad_block = MagicMock()
+    bad_block.side_effect = Exception("Instantiation failed")
+
+    mock_blocks = {"good-block": good_block, "bad-block": bad_block}
+
+    with patch(
+        "backend.data.block.get_blocks",
+        return_value=mock_blocks,
+    ):
+        with patch(
+            "backend.api.features.store.content_handlers.query_raw_with_schema",
+            return_value=[],
+        ):
+            items = await handler.get_missing_items(batch_size=10)
+
+            # Should only get the good block
+            assert len(items) == 1
+            assert items[0].content_id == "good-block"
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_documentation_handler_missing_docs_directory():
+    """Test DocumentationHandler handles missing docs directory gracefully."""
+    handler = DocumentationHandler()
+
+    # Mock _get_docs_root to return non-existent path
+    fake_path = Path("/nonexistent/docs")
+    with patch.object(handler, "_get_docs_root", return_value=fake_path):
+        items = await handler.get_missing_items(batch_size=10)
+        assert items == []
+
+        stats = await handler.get_stats()
+        assert stats["total"] == 0
+        assert stats["with_embeddings"] == 0
+        assert stats["without_embeddings"] == 0

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

@@ -14,6 +14,7 @@ import prisma
 from prisma.enums import ContentType
 from tiktoken import encoding_for_model
 
+from backend.api.features.store.content_handlers import CONTENT_HANDLERS
 from backend.data.db import execute_raw_with_schema, query_raw_with_schema
 from backend.util.clients import get_openai_client
 from backend.util.json import dumps
@@ -23,6 +24,9 @@ logger = logging.getLogger(__name__)
 
 # OpenAI embedding model configuration
 EMBEDDING_MODEL = "text-embedding-3-small"
+# Embedding dimension for the model above
+# text-embedding-3-small: 1536, text-embedding-3-large: 3072
+EMBEDDING_DIM = 1536
 # OpenAI embedding token limit (8,191 with 1 token buffer for safety)
 EMBEDDING_MAX_TOKENS = 8191
 
@@ -369,55 +373,69 @@ async def delete_content_embedding(
 
 async def get_embedding_stats() -> dict[str, Any]:
     """
-    Get statistics about embedding coverage.
+    Get statistics about embedding coverage for all content types.
 
-    Returns counts of:
-    - Total approved listing versions
-    - Versions with embeddings
-    - Versions without embeddings
+    Returns stats per content type and overall totals.
     """
     try:
-        # Count approved versions
-        approved_result = await query_raw_with_schema(
-            """
-            SELECT COUNT(*) as count
-            FROM {schema_prefix}"StoreListingVersion"
-            WHERE "submissionStatus" = 'APPROVED'
-            AND "isDeleted" = false
-            """
-        )
-        total_approved = approved_result[0]["count"] if approved_result else 0
+        stats_by_type = {}
+        total_items = 0
+        total_with_embeddings = 0
+        total_without_embeddings = 0
 
-        # Count versions with embeddings
-        embedded_result = await query_raw_with_schema(
-            """
-            SELECT COUNT(*) as count
-            FROM {schema_prefix}"StoreListingVersion" slv
-            JOIN {schema_prefix}"UnifiedContentEmbedding" uce ON slv.id = uce."contentId" AND uce."contentType" = 'STORE_AGENT'::{schema_prefix}"ContentType"
-            WHERE slv."submissionStatus" = 'APPROVED'
-            AND slv."isDeleted" = false
-            """
-        )
-        with_embeddings = embedded_result[0]["count"] if embedded_result else 0
+        # Aggregate stats from all handlers
+        for content_type, handler in CONTENT_HANDLERS.items():
+            try:
+                stats = await handler.get_stats()
+                stats_by_type[content_type.value] = {
+                    "total": stats["total"],
+                    "with_embeddings": stats["with_embeddings"],
+                    "without_embeddings": stats["without_embeddings"],
+                    "coverage_percent": (
+                        round(stats["with_embeddings"] / stats["total"] * 100, 1)
+                        if stats["total"] > 0
+                        else 0
+                    ),
+                }
+
+                total_items += stats["total"]
+                total_with_embeddings += stats["with_embeddings"]
+                total_without_embeddings += stats["without_embeddings"]
+
+            except Exception as e:
+                logger.error(f"Failed to get stats for {content_type.value}: {e}")
+                stats_by_type[content_type.value] = {
+                    "total": 0,
+                    "with_embeddings": 0,
+                    "without_embeddings": 0,
+                    "coverage_percent": 0,
+                    "error": str(e),
+                }
 
         return {
-            "total_approved": total_approved,
-            "with_embeddings": with_embeddings,
-            "without_embeddings": total_approved - with_embeddings,
-            "coverage_percent": (
-                round(with_embeddings / total_approved * 100, 1)
-                if total_approved > 0
-                else 0
-            ),
+            "by_type": stats_by_type,
+            "totals": {
+                "total": total_items,
+                "with_embeddings": total_with_embeddings,
+                "without_embeddings": total_without_embeddings,
+                "coverage_percent": (
+                    round(total_with_embeddings / total_items * 100, 1)
+                    if total_items > 0
+                    else 0
+                ),
+            },
         }
 
     except Exception as e:
         logger.error(f"Failed to get embedding stats: {e}")
         return {
-            "total_approved": 0,
-            "with_embeddings": 0,
-            "without_embeddings": 0,
-            "coverage_percent": 0,
+            "by_type": {},
+            "totals": {
+                "total": 0,
+                "with_embeddings": 0,
+                "without_embeddings": 0,
+                "coverage_percent": 0,
+            },
             "error": str(e),
         }
 
@@ -426,73 +444,118 @@ async def backfill_missing_embeddings(batch_size: int = 10) -> dict[str, Any]:
     """
     Generate embeddings for approved listings that don't have them.
 
+    BACKWARD COMPATIBILITY: Maintained for existing usage.
+    This now delegates to backfill_all_content_types() to process all content types.
+
     Args:
-        batch_size: Number of embeddings to generate in one call
+        batch_size: Number of embeddings to generate per content type
 
     Returns:
-        Dict with success/failure counts
+        Dict with success/failure counts aggregated across all content types
     """
-    try:
-        # Find approved versions without embeddings
-        missing = await query_raw_with_schema(
-            """
-            SELECT
-                slv.id,
-                slv.name,
-                slv.description,
-                slv."subHeading",
-                slv.categories
-            FROM {schema_prefix}"StoreListingVersion" slv
-            LEFT JOIN {schema_prefix}"UnifiedContentEmbedding" uce
-                ON slv.id = uce."contentId" AND uce."contentType" = 'STORE_AGENT'::{schema_prefix}"ContentType"
-            WHERE slv."submissionStatus" = 'APPROVED'
-            AND slv."isDeleted" = false
-            AND uce."contentId" IS NULL
-            LIMIT $1
-            """,
-            batch_size,
-        )
+    # Delegate to the new generic backfill system
+    result = await backfill_all_content_types(batch_size)
 
-        if not missing:
-            return {
+    # Return in the old format for backward compatibility
+    return result["totals"]
+
+
+async def backfill_all_content_types(batch_size: int = 10) -> dict[str, Any]:
+    """
+    Generate embeddings for all content types using registered handlers.
+
+    Processes content types in order: BLOCK → STORE_AGENT → DOCUMENTATION.
+    This ensures foundational content (blocks) are searchable first.
+
+    Args:
+        batch_size: Number of embeddings to generate per content type
+
+    Returns:
+        Dict with stats per content type and overall totals
+    """
+    results_by_type = {}
+    total_processed = 0
+    total_success = 0
+    total_failed = 0
+
+    # Process content types in explicit order
+    processing_order = [
+        ContentType.BLOCK,
+        ContentType.STORE_AGENT,
+        ContentType.DOCUMENTATION,
+    ]
+
+    for content_type in processing_order:
+        handler = CONTENT_HANDLERS.get(content_type)
+        if not handler:
+            logger.warning(f"No handler registered for {content_type.value}")
+            continue
+        try:
+            logger.info(f"Processing {content_type.value} content type...")
+
+            # Get missing items from handler
+            missing_items = await handler.get_missing_items(batch_size)
+
+            if not missing_items:
+                results_by_type[content_type.value] = {
+                    "processed": 0,
+                    "success": 0,
+                    "failed": 0,
+                    "message": "No missing embeddings",
+                }
+                continue
+
+            # Process embeddings concurrently for better performance
+            embedding_tasks = [
+                ensure_content_embedding(
+                    content_type=item.content_type,
+                    content_id=item.content_id,
+                    searchable_text=item.searchable_text,
+                    metadata=item.metadata,
+                    user_id=item.user_id,
+                )
+                for item in missing_items
+            ]
+
+            results = await asyncio.gather(*embedding_tasks, return_exceptions=True)
+
+            success = sum(1 for result in results if result is True)
+            failed = len(results) - success
+
+            results_by_type[content_type.value] = {
+                "processed": len(missing_items),
+                "success": success,
+                "failed": failed,
+                "message": f"Backfilled {success} embeddings, {failed} failed",
+            }
+
+            total_processed += len(missing_items)
+            total_success += success
+            total_failed += failed
+
+            logger.info(
+                f"{content_type.value}: processed {len(missing_items)}, "
+                f"success {success}, failed {failed}"
+            )
+
+        except Exception as e:
+            logger.error(f"Failed to process {content_type.value}: {e}")
+            results_by_type[content_type.value] = {
                 "processed": 0,
                 "success": 0,
                 "failed": 0,
-                "message": "No missing embeddings",
+                "error": str(e),
             }
 
-        # Process embeddings concurrently for better performance
-        embedding_tasks = [
-            ensure_embedding(
-                version_id=row["id"],
-                name=row["name"],
-                description=row["description"],
-                sub_heading=row["subHeading"],
-                categories=row["categories"] or [],
-            )
-            for row in missing
-        ]
-
-        results = await asyncio.gather(*embedding_tasks, return_exceptions=True)
-
-        success = sum(1 for result in results if result is True)
-        failed = len(results) - success
-
-        return {
-            "processed": len(missing),
-            "success": success,
-            "failed": failed,
-            "message": f"Backfilled {success} embeddings, {failed} failed",
-        }
-
-    except Exception as e:
-        logger.error(f"Failed to backfill embeddings: {e}")
-        return {
-            "processed": 0,
-            "success": 0,
-            "failed": 0,
-            "error": str(e),
-        }
+    return {
+        "by_type": results_by_type,
+        "totals": {
+            "processed": total_processed,
+            "success": total_success,
+            "failed": total_failed,
+            "message": f"Overall: {total_success} succeeded, {total_failed} failed",
+        },
+    }
 
 
 async def embed_query(query: str) -> list[float] | None:
@@ -566,3 +629,334 @@ async def ensure_content_embedding(
     except Exception as e:
         logger.error(f"Failed to ensure embedding for {content_type}:{content_id}: {e}")
         return False
+
+
+async def cleanup_orphaned_embeddings() -> dict[str, Any]:
+    """
+    Clean up embeddings for content that no longer exists or is no longer valid.
+
+    Compares current content with embeddings in database and removes orphaned records:
+    - STORE_AGENT: Removes embeddings for rejected/deleted store listings
+    - BLOCK: Removes embeddings for blocks no longer registered
+    - DOCUMENTATION: Removes embeddings for deleted doc files
+
+    Returns:
+        Dict with cleanup statistics per content type
+    """
+    results_by_type = {}
+    total_deleted = 0
+
+    # Cleanup orphaned embeddings for all content types
+    cleanup_types = [
+        ContentType.STORE_AGENT,
+        ContentType.BLOCK,
+        ContentType.DOCUMENTATION,
+    ]
+
+    for content_type in cleanup_types:
+        try:
+            handler = CONTENT_HANDLERS.get(content_type)
+            if not handler:
+                logger.warning(f"No handler registered for {content_type}")
+                results_by_type[content_type.value] = {
+                    "deleted": 0,
+                    "error": "No handler registered",
+                }
+                continue
+
+            # Get all current content IDs from handler
+            if content_type == ContentType.STORE_AGENT:
+                # Get IDs of approved store listing versions from non-deleted listings
+                valid_agents = await query_raw_with_schema(
+                    """
+                    SELECT slv.id
+                    FROM {schema_prefix}"StoreListingVersion" slv
+                    JOIN {schema_prefix}"StoreListing" sl ON slv."storeListingId" = sl.id
+                    WHERE slv."submissionStatus" = 'APPROVED'
+                      AND slv."isDeleted" = false
+                      AND sl."isDeleted" = false
+                    """,
+                )
+                current_ids = {row["id"] for row in valid_agents}
+            elif content_type == ContentType.BLOCK:
+                from backend.data.block import get_blocks
+
+                current_ids = set(get_blocks().keys())
+            elif content_type == ContentType.DOCUMENTATION:
+                # Use DocumentationHandler to get section-based content IDs
+                from backend.api.features.store.content_handlers import (
+                    DocumentationHandler,
+                )
+
+                doc_handler = CONTENT_HANDLERS.get(ContentType.DOCUMENTATION)
+                if isinstance(doc_handler, DocumentationHandler):
+                    docs_root = doc_handler._get_docs_root()
+                    if docs_root.exists():
+                        current_ids = doc_handler._get_all_section_content_ids(
+                            docs_root
+                        )
+                    else:
+                        current_ids = set()
+                else:
+                    current_ids = set()
+            else:
+                # Skip unknown content types to avoid accidental deletion
+                logger.warning(
+                    f"Skipping cleanup for unknown content type: {content_type}"
+                )
+                results_by_type[content_type.value] = {
+                    "deleted": 0,
+                    "error": "Unknown content type - skipped for safety",
+                }
+                continue
+
+            # Get all embedding IDs from database
+            db_embeddings = await query_raw_with_schema(
+                """
+                SELECT "contentId"
+                FROM {schema_prefix}"UnifiedContentEmbedding"
+                WHERE "contentType" = $1::{schema_prefix}"ContentType"
+                """,
+                content_type,
+            )
+
+            db_ids = {row["contentId"] for row in db_embeddings}
+
+            # Find orphaned embeddings (in DB but not in current content)
+            orphaned_ids = db_ids - current_ids
+
+            if not orphaned_ids:
+                logger.info(f"{content_type.value}: No orphaned embeddings found")
+                results_by_type[content_type.value] = {
+                    "deleted": 0,
+                    "message": "No orphaned embeddings",
+                }
+                continue
+
+            # Delete orphaned embeddings in batch for better performance
+            orphaned_list = list(orphaned_ids)
+            try:
+                await execute_raw_with_schema(
+                    """
+                    DELETE FROM {schema_prefix}"UnifiedContentEmbedding"
+                    WHERE "contentType" = $1::{schema_prefix}"ContentType"
+                      AND "contentId" = ANY($2::text[])
+                    """,
+                    content_type,
+                    orphaned_list,
+                )
+                deleted = len(orphaned_list)
+            except Exception as e:
+                logger.error(f"Failed to batch delete orphaned embeddings: {e}")
+                deleted = 0
+
+            logger.info(
+                f"{content_type.value}: Deleted {deleted}/{len(orphaned_ids)} orphaned embeddings"
+            )
+            results_by_type[content_type.value] = {
+                "deleted": deleted,
+                "orphaned": len(orphaned_ids),
+                "message": f"Deleted {deleted} orphaned embeddings",
+            }
+
+            total_deleted += deleted
+
+        except Exception as e:
+            logger.error(f"Failed to cleanup {content_type.value}: {e}")
+            results_by_type[content_type.value] = {
+                "deleted": 0,
+                "error": str(e),
+            }
+
+    return {
+        "by_type": results_by_type,
+        "totals": {
+            "deleted": total_deleted,
+            "message": f"Deleted {total_deleted} orphaned embeddings",
+        },
+    }
+
+
+async def semantic_search(
+    query: str,
+    content_types: list[ContentType] | None = None,
+    user_id: str | None = None,
+    limit: int = 20,
+    min_similarity: float = 0.5,
+) -> list[dict[str, Any]]:
+    """
+    Semantic search across content types using embeddings.
+
+    Performs vector similarity search on UnifiedContentEmbedding table.
+    Used directly for blocks/docs/library agents, or as the semantic component
+    within hybrid_search for store agents.
+
+    If embedding generation fails, falls back to lexical search on searchableText.
+
+    Args:
+        query: Search query string
+        content_types: List of ContentType to search. Defaults to [BLOCK, STORE_AGENT, DOCUMENTATION]
+        user_id: Optional user ID for searching private content (library agents)
+        limit: Maximum number of results to return (default: 20)
+        min_similarity: Minimum cosine similarity threshold (0-1, default: 0.5)
+
+    Returns:
+        List of search results with the following structure:
+        [
+            {
+                "content_id": str,
+                "content_type": str,  # "BLOCK", "STORE_AGENT", "DOCUMENTATION", or "LIBRARY_AGENT"
+                "searchable_text": str,
+                "metadata": dict,
+                "similarity": float,  # Cosine similarity score (0-1)
+            },
+            ...
+        ]
+
+    Examples:
+        # Search blocks only
+        results = await semantic_search("calculate", content_types=[ContentType.BLOCK])
+
+        # Search blocks and documentation
+        results = await semantic_search(
+            "how to use API",
+            content_types=[ContentType.BLOCK, ContentType.DOCUMENTATION]
+        )
+
+        # Search all public content (default)
+        results = await semantic_search("AI agent")
+
+        # Search user's library agents
+        results = await semantic_search(
+            "my custom agent",
+            content_types=[ContentType.LIBRARY_AGENT],
+            user_id="user123"
+        )
+    """
+    # Default to searching all public content types
+    if content_types is None:
+        content_types = [
+            ContentType.BLOCK,
+            ContentType.STORE_AGENT,
+            ContentType.DOCUMENTATION,
+        ]
+
+    # Validate inputs
+    if not content_types:
+        return []  # Empty content_types would cause invalid SQL (IN ())
+
+    query = query.strip()
+    if not query:
+        return []
+
+    if limit < 1:
+        limit = 1
+    if limit > 100:
+        limit = 100
+
+    # Generate query embedding
+    query_embedding = await embed_query(query)
+
+    if query_embedding is not None:
+        # Semantic search with embeddings
+        embedding_str = embedding_to_vector_string(query_embedding)
+
+        # Build params in order: limit, then user_id (if provided), then content types
+        params: list[Any] = [limit]
+        user_filter = ""
+        if user_id is not None:
+            user_filter = 'AND "userId" = ${}'.format(len(params) + 1)
+            params.append(user_id)
+
+        # Add content type parameters and build placeholders dynamically
+        content_type_start_idx = len(params) + 1
+        content_type_placeholders = ", ".join(
+            f'${content_type_start_idx + i}::{{{{schema_prefix}}}}"ContentType"'
+            for i in range(len(content_types))
+        )
+        params.extend([ct.value for ct in content_types])
+
+        sql = f"""
+            SELECT
+                "contentId" as content_id,
+                "contentType" as content_type,
+                "searchableText" as searchable_text,
+                metadata,
+                1 - (embedding <=> '{embedding_str}'::vector) as similarity
+            FROM {{{{schema_prefix}}}}"UnifiedContentEmbedding"
+            WHERE "contentType" IN ({content_type_placeholders})
+            {user_filter}
+            AND 1 - (embedding <=> '{embedding_str}'::vector) >= ${len(params) + 1}
+            ORDER BY similarity DESC
+            LIMIT $1
+        """
+        params.append(min_similarity)
+
+        try:
+            results = await query_raw_with_schema(
+                sql, *params, set_public_search_path=True
+            )
+            return [
+                {
+                    "content_id": row["content_id"],
+                    "content_type": row["content_type"],
+                    "searchable_text": row["searchable_text"],
+                    "metadata": row["metadata"],
+                    "similarity": float(row["similarity"]),
+                }
+                for row in results
+            ]
+        except Exception as e:
+            logger.error(f"Semantic search failed: {e}")
+            # Fall through to lexical search below
+
+    # Fallback to lexical search if embeddings unavailable
+    logger.warning("Falling back to lexical search (embeddings unavailable)")
+
+    params_lexical: list[Any] = [limit]
+    user_filter = ""
+    if user_id is not None:
+        user_filter = 'AND "userId" = ${}'.format(len(params_lexical) + 1)
+        params_lexical.append(user_id)
+
+    # Add content type parameters and build placeholders dynamically
+    content_type_start_idx = len(params_lexical) + 1
+    content_type_placeholders_lexical = ", ".join(
+        f'${content_type_start_idx + i}::{{{{schema_prefix}}}}"ContentType"'
+        for i in range(len(content_types))
+    )
+    params_lexical.extend([ct.value for ct in content_types])
+
+    sql_lexical = f"""
+        SELECT
+            "contentId" as content_id,
+            "contentType" as content_type,
+            "searchableText" as searchable_text,
+            metadata,
+            0.0 as similarity
+        FROM {{{{schema_prefix}}}}"UnifiedContentEmbedding"
+        WHERE "contentType" IN ({content_type_placeholders_lexical})
+        {user_filter}
+        AND "searchableText" ILIKE ${len(params_lexical) + 1}
+        ORDER BY "updatedAt" DESC
+        LIMIT $1
+    """
+    params_lexical.append(f"%{query}%")
+
+    try:
+        results = await query_raw_with_schema(
+            sql_lexical, *params_lexical, set_public_search_path=True
+        )
+        return [
+            {
+                "content_id": row["content_id"],
+                "content_type": row["content_type"],
+                "searchable_text": row["searchable_text"],
+                "metadata": row["metadata"],
+                "similarity": 0.0,  # Lexical search doesn't provide similarity
+            }
+            for row in results
+        ]
+    except Exception as e:
+        logger.error(f"Lexical search failed: {e}")
+        return []

autogpt_platform/backend/backend/api/features/store/embeddings_e2e_test.py

@@ -0,0 +1,666 @@
+"""
+End-to-end database tests for embeddings and hybrid search.
+
+These tests hit the actual database to verify SQL queries work correctly.
+Tests cover:
+1. Embedding storage (store_content_embedding)
+2. Embedding retrieval (get_content_embedding)
+3. Embedding deletion (delete_content_embedding)
+4. Unified hybrid search across content types
+5. Store agent hybrid search
+"""
+
+import uuid
+from typing import AsyncGenerator
+
+import pytest
+from prisma.enums import ContentType
+
+from backend.api.features.store import embeddings
+from backend.api.features.store.embeddings import EMBEDDING_DIM
+from backend.api.features.store.hybrid_search import (
+    hybrid_search,
+    unified_hybrid_search,
+)
+
+# ============================================================================
+# Test Fixtures
+# ============================================================================
+
+
+@pytest.fixture
+def test_content_id() -> str:
+    """Generate unique content ID for test isolation."""
+    return f"test-content-{uuid.uuid4()}"
+
+
+@pytest.fixture
+def test_user_id() -> str:
+    """Generate unique user ID for test isolation."""
+    return f"test-user-{uuid.uuid4()}"
+
+
+@pytest.fixture
+def mock_embedding() -> list[float]:
+    """Generate a mock embedding vector."""
+    # Create a normalized embedding vector
+    import math
+
+    raw = [float(i % 10) / 10.0 for i in range(EMBEDDING_DIM)]
+    # Normalize to unit length (required for cosine similarity)
+    magnitude = math.sqrt(sum(x * x for x in raw))
+    return [x / magnitude for x in raw]
+
+
+@pytest.fixture
+def similar_embedding() -> list[float]:
+    """Generate an embedding similar to mock_embedding."""
+    import math
+
+    # Similar but slightly different values
+    raw = [float(i % 10) / 10.0 + 0.01 for i in range(EMBEDDING_DIM)]
+    magnitude = math.sqrt(sum(x * x for x in raw))
+    return [x / magnitude for x in raw]
+
+
+@pytest.fixture
+def different_embedding() -> list[float]:
+    """Generate an embedding very different from mock_embedding."""
+    import math
+
+    # Reversed pattern to be maximally different
+    raw = [float((EMBEDDING_DIM - i) % 10) / 10.0 for i in range(EMBEDDING_DIM)]
+    magnitude = math.sqrt(sum(x * x for x in raw))
+    return [x / magnitude for x in raw]
+
+
+@pytest.fixture
+async def cleanup_embeddings(
+    server,
+) -> AsyncGenerator[list[tuple[ContentType, str, str | None]], None]:
+    """
+    Fixture that tracks created embeddings and cleans them up after tests.
+
+    Yields a list to which tests can append (content_type, content_id, user_id) tuples.
+    """
+    created_embeddings: list[tuple[ContentType, str, str | None]] = []
+    yield created_embeddings
+
+    # Cleanup all created embeddings
+    for content_type, content_id, user_id in created_embeddings:
+        try:
+            await embeddings.delete_content_embedding(content_type, content_id, user_id)
+        except Exception:
+            pass  # Ignore cleanup errors
+
+
+# ============================================================================
+# store_content_embedding Tests
+# ============================================================================
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_store_content_embedding_store_agent(
+    server,
+    test_content_id: str,
+    mock_embedding: list[float],
+    cleanup_embeddings: list,
+):
+    """Test storing embedding for STORE_AGENT content type."""
+    # Track for cleanup
+    cleanup_embeddings.append((ContentType.STORE_AGENT, test_content_id, None))
+
+    result = await embeddings.store_content_embedding(
+        content_type=ContentType.STORE_AGENT,
+        content_id=test_content_id,
+        embedding=mock_embedding,
+        searchable_text="AI assistant for productivity tasks",
+        metadata={"name": "Test Agent", "categories": ["productivity"]},
+        user_id=None,  # Store agents are public
+    )
+
+    assert result is True
+
+    # Verify it was stored
+    stored = await embeddings.get_content_embedding(
+        ContentType.STORE_AGENT, test_content_id, user_id=None
+    )
+    assert stored is not None
+    assert stored["contentId"] == test_content_id
+    assert stored["contentType"] == "STORE_AGENT"
+    assert stored["searchableText"] == "AI assistant for productivity tasks"
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_store_content_embedding_block(
+    server,
+    test_content_id: str,
+    mock_embedding: list[float],
+    cleanup_embeddings: list,
+):
+    """Test storing embedding for BLOCK content type."""
+    cleanup_embeddings.append((ContentType.BLOCK, test_content_id, None))
+
+    result = await embeddings.store_content_embedding(
+        content_type=ContentType.BLOCK,
+        content_id=test_content_id,
+        embedding=mock_embedding,
+        searchable_text="HTTP request block for API calls",
+        metadata={"name": "HTTP Request Block"},
+        user_id=None,  # Blocks are public
+    )
+
+    assert result is True
+
+    stored = await embeddings.get_content_embedding(
+        ContentType.BLOCK, test_content_id, user_id=None
+    )
+    assert stored is not None
+    assert stored["contentType"] == "BLOCK"
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_store_content_embedding_documentation(
+    server,
+    test_content_id: str,
+    mock_embedding: list[float],
+    cleanup_embeddings: list,
+):
+    """Test storing embedding for DOCUMENTATION content type."""
+    cleanup_embeddings.append((ContentType.DOCUMENTATION, test_content_id, None))
+
+    result = await embeddings.store_content_embedding(
+        content_type=ContentType.DOCUMENTATION,
+        content_id=test_content_id,
+        embedding=mock_embedding,
+        searchable_text="Getting started guide for AutoGPT platform",
+        metadata={"title": "Getting Started", "url": "/docs/getting-started"},
+        user_id=None,  # Docs are public
+    )
+
+    assert result is True
+
+    stored = await embeddings.get_content_embedding(
+        ContentType.DOCUMENTATION, test_content_id, user_id=None
+    )
+    assert stored is not None
+    assert stored["contentType"] == "DOCUMENTATION"
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_store_content_embedding_upsert(
+    server,
+    test_content_id: str,
+    mock_embedding: list[float],
+    cleanup_embeddings: list,
+):
+    """Test that storing embedding twice updates instead of duplicates."""
+    cleanup_embeddings.append((ContentType.BLOCK, test_content_id, None))
+
+    # Store first time
+    result1 = await embeddings.store_content_embedding(
+        content_type=ContentType.BLOCK,
+        content_id=test_content_id,
+        embedding=mock_embedding,
+        searchable_text="Original text",
+        metadata={"version": 1},
+        user_id=None,
+    )
+    assert result1 is True
+
+    # Store again with different text (upsert)
+    result2 = await embeddings.store_content_embedding(
+        content_type=ContentType.BLOCK,
+        content_id=test_content_id,
+        embedding=mock_embedding,
+        searchable_text="Updated text",
+        metadata={"version": 2},
+        user_id=None,
+    )
+    assert result2 is True
+
+    # Verify only one record with updated text
+    stored = await embeddings.get_content_embedding(
+        ContentType.BLOCK, test_content_id, user_id=None
+    )
+    assert stored is not None
+    assert stored["searchableText"] == "Updated text"
+
+
+# ============================================================================
+# get_content_embedding Tests
+# ============================================================================
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_get_content_embedding_not_found(server):
+    """Test retrieving non-existent embedding returns None."""
+    result = await embeddings.get_content_embedding(
+        ContentType.STORE_AGENT, "non-existent-id", user_id=None
+    )
+    assert result is None
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_get_content_embedding_with_metadata(
+    server,
+    test_content_id: str,
+    mock_embedding: list[float],
+    cleanup_embeddings: list,
+):
+    """Test that metadata is correctly stored and retrieved."""
+    cleanup_embeddings.append((ContentType.STORE_AGENT, test_content_id, None))
+
+    metadata = {
+        "name": "Test Agent",
+        "subHeading": "A test agent",
+        "categories": ["ai", "productivity"],
+        "customField": 123,
+    }
+
+    await embeddings.store_content_embedding(
+        content_type=ContentType.STORE_AGENT,
+        content_id=test_content_id,
+        embedding=mock_embedding,
+        searchable_text="test",
+        metadata=metadata,
+        user_id=None,
+    )
+
+    stored = await embeddings.get_content_embedding(
+        ContentType.STORE_AGENT, test_content_id, user_id=None
+    )
+
+    assert stored is not None
+    assert stored["metadata"]["name"] == "Test Agent"
+    assert stored["metadata"]["categories"] == ["ai", "productivity"]
+    assert stored["metadata"]["customField"] == 123
+
+
+# ============================================================================
+# delete_content_embedding Tests
+# ============================================================================
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_delete_content_embedding(
+    server,
+    test_content_id: str,
+    mock_embedding: list[float],
+):
+    """Test deleting embedding removes it from database."""
+    # Store embedding
+    await embeddings.store_content_embedding(
+        content_type=ContentType.BLOCK,
+        content_id=test_content_id,
+        embedding=mock_embedding,
+        searchable_text="To be deleted",
+        metadata=None,
+        user_id=None,
+    )
+
+    # Verify it exists
+    stored = await embeddings.get_content_embedding(
+        ContentType.BLOCK, test_content_id, user_id=None
+    )
+    assert stored is not None
+
+    # Delete it
+    result = await embeddings.delete_content_embedding(
+        ContentType.BLOCK, test_content_id, user_id=None
+    )
+    assert result is True
+
+    # Verify it's gone
+    stored = await embeddings.get_content_embedding(
+        ContentType.BLOCK, test_content_id, user_id=None
+    )
+    assert stored is None
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_delete_content_embedding_not_found(server):
+    """Test deleting non-existent embedding doesn't error."""
+    result = await embeddings.delete_content_embedding(
+        ContentType.BLOCK, "non-existent-id", user_id=None
+    )
+    # Should succeed even if nothing to delete
+    assert result is True
+
+
+# ============================================================================
+# unified_hybrid_search Tests
+# ============================================================================
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_unified_hybrid_search_finds_matching_content(
+    server,
+    mock_embedding: list[float],
+    cleanup_embeddings: list,
+):
+    """Test unified search finds content matching the query."""
+    # Create unique content IDs
+    agent_id = f"test-agent-{uuid.uuid4()}"
+    block_id = f"test-block-{uuid.uuid4()}"
+    doc_id = f"test-doc-{uuid.uuid4()}"
+
+    cleanup_embeddings.append((ContentType.STORE_AGENT, agent_id, None))
+    cleanup_embeddings.append((ContentType.BLOCK, block_id, None))
+    cleanup_embeddings.append((ContentType.DOCUMENTATION, doc_id, None))
+
+    # Store embeddings for different content types
+    await embeddings.store_content_embedding(
+        content_type=ContentType.STORE_AGENT,
+        content_id=agent_id,
+        embedding=mock_embedding,
+        searchable_text="AI writing assistant for blog posts",
+        metadata={"name": "Writing Assistant"},
+        user_id=None,
+    )
+
+    await embeddings.store_content_embedding(
+        content_type=ContentType.BLOCK,
+        content_id=block_id,
+        embedding=mock_embedding,
+        searchable_text="Text generation block for creative writing",
+        metadata={"name": "Text Generator"},
+        user_id=None,
+    )
+
+    await embeddings.store_content_embedding(
+        content_type=ContentType.DOCUMENTATION,
+        content_id=doc_id,
+        embedding=mock_embedding,
+        searchable_text="How to use writing blocks in AutoGPT",
+        metadata={"title": "Writing Guide"},
+        user_id=None,
+    )
+
+    # Search for "writing" - should find all three
+    results, total = await unified_hybrid_search(
+        query="writing",
+        page=1,
+        page_size=20,
+    )
+
+    # Should find at least our test content (may find others too)
+    content_ids = [r["content_id"] for r in results]
+    assert agent_id in content_ids or total >= 1  # Lexical search should find it
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_unified_hybrid_search_filter_by_content_type(
+    server,
+    mock_embedding: list[float],
+    cleanup_embeddings: list,
+):
+    """Test unified search can filter by content type."""
+    agent_id = f"test-agent-{uuid.uuid4()}"
+    block_id = f"test-block-{uuid.uuid4()}"
+
+    cleanup_embeddings.append((ContentType.STORE_AGENT, agent_id, None))
+    cleanup_embeddings.append((ContentType.BLOCK, block_id, None))
+
+    # Store both types with same searchable text
+    await embeddings.store_content_embedding(
+        content_type=ContentType.STORE_AGENT,
+        content_id=agent_id,
+        embedding=mock_embedding,
+        searchable_text="unique_search_term_xyz123",
+        metadata={},
+        user_id=None,
+    )
+
+    await embeddings.store_content_embedding(
+        content_type=ContentType.BLOCK,
+        content_id=block_id,
+        embedding=mock_embedding,
+        searchable_text="unique_search_term_xyz123",
+        metadata={},
+        user_id=None,
+    )
+
+    # Search only for BLOCK type
+    results, total = await unified_hybrid_search(
+        query="unique_search_term_xyz123",
+        content_types=[ContentType.BLOCK],
+        page=1,
+        page_size=20,
+    )
+
+    # All results should be BLOCK type
+    for r in results:
+        assert r["content_type"] == "BLOCK"
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_unified_hybrid_search_empty_query(server):
+    """Test unified search with empty query returns empty results."""
+    results, total = await unified_hybrid_search(
+        query="",
+        page=1,
+        page_size=20,
+    )
+
+    assert results == []
+    assert total == 0
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_unified_hybrid_search_pagination(
+    server,
+    mock_embedding: list[float],
+    cleanup_embeddings: list,
+):
+    """Test unified search pagination works correctly."""
+    # Create multiple items
+    content_ids = []
+    for i in range(5):
+        content_id = f"test-pagination-{uuid.uuid4()}"
+        content_ids.append(content_id)
+        cleanup_embeddings.append((ContentType.BLOCK, content_id, None))
+
+        await embeddings.store_content_embedding(
+            content_type=ContentType.BLOCK,
+            content_id=content_id,
+            embedding=mock_embedding,
+            searchable_text=f"pagination test item number {i}",
+            metadata={"index": i},
+            user_id=None,
+        )
+
+    # Get first page
+    page1_results, total1 = await unified_hybrid_search(
+        query="pagination test",
+        content_types=[ContentType.BLOCK],
+        page=1,
+        page_size=2,
+    )
+
+    # Get second page
+    page2_results, total2 = await unified_hybrid_search(
+        query="pagination test",
+        content_types=[ContentType.BLOCK],
+        page=2,
+        page_size=2,
+    )
+
+    # Total should be consistent
+    assert total1 == total2
+
+    # Pages should have different content (if we have enough results)
+    if len(page1_results) > 0 and len(page2_results) > 0:
+        page1_ids = {r["content_id"] for r in page1_results}
+        page2_ids = {r["content_id"] for r in page2_results}
+        # No overlap between pages
+        assert page1_ids.isdisjoint(page2_ids)
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_unified_hybrid_search_min_score_filtering(
+    server,
+    mock_embedding: list[float],
+    cleanup_embeddings: list,
+):
+    """Test unified search respects min_score threshold."""
+    content_id = f"test-minscore-{uuid.uuid4()}"
+    cleanup_embeddings.append((ContentType.BLOCK, content_id, None))
+
+    await embeddings.store_content_embedding(
+        content_type=ContentType.BLOCK,
+        content_id=content_id,
+        embedding=mock_embedding,
+        searchable_text="completely unrelated content about bananas",
+        metadata={},
+        user_id=None,
+    )
+
+    # Search with very high min_score - should filter out low relevance
+    results_high, _ = await unified_hybrid_search(
+        query="quantum computing algorithms",
+        content_types=[ContentType.BLOCK],
+        min_score=0.9,  # Very high threshold
+        page=1,
+        page_size=20,
+    )
+
+    # Search with low min_score
+    results_low, _ = await unified_hybrid_search(
+        query="quantum computing algorithms",
+        content_types=[ContentType.BLOCK],
+        min_score=0.01,  # Very low threshold
+        page=1,
+        page_size=20,
+    )
+
+    # High threshold should have fewer or equal results
+    assert len(results_high) <= len(results_low)
+
+
+# ============================================================================
+# hybrid_search (Store Agents) Tests
+# ============================================================================
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_hybrid_search_store_agents_sql_valid(server):
+    """Test that hybrid_search SQL executes without errors."""
+    # This test verifies the SQL is syntactically correct
+    # even if no results are found
+    results, total = await hybrid_search(
+        query="test agent",
+        page=1,
+        page_size=20,
+    )
+
+    # Should not raise - verifies SQL is valid
+    assert isinstance(results, list)
+    assert isinstance(total, int)
+    assert total >= 0
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_hybrid_search_with_filters(server):
+    """Test hybrid_search with various filter options."""
+    # Test with all filter types
+    results, total = await hybrid_search(
+        query="productivity",
+        featured=True,
+        creators=["test-creator"],
+        category="productivity",
+        page=1,
+        page_size=10,
+    )
+
+    # Should not raise - verifies filter SQL is valid
+    assert isinstance(results, list)
+    assert isinstance(total, int)
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_hybrid_search_pagination(server):
+    """Test hybrid_search pagination."""
+    # Page 1
+    results1, total1 = await hybrid_search(
+        query="agent",
+        page=1,
+        page_size=5,
+    )
+
+    # Page 2
+    results2, total2 = await hybrid_search(
+        query="agent",
+        page=2,
+        page_size=5,
+    )
+
+    # Verify SQL executes without error
+    assert isinstance(results1, list)
+    assert isinstance(results2, list)
+    assert isinstance(total1, int)
+    assert isinstance(total2, int)
+
+    # If page 1 has results, total should be > 0
+    # Note: total from page 2 may be 0 if no results on that page (COUNT(*) OVER limitation)
+    if results1:
+        assert total1 > 0
+
+
+# ============================================================================
+# SQL Validity Tests (verify queries don't break)
+# ============================================================================
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_all_content_types_searchable(server):
+    """Test that all content types can be searched without SQL errors."""
+    for content_type in [
+        ContentType.STORE_AGENT,
+        ContentType.BLOCK,
+        ContentType.DOCUMENTATION,
+    ]:
+        results, total = await unified_hybrid_search(
+            query="test",
+            content_types=[content_type],
+            page=1,
+            page_size=10,
+        )
+
+        # Should not raise
+        assert isinstance(results, list)
+        assert isinstance(total, int)
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_multiple_content_types_searchable(server):
+    """Test searching multiple content types at once."""
+    results, total = await unified_hybrid_search(
+        query="test",
+        content_types=[ContentType.BLOCK, ContentType.DOCUMENTATION],
+        page=1,
+        page_size=20,
+    )
+
+    # Should not raise
+    assert isinstance(results, list)
+    assert isinstance(total, int)
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_search_all_content_types_default(server):
+    """Test searching all content types (default behavior)."""
+    results, total = await unified_hybrid_search(
+        query="test",
+        content_types=None,  # Should search all
+        page=1,
+        page_size=20,
+    )
+
+    # Should not raise
+    assert isinstance(results, list)
+    assert isinstance(total, int)
+
+
+if __name__ == "__main__":
+    pytest.main([__file__, "-v", "-s"])

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

@@ -4,12 +4,13 @@ Integration tests for embeddings with schema handling.
 These tests verify that embeddings operations work correctly across different database schemas.
 """
 
-from unittest.mock import AsyncMock, patch
+from unittest.mock import AsyncMock, MagicMock, patch
 
 import pytest
 from prisma.enums import ContentType
 
 from backend.api.features.store import embeddings
+from backend.api.features.store.embeddings import EMBEDDING_DIM
 
 # Schema prefix tests removed - functionality moved to db.raw_with_schema() helper
 
@@ -28,7 +29,7 @@ async def test_store_content_embedding_with_schema():
             result = await embeddings.store_content_embedding(
                 content_type=ContentType.STORE_AGENT,
                 content_id="test-id",
-                embedding=[0.1] * 1536,
+                embedding=[0.1] * EMBEDDING_DIM,
                 searchable_text="test text",
                 metadata={"test": "data"},
                 user_id=None,
@@ -125,84 +126,69 @@ async def test_delete_content_embedding_with_schema():
 @pytest.mark.asyncio(loop_scope="session")
 @pytest.mark.integration
 async def test_get_embedding_stats_with_schema():
-    """Test embedding statistics with proper schema handling."""
-    with patch("backend.data.db.get_database_schema") as mock_schema:
-        mock_schema.return_value = "platform"
+    """Test embedding statistics with proper schema handling via content handlers."""
+    # Mock handler to return stats
+    mock_handler = MagicMock()
+    mock_handler.get_stats = AsyncMock(
+        return_value={
+            "total": 100,
+            "with_embeddings": 80,
+            "without_embeddings": 20,
+        }
+    )
 
-        with patch("prisma.get_client") as mock_get_client:
-            mock_client = AsyncMock()
-            # Mock both query results
-            mock_client.query_raw.side_effect = [
-                [{"count": 100}],  # total_approved
-                [{"count": 80}],  # with_embeddings
-            ]
-            mock_get_client.return_value = mock_client
+    with patch(
+        "backend.api.features.store.embeddings.CONTENT_HANDLERS",
+        {ContentType.STORE_AGENT: mock_handler},
+    ):
+        result = await embeddings.get_embedding_stats()
 
-            result = await embeddings.get_embedding_stats()
+        # Verify handler was called
+        mock_handler.get_stats.assert_called_once()
 
-            # Verify both queries were called
-            assert mock_client.query_raw.call_count == 2
-
-            # Get both SQL queries
-            first_call = mock_client.query_raw.call_args_list[0]
-            second_call = mock_client.query_raw.call_args_list[1]
-
-            first_sql = first_call[0][0]
-            second_sql = second_call[0][0]
-
-            # Verify schema prefix in both queries
-            assert '"platform"."StoreListingVersion"' in first_sql
-            assert '"platform"."StoreListingVersion"' in second_sql
-            assert '"platform"."UnifiedContentEmbedding"' in second_sql
-
-            # Verify results
-            assert result["total_approved"] == 100
-            assert result["with_embeddings"] == 80
-            assert result["without_embeddings"] == 20
-            assert result["coverage_percent"] == 80.0
+        # Verify new result structure
+        assert "by_type" in result
+        assert "totals" in result
+        assert result["totals"]["total"] == 100
+        assert result["totals"]["with_embeddings"] == 80
+        assert result["totals"]["without_embeddings"] == 20
+        assert result["totals"]["coverage_percent"] == 80.0
 
 
 @pytest.mark.asyncio(loop_scope="session")
 @pytest.mark.integration
 async def test_backfill_missing_embeddings_with_schema():
-    """Test backfilling embeddings with proper schema handling."""
-    with patch("backend.data.db.get_database_schema") as mock_schema:
-        mock_schema.return_value = "platform"
+    """Test backfilling embeddings via content handlers."""
+    from backend.api.features.store.content_handlers import ContentItem
 
-        with patch("prisma.get_client") as mock_get_client:
-            mock_client = AsyncMock()
-            # Mock missing embeddings query
-            mock_client.query_raw.return_value = [
-                {
-                    "id": "version-1",
-                    "name": "Test Agent",
-                    "description": "Test description",
-                    "subHeading": "Test heading",
-                    "categories": ["test"],
-                }
-            ]
-            mock_get_client.return_value = mock_client
+    # Create mock content item
+    mock_item = ContentItem(
+        content_id="version-1",
+        content_type=ContentType.STORE_AGENT,
+        searchable_text="Test Agent Test description",
+        metadata={"name": "Test Agent"},
+    )
 
+    # Mock handler
+    mock_handler = MagicMock()
+    mock_handler.get_missing_items = AsyncMock(return_value=[mock_item])
+
+    with patch(
+        "backend.api.features.store.embeddings.CONTENT_HANDLERS",
+        {ContentType.STORE_AGENT: mock_handler},
+    ):
+        with patch(
+            "backend.api.features.store.embeddings.generate_embedding",
+            return_value=[0.1] * EMBEDDING_DIM,
+        ):
             with patch(
-                "backend.api.features.store.embeddings.ensure_embedding"
-            ) as mock_ensure:
-                mock_ensure.return_value = True
-
+                "backend.api.features.store.embeddings.store_content_embedding",
+                return_value=True,
+            ):
                 result = await embeddings.backfill_missing_embeddings(batch_size=10)
 
-                # Verify the query was called
-                assert mock_client.query_raw.called
-
-                # Get the SQL query
-                call_args = mock_client.query_raw.call_args
-                sql_query = call_args[0][0]
-
-                # Verify schema prefix in query
-                assert '"platform"."StoreListingVersion"' in sql_query
-                assert '"platform"."UnifiedContentEmbedding"' in sql_query
-
-                # Verify ensure_embedding was called
-                assert mock_ensure.called
+                # Verify handler was called
+                mock_handler.get_missing_items.assert_called_once_with(10)
 
                 # Verify results
                 assert result["processed"] == 1
@@ -226,7 +212,7 @@ async def test_ensure_content_embedding_with_schema():
             with patch(
                 "backend.api.features.store.embeddings.generate_embedding"
             ) as mock_generate:
-                mock_generate.return_value = [0.1] * 1536
+                mock_generate.return_value = [0.1] * EMBEDDING_DIM
 
                 with patch(
                     "backend.api.features.store.embeddings.store_content_embedding"
@@ -260,7 +246,7 @@ async def test_backward_compatibility_store_embedding():
 
         result = await embeddings.store_embedding(
             version_id="test-version-id",
-            embedding=[0.1] * 1536,
+            embedding=[0.1] * EMBEDDING_DIM,
             tx=None,
         )
 
@@ -315,7 +301,7 @@ async def test_schema_handling_error_cases():
             result = await embeddings.store_content_embedding(
                 content_type=ContentType.STORE_AGENT,
                 content_id="test-id",
-                embedding=[0.1] * 1536,
+                embedding=[0.1] * EMBEDDING_DIM,
                 searchable_text="test",
                 metadata=None,
                 user_id=None,

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

@@ -63,7 +63,7 @@ async def test_generate_embedding_success():
         result = await embeddings.generate_embedding("test text")
 
         assert result is not None
-        assert len(result) == 1536
+        assert len(result) == embeddings.EMBEDDING_DIM
         assert result[0] == 0.1
 
         mock_client.embeddings.create.assert_called_once_with(
@@ -110,7 +110,7 @@ async def test_generate_embedding_text_truncation():
     mock_client = MagicMock()
     mock_response = MagicMock()
     mock_response.data = [MagicMock()]
-    mock_response.data[0].embedding = [0.1] * 1536
+    mock_response.data[0].embedding = [0.1] * embeddings.EMBEDDING_DIM
 
     # Use AsyncMock for async embeddings.create method
     mock_client.embeddings.create = AsyncMock(return_value=mock_response)
@@ -297,72 +297,92 @@ async def test_ensure_embedding_generation_fails(mock_get, mock_generate):
 @pytest.mark.asyncio(loop_scope="session")
 async def test_get_embedding_stats():
     """Test embedding statistics retrieval."""
-    # Mock approved count query and embedded count query
-    mock_approved_result = [{"count": 100}]
-    mock_embedded_result = [{"count": 75}]
+    # Mock handler stats for each content type
+    mock_handler = MagicMock()
+    mock_handler.get_stats = AsyncMock(
+        return_value={
+            "total": 100,
+            "with_embeddings": 75,
+            "without_embeddings": 25,
+        }
+    )
 
+    # Patch the CONTENT_HANDLERS where it's used (in embeddings module)
     with patch(
-        "backend.api.features.store.embeddings.query_raw_with_schema",
-        side_effect=[mock_approved_result, mock_embedded_result],
+        "backend.api.features.store.embeddings.CONTENT_HANDLERS",
+        {ContentType.STORE_AGENT: mock_handler},
     ):
         result = await embeddings.get_embedding_stats()
 
-        assert result["total_approved"] == 100
-        assert result["with_embeddings"] == 75
-        assert result["without_embeddings"] == 25
-        assert result["coverage_percent"] == 75.0
+        assert "by_type" in result
+        assert "totals" in result
+        assert result["totals"]["total"] == 100
+        assert result["totals"]["with_embeddings"] == 75
+        assert result["totals"]["without_embeddings"] == 25
+        assert result["totals"]["coverage_percent"] == 75.0
 
 
 @pytest.mark.asyncio(loop_scope="session")
-@patch("backend.api.features.store.embeddings.ensure_embedding")
-async def test_backfill_missing_embeddings_success(mock_ensure):
+@patch("backend.api.features.store.embeddings.store_content_embedding")
+async def test_backfill_missing_embeddings_success(mock_store):
     """Test backfill with successful embedding generation."""
-    # Mock missing embeddings query
-    mock_missing = [
-        {
-            "id": "version-1",
-            "name": "Agent 1",
-            "description": "Description 1",
-            "subHeading": "Heading 1",
-            "categories": ["AI"],
-        },
-        {
-            "id": "version-2",
-            "name": "Agent 2",
-            "description": "Description 2",
-            "subHeading": "Heading 2",
-            "categories": ["Productivity"],
-        },
+    # Mock ContentItem from handlers
+    from backend.api.features.store.content_handlers import ContentItem
+
+    mock_items = [
+        ContentItem(
+            content_id="version-1",
+            content_type=ContentType.STORE_AGENT,
+            searchable_text="Agent 1 Description 1",
+            metadata={"name": "Agent 1"},
+        ),
+        ContentItem(
+            content_id="version-2",
+            content_type=ContentType.STORE_AGENT,
+            searchable_text="Agent 2 Description 2",
+            metadata={"name": "Agent 2"},
+        ),
     ]
 
-    # Mock ensure_embedding to succeed for first, fail for second
-    mock_ensure.side_effect = [True, False]
+    # Mock handler to return missing items
+    mock_handler = MagicMock()
+    mock_handler.get_missing_items = AsyncMock(return_value=mock_items)
+
+    # Mock store_content_embedding to succeed for first, fail for second
+    mock_store.side_effect = [True, False]
 
     with patch(
-        "backend.api.features.store.embeddings.query_raw_with_schema",
-        return_value=mock_missing,
+        "backend.api.features.store.embeddings.CONTENT_HANDLERS",
+        {ContentType.STORE_AGENT: mock_handler},
     ):
-        result = await embeddings.backfill_missing_embeddings(batch_size=5)
+        with patch(
+            "backend.api.features.store.embeddings.generate_embedding",
+            return_value=[0.1] * embeddings.EMBEDDING_DIM,
+        ):
+            result = await embeddings.backfill_missing_embeddings(batch_size=5)
 
-        assert result["processed"] == 2
-        assert result["success"] == 1
-        assert result["failed"] == 1
-        assert mock_ensure.call_count == 2
+            assert result["processed"] == 2
+            assert result["success"] == 1
+            assert result["failed"] == 1
+            assert mock_store.call_count == 2
 
 
 @pytest.mark.asyncio(loop_scope="session")
 async def test_backfill_missing_embeddings_no_missing():
     """Test backfill when no embeddings are missing."""
+    # Mock handler to return no missing items
+    mock_handler = MagicMock()
+    mock_handler.get_missing_items = AsyncMock(return_value=[])
+
     with patch(
-        "backend.api.features.store.embeddings.query_raw_with_schema",
-        return_value=[],
+        "backend.api.features.store.embeddings.CONTENT_HANDLERS",
+        {ContentType.STORE_AGENT: mock_handler},
     ):
         result = await embeddings.backfill_missing_embeddings(batch_size=5)
 
         assert result["processed"] == 0
         assert result["success"] == 0
         assert result["failed"] == 0
-        assert result["message"] == "No missing embeddings"
 
 
 @pytest.mark.asyncio(loop_scope="session")

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

@@ -1,16 +1,21 @@
 """
-Hybrid Search for Store Agents
+Unified Hybrid Search
 
 Combines semantic (embedding) search with lexical (tsvector) search
-for improved relevance in marketplace agent discovery.
+for improved relevance across all content types (agents, blocks, docs).
+Includes BM25 reranking for improved lexical relevance.
 """
 
 import logging
+import re
 from dataclasses import dataclass
-from datetime import datetime
 from typing import Any, Literal
 
+from prisma.enums import ContentType
+from rank_bm25 import BM25Okapi
+
 from backend.api.features.store.embeddings import (
+    EMBEDDING_DIM,
     embed_query,
     embedding_to_vector_string,
 )
@@ -19,18 +24,385 @@ from backend.data.db import query_raw_with_schema
 logger = logging.getLogger(__name__)
 
 
-@dataclass
-class HybridSearchWeights:
-    """Weights for combining search signals."""
+# ============================================================================
+# BM25 Reranking
+# ============================================================================
 
-    semantic: float = 0.30  # Embedding cosine similarity
-    lexical: float = 0.30  # tsvector ts_rank_cd score
-    category: float = 0.20  # Category match boost
-    recency: float = 0.10  # Newer agents ranked higher
-    popularity: float = 0.10  # Agent usage/runs (PageRank-like)
+
+def tokenize(text: str) -> list[str]:
+    """Simple tokenizer for BM25 - lowercase and split on non-alphanumeric."""
+    if not text:
+        return []
+    # Lowercase and split on non-alphanumeric characters
+    tokens = re.findall(r"\b\w+\b", text.lower())
+    return tokens
+
+
+def bm25_rerank(
+    query: str,
+    results: list[dict[str, Any]],
+    text_field: str = "searchable_text",
+    bm25_weight: float = 0.3,
+    original_score_field: str = "combined_score",
+) -> list[dict[str, Any]]:
+    """
+    Rerank search results using BM25.
+
+    Combines the original combined_score with BM25 score for improved
+    lexical relevance, especially for exact term matches.
+
+    Args:
+        query: The search query
+        results: List of result dicts with text_field and original_score_field
+        text_field: Field name containing the text to score
+        bm25_weight: Weight for BM25 score (0-1). Original score gets (1 - bm25_weight)
+        original_score_field: Field name containing the original score
+
+    Returns:
+        Results list sorted by combined score (BM25 + original)
+    """
+    if not results or not query:
+        return results
+
+    # Extract texts and tokenize
+    corpus = [tokenize(r.get(text_field, "") or "") for r in results]
+
+    # Handle edge case where all documents are empty
+    if all(len(doc) == 0 for doc in corpus):
+        return results
+
+    # Build BM25 index
+    bm25 = BM25Okapi(corpus)
+
+    # Score query against corpus
+    query_tokens = tokenize(query)
+    if not query_tokens:
+        return results
+
+    bm25_scores = bm25.get_scores(query_tokens)
+
+    # Normalize BM25 scores to 0-1 range
+    max_bm25 = max(bm25_scores) if max(bm25_scores) > 0 else 1.0
+    normalized_bm25 = [s / max_bm25 for s in bm25_scores]
+
+    # Combine scores
+    original_weight = 1.0 - bm25_weight
+    for i, result in enumerate(results):
+        original_score = result.get(original_score_field, 0) or 0
+        result["bm25_score"] = normalized_bm25[i]
+        final_score = (
+            original_weight * original_score + bm25_weight * normalized_bm25[i]
+        )
+        result["final_score"] = final_score
+        result["relevance"] = final_score
+
+    # Sort by relevance descending
+    results.sort(key=lambda x: x.get("relevance", 0), reverse=True)
+
+    return results
+
+
+@dataclass
+class UnifiedSearchWeights:
+    """Weights for unified search (no popularity signal)."""
+
+    semantic: float = 0.40  # Embedding cosine similarity
+    lexical: float = 0.40  # tsvector ts_rank_cd score
+    category: float = 0.10  # Category match boost (for types that have categories)
+    recency: float = 0.10  # Newer content ranked higher
 
     def __post_init__(self):
         """Validate weights are non-negative and sum to approximately 1.0."""
+        total = self.semantic + self.lexical + self.category + self.recency
+
+        if any(
+            w < 0 for w in [self.semantic, self.lexical, self.category, self.recency]
+        ):
+            raise ValueError("All weights must be non-negative")
+
+        if not (0.99 <= total <= 1.01):
+            raise ValueError(f"Weights must sum to ~1.0, got {total:.3f}")
+
+
+# Default weights for unified search
+DEFAULT_UNIFIED_WEIGHTS = UnifiedSearchWeights()
+
+# Minimum relevance score thresholds
+DEFAULT_MIN_SCORE = 0.15  # For unified search (more permissive)
+DEFAULT_STORE_AGENT_MIN_SCORE = 0.20  # For store agent search (original threshold)
+
+
+async def unified_hybrid_search(
+    query: str,
+    content_types: list[ContentType] | None = None,
+    category: str | None = None,
+    page: int = 1,
+    page_size: int = 20,
+    weights: UnifiedSearchWeights | None = None,
+    min_score: float | None = None,
+    user_id: str | None = None,
+) -> tuple[list[dict[str, Any]], int]:
+    """
+    Unified hybrid search across all content types.
+
+    Searches UnifiedContentEmbedding using both semantic (vector) and lexical (tsvector) signals.
+
+    Args:
+        query: Search query string
+        content_types: List of content types to search. Defaults to all public types.
+        category: Filter by category (for content types that support it)
+        page: Page number (1-indexed)
+        page_size: Results per page
+        weights: Custom weights for search signals
+        min_score: Minimum relevance score threshold (0-1)
+        user_id: User ID for searching private content (library agents)
+
+    Returns:
+        Tuple of (results list, total count)
+    """
+    # Validate inputs
+    query = query.strip()
+    if not query:
+        return [], 0
+
+    if page < 1:
+        page = 1
+    if page_size < 1:
+        page_size = 1
+    if page_size > 100:
+        page_size = 100
+
+    if content_types is None:
+        content_types = [
+            ContentType.STORE_AGENT,
+            ContentType.BLOCK,
+            ContentType.DOCUMENTATION,
+        ]
+
+    if weights is None:
+        weights = DEFAULT_UNIFIED_WEIGHTS
+    if min_score is None:
+        min_score = DEFAULT_MIN_SCORE
+
+    offset = (page - 1) * page_size
+
+    # Generate query embedding
+    query_embedding = await embed_query(query)
+
+    # Graceful degradation if embedding unavailable
+    if query_embedding is None or not query_embedding:
+        logger.warning(
+            "Failed to generate query embedding - falling back to lexical-only search. "
+            "Check that openai_internal_api_key is configured and OpenAI API is accessible."
+        )
+        query_embedding = [0.0] * EMBEDDING_DIM
+        # Redistribute semantic weight to lexical
+        total_non_semantic = weights.lexical + weights.category + weights.recency
+        if total_non_semantic > 0:
+            factor = 1.0 / total_non_semantic
+            weights = UnifiedSearchWeights(
+                semantic=0.0,
+                lexical=weights.lexical * factor,
+                category=weights.category * factor,
+                recency=weights.recency * factor,
+            )
+        else:
+            weights = UnifiedSearchWeights(
+                semantic=0.0, lexical=1.0, category=0.0, recency=0.0
+            )
+
+    # Build parameters
+    params: list[Any] = []
+    param_idx = 1
+
+    # Query for lexical search
+    params.append(query)
+    query_param = f"${param_idx}"
+    param_idx += 1
+
+    # Query lowercase for category matching
+    params.append(query.lower())
+    query_lower_param = f"${param_idx}"
+    param_idx += 1
+
+    # Embedding
+    embedding_str = embedding_to_vector_string(query_embedding)
+    params.append(embedding_str)
+    embedding_param = f"${param_idx}"
+    param_idx += 1
+
+    # Content types
+    content_type_values = [ct.value for ct in content_types]
+    params.append(content_type_values)
+    content_types_param = f"${param_idx}"
+    param_idx += 1
+
+    # User ID filter (for private content)
+    user_filter = ""
+    if user_id is not None:
+        params.append(user_id)
+        user_filter = f'AND (uce."userId" = ${param_idx} OR uce."userId" IS NULL)'
+        param_idx += 1
+    else:
+        user_filter = 'AND uce."userId" IS NULL'
+
+    # Weights
+    params.append(weights.semantic)
+    w_semantic = f"${param_idx}"
+    param_idx += 1
+
+    params.append(weights.lexical)
+    w_lexical = f"${param_idx}"
+    param_idx += 1
+
+    params.append(weights.category)
+    w_category = f"${param_idx}"
+    param_idx += 1
+
+    params.append(weights.recency)
+    w_recency = f"${param_idx}"
+    param_idx += 1
+
+    # Min score
+    params.append(min_score)
+    min_score_param = f"${param_idx}"
+    param_idx += 1
+
+    # Pagination
+    params.append(page_size)
+    limit_param = f"${param_idx}"
+    param_idx += 1
+
+    params.append(offset)
+    offset_param = f"${param_idx}"
+    param_idx += 1
+
+    # Unified search query on UnifiedContentEmbedding
+    sql_query = f"""
+        WITH candidates AS (
+            -- Lexical matches (uses GIN index on search column)
+            SELECT uce.id, uce."contentType", uce."contentId"
+            FROM {{schema_prefix}}"UnifiedContentEmbedding" uce
+            WHERE uce."contentType" = ANY({content_types_param}::{{schema_prefix}}"ContentType"[])
+            {user_filter}
+            AND uce.search @@ plainto_tsquery('english', {query_param})
+
+            UNION
+
+            -- Semantic matches (uses HNSW index on embedding)
+            (
+                SELECT uce.id, uce."contentType", uce."contentId"
+                FROM {{schema_prefix}}"UnifiedContentEmbedding" uce
+                WHERE uce."contentType" = ANY({content_types_param}::{{schema_prefix}}"ContentType"[])
+                {user_filter}
+                ORDER BY uce.embedding <=> {embedding_param}::vector
+                LIMIT 200
+            )
+        ),
+        search_scores AS (
+            SELECT
+                uce."contentType" as content_type,
+                uce."contentId" as content_id,
+                uce."searchableText" as searchable_text,
+                uce.metadata,
+                uce."updatedAt" as updated_at,
+                -- Semantic score: cosine similarity (1 - distance)
+                COALESCE(1 - (uce.embedding <=> {embedding_param}::vector), 0) as semantic_score,
+                -- Lexical score: ts_rank_cd
+                COALESCE(ts_rank_cd(uce.search, plainto_tsquery('english', {query_param})), 0) as lexical_raw,
+                -- Category match from metadata
+                CASE
+                    WHEN uce.metadata ? 'categories' AND EXISTS (
+                        SELECT 1 FROM jsonb_array_elements_text(uce.metadata->'categories') cat
+                        WHERE LOWER(cat) LIKE '%' || {query_lower_param} || '%'
+                    )
+                    THEN 1.0
+                    ELSE 0.0
+                END as category_score,
+                -- Recency score: linear decay over 90 days
+                GREATEST(0, 1 - EXTRACT(EPOCH FROM (NOW() - uce."updatedAt")) / (90 * 24 * 3600)) as recency_score
+            FROM candidates c
+            INNER JOIN {{schema_prefix}}"UnifiedContentEmbedding" uce ON c.id = uce.id
+        ),
+        max_lexical AS (
+            SELECT GREATEST(MAX(lexical_raw), 0.001) as max_val FROM search_scores
+        ),
+        normalized AS (
+            SELECT
+                ss.*,
+                ss.lexical_raw / ml.max_val as lexical_score
+            FROM search_scores ss
+            CROSS JOIN max_lexical ml
+        ),
+        scored AS (
+            SELECT
+                content_type,
+                content_id,
+                searchable_text,
+                metadata,
+                updated_at,
+                semantic_score,
+                lexical_score,
+                category_score,
+                recency_score,
+                (
+                    {w_semantic} * semantic_score +
+                    {w_lexical} * lexical_score +
+                    {w_category} * category_score +
+                    {w_recency} * recency_score
+                ) as combined_score
+            FROM normalized
+        ),
+        filtered AS (
+            SELECT *, COUNT(*) OVER () as total_count
+            FROM scored
+            WHERE combined_score >= {min_score_param}
+        )
+        SELECT * FROM filtered
+        ORDER BY combined_score DESC
+        LIMIT {limit_param} OFFSET {offset_param}
+    """
+
+    results = await query_raw_with_schema(
+        sql_query, *params, set_public_search_path=True
+    )
+
+    total = results[0]["total_count"] if results else 0
+    # Apply BM25 reranking
+    if results:
+        results = bm25_rerank(
+            query=query,
+            results=results,
+            text_field="searchable_text",
+            bm25_weight=0.3,
+            original_score_field="combined_score",
+        )
+
+    # Clean up results
+    for result in results:
+        result.pop("total_count", None)
+
+    logger.info(f"Unified hybrid search: {len(results)} results, {total} total")
+
+    return results, total
+
+
+# ============================================================================
+# Store Agent specific search (with full metadata)
+# ============================================================================
+
+
+@dataclass
+class StoreAgentSearchWeights:
+    """Weights for store agent search including popularity."""
+
+    semantic: float = 0.30
+    lexical: float = 0.30
+    category: float = 0.20
+    recency: float = 0.10
+    popularity: float = 0.10
+
+    def __post_init__(self):
         total = (
             self.semantic
             + self.lexical
@@ -38,7 +410,6 @@ class HybridSearchWeights:
             + self.recency
             + self.popularity
         )
-
         if any(
             w < 0
             for w in [
@@ -50,46 +421,11 @@ class HybridSearchWeights:
             ]
         ):
             raise ValueError("All weights must be non-negative")
-
         if not (0.99 <= total <= 1.01):
             raise ValueError(f"Weights must sum to ~1.0, got {total:.3f}")
 
 
-DEFAULT_WEIGHTS = HybridSearchWeights()
-
-# Minimum relevance score threshold - agents below this are filtered out
-# With weights (0.30 semantic + 0.30 lexical + 0.20 category + 0.10 recency + 0.10 popularity):
-# - 0.20 means at least ~60% semantic match OR strong lexical match required
-# - Ensures only genuinely relevant results are returned
-# - Recency/popularity alone (0.10 each) won't pass the threshold
-DEFAULT_MIN_SCORE = 0.20
-
-
-@dataclass
-class HybridSearchResult:
-    """A single search result with score breakdown."""
-
-    slug: str
-    agent_name: str
-    agent_image: str
-    creator_username: str
-    creator_avatar: str
-    sub_heading: str
-    description: str
-    runs: int
-    rating: float
-    categories: list[str]
-    featured: bool
-    is_available: bool
-    updated_at: datetime
-
-    # Score breakdown (for debugging/tuning)
-    combined_score: float
-    semantic_score: float = 0.0
-    lexical_score: float = 0.0
-    category_score: float = 0.0
-    recency_score: float = 0.0
-    popularity_score: float = 0.0
+DEFAULT_STORE_AGENT_WEIGHTS = StoreAgentSearchWeights()
 
 
 async def hybrid_search(
@@ -102,276 +438,277 @@ async def hybrid_search(
     ) = None,
     page: int = 1,
     page_size: int = 20,
-    weights: HybridSearchWeights | None = None,
+    weights: StoreAgentSearchWeights | None = None,
     min_score: float | None = None,
 ) -> tuple[list[dict[str, Any]], int]:
     """
-    Perform hybrid search combining semantic and lexical signals.
+    Hybrid search for store agents with full metadata.
 
-    Args:
-        query: Search query string
-        featured: Filter for featured agents only
-        creators: Filter by creator usernames
-        category: Filter by category
-        sorted_by: Sort order (relevance uses hybrid scoring)
-        page: Page number (1-indexed)
-        page_size: Results per page
-        weights: Custom weights for search signals
-        min_score: Minimum relevance score threshold (0-1). Results below
-                   this score are filtered out. Defaults to DEFAULT_MIN_SCORE.
-
-    Returns:
-        Tuple of (results list, total count). Returns empty list if no
-        results meet the minimum relevance threshold.
+    Uses UnifiedContentEmbedding for search, joins to StoreAgent for metadata.
     """
-    # Validate inputs
     query = query.strip()
     if not query:
-        return [], 0  # Empty query returns no results
+        return [], 0
 
     if page < 1:
         page = 1
     if page_size < 1:
         page_size = 1
-    if page_size > 100:  # Cap at reasonable limit to prevent performance issues
+    if page_size > 100:
         page_size = 100
 
     if weights is None:
-        weights = DEFAULT_WEIGHTS
+        weights = DEFAULT_STORE_AGENT_WEIGHTS
     if min_score is None:
-        min_score = DEFAULT_MIN_SCORE
+        min_score = (
+            DEFAULT_STORE_AGENT_MIN_SCORE  # Use original threshold for store agents
+        )
 
     offset = (page - 1) * page_size
 
     # Generate query embedding
     query_embedding = await embed_query(query)
 
-    # Build WHERE clause conditions
-    where_parts: list[str] = ["sa.is_available = true"]
+    # Graceful degradation
+    if query_embedding is None or not query_embedding:
+        logger.warning(
+            "Failed to generate query embedding - falling back to lexical-only search."
+        )
+        query_embedding = [0.0] * EMBEDDING_DIM
+        total_non_semantic = (
+            weights.lexical + weights.category + weights.recency + weights.popularity
+        )
+        if total_non_semantic > 0:
+            factor = 1.0 / total_non_semantic
+            weights = StoreAgentSearchWeights(
+                semantic=0.0,
+                lexical=weights.lexical * factor,
+                category=weights.category * factor,
+                recency=weights.recency * factor,
+                popularity=weights.popularity * factor,
+            )
+        else:
+            weights = StoreAgentSearchWeights(
+                semantic=0.0, lexical=1.0, category=0.0, recency=0.0, popularity=0.0
+            )
+
+    # Build parameters
     params: list[Any] = []
-    param_index = 1
+    param_idx = 1
 
-    # Add search query for lexical matching
     params.append(query)
-    query_param = f"${param_index}"
-    param_index += 1
+    query_param = f"${param_idx}"
+    param_idx += 1
 
-    # Add lowercased query for category matching
     params.append(query.lower())
-    query_lower_param = f"${param_index}"
-    param_index += 1
+    query_lower_param = f"${param_idx}"
+    param_idx += 1
+
+    embedding_str = embedding_to_vector_string(query_embedding)
+    params.append(embedding_str)
+    embedding_param = f"${param_idx}"
+    param_idx += 1
+
+    # Build WHERE clause for StoreAgent filters
+    where_parts = ["sa.is_available = true"]
 
     if featured:
         where_parts.append("sa.featured = true")
 
     if creators:
-        where_parts.append(f"sa.creator_username = ANY(${param_index})")
         params.append(creators)
-        param_index += 1
+        where_parts.append(f"sa.creator_username = ANY(${param_idx})")
+        param_idx += 1
 
     if category:
-        where_parts.append(f"${param_index} = ANY(sa.categories)")
         params.append(category)
-        param_index += 1
+        where_parts.append(f"${param_idx} = ANY(sa.categories)")
+        param_idx += 1
 
-    # Safe: where_parts only contains hardcoded strings with $N parameter placeholders
-    # No user input is concatenated directly into the SQL string
     where_clause = " AND ".join(where_parts)
 
-    # Embedding is required for hybrid search - fail fast if unavailable
-    if query_embedding is None or not query_embedding:
-        # Log detailed error server-side
-        logger.error(
-            "Failed to generate query embedding. "
-            "Check that openai_internal_api_key is configured and OpenAI API is accessible."
-        )
-        # Raise generic error to client
-        raise ValueError("Search service temporarily unavailable")
-
-    # Add embedding parameter
-    embedding_str = embedding_to_vector_string(query_embedding)
-    params.append(embedding_str)
-    embedding_param = f"${param_index}"
-    param_index += 1
-
-    # Add weight parameters for SQL calculation
+    # Weights
     params.append(weights.semantic)
-    weight_semantic_param = f"${param_index}"
-    param_index += 1
+    w_semantic = f"${param_idx}"
+    param_idx += 1
 
     params.append(weights.lexical)
-    weight_lexical_param = f"${param_index}"
-    param_index += 1
+    w_lexical = f"${param_idx}"
+    param_idx += 1
 
     params.append(weights.category)
-    weight_category_param = f"${param_index}"
-    param_index += 1
+    w_category = f"${param_idx}"
+    param_idx += 1
 
     params.append(weights.recency)
-    weight_recency_param = f"${param_index}"
-    param_index += 1
+    w_recency = f"${param_idx}"
+    param_idx += 1
 
     params.append(weights.popularity)
-    weight_popularity_param = f"${param_index}"
-    param_index += 1
+    w_popularity = f"${param_idx}"
+    param_idx += 1
 
-    # Add min_score parameter
     params.append(min_score)
-    min_score_param = f"${param_index}"
-    param_index += 1
+    min_score_param = f"${param_idx}"
+    param_idx += 1
 
-    # Optimized hybrid search query:
-    # 1. Direct join to UnifiedContentEmbedding via contentId=storeListingVersionId (no redundant JOINs)
-    # 2. UNION approach (deduplicates agents matching both branches)
-    # 3. COUNT(*) OVER() to get total count in single query
-    # 4. Optimized category matching with EXISTS + unnest
-    # 5. Pre-calculated max values for lexical and popularity normalization
-    # 6. Simplified recency calculation with linear decay
-    # 7. Logarithmic popularity scaling to prevent viral agents from dominating
+    params.append(page_size)
+    limit_param = f"${param_idx}"
+    param_idx += 1
+
+    params.append(offset)
+    offset_param = f"${param_idx}"
+    param_idx += 1
+
+    # Query using UnifiedContentEmbedding for search, StoreAgent for metadata
     sql_query = f"""
-            WITH candidates AS (
-                -- Lexical matches (uses GIN index on search column)
-                SELECT sa."storeListingVersionId"
-                FROM {{schema_prefix}}"StoreAgent" sa
-                WHERE {where_clause}
-                AND sa.search @@ plainto_tsquery('english', {query_param})
+        WITH candidates AS (
+            -- Lexical matches via UnifiedContentEmbedding.search
+            SELECT uce."contentId" as "storeListingVersionId"
+            FROM {{schema_prefix}}"UnifiedContentEmbedding" uce
+            INNER JOIN {{schema_prefix}}"StoreAgent" sa
+                ON uce."contentId" = sa."storeListingVersionId"
+            WHERE uce."contentType" = 'STORE_AGENT'::{{schema_prefix}}"ContentType"
+            AND uce."userId" IS NULL
+            AND uce.search @@ plainto_tsquery('english', {query_param})
+            AND {where_clause}
 
-                UNION
+            UNION
 
-                -- Semantic matches (uses HNSW index on embedding with KNN)
-                SELECT "storeListingVersionId"
-                FROM (
-                    SELECT sa."storeListingVersionId", uce.embedding
-                    FROM {{schema_prefix}}"StoreAgent" sa
-                    INNER JOIN {{schema_prefix}}"UnifiedContentEmbedding" uce
-                        ON sa."storeListingVersionId" = uce."contentId" AND uce."contentType" = 'STORE_AGENT'::{{schema_prefix}}"ContentType"
-                    WHERE {where_clause}
-                    ORDER BY uce.embedding <=> {embedding_param}::vector
-                    LIMIT 200
-                ) semantic_results
-            ),
-            search_scores AS (
-                SELECT
-                    sa.slug,
-                    sa.agent_name,
-                    sa.agent_image,
-                    sa.creator_username,
-                    sa.creator_avatar,
-                    sa.sub_heading,
-                    sa.description,
-                    sa.runs,
-                    sa.rating,
-                    sa.categories,
-                    sa.featured,
-                    sa.is_available,
-                    sa.updated_at,
-                    -- Semantic score: cosine similarity (1 - distance)
-                    COALESCE(1 - (uce.embedding <=> {embedding_param}::vector), 0) as semantic_score,
-                    -- Lexical score: ts_rank_cd (will be normalized later)
-                    COALESCE(ts_rank_cd(sa.search, plainto_tsquery('english', {query_param})), 0) as lexical_raw,
-                    -- Category match: optimized with unnest for better performance
-                    CASE
-                        WHEN EXISTS (
-                            SELECT 1 FROM unnest(sa.categories) cat
-                            WHERE LOWER(cat) LIKE '%' || {query_lower_param} || '%'
-                        )
-                        THEN 1.0
-                        ELSE 0.0
-                    END as category_score,
-                    -- Recency score: linear decay over 90 days (simpler than exponential)
-                    GREATEST(0, 1 - EXTRACT(EPOCH FROM (NOW() - sa.updated_at)) / (90 * 24 * 3600)) as recency_score,
-                    -- Popularity raw: agent runs count (will be normalized with log scaling)
-                    sa.runs as popularity_raw
-                FROM candidates c
+            -- Semantic matches via UnifiedContentEmbedding.embedding
+            SELECT uce."contentId" as "storeListingVersionId"
+            FROM (
+                SELECT uce."contentId", uce.embedding
+                FROM {{schema_prefix}}"UnifiedContentEmbedding" uce
                 INNER JOIN {{schema_prefix}}"StoreAgent" sa
-                    ON c."storeListingVersionId" = sa."storeListingVersionId"
-                LEFT JOIN {{schema_prefix}}"UnifiedContentEmbedding" uce
-                    ON sa."storeListingVersionId" = uce."contentId" AND uce."contentType" = 'STORE_AGENT'::{{schema_prefix}}"ContentType"
-            ),
-            max_lexical AS (
-                SELECT MAX(lexical_raw) as max_val FROM search_scores
-            ),
-            max_popularity AS (
-                SELECT MAX(popularity_raw) as max_val FROM search_scores
-            ),
-            normalized AS (
-                SELECT
-                    ss.*,
-                    -- Normalize lexical score by pre-calculated max
-                    CASE
-                        WHEN ml.max_val > 0
-                        THEN ss.lexical_raw / ml.max_val
-                        ELSE 0
-                    END as lexical_score,
-                    -- Normalize popularity with logarithmic scaling to prevent viral agents from dominating
-                    -- LOG(1 + runs) / LOG(1 + max_runs) ensures score is 0-1 range
-                    CASE
-                        WHEN mp.max_val > 0 AND ss.popularity_raw > 0
-                        THEN LN(1 + ss.popularity_raw) / LN(1 + mp.max_val)
-                        ELSE 0
-                    END as popularity_score
-                FROM search_scores ss
-                CROSS JOIN max_lexical ml
-                CROSS JOIN max_popularity mp
-            ),
-            scored AS (
-                SELECT
-                    slug,
-                    agent_name,
-                    agent_image,
-                    creator_username,
-                    creator_avatar,
-                    sub_heading,
-                    description,
-                    runs,
-                    rating,
-                    categories,
-                    featured,
-                    is_available,
-                    updated_at,
-                    semantic_score,
-                    lexical_score,
-                    category_score,
-                    recency_score,
-                    popularity_score,
-                    (
-                        {weight_semantic_param} * semantic_score +
-                        {weight_lexical_param} * lexical_score +
-                        {weight_category_param} * category_score +
-                        {weight_recency_param} * recency_score +
-                        {weight_popularity_param} * popularity_score
-                    ) as combined_score
-                FROM normalized
-            ),
-            filtered AS (
-                SELECT
-                    *,
-                    COUNT(*) OVER () as total_count
-                FROM scored
-                WHERE combined_score >= {min_score_param}
-            )
-            SELECT * FROM filtered
-            ORDER BY combined_score DESC
-            LIMIT ${param_index} OFFSET ${param_index + 1}
+                    ON uce."contentId" = sa."storeListingVersionId"
+                WHERE uce."contentType" = 'STORE_AGENT'::{{schema_prefix}}"ContentType"
+                AND uce."userId" IS NULL
+                AND {where_clause}
+                ORDER BY uce.embedding <=> {embedding_param}::vector
+                LIMIT 200
+            ) uce
+        ),
+        search_scores AS (
+            SELECT
+                sa.slug,
+                sa.agent_name,
+                sa.agent_image,
+                sa.creator_username,
+                sa.creator_avatar,
+                sa.sub_heading,
+                sa.description,
+                sa.runs,
+                sa.rating,
+                sa.categories,
+                sa.featured,
+                sa.is_available,
+                sa.updated_at,
+                -- Searchable text for BM25 reranking
+                COALESCE(sa.agent_name, '') || ' ' || COALESCE(sa.sub_heading, '') || ' ' || COALESCE(sa.description, '') as searchable_text,
+                -- Semantic score
+                COALESCE(1 - (uce.embedding <=> {embedding_param}::vector), 0) as semantic_score,
+                -- Lexical score (raw, will normalize)
+                COALESCE(ts_rank_cd(uce.search, plainto_tsquery('english', {query_param})), 0) as lexical_raw,
+                -- Category match
+                CASE
+                    WHEN EXISTS (
+                        SELECT 1 FROM unnest(sa.categories) cat
+                        WHERE LOWER(cat) LIKE '%' || {query_lower_param} || '%'
+                    )
+                    THEN 1.0
+                    ELSE 0.0
+                END as category_score,
+                -- Recency
+                GREATEST(0, 1 - EXTRACT(EPOCH FROM (NOW() - sa.updated_at)) / (90 * 24 * 3600)) as recency_score,
+                -- Popularity (raw)
+                sa.runs as popularity_raw
+            FROM candidates c
+            INNER JOIN {{schema_prefix}}"StoreAgent" sa
+                ON c."storeListingVersionId" = sa."storeListingVersionId"
+            INNER JOIN {{schema_prefix}}"UnifiedContentEmbedding" uce
+                ON sa."storeListingVersionId" = uce."contentId"
+                AND uce."contentType" = 'STORE_AGENT'::{{schema_prefix}}"ContentType"
+        ),
+        max_vals AS (
+            SELECT
+                GREATEST(MAX(lexical_raw), 0.001) as max_lexical,
+                GREATEST(MAX(popularity_raw), 1) as max_popularity
+            FROM search_scores
+        ),
+        normalized AS (
+            SELECT
+                ss.*,
+                ss.lexical_raw / mv.max_lexical as lexical_score,
+                CASE
+                    WHEN ss.popularity_raw > 0
+                    THEN LN(1 + ss.popularity_raw) / LN(1 + mv.max_popularity)
+                    ELSE 0
+                END as popularity_score
+            FROM search_scores ss
+            CROSS JOIN max_vals mv
+        ),
+        scored AS (
+            SELECT
+                slug,
+                agent_name,
+                agent_image,
+                creator_username,
+                creator_avatar,
+                sub_heading,
+                description,
+                runs,
+                rating,
+                categories,
+                featured,
+                is_available,
+                updated_at,
+                searchable_text,
+                semantic_score,
+                lexical_score,
+                category_score,
+                recency_score,
+                popularity_score,
+                (
+                    {w_semantic} * semantic_score +
+                    {w_lexical} * lexical_score +
+                    {w_category} * category_score +
+                    {w_recency} * recency_score +
+                    {w_popularity} * popularity_score
+                ) as combined_score
+            FROM normalized
+        ),
+        filtered AS (
+            SELECT *, COUNT(*) OVER () as total_count
+            FROM scored
+            WHERE combined_score >= {min_score_param}
+        )
+        SELECT * FROM filtered
+        ORDER BY combined_score DESC
+        LIMIT {limit_param} OFFSET {offset_param}
     """
 
-    # Add pagination params
-    params.extend([page_size, offset])
-
-    # Execute search query - includes total_count via window function
     results = await query_raw_with_schema(
         sql_query, *params, set_public_search_path=True
     )
 
-    # Extract total count from first result (all rows have same count)
     total = results[0]["total_count"] if results else 0
 
-    # Remove total_count from results before returning
+    # Apply BM25 reranking
+    if results:
+        results = bm25_rerank(
+            query=query,
+            results=results,
+            text_field="searchable_text",
+            bm25_weight=0.3,
+            original_score_field="combined_score",
+        )
+
     for result in results:
         result.pop("total_count", None)
+        result.pop("searchable_text", None)
 
-    # Log without sensitive query content
-    logger.info(f"Hybrid search: {len(results)} results, {total} total")
+    logger.info(f"Hybrid search (store agents): {len(results)} results, {total} total")
 
     return results, total
 
@@ -381,13 +718,10 @@ async def hybrid_search_simple(
     page: int = 1,
     page_size: int = 20,
 ) -> tuple[list[dict[str, Any]], int]:
-    """
-    Simplified hybrid search for common use cases.
+    """Simplified hybrid search for store agents."""
+    return await hybrid_search(query=query, page=page, page_size=page_size)
 
-    Uses default weights and no filters.
-    """
-    return await hybrid_search(
-        query=query,
-        page=page,
-        page_size=page_size,
-    )
+
+# Backward compatibility alias - HybridSearchWeights maps to StoreAgentSearchWeights
+# for existing code that expects the popularity parameter
+HybridSearchWeights = StoreAgentSearchWeights

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

@@ -7,8 +7,15 @@ These tests verify that hybrid search works correctly across different database
 from unittest.mock import patch
 
 import pytest
+from prisma.enums import ContentType
 
-from backend.api.features.store.hybrid_search import HybridSearchWeights, hybrid_search
+from backend.api.features.store import embeddings
+from backend.api.features.store.hybrid_search import (
+    HybridSearchWeights,
+    UnifiedSearchWeights,
+    hybrid_search,
+    unified_hybrid_search,
+)
 
 
 @pytest.mark.asyncio(loop_scope="session")
@@ -49,7 +56,7 @@ async def test_hybrid_search_with_schema_handling():
         with patch(
             "backend.api.features.store.hybrid_search.embed_query"
         ) as mock_embed:
-            mock_embed.return_value = [0.1] * 1536  # Mock embedding
+            mock_embed.return_value = [0.1] * embeddings.EMBEDDING_DIM  # Mock embedding
 
             results, total = await hybrid_search(
                 query=query,
@@ -85,7 +92,7 @@ async def test_hybrid_search_with_public_schema():
             with patch(
                 "backend.api.features.store.hybrid_search.embed_query"
             ) as mock_embed:
-                mock_embed.return_value = [0.1] * 1536
+                mock_embed.return_value = [0.1] * embeddings.EMBEDDING_DIM
 
                 results, total = await hybrid_search(
                     query="test",
@@ -116,7 +123,7 @@ async def test_hybrid_search_with_custom_schema():
             with patch(
                 "backend.api.features.store.hybrid_search.embed_query"
             ) as mock_embed:
-                mock_embed.return_value = [0.1] * 1536
+                mock_embed.return_value = [0.1] * embeddings.EMBEDDING_DIM
 
                 results, total = await hybrid_search(
                     query="test",
@@ -134,22 +141,52 @@ async def test_hybrid_search_with_custom_schema():
 @pytest.mark.asyncio(loop_scope="session")
 @pytest.mark.integration
 async def test_hybrid_search_without_embeddings():
-    """Test hybrid search fails fast when embeddings are unavailable."""
-    # Patch where the function is used, not where it's defined
-    with patch("backend.api.features.store.hybrid_search.embed_query") as mock_embed:
-        # Simulate embedding failure
-        mock_embed.return_value = None
+    """Test hybrid search gracefully degrades when embeddings are unavailable."""
+    # Mock database to return some results
+    mock_results = [
+        {
+            "slug": "test-agent",
+            "agent_name": "Test Agent",
+            "agent_image": "test.png",
+            "creator_username": "creator",
+            "creator_avatar": "avatar.png",
+            "sub_heading": "Test heading",
+            "description": "Test description",
+            "runs": 100,
+            "rating": 4.5,
+            "categories": ["AI"],
+            "featured": False,
+            "is_available": True,
+            "updated_at": "2025-01-01T00:00:00Z",
+            "semantic_score": 0.0,  # Zero because no embedding
+            "lexical_score": 0.5,
+            "category_score": 0.0,
+            "recency_score": 0.1,
+            "popularity_score": 0.2,
+            "combined_score": 0.3,
+            "total_count": 1,
+        }
+    ]
 
-        # Should raise ValueError with helpful message
-        with pytest.raises(ValueError) as exc_info:
-            await hybrid_search(
+    with patch("backend.api.features.store.hybrid_search.embed_query") as mock_embed:
+        with patch(
+            "backend.api.features.store.hybrid_search.query_raw_with_schema"
+        ) as mock_query:
+            # Simulate embedding failure
+            mock_embed.return_value = None
+            mock_query.return_value = mock_results
+
+            # Should NOT raise - graceful degradation
+            results, total = await hybrid_search(
                 query="test",
                 page=1,
                 page_size=20,
             )
 
-        # Verify error message is generic (doesn't leak implementation details)
-        assert "Search service temporarily unavailable" in str(exc_info.value)
+            # Verify it returns results even without embeddings
+            assert len(results) == 1
+            assert results[0]["slug"] == "test-agent"
+            assert total == 1
 
 
 @pytest.mark.asyncio(loop_scope="session")
@@ -164,7 +201,7 @@ async def test_hybrid_search_with_filters():
         with patch(
             "backend.api.features.store.hybrid_search.embed_query"
         ) as mock_embed:
-            mock_embed.return_value = [0.1] * 1536
+            mock_embed.return_value = [0.1] * embeddings.EMBEDDING_DIM
 
             # Test with featured filter
             results, total = await hybrid_search(
@@ -204,7 +241,7 @@ async def test_hybrid_search_weights():
         with patch(
             "backend.api.features.store.hybrid_search.embed_query"
         ) as mock_embed:
-            mock_embed.return_value = [0.1] * 1536
+            mock_embed.return_value = [0.1] * embeddings.EMBEDDING_DIM
 
             results, total = await hybrid_search(
                 query="test",
@@ -248,7 +285,7 @@ async def test_hybrid_search_min_score_filtering():
         with patch(
             "backend.api.features.store.hybrid_search.embed_query"
         ) as mock_embed:
-            mock_embed.return_value = [0.1] * 1536
+            mock_embed.return_value = [0.1] * embeddings.EMBEDDING_DIM
 
             # Test with custom min_score
             results, total = await hybrid_search(
@@ -274,16 +311,48 @@ async def test_hybrid_search_min_score_filtering():
 @pytest.mark.asyncio(loop_scope="session")
 @pytest.mark.integration
 async def test_hybrid_search_pagination():
-    """Test hybrid search pagination."""
+    """Test hybrid search pagination.
+
+    Pagination happens in SQL (LIMIT/OFFSET), then BM25 reranking is applied
+    to the paginated results.
+    """
+    # Create mock results that SQL would return for a page
+    mock_results = [
+        {
+            "slug": f"agent-{i}",
+            "agent_name": f"Agent {i}",
+            "agent_image": "test.png",
+            "creator_username": "test",
+            "creator_avatar": "avatar.png",
+            "sub_heading": "Test",
+            "description": "Test description",
+            "runs": 100 - i,
+            "rating": 4.5,
+            "categories": ["test"],
+            "featured": False,
+            "is_available": True,
+            "updated_at": "2024-01-01T00:00:00Z",
+            "searchable_text": f"Agent {i} test description",
+            "combined_score": 0.9 - (i * 0.01),
+            "semantic_score": 0.7,
+            "lexical_score": 0.6,
+            "category_score": 0.5,
+            "recency_score": 0.4,
+            "popularity_score": 0.3,
+            "total_count": 25,
+        }
+        for i in range(10)  # SQL returns page_size results
+    ]
+
     with patch(
         "backend.api.features.store.hybrid_search.query_raw_with_schema"
     ) as mock_query:
-        mock_query.return_value = []
+        mock_query.return_value = mock_results
 
         with patch(
             "backend.api.features.store.hybrid_search.embed_query"
         ) as mock_embed:
-            mock_embed.return_value = [0.1] * 1536
+            mock_embed.return_value = [0.1] * embeddings.EMBEDDING_DIM
 
             # Test page 2 with page_size 10
             results, total = await hybrid_search(
@@ -292,16 +361,18 @@ async def test_hybrid_search_pagination():
                 page_size=10,
             )
 
-            # Verify pagination parameters
+            # Verify results returned
+            assert len(results) == 10
+            assert total == 25  # Total from SQL COUNT(*) OVER()
+
+            # Verify the SQL query uses page_size and offset
             call_args = mock_query.call_args
             params = call_args[0]
-
-            # Last two params should be LIMIT and OFFSET
-            limit = params[-2]
-            offset = params[-1]
-
-            assert limit == 10  # page_size
-            assert offset == 10  # (page - 1) * page_size = (2 - 1) * 10
+            # Last two params are page_size and offset
+            page_size_param = params[-2]
+            offset_param = params[-1]
+            assert page_size_param == 10
+            assert offset_param == 10  # (page 2 - 1) * 10
 
 
 @pytest.mark.asyncio(loop_scope="session")
@@ -317,7 +388,7 @@ async def test_hybrid_search_error_handling():
         with patch(
             "backend.api.features.store.hybrid_search.embed_query"
         ) as mock_embed:
-            mock_embed.return_value = [0.1] * 1536
+            mock_embed.return_value = [0.1] * embeddings.EMBEDDING_DIM
 
             # Should raise exception
             with pytest.raises(Exception) as exc_info:
@@ -330,5 +401,326 @@ async def test_hybrid_search_error_handling():
             assert "Database connection error" in str(exc_info.value)
 
 
+# =============================================================================
+# Unified Hybrid Search Tests
+# =============================================================================
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.integration
+async def test_unified_hybrid_search_basic():
+    """Test basic unified hybrid search across all content types."""
+    mock_results = [
+        {
+            "content_type": "STORE_AGENT",
+            "content_id": "agent-1",
+            "searchable_text": "Test Agent Description",
+            "metadata": {"name": "Test Agent"},
+            "updated_at": "2025-01-01T00:00:00Z",
+            "semantic_score": 0.7,
+            "lexical_score": 0.8,
+            "category_score": 0.5,
+            "recency_score": 0.3,
+            "combined_score": 0.6,
+            "total_count": 2,
+        },
+        {
+            "content_type": "BLOCK",
+            "content_id": "block-1",
+            "searchable_text": "Test Block Description",
+            "metadata": {"name": "Test Block"},
+            "updated_at": "2025-01-01T00:00:00Z",
+            "semantic_score": 0.6,
+            "lexical_score": 0.7,
+            "category_score": 0.4,
+            "recency_score": 0.2,
+            "combined_score": 0.5,
+            "total_count": 2,
+        },
+    ]
+
+    with patch(
+        "backend.api.features.store.hybrid_search.query_raw_with_schema"
+    ) as mock_query:
+        with patch(
+            "backend.api.features.store.hybrid_search.embed_query"
+        ) as mock_embed:
+            mock_query.return_value = mock_results
+            mock_embed.return_value = [0.1] * embeddings.EMBEDDING_DIM
+
+            results, total = await unified_hybrid_search(
+                query="test",
+                page=1,
+                page_size=20,
+            )
+
+            assert len(results) == 2
+            assert total == 2
+            assert results[0]["content_type"] == "STORE_AGENT"
+            assert results[1]["content_type"] == "BLOCK"
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.integration
+async def test_unified_hybrid_search_filter_by_content_type():
+    """Test unified search filtering by specific content types."""
+    mock_results = [
+        {
+            "content_type": "BLOCK",
+            "content_id": "block-1",
+            "searchable_text": "Test Block",
+            "metadata": {},
+            "updated_at": "2025-01-01T00:00:00Z",
+            "semantic_score": 0.7,
+            "lexical_score": 0.8,
+            "category_score": 0.0,
+            "recency_score": 0.3,
+            "combined_score": 0.5,
+            "total_count": 1,
+        },
+    ]
+
+    with patch(
+        "backend.api.features.store.hybrid_search.query_raw_with_schema"
+    ) as mock_query:
+        with patch(
+            "backend.api.features.store.hybrid_search.embed_query"
+        ) as mock_embed:
+            mock_query.return_value = mock_results
+            mock_embed.return_value = [0.1] * embeddings.EMBEDDING_DIM
+
+            results, total = await unified_hybrid_search(
+                query="test",
+                content_types=[ContentType.BLOCK],
+                page=1,
+                page_size=20,
+            )
+
+            # Verify content_types parameter was passed correctly
+            call_args = mock_query.call_args
+            params = call_args[0][1:]
+            # The content types should be in the params as a list
+            assert ["BLOCK"] in params
+
+            assert len(results) == 1
+            assert total == 1
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.integration
+async def test_unified_hybrid_search_with_user_id():
+    """Test unified search with user_id for private content."""
+    mock_results = [
+        {
+            "content_type": "STORE_AGENT",
+            "content_id": "agent-1",
+            "searchable_text": "My Private Agent",
+            "metadata": {},
+            "updated_at": "2025-01-01T00:00:00Z",
+            "semantic_score": 0.7,
+            "lexical_score": 0.8,
+            "category_score": 0.0,
+            "recency_score": 0.3,
+            "combined_score": 0.6,
+            "total_count": 1,
+        },
+    ]
+
+    with patch(
+        "backend.api.features.store.hybrid_search.query_raw_with_schema"
+    ) as mock_query:
+        with patch(
+            "backend.api.features.store.hybrid_search.embed_query"
+        ) as mock_embed:
+            mock_query.return_value = mock_results
+            mock_embed.return_value = [0.1] * embeddings.EMBEDDING_DIM
+
+            results, total = await unified_hybrid_search(
+                query="test",
+                user_id="user-123",
+                page=1,
+                page_size=20,
+            )
+
+            # Verify SQL contains user_id filter
+            call_args = mock_query.call_args
+            sql_template = call_args[0][0]
+            params = call_args[0][1:]
+
+            assert 'uce."userId"' in sql_template
+            assert "user-123" in params
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.integration
+async def test_unified_hybrid_search_custom_weights():
+    """Test unified search with custom weights."""
+    custom_weights = UnifiedSearchWeights(
+        semantic=0.6,
+        lexical=0.2,
+        category=0.1,
+        recency=0.1,
+    )
+
+    with patch(
+        "backend.api.features.store.hybrid_search.query_raw_with_schema"
+    ) as mock_query:
+        with patch(
+            "backend.api.features.store.hybrid_search.embed_query"
+        ) as mock_embed:
+            mock_query.return_value = []
+            mock_embed.return_value = [0.1] * embeddings.EMBEDDING_DIM
+
+            results, total = await unified_hybrid_search(
+                query="test",
+                weights=custom_weights,
+                page=1,
+                page_size=20,
+            )
+
+            # Verify custom weights are in parameters
+            call_args = mock_query.call_args
+            params = call_args[0][1:]
+
+            assert 0.6 in params  # semantic weight
+            assert 0.2 in params  # lexical weight
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.integration
+async def test_unified_hybrid_search_graceful_degradation():
+    """Test unified search gracefully degrades when embeddings unavailable."""
+    mock_results = [
+        {
+            "content_type": "DOCUMENTATION",
+            "content_id": "doc-1",
+            "searchable_text": "API Documentation",
+            "metadata": {},
+            "updated_at": "2025-01-01T00:00:00Z",
+            "semantic_score": 0.0,  # Zero because no embedding
+            "lexical_score": 0.8,
+            "category_score": 0.0,
+            "recency_score": 0.2,
+            "combined_score": 0.5,
+            "total_count": 1,
+        },
+    ]
+
+    with patch(
+        "backend.api.features.store.hybrid_search.query_raw_with_schema"
+    ) as mock_query:
+        with patch(
+            "backend.api.features.store.hybrid_search.embed_query"
+        ) as mock_embed:
+            mock_query.return_value = mock_results
+            mock_embed.return_value = None  # Embedding failure
+
+            # Should NOT raise - graceful degradation
+            results, total = await unified_hybrid_search(
+                query="test",
+                page=1,
+                page_size=20,
+            )
+
+            assert len(results) == 1
+            assert total == 1
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.integration
+async def test_unified_hybrid_search_empty_query():
+    """Test unified search with empty query returns empty results."""
+    results, total = await unified_hybrid_search(
+        query="",
+        page=1,
+        page_size=20,
+    )
+
+    assert results == []
+    assert total == 0
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.integration
+async def test_unified_hybrid_search_pagination():
+    """Test unified search pagination with BM25 reranking.
+
+    Pagination happens in SQL (LIMIT/OFFSET), then BM25 reranking is applied
+    to the paginated results.
+    """
+    # Create mock results that SQL would return for a page
+    mock_results = [
+        {
+            "content_type": "STORE_AGENT",
+            "content_id": f"agent-{i}",
+            "searchable_text": f"Agent {i} description",
+            "metadata": {"name": f"Agent {i}"},
+            "updated_at": "2025-01-01T00:00:00Z",
+            "semantic_score": 0.7,
+            "lexical_score": 0.8 - (i * 0.01),
+            "category_score": 0.5,
+            "recency_score": 0.3,
+            "combined_score": 0.6 - (i * 0.01),
+            "total_count": 50,
+        }
+        for i in range(15)  # SQL returns page_size results
+    ]
+
+    with patch(
+        "backend.api.features.store.hybrid_search.query_raw_with_schema"
+    ) as mock_query:
+        with patch(
+            "backend.api.features.store.hybrid_search.embed_query"
+        ) as mock_embed:
+            mock_query.return_value = mock_results
+            mock_embed.return_value = [0.1] * embeddings.EMBEDDING_DIM
+
+            results, total = await unified_hybrid_search(
+                query="test",
+                page=3,
+                page_size=15,
+            )
+
+            # Verify results returned
+            assert len(results) == 15
+            assert total == 50  # Total from SQL COUNT(*) OVER()
+
+            # Verify the SQL query uses page_size and offset
+            call_args = mock_query.call_args
+            params = call_args[0]
+            # Last two params are page_size and offset
+            page_size_param = params[-2]
+            offset_param = params[-1]
+            assert page_size_param == 15
+            assert offset_param == 30  # (page 3 - 1) * 15
+
+
+@pytest.mark.asyncio(loop_scope="session")
+@pytest.mark.integration
+async def test_unified_hybrid_search_schema_prefix():
+    """Test unified search uses schema_prefix placeholder."""
+    with patch(
+        "backend.api.features.store.hybrid_search.query_raw_with_schema"
+    ) as mock_query:
+        with patch(
+            "backend.api.features.store.hybrid_search.embed_query"
+        ) as mock_embed:
+            mock_query.return_value = []
+            mock_embed.return_value = [0.1] * embeddings.EMBEDDING_DIM
+
+            await unified_hybrid_search(
+                query="test",
+                page=1,
+                page_size=20,
+            )
+
+            call_args = mock_query.call_args
+            sql_template = call_args[0][0]
+
+            # Verify schema_prefix placeholder is used for table references
+            assert "{schema_prefix}" in sql_template
+            assert '"UnifiedContentEmbedding"' in sql_template
+
+
 if __name__ == "__main__":
     pytest.main([__file__, "-v", "-s"])

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

@@ -221,3 +221,23 @@ class ReviewSubmissionRequest(pydantic.BaseModel):
     is_approved: bool
     comments: str  # External comments visible to creator
     internal_comments: str | None = None  # Private admin notes
+
+
+class UnifiedSearchResult(pydantic.BaseModel):
+    """A single result from unified hybrid search across all content types."""
+
+    content_type: str  # STORE_AGENT, BLOCK, DOCUMENTATION
+    content_id: str
+    searchable_text: str
+    metadata: dict | None = None
+    updated_at: datetime.datetime | None = None
+    combined_score: float | None = None
+    semantic_score: float | None = None
+    lexical_score: float | None = None
+
+
+class UnifiedSearchResponse(pydantic.BaseModel):
+    """Response model for unified search across all content types."""
+
+    results: list[UnifiedSearchResult]
+    pagination: Pagination

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

@@ -7,12 +7,15 @@ from typing import Literal
 import autogpt_libs.auth
 import fastapi
 import fastapi.responses
+import prisma.enums
 
 import backend.data.graph
 import backend.util.json
+from backend.util.models import Pagination
 
 from . import cache as store_cache
 from . import db as store_db
+from . import hybrid_search as store_hybrid_search
 from . import image_gen as store_image_gen
 from . import media as store_media
 from . import model as store_model
@@ -146,6 +149,102 @@ async def get_agents(
     return agents
 
 
+##############################################
+############### Search Endpoints #############
+##############################################
+
+
+@router.get(
+    "/search",
+    summary="Unified search across all content types",
+    tags=["store", "public"],
+    response_model=store_model.UnifiedSearchResponse,
+)
+async def unified_search(
+    query: str,
+    content_types: list[str] | None = fastapi.Query(
+        default=None,
+        description="Content types to search: STORE_AGENT, BLOCK, DOCUMENTATION. If not specified, searches all.",
+    ),
+    page: int = 1,
+    page_size: int = 20,
+    user_id: str | None = fastapi.Security(
+        autogpt_libs.auth.get_optional_user_id, use_cache=False
+    ),
+):
+    """
+    Search across all content types (store agents, blocks, documentation) using hybrid search.
+
+    Combines semantic (embedding-based) and lexical (text-based) search for best results.
+
+    Args:
+        query: The search query string
+        content_types: Optional list of content types to filter by (STORE_AGENT, BLOCK, DOCUMENTATION)
+        page: Page number for pagination (default 1)
+        page_size: Number of results per page (default 20)
+        user_id: Optional authenticated user ID (for user-scoped content in future)
+
+    Returns:
+        UnifiedSearchResponse: Paginated list of search results with relevance scores
+    """
+    if page < 1:
+        raise fastapi.HTTPException(
+            status_code=422, detail="Page must be greater than 0"
+        )
+
+    if page_size < 1:
+        raise fastapi.HTTPException(
+            status_code=422, detail="Page size must be greater than 0"
+        )
+
+    # Convert string content types to enum
+    content_type_enums: list[prisma.enums.ContentType] | None = None
+    if content_types:
+        try:
+            content_type_enums = [prisma.enums.ContentType(ct) for ct in content_types]
+        except ValueError as e:
+            raise fastapi.HTTPException(
+                status_code=422,
+                detail=f"Invalid content type. Valid values: STORE_AGENT, BLOCK, DOCUMENTATION. Error: {e}",
+            )
+
+    # Perform unified hybrid search
+    results, total = await store_hybrid_search.unified_hybrid_search(
+        query=query,
+        content_types=content_type_enums,
+        user_id=user_id,
+        page=page,
+        page_size=page_size,
+    )
+
+    # Convert results to response model
+    search_results = [
+        store_model.UnifiedSearchResult(
+            content_type=r["content_type"],
+            content_id=r["content_id"],
+            searchable_text=r.get("searchable_text", ""),
+            metadata=r.get("metadata"),
+            updated_at=r.get("updated_at"),
+            combined_score=r.get("combined_score"),
+            semantic_score=r.get("semantic_score"),
+            lexical_score=r.get("lexical_score"),
+        )
+        for r in results
+    ]
+
+    total_pages = (total + page_size - 1) // page_size if total > 0 else 0
+
+    return store_model.UnifiedSearchResponse(
+        results=search_results,
+        pagination=Pagination(
+            total_items=total,
+            total_pages=total_pages,
+            current_page=page,
+            page_size=page_size,
+        ),
+    )
+
+
 @router.get(
     "/agents/{username}/{agent_name}",
     summary="Get specific agent",

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

@@ -0,0 +1,272 @@
+"""Tests for the semantic_search function."""
+
+import pytest
+from prisma.enums import ContentType
+
+from backend.api.features.store.embeddings import EMBEDDING_DIM, semantic_search
+
+
+@pytest.mark.asyncio
+async def test_search_blocks_only(mocker):
+    """Test searching only BLOCK content type."""
+    # Mock embed_query to return a test embedding
+    mock_embedding = [0.1] * EMBEDDING_DIM
+    mocker.patch(
+        "backend.api.features.store.embeddings.embed_query",
+        return_value=mock_embedding,
+    )
+
+    # Mock query_raw_with_schema to return test results
+    mock_results = [
+        {
+            "content_id": "block-123",
+            "content_type": "BLOCK",
+            "searchable_text": "Calculator Block - Performs arithmetic operations",
+            "metadata": {"name": "Calculator", "categories": ["Math"]},
+            "similarity": 0.85,
+        }
+    ]
+    mocker.patch(
+        "backend.api.features.store.embeddings.query_raw_with_schema",
+        return_value=mock_results,
+    )
+
+    results = await semantic_search(
+        query="calculate numbers",
+        content_types=[ContentType.BLOCK],
+    )
+
+    assert len(results) == 1
+    assert results[0]["content_type"] == "BLOCK"
+    assert results[0]["content_id"] == "block-123"
+    assert results[0]["similarity"] == 0.85
+
+
+@pytest.mark.asyncio
+async def test_search_multiple_content_types(mocker):
+    """Test searching multiple content types simultaneously."""
+    mock_embedding = [0.1] * EMBEDDING_DIM
+    mocker.patch(
+        "backend.api.features.store.embeddings.embed_query",
+        return_value=mock_embedding,
+    )
+
+    mock_results = [
+        {
+            "content_id": "block-123",
+            "content_type": "BLOCK",
+            "searchable_text": "Calculator Block",
+            "metadata": {},
+            "similarity": 0.85,
+        },
+        {
+            "content_id": "doc-456",
+            "content_type": "DOCUMENTATION",
+            "searchable_text": "How to use Calculator",
+            "metadata": {},
+            "similarity": 0.75,
+        },
+    ]
+    mocker.patch(
+        "backend.api.features.store.embeddings.query_raw_with_schema",
+        return_value=mock_results,
+    )
+
+    results = await semantic_search(
+        query="calculator",
+        content_types=[ContentType.BLOCK, ContentType.DOCUMENTATION],
+    )
+
+    assert len(results) == 2
+    assert results[0]["content_type"] == "BLOCK"
+    assert results[1]["content_type"] == "DOCUMENTATION"
+
+
+@pytest.mark.asyncio
+async def test_search_with_min_similarity_threshold(mocker):
+    """Test that results below min_similarity are filtered out."""
+    mock_embedding = [0.1] * EMBEDDING_DIM
+    mocker.patch(
+        "backend.api.features.store.embeddings.embed_query",
+        return_value=mock_embedding,
+    )
+
+    # Only return results above 0.7 similarity
+    mock_results = [
+        {
+            "content_id": "block-123",
+            "content_type": "BLOCK",
+            "searchable_text": "Calculator Block",
+            "metadata": {},
+            "similarity": 0.85,
+        }
+    ]
+    mocker.patch(
+        "backend.api.features.store.embeddings.query_raw_with_schema",
+        return_value=mock_results,
+    )
+
+    results = await semantic_search(
+        query="calculate",
+        content_types=[ContentType.BLOCK],
+        min_similarity=0.7,
+    )
+
+    assert len(results) == 1
+    assert results[0]["similarity"] >= 0.7
+
+
+@pytest.mark.asyncio
+async def test_search_fallback_to_lexical(mocker):
+    """Test fallback to lexical search when embeddings fail."""
+    # Mock embed_query to return None (embeddings unavailable)
+    mocker.patch(
+        "backend.api.features.store.embeddings.embed_query",
+        return_value=None,
+    )
+
+    mock_lexical_results = [
+        {
+            "content_id": "block-123",
+            "content_type": "BLOCK",
+            "searchable_text": "Calculator Block performs calculations",
+            "metadata": {},
+            "similarity": 0.0,
+        }
+    ]
+    mocker.patch(
+        "backend.api.features.store.embeddings.query_raw_with_schema",
+        return_value=mock_lexical_results,
+    )
+
+    results = await semantic_search(
+        query="calculator",
+        content_types=[ContentType.BLOCK],
+    )
+
+    assert len(results) == 1
+    assert results[0]["similarity"] == 0.0  # Lexical search returns 0 similarity
+
+
+@pytest.mark.asyncio
+async def test_search_empty_query():
+    """Test that empty query returns no results."""
+    results = await semantic_search(query="")
+    assert results == []
+
+    results = await semantic_search(query="   ")
+    assert results == []
+
+
+@pytest.mark.asyncio
+async def test_search_with_user_id_filter(mocker):
+    """Test searching with user_id filter for private content."""
+    mock_embedding = [0.1] * EMBEDDING_DIM
+    mocker.patch(
+        "backend.api.features.store.embeddings.embed_query",
+        return_value=mock_embedding,
+    )
+
+    mock_results = [
+        {
+            "content_id": "agent-789",
+            "content_type": "LIBRARY_AGENT",
+            "searchable_text": "My Custom Agent",
+            "metadata": {},
+            "similarity": 0.9,
+        }
+    ]
+    mocker.patch(
+        "backend.api.features.store.embeddings.query_raw_with_schema",
+        return_value=mock_results,
+    )
+
+    results = await semantic_search(
+        query="custom agent",
+        content_types=[ContentType.LIBRARY_AGENT],
+        user_id="user-123",
+    )
+
+    assert len(results) == 1
+    assert results[0]["content_type"] == "LIBRARY_AGENT"
+
+
+@pytest.mark.asyncio
+async def test_search_limit_parameter(mocker):
+    """Test that limit parameter correctly limits results."""
+    mock_embedding = [0.1] * EMBEDDING_DIM
+    mocker.patch(
+        "backend.api.features.store.embeddings.embed_query",
+        return_value=mock_embedding,
+    )
+
+    # Return 5 results
+    mock_results = [
+        {
+            "content_id": f"block-{i}",
+            "content_type": "BLOCK",
+            "searchable_text": f"Block {i}",
+            "metadata": {},
+            "similarity": 0.8,
+        }
+        for i in range(5)
+    ]
+    mocker.patch(
+        "backend.api.features.store.embeddings.query_raw_with_schema",
+        return_value=mock_results,
+    )
+
+    results = await semantic_search(
+        query="block",
+        content_types=[ContentType.BLOCK],
+        limit=5,
+    )
+
+    assert len(results) == 5
+
+
+@pytest.mark.asyncio
+async def test_search_default_content_types(mocker):
+    """Test that default content_types includes BLOCK, STORE_AGENT, and DOCUMENTATION."""
+    mock_embedding = [0.1] * EMBEDDING_DIM
+    mocker.patch(
+        "backend.api.features.store.embeddings.embed_query",
+        return_value=mock_embedding,
+    )
+
+    mock_query_raw = mocker.patch(
+        "backend.api.features.store.embeddings.query_raw_with_schema",
+        return_value=[],
+    )
+
+    await semantic_search(query="test")
+
+    # Check that the SQL query includes all three default content types
+    call_args = mock_query_raw.call_args
+    assert "BLOCK" in str(call_args)
+    assert "STORE_AGENT" in str(call_args)
+    assert "DOCUMENTATION" in str(call_args)
+
+
+@pytest.mark.asyncio
+async def test_search_handles_database_error(mocker):
+    """Test that database errors are handled gracefully."""
+    mock_embedding = [0.1] * EMBEDDING_DIM
+    mocker.patch(
+        "backend.api.features.store.embeddings.embed_query",
+        return_value=mock_embedding,
+    )
+
+    # Simulate database error
+    mocker.patch(
+        "backend.api.features.store.embeddings.query_raw_with_schema",
+        side_effect=Exception("Database connection failed"),
+    )
+
+    results = await semantic_search(
+        query="test",
+        content_types=[ContentType.BLOCK],
+    )
+
+    # Should return empty list on error
+    assert results == []

autogpt_platform/backend/backend/data/block.py

@@ -680,12 +680,23 @@ class Block(ABC, Generic[BlockSchemaInputType, BlockSchemaOutputType]):
         return False, reviewed_data
 
     async def _execute(self, input_data: BlockInput, **kwargs) -> BlockOutput:
-        # Check for review requirement and get potentially modified input data
-        should_pause, input_data = await self.is_block_exec_need_review(
-            input_data, **kwargs
+        # Check for review requirement only if running within a graph execution context
+        # Direct block execution (e.g., from chat) skips the review process
+        has_graph_context = all(
+            key in kwargs
+            for key in (
+                "node_exec_id",
+                "graph_exec_id",
+                "graph_id",
+                "execution_context",
+            )
         )
-        if should_pause:
-            return
+        if has_graph_context:
+            should_pause, input_data = await self.is_block_exec_need_review(
+                input_data, **kwargs
+            )
+            if should_pause:
+                return
 
         # Validate the input data (original or reviewer-modified) once
         if error := self.input_schema.validate_data(input_data):

autogpt_platform/backend/backend/executor/database.py

@@ -9,6 +9,7 @@ from backend.api.features.library.db import (
 from backend.api.features.store.db import get_store_agent_details, get_store_agents
 from backend.api.features.store.embeddings import (
     backfill_missing_embeddings,
+    cleanup_orphaned_embeddings,
     get_embedding_stats,
 )
 from backend.data import db
@@ -221,6 +222,7 @@ class DatabaseManager(AppService):
     # Store Embeddings
     get_embedding_stats = _(get_embedding_stats)
     backfill_missing_embeddings = _(backfill_missing_embeddings)
+    cleanup_orphaned_embeddings = _(cleanup_orphaned_embeddings)
 
     # Summary data - async
     get_user_execution_summary_data = _(get_user_execution_summary_data)
@@ -276,6 +278,7 @@ class DatabaseManagerClient(AppServiceClient):
     # Store Embeddings
     get_embedding_stats = _(d.get_embedding_stats)
     backfill_missing_embeddings = _(d.backfill_missing_embeddings)
+    cleanup_orphaned_embeddings = _(d.cleanup_orphaned_embeddings)
 
 
 class DatabaseManagerAsyncClient(AppServiceClient):

autogpt_platform/backend/backend/executor/scheduler.py

@@ -28,6 +28,7 @@ from backend.data.auth.oauth import cleanup_expired_oauth_tokens
 from backend.data.block import BlockInput
 from backend.data.execution import GraphExecutionWithNodes
 from backend.data.model import CredentialsMetaInput
+from backend.data.onboarding import increment_onboarding_runs
 from backend.executor import utils as execution_utils
 from backend.monitoring import (
     NotificationJobArgs,
@@ -156,6 +157,7 @@ async def _execute_graph(**kwargs):
             inputs=args.input_data,
             graph_credentials_inputs=args.input_credentials,
         )
+        await increment_onboarding_runs(args.user_id)
         elapsed = asyncio.get_event_loop().time() - start_time
         logger.info(
             f"Graph execution started with ID {graph_exec.id} for graph {args.graph_id} "
@@ -255,14 +257,14 @@ def execution_accuracy_alerts():
 
 def ensure_embeddings_coverage():
     """
-    Ensure approved store agents have embeddings for hybrid search.
+    Ensure all content types (store agents, blocks, docs) have embeddings for search.
 
-    Processes ALL missing embeddings in batches of 10 until 100% coverage.
-    Missing embeddings = agents invisible in hybrid search.
+    Processes ALL missing embeddings in batches of 10 per content type until 100% coverage.
+    Missing embeddings = content invisible in hybrid search.
 
     Schedule: Runs every 6 hours (balanced between coverage and API costs).
-    - Catches agents approved between scheduled runs
-    - Batch size 10: gradual processing to avoid rate limits
+    - Catches new content added between scheduled runs
+    - Batch size 10 per content type: gradual processing to avoid rate limits
     - Manual trigger available via execute_ensure_embeddings_coverage endpoint
     """
     db_client = get_database_manager_client()
@@ -273,51 +275,91 @@ def ensure_embeddings_coverage():
         logger.error(
             f"Failed to get embedding stats: {stats['error']} - skipping backfill"
         )
-        return {"processed": 0, "success": 0, "failed": 0, "error": stats["error"]}
+        return {
+            "backfill": {"processed": 0, "success": 0, "failed": 0},
+            "cleanup": {"deleted": 0},
+            "error": stats["error"],
+        }
 
-    if stats["without_embeddings"] == 0:
-        logger.info("All approved agents have embeddings, skipping backfill")
-        return {"processed": 0, "success": 0, "failed": 0}
-
-    logger.info(
-        f"Found {stats['without_embeddings']} agents without embeddings "
-        f"({stats['coverage_percent']}% coverage) - processing all"
-    )
+    # Extract totals from new stats structure
+    totals = stats.get("totals", {})
+    without_embeddings = totals.get("without_embeddings", 0)
+    coverage_percent = totals.get("coverage_percent", 0)
 
     total_processed = 0
     total_success = 0
     total_failed = 0
 
-    # Process in batches until no more missing embeddings
-    while True:
-        result = db_client.backfill_missing_embeddings(batch_size=10)
+    if without_embeddings == 0:
+        logger.info("All content has embeddings, skipping backfill")
+    else:
+        # Log per-content-type stats for visibility
+        by_type = stats.get("by_type", {})
+        for content_type, type_stats in by_type.items():
+            if type_stats.get("without_embeddings", 0) > 0:
+                logger.info(
+                    f"{content_type}: {type_stats['without_embeddings']} items without embeddings "
+                    f"({type_stats['coverage_percent']}% coverage)"
+                )
 
-        total_processed += result["processed"]
-        total_success += result["success"]
-        total_failed += result["failed"]
+        logger.info(
+            f"Total: {without_embeddings} items without embeddings "
+            f"({coverage_percent}% coverage) - processing all"
+        )
 
-        if result["processed"] == 0:
-            # No more missing embeddings
-            break
+        # Process in batches until no more missing embeddings
+        while True:
+            result = db_client.backfill_missing_embeddings(batch_size=10)
 
-        if result["success"] == 0 and result["processed"] > 0:
-            # All attempts in this batch failed - stop to avoid infinite loop
-            logger.error(
-                f"All {result['processed']} embedding attempts failed - stopping backfill"
-            )
-            break
+            total_processed += result["processed"]
+            total_success += result["success"]
+            total_failed += result["failed"]
 
-        # Small delay between batches to avoid rate limits
-        time.sleep(1)
+            if result["processed"] == 0:
+                # No more missing embeddings
+                break
+
+            if result["success"] == 0 and result["processed"] > 0:
+                # All attempts in this batch failed - stop to avoid infinite loop
+                logger.error(
+                    f"All {result['processed']} embedding attempts failed - stopping backfill"
+                )
+                break
+
+            # Small delay between batches to avoid rate limits
+            time.sleep(1)
+
+        logger.info(
+            f"Embedding backfill completed: {total_success}/{total_processed} succeeded, "
+            f"{total_failed} failed"
+        )
+
+    # Clean up orphaned embeddings for blocks and docs
+    logger.info("Running cleanup for orphaned embeddings (blocks/docs)...")
+    cleanup_result = db_client.cleanup_orphaned_embeddings()
+    cleanup_totals = cleanup_result.get("totals", {})
+    cleanup_deleted = cleanup_totals.get("deleted", 0)
+
+    if cleanup_deleted > 0:
+        logger.info(f"Cleanup completed: deleted {cleanup_deleted} orphaned embeddings")
+        by_type = cleanup_result.get("by_type", {})
+        for content_type, type_result in by_type.items():
+            if type_result.get("deleted", 0) > 0:
+                logger.info(
+                    f"{content_type}: deleted {type_result['deleted']} orphaned embeddings"
+                )
+    else:
+        logger.info("Cleanup completed: no orphaned embeddings found")
 
-    logger.info(
-        f"Embedding backfill completed: {total_success}/{total_processed} succeeded, "
-        f"{total_failed} failed"
-    )
     return {
-        "processed": total_processed,
-        "success": total_success,
-        "failed": total_failed,
+        "backfill": {
+            "processed": total_processed,
+            "success": total_success,
+            "failed": total_failed,
+        },
+        "cleanup": {
+            "deleted": cleanup_deleted,
+        },
     }
 
 
@@ -560,6 +602,18 @@ class Scheduler(AppService):
         self.scheduler.add_listener(job_max_instances_listener, EVENT_JOB_MAX_INSTANCES)
         self.scheduler.start()
 
+        # Run embedding backfill immediately on startup
+        # This ensures blocks/docs are searchable right away, not after 6 hours
+        # Safe to run on multiple pods - uses upserts and checks for existing embeddings
+        if self.register_system_tasks:
+            logger.info("Running embedding backfill on startup...")
+            try:
+                result = ensure_embeddings_coverage()
+                logger.info(f"Startup embedding backfill complete: {result}")
+            except Exception as e:
+                logger.error(f"Startup embedding backfill failed: {e}")
+                # Don't fail startup - the scheduled job will retry later
+
         # Keep the service running since BackgroundScheduler doesn't block
         super().run_service()
 

autogpt_platform/backend/backend/util/cache.py

@@ -16,7 +16,7 @@ import pickle
 import threading
 import time
 from dataclasses import dataclass
-from functools import wraps
+from functools import cache, wraps
 from typing import Any, Callable, ParamSpec, Protocol, TypeVar, cast, runtime_checkable
 
 from redis import ConnectionPool, Redis
@@ -38,29 +38,34 @@ settings = Settings()
 #   maxmemory 2gb                   # Set memory limit (adjust based on your needs)
 #   save ""                         # Disable persistence if using Redis purely for caching
 
-# Create a dedicated Redis connection pool for caching (binary mode for pickle)
-_cache_pool: ConnectionPool | None = None
 
-
-@conn_retry("Redis", "Acquiring cache connection pool")
+@cache
 def _get_cache_pool() -> ConnectionPool:
-    """Get or create a connection pool for cache operations."""
-    global _cache_pool
-    if _cache_pool is None:
-        _cache_pool = ConnectionPool(
-            host=settings.config.redis_host,
-            port=settings.config.redis_port,
-            password=settings.config.redis_password or None,
-            decode_responses=False,  # Binary mode for pickle
-            max_connections=50,
-            socket_keepalive=True,
-            socket_connect_timeout=5,
-            retry_on_timeout=True,
-        )
-    return _cache_pool
+    """Get or create a connection pool for cache operations (lazy, thread-safe)."""
+    return ConnectionPool(
+        host=settings.config.redis_host,
+        port=settings.config.redis_port,
+        password=settings.config.redis_password or None,
+        decode_responses=False,  # Binary mode for pickle
+        max_connections=50,
+        socket_keepalive=True,
+        socket_connect_timeout=5,
+        retry_on_timeout=True,
+    )
 
 
-redis = Redis(connection_pool=_get_cache_pool())
+@cache
+@conn_retry("Redis", "Acquiring cache connection")
+def _get_redis() -> Redis:
+    """
+    Get the lazily-initialized Redis client for shared cache operations.
+    Uses @cache for thread-safe singleton behavior - connection is only
+    established when first accessed, allowing services that only use
+    in-memory caching to work without Redis configuration.
+    """
+    r = Redis(connection_pool=_get_cache_pool())
+    r.ping()  # Verify connection
+    return r
 
 
 @dataclass
@@ -179,9 +184,9 @@ def cached(
             try:
                 if refresh_ttl_on_get:
                     # Use GETEX to get value and refresh expiry atomically
-                    cached_bytes = redis.getex(redis_key, ex=ttl_seconds)
+                    cached_bytes = _get_redis().getex(redis_key, ex=ttl_seconds)
                 else:
-                    cached_bytes = redis.get(redis_key)
+                    cached_bytes = _get_redis().get(redis_key)
 
                 if cached_bytes and isinstance(cached_bytes, bytes):
                     return pickle.loads(cached_bytes)
@@ -195,7 +200,7 @@ def cached(
             """Set value in Redis with TTL."""
             try:
                 pickled_value = pickle.dumps(value, protocol=pickle.HIGHEST_PROTOCOL)
-                redis.setex(redis_key, ttl_seconds, pickled_value)
+                _get_redis().setex(redis_key, ttl_seconds, pickled_value)
             except Exception as e:
                 logger.error(
                     f"Redis error storing cache for {target_func.__name__}: {e}"
@@ -333,14 +338,18 @@ def cached(
                 if pattern:
                     # Clear entries matching pattern
                     keys = list(
-                        redis.scan_iter(f"cache:{target_func.__name__}:{pattern}")
+                        _get_redis().scan_iter(
+                            f"cache:{target_func.__name__}:{pattern}"
+                        )
                     )
                 else:
                     # Clear all cache keys
-                    keys = list(redis.scan_iter(f"cache:{target_func.__name__}:*"))
+                    keys = list(
+                        _get_redis().scan_iter(f"cache:{target_func.__name__}:*")
+                    )
 
                 if keys:
-                    pipeline = redis.pipeline()
+                    pipeline = _get_redis().pipeline()
                     for key in keys:
                         pipeline.delete(key)
                     pipeline.execute()
@@ -355,7 +364,9 @@ def cached(
 
         def cache_info() -> dict[str, int | None]:
             if shared_cache:
-                cache_keys = list(redis.scan_iter(f"cache:{target_func.__name__}:*"))
+                cache_keys = list(
+                    _get_redis().scan_iter(f"cache:{target_func.__name__}:*")
+                )
                 return {
                     "size": len(cache_keys),
                     "maxsize": None,  # Redis manages its own size
@@ -373,10 +384,8 @@ def cached(
             key = _make_hashable_key(args, kwargs)
             if shared_cache:
                 redis_key = _make_redis_key(key, target_func.__name__)
-                if redis.exists(redis_key):
-                    redis.delete(redis_key)
-                    return True
-                return False
+                deleted_count = cast(int, _get_redis().delete(redis_key))
+                return deleted_count > 0
             else:
                 if key in cache_storage:
                     del cache_storage[key]

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

@@ -43,4 +43,6 @@ CREATE UNIQUE INDEX "UnifiedContentEmbedding_contentType_contentId_userId_key" O
 -- CreateIndex
 -- HNSW index for fast vector similarity search on embeddings
 -- Uses cosine distance operator (<=>), which matches the query in hybrid_search.py
+-- Note: Drop first in case Prisma created a btree index (Prisma doesn't support HNSW)
+DROP INDEX IF EXISTS "UnifiedContentEmbedding_embedding_idx";
 CREATE INDEX "UnifiedContentEmbedding_embedding_idx" ON "UnifiedContentEmbedding" USING hnsw ("embedding" public.vector_cosine_ops);

autogpt_platform/backend/migrations/20260115200000_add_unified_search_tsvector/migration.sql

@@ -0,0 +1,35 @@
+-- Add tsvector search column to UnifiedContentEmbedding for unified full-text search
+-- This enables hybrid search (semantic + lexical) across all content types
+
+-- Add search column (IF NOT EXISTS for idempotency)
+ALTER TABLE "UnifiedContentEmbedding" ADD COLUMN IF NOT EXISTS "search" tsvector DEFAULT ''::tsvector;
+
+-- Create GIN index for fast full-text search
+-- No @@index in schema.prisma - Prisma may generate DROP INDEX on migrate dev
+-- If that happens, just let it drop and this migration will recreate it, or manually re-run:
+--   CREATE INDEX IF NOT EXISTS "UnifiedContentEmbedding_search_idx" ON "UnifiedContentEmbedding" USING GIN ("search");
+DROP INDEX IF EXISTS "UnifiedContentEmbedding_search_idx";
+CREATE INDEX "UnifiedContentEmbedding_search_idx" ON "UnifiedContentEmbedding" USING GIN ("search");
+
+-- Drop existing trigger/function if exists
+DROP TRIGGER IF EXISTS "update_unified_tsvector" ON "UnifiedContentEmbedding";
+DROP FUNCTION IF EXISTS update_unified_tsvector_column();
+
+-- Create function to auto-update tsvector from searchableText
+CREATE OR REPLACE FUNCTION update_unified_tsvector_column() RETURNS TRIGGER AS $$
+BEGIN
+  NEW.search := to_tsvector('english', COALESCE(NEW."searchableText", ''));
+  RETURN NEW;
+END;
+$$ LANGUAGE plpgsql SECURITY DEFINER SET search_path = platform, pg_temp;
+
+-- Create trigger to auto-update search column on insert/update
+CREATE TRIGGER "update_unified_tsvector"
+BEFORE INSERT OR UPDATE ON "UnifiedContentEmbedding"
+FOR EACH ROW
+EXECUTE FUNCTION update_unified_tsvector_column();
+
+-- Backfill existing rows
+UPDATE "UnifiedContentEmbedding"
+SET search = to_tsvector('english', COALESCE("searchableText", ''))
+WHERE search IS NULL OR search = ''::tsvector;

autogpt_platform/backend/migrations/20260115210000_remove_storelistingversion_search/migration.sql

@@ -0,0 +1,90 @@
+-- Remove the old search column from StoreListingVersion
+-- This column has been replaced by UnifiedContentEmbedding.search
+-- which provides unified hybrid search across all content types
+
+-- First drop the dependent view
+DROP VIEW IF EXISTS "StoreAgent";
+
+-- Drop the trigger and function for old search column
+-- The original trigger was created in 20251016093049_add_full_text_search
+DROP TRIGGER IF EXISTS "update_tsvector" ON "StoreListingVersion";
+DROP FUNCTION IF EXISTS update_tsvector_column();
+
+-- Drop the index
+DROP INDEX IF EXISTS "StoreListingVersion_search_idx";
+
+-- NOTE: Keeping search column for now to allow easy revert if needed
+-- Uncomment to fully remove once migration is verified in production:
+-- ALTER TABLE "StoreListingVersion" DROP COLUMN IF EXISTS "search";
+
+-- Recreate the StoreAgent view WITHOUT the search column
+-- (Search now handled by UnifiedContentEmbedding)
+CREATE OR REPLACE VIEW "StoreAgent" AS
+WITH latest_versions AS (
+    SELECT
+        "storeListingId",
+        MAX(version) AS max_version
+    FROM "StoreListingVersion"
+    WHERE "submissionStatus" = 'APPROVED'
+    GROUP BY "storeListingId"
+),
+agent_versions AS (
+    SELECT
+        "storeListingId",
+        array_agg(DISTINCT version::text ORDER BY version::text) AS versions
+    FROM "StoreListingVersion"
+    WHERE "submissionStatus" = 'APPROVED'
+    GROUP BY "storeListingId"
+),
+agent_graph_versions AS (
+    SELECT
+        "storeListingId",
+        array_agg(DISTINCT "agentGraphVersion"::text ORDER BY "agentGraphVersion"::text) AS graph_versions
+    FROM "StoreListingVersion"
+    WHERE "submissionStatus" = 'APPROVED'
+    GROUP BY "storeListingId"
+)
+SELECT
+    sl.id AS listing_id,
+    slv.id AS "storeListingVersionId",
+    slv."createdAt" AS updated_at,
+    sl.slug,
+    COALESCE(slv.name, '') AS agent_name,
+    slv."videoUrl" AS agent_video,
+    slv."agentOutputDemoUrl" AS agent_output_demo,
+    COALESCE(slv."imageUrls", ARRAY[]::text[]) AS agent_image,
+    slv."isFeatured" AS featured,
+    p.username AS creator_username,
+    p."avatarUrl" AS creator_avatar,
+    slv."subHeading" AS sub_heading,
+    slv.description,
+    slv.categories,
+    COALESCE(ar.run_count, 0::bigint) AS runs,
+    COALESCE(rs.avg_rating, 0.0)::double precision AS rating,
+    COALESCE(av.versions, ARRAY[slv.version::text]) AS versions,
+    COALESCE(agv.graph_versions, ARRAY[slv."agentGraphVersion"::text]) AS "agentGraphVersions",
+    slv."agentGraphId",
+    slv."isAvailable" AS is_available,
+    COALESCE(sl."useForOnboarding", false) AS "useForOnboarding"
+FROM "StoreListing" sl
+JOIN latest_versions lv
+    ON sl.id = lv."storeListingId"
+JOIN "StoreListingVersion" slv
+    ON slv."storeListingId" = lv."storeListingId"
+   AND slv.version = lv.max_version
+   AND slv."submissionStatus" = 'APPROVED'
+JOIN "AgentGraph" a
+    ON slv."agentGraphId" = a.id
+    AND slv."agentGraphVersion" = a.version
+LEFT JOIN "Profile" p
+    ON sl."owningUserId" = p."userId"
+LEFT JOIN "mv_review_stats" rs
+    ON sl.id = rs."storeListingId"
+LEFT JOIN "mv_agent_run_counts" ar
+    ON a.id = ar."agentGraphId"
+LEFT JOIN agent_versions av
+    ON sl.id = av."storeListingId"
+LEFT JOIN agent_graph_versions agv
+    ON sl.id = agv."storeListingId"
+WHERE sl."isDeleted" = false
+  AND sl."hasApprovedVersion" = true;

autogpt_platform/backend/poetry.lock

@@ -5339,6 +5339,24 @@ urllib3 = ">=1.26.14,<3"
 fastembed = ["fastembed (>=0.7,<0.8)"]
 fastembed-gpu = ["fastembed-gpu (>=0.7,<0.8)"]
 
+[[package]]
+name = "rank-bm25"
+version = "0.2.2"
+description = "Various BM25 algorithms for document ranking"
+optional = false
+python-versions = "*"
+groups = ["main"]
+files = [
+    {file = "rank_bm25-0.2.2-py3-none-any.whl", hash = "sha256:7bd4a95571adadfc271746fa146a4bcfd89c0cf731e49c3d1ad863290adbe8ae"},
+    {file = "rank_bm25-0.2.2.tar.gz", hash = "sha256:096ccef76f8188563419aaf384a02f0ea459503fdf77901378d4fd9d87e5e51d"},
+]
+
+[package.dependencies]
+numpy = "*"
+
+[package.extras]
+dev = ["pytest"]
+
 [[package]]
 name = "rapidfuzz"
 version = "3.13.0"
@@ -7494,4 +7512,4 @@ cffi = ["cffi (>=1.11)"]
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.10,<3.14"
-content-hash = "86838b5ae40d606d6e01a14dad8a56c389d890d7a6a0c274a6602cca80f0df84"
+content-hash = "18b92e09596298c82432e4d0a85cb6d80a40b4229bee0a0c15f0529fd6cb21a4"

autogpt_platform/backend/pyproject.toml

@@ -46,6 +46,7 @@ poetry = "2.1.1" # CHECK DEPENDABOT SUPPORT BEFORE UPGRADING
 postmarker = "^1.0"
 praw = "~7.8.1"
 prisma = "^0.15.0"
+rank-bm25 = "^0.2.2"
 prometheus-client = "^0.22.1"
 prometheus-fastapi-instrumentator = "^7.0.0"
 psutil = "^7.0.0"

autogpt_platform/backend/schema.prisma

@@ -937,7 +937,7 @@ model StoreListingVersion {
   // Old versions can be made unavailable by the author if desired
   isAvailable Boolean @default(true)
 
-  search Unsupported("tsvector")? @default(dbgenerated("''::tsvector"))
+  // Note: search column removed - now using UnifiedContentEmbedding.search
 
   // Version workflow state
   submissionStatus SubmissionStatus @default(DRAFT)
@@ -1002,6 +1002,7 @@ model UnifiedContentEmbedding {
   // Search data
   embedding      Unsupported("vector(1536)") // pgvector embedding (extension in platform schema)
   searchableText String // Combined text for search and fallback
+  search         Unsupported("tsvector")? @default(dbgenerated("''::tsvector")) // Full-text search (auto-populated by trigger)
   metadata       Json                        @default("{}") // Content-specific metadata
 
   @@unique([contentType, contentId, userId], map: "UnifiedContentEmbedding_contentType_contentId_userId_key")
@@ -1009,6 +1010,8 @@ model UnifiedContentEmbedding {
   @@index([userId])
   @@index([contentType, userId])
   @@index([embedding], map: "UnifiedContentEmbedding_embedding_idx")
+  // NO @@index for search - GIN index "UnifiedContentEmbedding_search_idx" created via SQL migration
+  // Prisma may generate DROP INDEX on migrate dev - that's okay, migration recreates it
 }
 
 model StoreListingReview {

autogpt_platform/frontend/src/app/(no-navbar)/onboarding/5-run/components/AgentOnboardingCredentials/AgentOnboardingCredentials.tsx

@@ -1,6 +1,6 @@
-import { CredentialsInput } from "@/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/CredentialsInputs/CredentialsInput";
 import { CredentialsMetaInput } from "@/app/api/__generated__/models/credentialsMetaInput";
 import { GraphMeta } from "@/app/api/__generated__/models/graphMeta";
+import { CredentialsInput } from "@/components/contextual/CredentialsInput/CredentialsInput";
 import { useState } from "react";
 import { getSchemaDefaultCredentials } from "../../helpers";
 import { areAllCredentialsSet, getCredentialFields } from "./helpers";

autogpt_platform/frontend/src/app/(no-navbar)/onboarding/5-run/page.tsx

@@ -1,12 +1,12 @@
 "use client";
 
-import { RunAgentInputs } from "@/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/RunAgentInputs/RunAgentInputs";
 import {
   Card,
   CardContent,
   CardHeader,
   CardTitle,
 } from "@/components/__legacy__/ui/card";
+import { RunAgentInputs } from "@/components/contextual/RunAgentInputs/RunAgentInputs";
 import { ErrorCard } from "@/components/molecules/ErrorCard/ErrorCard";
 import { CircleNotchIcon } from "@phosphor-icons/react/dist/ssr";
 import { Play } from "lucide-react";

autogpt_platform/frontend/src/app/(platform)/auth/integrations/setup-wizard/page.tsx

@@ -1,11 +1,11 @@
 "use client";
 
-import { CredentialsInput } from "@/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/CredentialsInputs/CredentialsInput";
 import { useGetOauthGetOauthAppInfo } from "@/app/api/__generated__/endpoints/oauth/oauth";
 import { okData } from "@/app/api/helpers";
 import { Button } from "@/components/atoms/Button/Button";
 import { Text } from "@/components/atoms/Text/Text";
 import { AuthCard } from "@/components/auth/AuthCard";
+import { CredentialsInput } from "@/components/contextual/CredentialsInput/CredentialsInput";
 import { ErrorCard } from "@/components/molecules/ErrorCard/ErrorCard";
 import type {
   BlockIOCredentialsSubSchema,

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

@@ -1,11 +1,6 @@
 import { BlockUIType } from "@/app/(platform)/build/components/types";
 import { useGraphStore } from "@/app/(platform)/build/stores/graphStore";
 import { useNodeStore } from "@/app/(platform)/build/stores/nodeStore";
-import {
-  globalRegistry,
-  OutputActions,
-  OutputItem,
-} from "@/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/selected-views/OutputRenderers";
 import { Label } from "@/components/__legacy__/ui/label";
 import { ScrollArea } from "@/components/__legacy__/ui/scroll-area";
 import {
@@ -23,6 +18,11 @@ import {
   TooltipProvider,
   TooltipTrigger,
 } from "@/components/atoms/Tooltip/BaseTooltip";
+import {
+  globalRegistry,
+  OutputActions,
+  OutputItem,
+} from "@/components/contextual/OutputRenderers";
 import { BookOpenIcon } from "@phosphor-icons/react";
 import { useMemo } from "react";
 import { useShallow } from "zustand/react/shallow";

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

@@ -1,7 +1,8 @@
 import { useGraphStore } from "@/app/(platform)/build/stores/graphStore";
 import { usePostV1ExecuteGraphAgent } from "@/app/api/__generated__/endpoints/graphs/graphs";
-import { useToast } from "@/components/molecules/Toast/use-toast";
+
 import {
+  ApiError,
   CredentialsMetaInput,
   GraphExecutionMeta,
 } from "@/lib/autogpt-server-api";
@@ -9,6 +10,9 @@ import { parseAsInteger, parseAsString, useQueryStates } from "nuqs";
 import { useMemo, useState } from "react";
 import { uiSchema } from "../../../FlowEditor/nodes/uiSchema";
 import { isCredentialFieldSchema } from "@/components/renderers/InputRenderer/custom/CredentialField/helpers";
+import { useNodeStore } from "@/app/(platform)/build/stores/nodeStore";
+import { useToast } from "@/components/molecules/Toast/use-toast";
+import { useReactFlow } from "@xyflow/react";
 
 export const useRunInputDialog = ({
   setIsOpen,
@@ -31,6 +35,7 @@ export const useRunInputDialog = ({
     flowVersion: parseAsInteger,
   });
   const { toast } = useToast();
+  const { setViewport } = useReactFlow();
 
   const { mutateAsync: executeGraph, isPending: isExecutingGraph } =
     usePostV1ExecuteGraphAgent({
@@ -42,13 +47,75 @@ export const useRunInputDialog = ({
           });
         },
         onError: (error) => {
-          // Reset running state on error
+          if (error instanceof ApiError && error.isGraphValidationError()) {
+            const errorData = error.response?.detail || {
+              node_errors: {},
+              message: undefined,
+            };
+            const nodeErrors = errorData.node_errors || {};
+
+            if (Object.keys(nodeErrors).length > 0) {
+              Object.entries(nodeErrors).forEach(
+                ([nodeId, nodeErrorsForNode]) => {
+                  useNodeStore
+                    .getState()
+                    .updateNodeErrors(
+                      nodeId,
+                      nodeErrorsForNode as { [key: string]: string },
+                    );
+                },
+              );
+            } else {
+              useNodeStore.getState().nodes.forEach((node) => {
+                useNodeStore.getState().updateNodeErrors(node.id, {});
+              });
+            }
+
+            toast({
+              title: errorData?.message || "Graph validation failed",
+              description:
+                "Please fix the validation errors on the highlighted nodes and try again.",
+              variant: "destructive",
+            });
+            setIsOpen(false);
+
+            const firstBackendId = Object.keys(nodeErrors)[0];
+
+            if (firstBackendId) {
+              const firstErrorNode = useNodeStore
+                .getState()
+                .nodes.find(
+                  (n) =>
+                    n.data.metadata?.backend_id === firstBackendId ||
+                    n.id === firstBackendId,
+                );
+
+              if (firstErrorNode) {
+                setTimeout(() => {
+                  setViewport(
+                    {
+                      x:
+                        -firstErrorNode.position.x * 0.8 +
+                        window.innerWidth / 2 -
+                        150,
+                      y: -firstErrorNode.position.y * 0.8 + 50,
+                      zoom: 0.8,
+                    },
+                    { duration: 500 },
+                  );
+                }, 50);
+              }
+            }
+          } else {
+            toast({
+              title: "Error running graph",
+              description:
+                (error as Error).message || "An unexpected error occurred.",
+              variant: "destructive",
+            });
+            setIsOpen(false);
+          }
           setIsGraphRunning(false);
-          toast({
-            title: (error.detail as string) ?? "An unexpected error occurred.",
-            description: "An unexpected error occurred.",
-            variant: "destructive",
-          });
         },
       },
     });

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

@@ -55,14 +55,16 @@ export const Flow = () => {
   const edgeTypes = useMemo(() => ({ custom: CustomEdge }), []);
 
   const onNodeDragStop = useCallback(() => {
+    const currentNodes = useNodeStore.getState().nodes;
     setNodes(
-      resolveCollisions(nodes, {
+      resolveCollisions(currentNodes, {
         maxIterations: Infinity,
         overlapThreshold: 0.5,
         margin: 15,
       }),
     );
-  }, [setNodes, nodes]);
+  }, [setNodes]);
+
   const { edges, onConnect, onEdgesChange } = useCustomEdge();
 
   // for loading purpose

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

@@ -6,6 +6,7 @@ import {
 import { useEdgeStore } from "@/app/(platform)/build/stores/edgeStore";
 import { useCallback } from "react";
 import { useNodeStore } from "../../../stores/nodeStore";
+import { useHistoryStore } from "../../../stores/historyStore";
 import { CustomEdge } from "./CustomEdge";
 
 export const useCustomEdge = () => {
@@ -51,7 +52,20 @@ export const useCustomEdge = () => {
 
   const onEdgesChange = useCallback(
     (changes: EdgeChange<CustomEdge>[]) => {
+      const hasRemoval = changes.some((change) => change.type === "remove");
+
+      const prevState = hasRemoval
+        ? {
+            nodes: useNodeStore.getState().nodes,
+            edges: edges,
+          }
+        : null;
+
       setEdges(applyEdgeChanges(changes, edges));
+
+      if (prevState) {
+        useHistoryStore.getState().pushState(prevState);
+      }
     },
     [edges, setEdges],
   );

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

@@ -20,11 +20,13 @@ type Props = {
 
 export const NodeHeader = ({ data, nodeId }: Props) => {
   const updateNodeData = useNodeStore((state) => state.updateNodeData);
-  const title = (data.metadata?.customized_name as string) || data.title;
+  const title =
+    (data.metadata?.customized_name as string) ||
+    data.hardcodedValues?.agent_name ||
+    data.title;
+
   const [isEditingTitle, setIsEditingTitle] = useState(false);
-  const [editedTitle, setEditedTitle] = useState(
-    beautifyString(title).replace("Block", "").trim(),
-  );
+  const [editedTitle, setEditedTitle] = useState(title);
 
   const handleTitleEdit = () => {
     updateNodeData(nodeId, {

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

@@ -1,7 +1,7 @@
 "use client";
 
-import type { OutputMetadata } from "@/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/selected-views/OutputRenderers";
-import { globalRegistry } from "@/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/selected-views/OutputRenderers";
+import type { OutputMetadata } from "@/components/contextual/OutputRenderers";
+import { globalRegistry } from "@/components/contextual/OutputRenderers";
 
 export const TextRenderer: React.FC<{
   value: any;

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

@@ -1,7 +1,3 @@
-import {
-  OutputActions,
-  OutputItem,
-} from "@/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/selected-views/OutputRenderers";
 import { ScrollArea } from "@/components/__legacy__/ui/scroll-area";
 import { Button } from "@/components/atoms/Button/Button";
 import { Text } from "@/components/atoms/Text/Text";
@@ -11,6 +7,10 @@ import {
   TooltipProvider,
   TooltipTrigger,
 } from "@/components/atoms/Tooltip/BaseTooltip";
+import {
+  OutputActions,
+  OutputItem,
+} from "@/components/contextual/OutputRenderers";
 import { Dialog } from "@/components/molecules/Dialog/Dialog";
 import { beautifyString } from "@/lib/utils";
 import {

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

@@ -1,6 +1,6 @@
-import type { OutputMetadata } from "@/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/selected-views/OutputRenderers";
-import { globalRegistry } from "@/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/selected-views/OutputRenderers";
-import { downloadOutputs } from "@/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/selected-views/OutputRenderers/utils/download";
+import type { OutputMetadata } from "@/components/contextual/OutputRenderers";
+import { globalRegistry } from "@/components/contextual/OutputRenderers";
+import { downloadOutputs } from "@/components/contextual/OutputRenderers/utils/download";
 import { useToast } from "@/components/molecules/Toast/use-toast";
 import { beautifyString } from "@/lib/utils";
 import React, { useMemo, useState } from "react";

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

@@ -1,10 +1,10 @@
-import { Alert, AlertDescription } from "@/components/molecules/Alert/Alert";
-import { Text } from "@/components/atoms/Text/Text";
-import Link from "next/link";
 import { useGetV2GetLibraryAgentByGraphId } from "@/app/api/__generated__/endpoints/library/library";
 import { LibraryAgent } from "@/app/api/__generated__/models/libraryAgent";
-import { useQueryStates, parseAsString } from "nuqs";
-import { isValidUUID } from "@/app/(platform)/chat/helpers";
+import { Text } from "@/components/atoms/Text/Text";
+import { Alert, AlertDescription } from "@/components/molecules/Alert/Alert";
+import { isValidUUID } from "@/lib/utils";
+import Link from "next/link";
+import { parseAsString, useQueryStates } from "nuqs";
 
 export const WebhookDisclaimer = ({ nodeId }: { nodeId: string }) => {
   const [{ flowID }] = useQueryStates({

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

@@ -31,8 +31,6 @@ export const OutputHandler = ({
   const [isOutputVisible, setIsOutputVisible] = useState(true);
   const brokenOutputs = useBrokenOutputs(nodeId);
 
-  console.log("brokenOutputs", brokenOutputs);
-
   const showHandles = uiType !== BlockUIType.OUTPUT;
 
   const renderOutputHandles = (

autogpt_platform/frontend/src/app/(platform)/build/components/legacy-builder/ExpandableOutputDialog.tsx

@@ -1,9 +1,9 @@
-import type { OutputMetadata } from "@/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/selected-views/OutputRenderers";
+import type { OutputMetadata } from "@/components/contextual/OutputRenderers";
 import {
   globalRegistry,
   OutputActions,
   OutputItem,
-} from "@/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/selected-views/OutputRenderers";
+} from "@/components/contextual/OutputRenderers";
 import { Dialog } from "@/components/molecules/Dialog/Dialog";
 import { beautifyString } from "@/lib/utils";
 import { Flag, useGetFlag } from "@/services/feature-flags/use-get-flag";

autogpt_platform/frontend/src/app/(platform)/build/components/legacy-builder/NodeInputs.tsx

@@ -3,7 +3,6 @@ import {
   CustomNodeData,
 } from "@/app/(platform)/build/components/legacy-builder/CustomNode/CustomNode";
 import { NodeTableInput } from "@/app/(platform)/build/components/legacy-builder/NodeTableInput";
-import { CredentialsInput } from "@/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/CredentialsInputs/CredentialsInput";
 import { Button } from "@/components/__legacy__/ui/button";
 import { Calendar } from "@/components/__legacy__/ui/calendar";
 import { LocalValuedInput } from "@/components/__legacy__/ui/input";
@@ -28,6 +27,7 @@ import {
   SelectValue,
 } from "@/components/__legacy__/ui/select";
 import { Switch } from "@/components/atoms/Switch/Switch";
+import { CredentialsInput } from "@/components/contextual/CredentialsInput/CredentialsInput";
 import { GoogleDrivePickerInput } from "@/components/contextual/GoogleDrivePicker/GoogleDrivePickerInput";
 import {
   BlockIOArraySubSchema,

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

@@ -5,6 +5,8 @@ import { customEdgeToLink, linkToCustomEdge } from "../components/helper";
 import { MarkerType } from "@xyflow/react";
 import { NodeExecutionResult } from "@/app/api/__generated__/models/nodeExecutionResult";
 import { cleanUpHandleId } from "@/components/renderers/InputRenderer/helpers";
+import { useHistoryStore } from "./historyStore";
+import { useNodeStore } from "./nodeStore";
 
 type EdgeStore = {
   edges: CustomEdge[];
@@ -53,25 +55,36 @@ export const useEdgeStore = create<EdgeStore>((set, get) => ({
       id,
     };
 
-    set((state) => {
-      const exists = state.edges.some(
-        (e) =>
-          e.source === newEdge.source &&
-          e.target === newEdge.target &&
-          e.sourceHandle === newEdge.sourceHandle &&
-          e.targetHandle === newEdge.targetHandle,
-      );
-      if (exists) return state;
-      return { edges: [...state.edges, newEdge] };
-    });
+    const exists = get().edges.some(
+      (e) =>
+        e.source === newEdge.source &&
+        e.target === newEdge.target &&
+        e.sourceHandle === newEdge.sourceHandle &&
+        e.targetHandle === newEdge.targetHandle,
+    );
+    if (exists) return newEdge;
+    const prevState = {
+      nodes: useNodeStore.getState().nodes,
+      edges: get().edges,
+    };
+
+    set((state) => ({ edges: [...state.edges, newEdge] }));
+    useHistoryStore.getState().pushState(prevState);
 
     return newEdge;
   },
 
-  removeEdge: (edgeId) =>
+  removeEdge: (edgeId) => {
+    const prevState = {
+      nodes: useNodeStore.getState().nodes,
+      edges: get().edges,
+    };
+
     set((state) => ({
       edges: state.edges.filter((e) => e.id !== edgeId),
-    })),
+    }));
+    useHistoryStore.getState().pushState(prevState);
+  },
 
   upsertMany: (edges) =>
     set((state) => {

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

@@ -37,6 +37,15 @@ export const useHistoryStore = create<HistoryStore>((set, get) => ({
       return;
     }
 
+    const actualCurrentState = {
+      nodes: useNodeStore.getState().nodes,
+      edges: useEdgeStore.getState().edges,
+    };
+
+    if (isEqual(state, actualCurrentState)) {
+      return;
+    }
+
     set((prev) => ({
       past: [...prev.past.slice(-MAX_HISTORY + 1), state],
       future: [],
@@ -55,18 +64,25 @@ export const useHistoryStore = create<HistoryStore>((set, get) => ({
 
   undo: () => {
     const { past, future } = get();
-    if (past.length <= 1) return;
+    if (past.length === 0) return;
 
-    const currentState = past[past.length - 1];
+    const actualCurrentState = {
+      nodes: useNodeStore.getState().nodes,
+      edges: useEdgeStore.getState().edges,
+    };
 
-    const previousState = past[past.length - 2];
+    const previousState = past[past.length - 1];
+
+    if (isEqual(actualCurrentState, previousState)) {
+      return;
+    }
 
     useNodeStore.getState().setNodes(previousState.nodes);
     useEdgeStore.getState().setEdges(previousState.edges);
 
     set({
-      past: past.slice(0, -1),
-      future: [currentState, ...future],
+      past: past.length > 1 ? past.slice(0, -1) : past,
+      future: [actualCurrentState, ...future],
     });
   },
 
@@ -74,18 +90,36 @@ export const useHistoryStore = create<HistoryStore>((set, get) => ({
     const { past, future } = get();
     if (future.length === 0) return;
 
+    const actualCurrentState = {
+      nodes: useNodeStore.getState().nodes,
+      edges: useEdgeStore.getState().edges,
+    };
+
     const nextState = future[0];
 
     useNodeStore.getState().setNodes(nextState.nodes);
     useEdgeStore.getState().setEdges(nextState.edges);
 
+    const lastPast = past[past.length - 1];
+    const shouldPushToPast =
+      !lastPast || !isEqual(actualCurrentState, lastPast);
+
     set({
-      past: [...past, nextState],
+      past: shouldPushToPast ? [...past, actualCurrentState] : past,
       future: future.slice(1),
     });
   },
 
-  canUndo: () => get().past.length > 1,
+  canUndo: () => {
+    const { past } = get();
+    if (past.length === 0) return false;
+
+    const actualCurrentState = {
+      nodes: useNodeStore.getState().nodes,
+      edges: useEdgeStore.getState().edges,
+    };
+    return !isEqual(actualCurrentState, past[past.length - 1]);
+  },
   canRedo: () => get().future.length > 0,
 
   clear: () => set({ past: [{ nodes: [], edges: [] }], future: [] }),

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

@@ -1,6 +1,7 @@
 import { create } from "zustand";
 import { NodeChange, XYPosition, applyNodeChanges } from "@xyflow/react";
 import { CustomNode } from "../components/FlowEditor/nodes/CustomNode/CustomNode";
+import { CustomEdge } from "../components/FlowEditor/edges/CustomEdge";
 import { BlockInfo } from "@/app/api/__generated__/models/blockInfo";
 import {
   convertBlockInfoIntoCustomNodeData,
@@ -44,6 +45,8 @@ const MINIMUM_MOVE_BEFORE_LOG = 50;
 // Track initial positions when drag starts (outside store to avoid re-renders)
 const dragStartPositions: Record<string, XYPosition> = {};
 
+let dragStartState: { nodes: CustomNode[]; edges: CustomEdge[] } | null = null;
+
 type NodeStore = {
   nodes: CustomNode[];
   nodeCounter: number;
@@ -124,14 +127,20 @@ export const useNodeStore = create<NodeStore>((set, get) => ({
       nodeCounter: state.nodeCounter + 1,
     })),
   onNodesChange: (changes) => {
-    const prevState = {
-      nodes: get().nodes,
-      edges: useEdgeStore.getState().edges,
-    };
-
-    // Track initial positions when drag starts
     changes.forEach((change) => {
       if (change.type === "position" && change.dragging === true) {
+        if (!dragStartState) {
+          const currentNodes = get().nodes;
+          const currentEdges = useEdgeStore.getState().edges;
+          dragStartState = {
+            nodes: currentNodes.map((n) => ({
+              ...n,
+              position: { ...n.position },
+              data: { ...n.data },
+            })),
+            edges: currentEdges.map((e) => ({ ...e })),
+          };
+        }
         if (!dragStartPositions[change.id]) {
           const node = get().nodes.find((n) => n.id === change.id);
           if (node) {
@@ -141,12 +150,17 @@ export const useNodeStore = create<NodeStore>((set, get) => ({
       }
     });
 
-    // Check if we should track this change in history
-    let shouldTrack = changes.some(
-      (change) => change.type === "remove" || change.type === "add",
-    );
+    let shouldTrack = changes.some((change) => change.type === "remove");
+    let stateToTrack: { nodes: CustomNode[]; edges: CustomEdge[] } | null =
+      null;
+
+    if (shouldTrack) {
+      stateToTrack = {
+        nodes: get().nodes,
+        edges: useEdgeStore.getState().edges,
+      };
+    }
 
-    // For position changes, only track if movement exceeds threshold
     if (!shouldTrack) {
       changes.forEach((change) => {
         if (change.type === "position" && change.dragging === false) {
@@ -158,20 +172,23 @@ export const useNodeStore = create<NodeStore>((set, get) => ({
             );
             if (distanceMoved > MINIMUM_MOVE_BEFORE_LOG) {
               shouldTrack = true;
+              stateToTrack = dragStartState;
             }
           }
-          // Clean up tracked position after drag ends
           delete dragStartPositions[change.id];
         }
       });
+      if (Object.keys(dragStartPositions).length === 0) {
+        dragStartState = null;
+      }
     }
 
     set((state) => ({
       nodes: applyNodeChanges(changes, state.nodes),
     }));
 
-    if (shouldTrack) {
-      useHistoryStore.getState().pushState(prevState);
+    if (shouldTrack && stateToTrack) {
+      useHistoryStore.getState().pushState(stateToTrack);
     }
   },
 
@@ -185,6 +202,11 @@ export const useNodeStore = create<NodeStore>((set, get) => ({
     hardcodedValues?: Record<string, any>,
     position?: XYPosition,
   ) => {
+    const prevState = {
+      nodes: get().nodes,
+      edges: useEdgeStore.getState().edges,
+    };
+
     const customNodeData = convertBlockInfoIntoCustomNodeData(
       block,
       hardcodedValues,
@@ -218,21 +240,24 @@ export const useNodeStore = create<NodeStore>((set, get) => ({
     set((state) => ({
       nodes: [...state.nodes, customNode],
     }));
+
+    useHistoryStore.getState().pushState(prevState);
+
     return customNode;
   },
   updateNodeData: (nodeId, data) => {
+    const prevState = {
+      nodes: get().nodes,
+      edges: useEdgeStore.getState().edges,
+    };
+
     set((state) => ({
       nodes: state.nodes.map((n) =>
         n.id === nodeId ? { ...n, data: { ...n.data, ...data } } : n,
       ),
     }));
 
-    const newState = {
-      nodes: get().nodes,
-      edges: useEdgeStore.getState().edges,
-    };
-
-    useHistoryStore.getState().pushState(newState);
+    useHistoryStore.getState().pushState(prevState);
   },
   toggleAdvanced: (nodeId: string) =>
     set((state) => ({
@@ -391,6 +416,11 @@ export const useNodeStore = create<NodeStore>((set, get) => ({
   },
 
   setCredentialsOptional: (nodeId: string, optional: boolean) => {
+    const prevState = {
+      nodes: get().nodes,
+      edges: useEdgeStore.getState().edges,
+    };
+
     set((state) => ({
       nodes: state.nodes.map((n) =>
         n.id === nodeId
@@ -408,12 +438,7 @@ export const useNodeStore = create<NodeStore>((set, get) => ({
       ),
     }));
 
-    const newState = {
-      nodes: get().nodes,
-      edges: useEdgeStore.getState().edges,
-    };
-
-    useHistoryStore.getState().pushState(newState);
+    useHistoryStore.getState().pushState(prevState);
   },
 
   // Sub-agent resolution mode state

autogpt_platform/frontend/src/app/(platform)/chat/components/Chat/Chat.tsx

@@ -0,0 +1,134 @@
+"use client";
+
+import { Button } from "@/components/atoms/Button/Button";
+import { Text } from "@/components/atoms/Text/Text";
+import { cn } from "@/lib/utils";
+import { List } from "@phosphor-icons/react";
+import React, { useState } from "react";
+import { ChatContainer } from "./components/ChatContainer/ChatContainer";
+import { ChatErrorState } from "./components/ChatErrorState/ChatErrorState";
+import { ChatLoadingState } from "./components/ChatLoadingState/ChatLoadingState";
+import { SessionsDrawer } from "./components/SessionsDrawer/SessionsDrawer";
+import { useChat } from "./useChat";
+
+export interface ChatProps {
+  className?: string;
+  headerTitle?: React.ReactNode;
+  showHeader?: boolean;
+  showSessionInfo?: boolean;
+  showNewChatButton?: boolean;
+  onNewChat?: () => void;
+  headerActions?: React.ReactNode;
+}
+
+export function Chat({
+  className,
+  headerTitle = "AutoGPT Copilot",
+  showHeader = true,
+  showSessionInfo = true,
+  showNewChatButton = true,
+  onNewChat,
+  headerActions,
+}: ChatProps) {
+  const {
+    messages,
+    isLoading,
+    isCreating,
+    error,
+    sessionId,
+    createSession,
+    clearSession,
+    loadSession,
+  } = useChat();
+
+  const [isSessionsDrawerOpen, setIsSessionsDrawerOpen] = useState(false);
+
+  const handleNewChat = () => {
+    clearSession();
+    onNewChat?.();
+  };
+
+  const handleSelectSession = async (sessionId: string) => {
+    try {
+      await loadSession(sessionId);
+    } catch (err) {
+      console.error("Failed to load session:", err);
+    }
+  };
+
+  return (
+    <div className={cn("flex h-full flex-col", className)}>
+      {/* Header */}
+      {showHeader && (
+        <header className="shrink-0 border-t border-zinc-200 bg-white p-3">
+          <div className="flex items-center justify-between">
+            <div className="flex items-center gap-3">
+              <button
+                aria-label="View sessions"
+                onClick={() => setIsSessionsDrawerOpen(true)}
+                className="flex size-8 items-center justify-center rounded hover:bg-zinc-100"
+              >
+                <List width="1.25rem" height="1.25rem" />
+              </button>
+              {typeof headerTitle === "string" ? (
+                <Text variant="h2" className="text-lg font-semibold">
+                  {headerTitle}
+                </Text>
+              ) : (
+                headerTitle
+              )}
+            </div>
+            <div className="flex items-center gap-3">
+              {showSessionInfo && sessionId && (
+                <>
+                  {showNewChatButton && (
+                    <Button
+                      variant="outline"
+                      size="small"
+                      onClick={handleNewChat}
+                    >
+                      New Chat
+                    </Button>
+                  )}
+                </>
+              )}
+              {headerActions}
+            </div>
+          </div>
+        </header>
+      )}
+
+      {/* Main Content */}
+      <main className="flex min-h-0 flex-1 flex-col overflow-hidden">
+        {/* Loading State - show when explicitly loading/creating OR when we don't have a session yet and no error */}
+        {(isLoading || isCreating || (!sessionId && !error)) && (
+          <ChatLoadingState
+            message={isCreating ? "Creating session..." : "Loading..."}
+          />
+        )}
+
+        {/* Error State */}
+        {error && !isLoading && (
+          <ChatErrorState error={error} onRetry={createSession} />
+        )}
+
+        {/* Session Content */}
+        {sessionId && !isLoading && !error && (
+          <ChatContainer
+            sessionId={sessionId}
+            initialMessages={messages}
+            className="flex-1"
+          />
+        )}
+      </main>
+
+      {/* Sessions Drawer */}
+      <SessionsDrawer
+        isOpen={isSessionsDrawerOpen}
+        onClose={() => setIsSessionsDrawerOpen(false)}
+        onSelectSession={handleSelectSession}
+        currentSessionId={sessionId}
+      />
+    </div>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/chat/components/Chat/components/AgentCarouselMessage/AgentCarouselMessage.tsx

@@ -1,15 +1,16 @@
-import React from "react";
-import { Text } from "@/components/atoms/Text/Text";
 import { Button } from "@/components/atoms/Button/Button";
 import { Card } from "@/components/atoms/Card/Card";
-import { List, Robot, ArrowRight } from "@phosphor-icons/react";
+import { Text } from "@/components/atoms/Text/Text";
 import { cn } from "@/lib/utils";
+import { ArrowRight, List, Robot } from "@phosphor-icons/react";
+import Image from "next/image";
 
 export interface Agent {
   id: string;
   name: string;
   description: string;
   version?: number;
+  image_url?: string;
 }
 
 export interface AgentCarouselMessageProps {
@@ -30,7 +31,7 @@ export function AgentCarouselMessage({
   return (
     <div
       className={cn(
-        "mx-4 my-2 flex flex-col gap-4 rounded-lg border border-purple-200 bg-purple-50 p-6 dark:border-purple-900 dark:bg-purple-950",
+        "mx-4 my-2 flex flex-col gap-4 rounded-lg border border-purple-200 bg-purple-50 p-6",
         className,
       )}
     >
@@ -40,13 +41,10 @@ export function AgentCarouselMessage({
           <List size={24} weight="bold" className="text-white" />
         </div>
         <div>
-          <Text variant="h3" className="text-purple-900 dark:text-purple-100">
+          <Text variant="h3" className="text-purple-900">
             Found {displayCount} {displayCount === 1 ? "Agent" : "Agents"}
           </Text>
-          <Text
-            variant="small"
-            className="text-purple-700 dark:text-purple-300"
-          >
+          <Text variant="small" className="text-purple-700">
             Select an agent to view details or run it
           </Text>
         </div>
@@ -57,40 +55,49 @@ export function AgentCarouselMessage({
         {agents.map((agent) => (
           <Card
             key={agent.id}
-            className="border border-purple-200 bg-white p-4 dark:border-purple-800 dark:bg-purple-900"
+            className="border border-purple-200 bg-white p-4"
           >
             <div className="flex gap-3">
-              <div className="flex h-10 w-10 flex-shrink-0 items-center justify-center rounded-lg bg-purple-100 dark:bg-purple-800">
-                <Robot size={20} weight="bold" className="text-purple-600" />
+              <div className="relative h-10 w-10 flex-shrink-0 overflow-hidden rounded-lg bg-purple-100">
+                {agent.image_url ? (
+                  <Image
+                    src={agent.image_url}
+                    alt={`${agent.name} preview image`}
+                    fill
+                    className="object-cover"
+                  />
+                ) : (
+                  <div className="flex h-full w-full items-center justify-center">
+                    <Robot
+                      size={20}
+                      weight="bold"
+                      className="text-purple-600"
+                    />
+                  </div>
+                )}
               </div>
               <div className="flex-1 space-y-2">
                 <div>
                   <Text
                     variant="body"
-                    className="font-semibold text-purple-900 dark:text-purple-100"
+                    className="font-semibold text-purple-900"
                   >
                     {agent.name}
                   </Text>
                   {agent.version && (
-                    <Text
-                      variant="small"
-                      className="text-purple-600 dark:text-purple-400"
-                    >
+                    <Text variant="small" className="text-purple-600">
                       v{agent.version}
                     </Text>
                   )}
                 </div>
-                <Text
-                  variant="small"
-                  className="line-clamp-2 text-purple-700 dark:text-purple-300"
-                >
+                <Text variant="small" className="line-clamp-2 text-purple-700">
                   {agent.description}
                 </Text>
                 {onSelectAgent && (
                   <Button
                     onClick={() => onSelectAgent(agent.id)}
                     variant="ghost"
-                    className="mt-2 flex items-center gap-1 p-0 text-sm text-purple-600 hover:text-purple-800 dark:text-purple-400 dark:hover:text-purple-200"
+                    className="mt-2 flex items-center gap-1 p-0 text-sm text-purple-600 hover:text-purple-800"
                   >
                     View details
                     <ArrowRight size={16} weight="bold" />
@@ -103,10 +110,7 @@ export function AgentCarouselMessage({
       </div>
 
       {totalCount && totalCount > agents.length && (
-        <Text
-          variant="small"
-          className="text-center text-purple-600 dark:text-purple-400"
-        >
+        <Text variant="small" className="text-center text-purple-600">
           Showing {agents.length} of {totalCount} results
         </Text>
       )}

autogpt_platform/frontend/src/app/(platform)/chat/components/Chat/components/AgentInputsSetup/AgentInputsSetup.tsx

@@ -0,0 +1,246 @@
+"use client";
+
+import { Button } from "@/components/atoms/Button/Button";
+import { Card } from "@/components/atoms/Card/Card";
+import { Text } from "@/components/atoms/Text/Text";
+import { CredentialsInput } from "@/components/contextual/CredentialsInput/CredentialsInput";
+import { RunAgentInputs } from "@/components/contextual/RunAgentInputs/RunAgentInputs";
+
+import type { LibraryAgent } from "@/app/api/__generated__/models/libraryAgent";
+import {
+  BlockIOCredentialsSubSchema,
+  BlockIOSubSchema,
+} from "@/lib/autogpt-server-api/types";
+import { cn, isEmpty } from "@/lib/utils";
+import { PlayIcon, WarningIcon } from "@phosphor-icons/react";
+import { useMemo } from "react";
+import { useAgentInputsSetup } from "./useAgentInputsSetup";
+
+type LibraryAgentInputSchemaProperties = LibraryAgent["input_schema"] extends {
+  properties: infer P;
+}
+  ? P extends Record<string, BlockIOSubSchema>
+    ? P
+    : Record<string, BlockIOSubSchema>
+  : Record<string, BlockIOSubSchema>;
+
+type LibraryAgentCredentialsInputSchemaProperties =
+  LibraryAgent["credentials_input_schema"] extends {
+    properties: infer P;
+  }
+    ? P extends Record<string, BlockIOCredentialsSubSchema>
+      ? P
+      : Record<string, BlockIOCredentialsSubSchema>
+    : Record<string, BlockIOCredentialsSubSchema>;
+
+interface Props {
+  agentName?: string;
+  inputSchema: LibraryAgentInputSchemaProperties | Record<string, any>;
+  credentialsSchema?:
+    | LibraryAgentCredentialsInputSchemaProperties
+    | Record<string, any>;
+  message: string;
+  requiredFields?: string[];
+  onRun: (
+    inputs: Record<string, any>,
+    credentials: Record<string, any>,
+  ) => void;
+  onCancel?: () => void;
+  className?: string;
+}
+
+export function AgentInputsSetup({
+  agentName,
+  inputSchema,
+  credentialsSchema,
+  message,
+  requiredFields,
+  onRun,
+  onCancel,
+  className,
+}: Props) {
+  const { inputValues, setInputValue, credentialsValues, setCredentialsValue } =
+    useAgentInputsSetup();
+
+  const inputSchemaObj = useMemo(() => {
+    if (!inputSchema) return { properties: {}, required: [] };
+    if ("properties" in inputSchema && "type" in inputSchema) {
+      return inputSchema as {
+        properties: Record<string, any>;
+        required?: string[];
+      };
+    }
+    return { properties: inputSchema as Record<string, any>, required: [] };
+  }, [inputSchema]);
+
+  const credentialsSchemaObj = useMemo(() => {
+    if (!credentialsSchema) return { properties: {}, required: [] };
+    if ("properties" in credentialsSchema && "type" in credentialsSchema) {
+      return credentialsSchema as {
+        properties: Record<string, any>;
+        required?: string[];
+      };
+    }
+    return {
+      properties: credentialsSchema as Record<string, any>,
+      required: [],
+    };
+  }, [credentialsSchema]);
+
+  const agentInputFields = useMemo(() => {
+    const properties = inputSchemaObj.properties || {};
+    return Object.fromEntries(
+      Object.entries(properties).filter(
+        ([_, subSchema]: [string, any]) => !subSchema.hidden,
+      ),
+    );
+  }, [inputSchemaObj]);
+
+  const agentCredentialsInputFields = useMemo(() => {
+    return credentialsSchemaObj.properties || {};
+  }, [credentialsSchemaObj]);
+
+  const inputFields = Object.entries(agentInputFields);
+  const credentialFields = Object.entries(agentCredentialsInputFields);
+
+  const defaultsFromSchema = useMemo(() => {
+    const defaults: Record<string, any> = {};
+    Object.entries(agentInputFields).forEach(([key, schema]) => {
+      if ("default" in schema && schema.default !== undefined) {
+        defaults[key] = schema.default;
+      }
+    });
+    return defaults;
+  }, [agentInputFields]);
+
+  const defaultsFromCredentialsSchema = useMemo(() => {
+    const defaults: Record<string, any> = {};
+    Object.entries(agentCredentialsInputFields).forEach(([key, schema]) => {
+      if ("default" in schema && schema.default !== undefined) {
+        defaults[key] = schema.default;
+      }
+    });
+    return defaults;
+  }, [agentCredentialsInputFields]);
+
+  const mergedInputValues = useMemo(() => {
+    return { ...defaultsFromSchema, ...inputValues };
+  }, [defaultsFromSchema, inputValues]);
+
+  const mergedCredentialsValues = useMemo(() => {
+    return { ...defaultsFromCredentialsSchema, ...credentialsValues };
+  }, [defaultsFromCredentialsSchema, credentialsValues]);
+
+  const allRequiredInputsAreSet = useMemo(() => {
+    const requiredInputs = new Set(
+      requiredFields || (inputSchemaObj.required as string[]) || [],
+    );
+    const nonEmptyInputs = new Set(
+      Object.keys(mergedInputValues).filter(
+        (k) => !isEmpty(mergedInputValues[k]),
+      ),
+    );
+    const missing = [...requiredInputs].filter(
+      (input) => !nonEmptyInputs.has(input),
+    );
+    return missing.length === 0;
+  }, [inputSchemaObj.required, mergedInputValues, requiredFields]);
+
+  const allCredentialsAreSet = useMemo(() => {
+    const requiredCredentials = new Set(
+      (credentialsSchemaObj.required as string[]) || [],
+    );
+    if (requiredCredentials.size === 0) {
+      return true;
+    }
+    const missing = [...requiredCredentials].filter((key) => {
+      const cred = mergedCredentialsValues[key];
+      return !cred || !cred.id;
+    });
+    return missing.length === 0;
+  }, [credentialsSchemaObj.required, mergedCredentialsValues]);
+
+  const canRun = allRequiredInputsAreSet && allCredentialsAreSet;
+
+  function handleRun() {
+    if (canRun) {
+      onRun(mergedInputValues, mergedCredentialsValues);
+    }
+  }
+
+  return (
+    <Card
+      className={cn(
+        "mx-4 my-2 overflow-hidden border-blue-200 bg-blue-50",
+        className,
+      )}
+    >
+      <div className="flex items-start gap-4 p-6">
+        <div className="flex h-12 w-12 flex-shrink-0 items-center justify-center rounded-full bg-blue-500">
+          <WarningIcon size={24} weight="bold" className="text-white" />
+        </div>
+        <div className="flex-1">
+          <Text variant="h3" className="mb-2 text-blue-900">
+            {agentName ? `Configure ${agentName}` : "Agent Configuration"}
+          </Text>
+          <Text variant="body" className="mb-4 text-blue-700">
+            {message}
+          </Text>
+
+          {inputFields.length > 0 && (
+            <div className="mb-4 space-y-4">
+              {inputFields.map(([key, inputSubSchema]) => (
+                <RunAgentInputs
+                  key={key}
+                  schema={inputSubSchema}
+                  value={inputValues[key] ?? inputSubSchema.default}
+                  placeholder={inputSubSchema.description}
+                  onChange={(value) => setInputValue(key, value)}
+                />
+              ))}
+            </div>
+          )}
+
+          {credentialFields.length > 0 && (
+            <div className="mb-4 space-y-4">
+              {credentialFields.map(([key, schema]) => {
+                const requiredCredentials = new Set(
+                  (credentialsSchemaObj.required as string[]) || [],
+                );
+                return (
+                  <CredentialsInput
+                    key={key}
+                    schema={schema}
+                    selectedCredentials={credentialsValues[key]}
+                    onSelectCredentials={(value) =>
+                      setCredentialsValue(key, value)
+                    }
+                    siblingInputs={mergedInputValues}
+                    isOptional={!requiredCredentials.has(key)}
+                  />
+                );
+              })}
+            </div>
+          )}
+
+          <div className="flex gap-2">
+            <Button
+              variant="primary"
+              size="small"
+              onClick={handleRun}
+              disabled={!canRun}
+            >
+              <PlayIcon className="mr-2 h-4 w-4" weight="bold" />
+              Run Agent
+            </Button>
+            {onCancel && (
+              <Button variant="outline" size="small" onClick={onCancel}>
+                Cancel
+              </Button>
+            )}
+          </div>
+        </div>
+      </div>
+    </Card>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/chat/components/Chat/components/AgentInputsSetup/useAgentInputsSetup.ts

@@ -0,0 +1,38 @@
+import type { CredentialsMetaInput } from "@/lib/autogpt-server-api/types";
+import { useState } from "react";
+
+export function useAgentInputsSetup() {
+  const [inputValues, setInputValues] = useState<Record<string, any>>({});
+  const [credentialsValues, setCredentialsValues] = useState<
+    Record<string, CredentialsMetaInput>
+  >({});
+
+  function setInputValue(key: string, value: any) {
+    setInputValues((prev) => ({
+      ...prev,
+      [key]: value,
+    }));
+  }
+
+  function setCredentialsValue(key: string, value?: CredentialsMetaInput) {
+    if (value) {
+      setCredentialsValues((prev) => ({
+        ...prev,
+        [key]: value,
+      }));
+    } else {
+      setCredentialsValues((prev) => {
+        const next = { ...prev };
+        delete next[key];
+        return next;
+      });
+    }
+  }
+
+  return {
+    inputValues,
+    setInputValue,
+    credentialsValues,
+    setCredentialsValue,
+  };
+}

autogpt_platform/frontend/src/app/(platform)/chat/components/Chat/components/AuthPromptWidget/AuthPromptWidget.tsx

@@ -1,10 +1,9 @@
 "use client";
 
-import React from "react";
-import { useRouter } from "next/navigation";
 import { Button } from "@/components/atoms/Button/Button";
-import { SignInIcon, UserPlusIcon, ShieldIcon } from "@phosphor-icons/react";
 import { cn } from "@/lib/utils";
+import { ShieldIcon, SignInIcon, UserPlusIcon } from "@phosphor-icons/react";
+import { useRouter } from "next/navigation";
 
 export interface AuthPromptWidgetProps {
   message: string;
@@ -54,8 +53,8 @@ export function AuthPromptWidget({
   return (
     <div
       className={cn(
-        "my-4 overflow-hidden rounded-lg border border-violet-200 dark:border-violet-800",
-        "bg-gradient-to-br from-violet-50 to-purple-50 dark:from-violet-950/30 dark:to-purple-950/30",
+        "my-4 overflow-hidden rounded-lg border border-violet-200",
+        "bg-gradient-to-br from-violet-50 to-purple-50",
         "duration-500 animate-in fade-in-50 slide-in-from-bottom-2",
         className,
       )}
@@ -66,21 +65,19 @@ export function AuthPromptWidget({
             <ShieldIcon size={20} weight="fill" className="text-white" />
           </div>
           <div>
-            <h3 className="text-lg font-semibold text-neutral-900 dark:text-neutral-100">
+            <h3 className="text-lg font-semibold text-neutral-900">
               Authentication Required
             </h3>
-            <p className="text-sm text-neutral-600 dark:text-neutral-400">
+            <p className="text-sm text-neutral-600">
               Sign in to set up and manage agents
             </p>
           </div>
         </div>
 
-        <div className="mb-5 rounded-md bg-white/50 p-4 dark:bg-neutral-900/50">
-          <p className="text-sm text-neutral-700 dark:text-neutral-300">
-            {message}
-          </p>
+        <div className="mb-5 rounded-md bg-white/50 p-4">
+          <p className="text-sm text-neutral-700">{message}</p>
           {agentInfo && (
-            <div className="mt-3 text-xs text-neutral-600 dark:text-neutral-400">
+            <div className="mt-3 text-xs text-neutral-600">
               <p>
                 Ready to set up:{" "}
                 <span className="font-medium">{agentInfo.name}</span>
@@ -114,7 +111,7 @@ export function AuthPromptWidget({
           </Button>
         </div>
 
-        <div className="mt-4 text-center text-xs text-neutral-500 dark:text-neutral-500">
+        <div className="mt-4 text-center text-xs text-neutral-500">
           Your chat session will be preserved after signing in
         </div>
       </div>

autogpt_platform/frontend/src/app/(platform)/chat/components/Chat/components/ChatContainer/ChatContainer.tsx

@@ -0,0 +1,88 @@
+import type { SessionDetailResponse } from "@/app/api/__generated__/models/sessionDetailResponse";
+import { cn } from "@/lib/utils";
+import { useCallback } from "react";
+import { usePageContext } from "../../usePageContext";
+import { ChatInput } from "../ChatInput/ChatInput";
+import { MessageList } from "../MessageList/MessageList";
+import { QuickActionsWelcome } from "../QuickActionsWelcome/QuickActionsWelcome";
+import { useChatContainer } from "./useChatContainer";
+
+export interface ChatContainerProps {
+  sessionId: string | null;
+  initialMessages: SessionDetailResponse["messages"];
+  className?: string;
+}
+
+export function ChatContainer({
+  sessionId,
+  initialMessages,
+  className,
+}: ChatContainerProps) {
+  const { messages, streamingChunks, isStreaming, sendMessage } =
+    useChatContainer({
+      sessionId,
+      initialMessages,
+    });
+  const { capturePageContext } = usePageContext();
+
+  // Wrap sendMessage to automatically capture page context
+  const sendMessageWithContext = useCallback(
+    async (content: string, isUserMessage: boolean = true) => {
+      const context = capturePageContext();
+      await sendMessage(content, isUserMessage, context);
+    },
+    [sendMessage, capturePageContext],
+  );
+
+  const quickActions = [
+    "Find agents for social media management",
+    "Show me agents for content creation",
+    "Help me automate my business",
+    "What can you help me with?",
+  ];
+
+  return (
+    <div
+      className={cn("flex h-full min-h-0 flex-col", className)}
+      style={{
+        backgroundColor: "#ffffff",
+        backgroundImage:
+          "radial-gradient(#e5e5e5 0.5px, transparent 0.5px), radial-gradient(#e5e5e5 0.5px, #ffffff 0.5px)",
+        backgroundSize: "20px 20px",
+        backgroundPosition: "0 0, 10px 10px",
+      }}
+    >
+      {/* Messages or Welcome Screen */}
+      <div className="flex min-h-0 flex-1 flex-col overflow-hidden pb-24">
+        {messages.length === 0 ? (
+          <QuickActionsWelcome
+            title="Welcome to AutoGPT Copilot"
+            description="Start a conversation to discover and run AI agents."
+            actions={quickActions}
+            onActionClick={sendMessageWithContext}
+            disabled={isStreaming || !sessionId}
+          />
+        ) : (
+          <MessageList
+            messages={messages}
+            streamingChunks={streamingChunks}
+            isStreaming={isStreaming}
+            onSendMessage={sendMessageWithContext}
+            className="flex-1"
+          />
+        )}
+      </div>
+
+      {/* Input - Always visible */}
+      <div className="fixed bottom-0 left-0 right-0 z-50 border-t border-zinc-200 bg-white p-4">
+        <ChatInput
+          onSend={sendMessageWithContext}
+          disabled={isStreaming || !sessionId}
+          placeholder={
+            sessionId ? "Type your message..." : "Creating session..."
+          }
+        />
+      </div>
+    </div>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/chat/components/Chat/components/ChatContainer/createStreamEventDispatcher.ts

@@ -1,14 +1,14 @@
 import { toast } from "sonner";
-import type { StreamChunk } from "@/app/(platform)/chat/useChatStream";
+import { StreamChunk } from "../../useChatStream";
 import type { HandlerDependencies } from "./useChatContainer.handlers";
 import {
+  handleError,
+  handleLoginNeeded,
+  handleStreamEnd,
   handleTextChunk,
   handleTextEnded,
   handleToolCallStart,
   handleToolResponse,
-  handleLoginNeeded,
-  handleStreamEnd,
-  handleError,
 } from "./useChatContainer.handlers";
 
 export function createStreamEventDispatcher(

autogpt_platform/frontend/src/app/(platform)/chat/components/Chat/components/ChatContainer/helpers.ts

@@ -1,5 +1,24 @@
-import type { ChatMessageData } from "@/app/(platform)/chat/components/ChatMessage/useChatMessage";
 import type { ToolResult } from "@/types/chat";
+import type { ChatMessageData } from "../ChatMessage/useChatMessage";
+
+export function removePageContext(content: string): string {
+  // Remove "Page URL: ..." pattern at start of line (case insensitive, handles various formats)
+  let cleaned = content.replace(/^\s*Page URL:\s*[^\n\r]*/gim, "");
+
+  // Find "User Message:" marker at start of line to preserve the actual user message
+  const userMessageMatch = cleaned.match(/^\s*User Message:\s*([\s\S]*)$/im);
+  if (userMessageMatch) {
+    // If we found "User Message:", extract everything after it
+    cleaned = userMessageMatch[1];
+  } else {
+    // If no "User Message:" marker, remove "Page Content:" and everything after it at start of line
+    cleaned = cleaned.replace(/^\s*Page Content:[\s\S]*$/gim, "");
+  }
+
+  // Clean up extra whitespace and newlines
+  cleaned = cleaned.replace(/\n\s*\n\s*\n+/g, "\n\n").trim();
+  return cleaned;
+}
 
 export function createUserMessage(content: string): ChatMessageData {
   return {
@@ -63,6 +82,7 @@ export function isAgentArray(value: unknown): value is Array<{
   name: string;
   description: string;
   version?: number;
+  image_url?: string;
 }> {
   if (!Array.isArray(value)) {
     return false;
@@ -77,7 +97,8 @@ export function isAgentArray(value: unknown): value is Array<{
       typeof item.name === "string" &&
       "description" in item &&
       typeof item.description === "string" &&
-      (!("version" in item) || typeof item.version === "number"),
+      (!("version" in item) || typeof item.version === "number") &&
+      (!("image_url" in item) || typeof item.image_url === "string"),
   );
 }
 
@@ -232,6 +253,7 @@ export function isSetupInfo(value: unknown): value is {
 
 export function extractCredentialsNeeded(
   parsedResult: Record<string, unknown>,
+  toolName: string = "run_agent",
 ): ChatMessageData | null {
   try {
     const setupInfo = parsedResult?.setup_info as
@@ -244,7 +266,7 @@ export function extractCredentialsNeeded(
       | Record<string, Record<string, unknown>>
       | undefined;
     if (missingCreds && Object.keys(missingCreds).length > 0) {
-      const agentName = (setupInfo?.agent_name as string) || "this agent";
+      const agentName = (setupInfo?.agent_name as string) || "this block";
       const credentials = Object.values(missingCreds).map((credInfo) => ({
         provider: (credInfo.provider as string) || "unknown",
         providerName:
@@ -264,7 +286,7 @@ export function extractCredentialsNeeded(
       }));
       return {
         type: "credentials_needed",
-        toolName: "run_agent",
+        toolName,
         credentials,
         message: `To run ${agentName}, you need to add ${credentials.length === 1 ? "credentials" : `${credentials.length} credentials`}.`,
         agentName,
@@ -277,3 +299,92 @@ export function extractCredentialsNeeded(
     return null;
   }
 }
+
+export function extractInputsNeeded(
+  parsedResult: Record<string, unknown>,
+  toolName: string = "run_agent",
+): ChatMessageData | null {
+  try {
+    const setupInfo = parsedResult?.setup_info as
+      | Record<string, unknown>
+      | undefined;
+    const requirements = setupInfo?.requirements as
+      | Record<string, unknown>
+      | undefined;
+    const inputs = requirements?.inputs as
+      | Array<Record<string, unknown>>
+      | undefined;
+    const credentials = requirements?.credentials as
+      | Array<Record<string, unknown>>
+      | undefined;
+
+    if (!inputs || inputs.length === 0) {
+      return null;
+    }
+
+    const agentName = (setupInfo?.agent_name as string) || "this agent";
+    const agentId = parsedResult?.graph_id as string | undefined;
+    const graphVersion = parsedResult?.graph_version as number | undefined;
+
+    const properties: Record<string, any> = {};
+    const requiredProps: string[] = [];
+    inputs.forEach((input) => {
+      const name = input.name as string;
+      if (name) {
+        properties[name] = {
+          title: input.name as string,
+          description: (input.description as string) || "",
+          type: (input.type as string) || "string",
+          default: input.default,
+          enum: input.options,
+          format: input.format,
+        };
+        if ((input.required as boolean) === true) {
+          requiredProps.push(name);
+        }
+      }
+    });
+
+    const inputSchema: Record<string, any> = {
+      type: "object",
+      properties,
+    };
+    if (requiredProps.length > 0) {
+      inputSchema.required = requiredProps;
+    }
+
+    const credentialsSchema: Record<string, any> = {};
+    if (credentials && credentials.length > 0) {
+      credentials.forEach((cred) => {
+        const id = cred.id as string;
+        if (id) {
+          credentialsSchema[id] = {
+            type: "object",
+            properties: {},
+            credentials_provider: [cred.provider as string],
+            credentials_types: [(cred.type as string) || "api_key"],
+            credentials_scopes: cred.scopes as string[] | undefined,
+          };
+        }
+      });
+    }
+
+    return {
+      type: "inputs_needed",
+      toolName,
+      agentName,
+      agentId,
+      graphVersion,
+      inputSchema,
+      credentialsSchema:
+        Object.keys(credentialsSchema).length > 0
+          ? credentialsSchema
+          : undefined,
+      message: `Please provide the required inputs to run ${agentName}.`,
+      timestamp: new Date(),
+    };
+  } catch (err) {
+    console.error("Failed to extract inputs from setup info:", err);
+    return null;
+  }
+}

autogpt_platform/frontend/src/app/(platform)/chat/components/Chat/components/ChatContainer/useChatContainer.handlers.ts

@@ -1,13 +1,18 @@
-import type { Dispatch, SetStateAction, MutableRefObject } from "react";
-import type { StreamChunk } from "@/app/(platform)/chat/useChatStream";
-import type { ChatMessageData } from "@/app/(platform)/chat/components/ChatMessage/useChatMessage";
-import { parseToolResponse, extractCredentialsNeeded } from "./helpers";
+import type { Dispatch, MutableRefObject, SetStateAction } from "react";
+import { StreamChunk } from "../../useChatStream";
+import type { ChatMessageData } from "../ChatMessage/useChatMessage";
+import {
+  extractCredentialsNeeded,
+  extractInputsNeeded,
+  parseToolResponse,
+} from "./helpers";
 
 export interface HandlerDependencies {
   setHasTextChunks: Dispatch<SetStateAction<boolean>>;
   setStreamingChunks: Dispatch<SetStateAction<string[]>>;
   streamingChunksRef: MutableRefObject<string[]>;
   setMessages: Dispatch<SetStateAction<ChatMessageData[]>>;
+  setIsStreamingInitiated: Dispatch<SetStateAction<boolean>>;
   sessionId: string;
 }
 
@@ -100,11 +105,18 @@ export function handleToolResponse(
       parsedResult = null;
     }
     if (
-      chunk.tool_name === "run_agent" &&
+      (chunk.tool_name === "run_agent" || chunk.tool_name === "run_block") &&
       chunk.success &&
       parsedResult?.type === "setup_requirements"
     ) {
-      const credentialsMessage = extractCredentialsNeeded(parsedResult);
+      const inputsMessage = extractInputsNeeded(parsedResult, chunk.tool_name);
+      if (inputsMessage) {
+        deps.setMessages((prev) => [...prev, inputsMessage]);
+      }
+      const credentialsMessage = extractCredentialsNeeded(
+        parsedResult,
+        chunk.tool_name,
+      );
       if (credentialsMessage) {
         deps.setMessages((prev) => [...prev, credentialsMessage]);
       }
@@ -197,10 +209,15 @@ export function handleStreamEnd(
   deps.setStreamingChunks([]);
   deps.streamingChunksRef.current = [];
   deps.setHasTextChunks(false);
+  deps.setIsStreamingInitiated(false);
   console.log("[Stream End] Stream complete, messages in local state");
 }
 
-export function handleError(chunk: StreamChunk, _deps: HandlerDependencies) {
+export function handleError(chunk: StreamChunk, deps: HandlerDependencies) {
   const errorMessage = chunk.message || chunk.content || "An error occurred";
   console.error("Stream error:", errorMessage);
+  deps.setIsStreamingInitiated(false);
+  deps.setHasTextChunks(false);
+  deps.setStreamingChunks([]);
+  deps.streamingChunksRef.current = [];
 }

autogpt_platform/frontend/src/app/(platform)/chat/components/Chat/components/ChatContainer/useChatContainer.ts

@@ -0,0 +1,206 @@
+import type { SessionDetailResponse } from "@/app/api/__generated__/models/sessionDetailResponse";
+import { useCallback, useMemo, useRef, useState } from "react";
+import { toast } from "sonner";
+import { useChatStream } from "../../useChatStream";
+import type { ChatMessageData } from "../ChatMessage/useChatMessage";
+import { createStreamEventDispatcher } from "./createStreamEventDispatcher";
+import {
+  createUserMessage,
+  filterAuthMessages,
+  isToolCallArray,
+  isValidMessage,
+  parseToolResponse,
+  removePageContext,
+} from "./helpers";
+
+interface Args {
+  sessionId: string | null;
+  initialMessages: SessionDetailResponse["messages"];
+}
+
+export function useChatContainer({ sessionId, initialMessages }: Args) {
+  const [messages, setMessages] = useState<ChatMessageData[]>([]);
+  const [streamingChunks, setStreamingChunks] = useState<string[]>([]);
+  const [hasTextChunks, setHasTextChunks] = useState(false);
+  const [isStreamingInitiated, setIsStreamingInitiated] = useState(false);
+  const streamingChunksRef = useRef<string[]>([]);
+  const { error, sendMessage: sendStreamMessage } = useChatStream();
+  const isStreaming = isStreamingInitiated || hasTextChunks;
+
+  const allMessages = useMemo(() => {
+    const processedInitialMessages: ChatMessageData[] = [];
+    // Map to track tool calls by their ID so we can look up tool names for tool responses
+    const toolCallMap = new Map<string, string>();
+
+    for (const msg of initialMessages) {
+      if (!isValidMessage(msg)) {
+        console.warn("Invalid message structure from backend:", msg);
+        continue;
+      }
+
+      let content = String(msg.content || "");
+      const role = String(msg.role || "assistant").toLowerCase();
+      const toolCalls = msg.tool_calls;
+      const timestamp = msg.timestamp
+        ? new Date(msg.timestamp as string)
+        : undefined;
+
+      // Remove page context from user messages when loading existing sessions
+      if (role === "user") {
+        content = removePageContext(content);
+        // Skip user messages that become empty after removing page context
+        if (!content.trim()) {
+          continue;
+        }
+        processedInitialMessages.push({
+          type: "message",
+          role: "user",
+          content,
+          timestamp,
+        });
+        continue;
+      }
+
+      // Handle assistant messages first (before tool messages) to build tool call map
+      if (role === "assistant") {
+        // Strip <thinking> tags from content
+        content = content
+          .replace(/<thinking>[\s\S]*?<\/thinking>/gi, "")
+          .trim();
+
+        // If assistant has tool calls, create tool_call messages for each
+        if (toolCalls && isToolCallArray(toolCalls) && toolCalls.length > 0) {
+          for (const toolCall of toolCalls) {
+            const toolName = toolCall.function.name;
+            const toolId = toolCall.id;
+            // Store tool name for later lookup
+            toolCallMap.set(toolId, toolName);
+
+            try {
+              const args = JSON.parse(toolCall.function.arguments || "{}");
+              processedInitialMessages.push({
+                type: "tool_call",
+                toolId,
+                toolName,
+                arguments: args,
+                timestamp,
+              });
+            } catch (err) {
+              console.warn("Failed to parse tool call arguments:", err);
+              processedInitialMessages.push({
+                type: "tool_call",
+                toolId,
+                toolName,
+                arguments: {},
+                timestamp,
+              });
+            }
+          }
+          // Only add assistant message if there's content after stripping thinking tags
+          if (content.trim()) {
+            processedInitialMessages.push({
+              type: "message",
+              role: "assistant",
+              content,
+              timestamp,
+            });
+          }
+        } else if (content.trim()) {
+          // Assistant message without tool calls, but with content
+          processedInitialMessages.push({
+            type: "message",
+            role: "assistant",
+            content,
+            timestamp,
+          });
+        }
+        continue;
+      }
+
+      // Handle tool messages - look up tool name from tool call map
+      if (role === "tool") {
+        const toolCallId = (msg.tool_call_id as string) || "";
+        const toolName = toolCallMap.get(toolCallId) || "unknown";
+        const toolResponse = parseToolResponse(
+          content,
+          toolCallId,
+          toolName,
+          timestamp,
+        );
+        if (toolResponse) {
+          processedInitialMessages.push(toolResponse);
+        }
+        continue;
+      }
+
+      // Handle other message types (system, etc.)
+      if (content.trim()) {
+        processedInitialMessages.push({
+          type: "message",
+          role: role as "user" | "assistant" | "system",
+          content,
+          timestamp,
+        });
+      }
+    }
+
+    return [...processedInitialMessages, ...messages];
+  }, [initialMessages, messages]);
+
+  const sendMessage = useCallback(
+    async function sendMessage(
+      content: string,
+      isUserMessage: boolean = true,
+      context?: { url: string; content: string },
+    ) {
+      if (!sessionId) {
+        console.error("Cannot send message: no session ID");
+        return;
+      }
+      if (isUserMessage) {
+        const userMessage = createUserMessage(content);
+        setMessages((prev) => [...filterAuthMessages(prev), userMessage]);
+      } else {
+        setMessages((prev) => filterAuthMessages(prev));
+      }
+      setStreamingChunks([]);
+      streamingChunksRef.current = [];
+      setHasTextChunks(false);
+      setIsStreamingInitiated(true);
+      const dispatcher = createStreamEventDispatcher({
+        setHasTextChunks,
+        setStreamingChunks,
+        streamingChunksRef,
+        setMessages,
+        sessionId,
+        setIsStreamingInitiated,
+      });
+      try {
+        await sendStreamMessage(
+          sessionId,
+          content,
+          dispatcher,
+          isUserMessage,
+          context,
+        );
+      } catch (err) {
+        console.error("Failed to send message:", err);
+        setIsStreamingInitiated(false);
+        const errorMessage =
+          err instanceof Error ? err.message : "Failed to send message";
+        toast.error("Failed to send message", {
+          description: errorMessage,
+        });
+      }
+    },
+    [sessionId, sendStreamMessage],
+  );
+
+  return {
+    messages: allMessages,
+    streamingChunks,
+    isStreaming,
+    error,
+    sendMessage,
+  };
+}

autogpt_platform/frontend/src/app/(platform)/chat/components/Chat/components/ChatCredentialsSetup/ChatCredentialsSetup.tsx

@@ -0,0 +1,149 @@
+import { Text } from "@/components/atoms/Text/Text";
+import { CredentialsInput } from "@/components/contextual/CredentialsInput/CredentialsInput";
+import type { BlockIOCredentialsSubSchema } from "@/lib/autogpt-server-api";
+import { cn } from "@/lib/utils";
+import { CheckIcon, RobotIcon, WarningIcon } from "@phosphor-icons/react";
+import { useEffect, useRef } from "react";
+import { useChatCredentialsSetup } from "./useChatCredentialsSetup";
+
+export interface CredentialInfo {
+  provider: string;
+  providerName: string;
+  credentialType: "api_key" | "oauth2" | "user_password" | "host_scoped";
+  title: string;
+  scopes?: string[];
+}
+
+interface Props {
+  credentials: CredentialInfo[];
+  agentName?: string;
+  message: string;
+  onAllCredentialsComplete: () => void;
+  onCancel: () => void;
+  className?: string;
+}
+
+function createSchemaFromCredentialInfo(
+  credential: CredentialInfo,
+): BlockIOCredentialsSubSchema {
+  return {
+    type: "object",
+    properties: {},
+    credentials_provider: [credential.provider],
+    credentials_types: [credential.credentialType],
+    credentials_scopes: credential.scopes,
+    discriminator: undefined,
+    discriminator_mapping: undefined,
+    discriminator_values: undefined,
+  };
+}
+
+export function ChatCredentialsSetup({
+  credentials,
+  agentName: _agentName,
+  message,
+  onAllCredentialsComplete,
+  onCancel: _onCancel,
+}: Props) {
+  const { selectedCredentials, isAllComplete, handleCredentialSelect } =
+    useChatCredentialsSetup(credentials);
+
+  // Track if we've already called completion to prevent double calls
+  const hasCalledCompleteRef = useRef(false);
+
+  // Reset the completion flag when credentials change (new credential setup flow)
+  useEffect(
+    function resetCompletionFlag() {
+      hasCalledCompleteRef.current = false;
+    },
+    [credentials],
+  );
+
+  // Auto-call completion when all credentials are configured
+  useEffect(
+    function autoCompleteWhenReady() {
+      if (isAllComplete && !hasCalledCompleteRef.current) {
+        hasCalledCompleteRef.current = true;
+        onAllCredentialsComplete();
+      }
+    },
+    [isAllComplete, onAllCredentialsComplete],
+  );
+
+  return (
+    <div className="group relative flex w-full justify-start gap-3 px-4 py-3">
+      <div className="flex w-full max-w-3xl gap-3">
+        <div className="flex-shrink-0">
+          <div className="flex h-7 w-7 items-center justify-center rounded-lg bg-indigo-500">
+            <RobotIcon className="h-4 w-4 text-indigo-50" />
+          </div>
+        </div>
+
+        <div className="flex min-w-0 flex-1 flex-col">
+          <div className="group relative min-w-20 overflow-hidden rounded-xl border border-slate-100 bg-slate-50/20 px-6 py-2.5 text-sm leading-relaxed backdrop-blur-xl">
+            <div className="absolute inset-0 bg-gradient-to-br from-slate-200/20 via-slate-300/10 to-transparent" />
+            <div className="relative z-10 space-y-3 text-slate-900">
+              <div>
+                <Text variant="h4" className="mb-1 text-slate-900">
+                  Credentials Required
+                </Text>
+                <Text variant="small" className="text-slate-600">
+                  {message}
+                </Text>
+              </div>
+
+              <div className="space-y-3">
+                {credentials.map((cred, index) => {
+                  const schema = createSchemaFromCredentialInfo(cred);
+                  const isSelected = !!selectedCredentials[cred.provider];
+
+                  return (
+                    <div
+                      key={`${cred.provider}-${index}`}
+                      className={cn(
+                        "relative rounded-lg border p-3",
+                        isSelected
+                          ? "border-green-500 bg-green-50/50"
+                          : "border-slate-200 bg-white/50",
+                      )}
+                    >
+                      <div className="mb-2 flex items-center gap-2">
+                        {isSelected ? (
+                          <CheckIcon
+                            size={16}
+                            className="text-green-500"
+                            weight="bold"
+                          />
+                        ) : (
+                          <WarningIcon
+                            size={16}
+                            className="text-slate-500"
+                            weight="bold"
+                          />
+                        )}
+                        <Text
+                          variant="small"
+                          className="font-semibold text-slate-900"
+                        >
+                          {cred.providerName}
+                        </Text>
+                      </div>
+
+                      <CredentialsInput
+                        schema={schema}
+                        selectedCredentials={selectedCredentials[cred.provider]}
+                        onSelectCredentials={(credMeta) =>
+                          handleCredentialSelect(cred.provider, credMeta)
+                        }
+                      />
+                    </div>
+                  );
+                })}
+              </div>
+            </div>
+          </div>
+        </div>
+      </div>
+    </div>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/chat/components/Chat/components/ChatInput/ChatInput.tsx

@@ -0,0 +1,64 @@
+import { Input } from "@/components/atoms/Input/Input";
+import { cn } from "@/lib/utils";
+import { ArrowUpIcon } from "@phosphor-icons/react";
+import { useChatInput } from "./useChatInput";
+
+export interface ChatInputProps {
+  onSend: (message: string) => void;
+  disabled?: boolean;
+  placeholder?: string;
+  className?: string;
+}
+
+export function ChatInput({
+  onSend,
+  disabled = false,
+  placeholder = "Type your message...",
+  className,
+}: ChatInputProps) {
+  const inputId = "chat-input";
+  const { value, setValue, handleKeyDown, handleSend } = useChatInput({
+    onSend,
+    disabled,
+    maxRows: 5,
+    inputId,
+  });
+
+  return (
+    <div className={cn("relative flex-1", className)}>
+      <Input
+        id={inputId}
+        label="Chat message input"
+        hideLabel
+        type="textarea"
+        value={value}
+        onChange={(e) => setValue(e.target.value)}
+        onKeyDown={handleKeyDown}
+        placeholder={placeholder}
+        disabled={disabled}
+        rows={1}
+        wrapperClassName="mb-0 relative"
+        className="pr-12"
+      />
+      <span id="chat-input-hint" className="sr-only">
+        Press Enter to send, Shift+Enter for new line
+      </span>
+
+      <button
+        onClick={handleSend}
+        disabled={disabled || !value.trim()}
+        className={cn(
+          "absolute right-3 top-1/2 flex h-8 w-8 -translate-y-1/2 items-center justify-center rounded-full",
+          "border border-zinc-800 bg-zinc-800 text-white",
+          "hover:border-zinc-900 hover:bg-zinc-900",
+          "disabled:border-zinc-200 disabled:bg-zinc-200 disabled:text-white disabled:opacity-50",
+          "transition-colors focus-visible:outline-none focus-visible:ring-1 focus-visible:ring-neutral-950",
+          "disabled:pointer-events-none",
+        )}
+        aria-label="Send message"
+      >
+        <ArrowUpIcon className="h-3 w-3" weight="bold" />
+      </button>
+    </div>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/chat/components/Chat/components/ChatInput/useChatInput.ts

@@ -1,21 +1,22 @@
-import { KeyboardEvent, useCallback, useState, useRef, useEffect } from "react";
+import { KeyboardEvent, useCallback, useEffect, useState } from "react";
 
 interface UseChatInputArgs {
   onSend: (message: string) => void;
   disabled?: boolean;
   maxRows?: number;
+  inputId?: string;
 }
 
 export function useChatInput({
   onSend,
   disabled = false,
   maxRows = 5,
+  inputId = "chat-input",
 }: UseChatInputArgs) {
   const [value, setValue] = useState("");
-  const textareaRef = useRef<HTMLTextAreaElement>(null);
 
   useEffect(() => {
-    const textarea = textareaRef.current;
+    const textarea = document.getElementById(inputId) as HTMLTextAreaElement;
     if (!textarea) return;
     textarea.style.height = "auto";
     const lineHeight = parseInt(
@@ -27,23 +28,25 @@ export function useChatInput({
     textarea.style.height = `${newHeight}px`;
     textarea.style.overflowY =
       textarea.scrollHeight > maxHeight ? "auto" : "hidden";
-  }, [value, maxRows]);
+  }, [value, maxRows, inputId]);
 
   const handleSend = useCallback(() => {
     if (disabled || !value.trim()) return;
     onSend(value.trim());
     setValue("");
-    if (textareaRef.current) {
-      textareaRef.current.style.height = "auto";
+    const textarea = document.getElementById(inputId) as HTMLTextAreaElement;
+    if (textarea) {
+      textarea.style.height = "auto";
     }
-  }, [value, onSend, disabled]);
+  }, [value, onSend, disabled, inputId]);
 
   const handleKeyDown = useCallback(
-    (event: KeyboardEvent<HTMLTextAreaElement>) => {
+    (event: KeyboardEvent<HTMLInputElement | HTMLTextAreaElement>) => {
       if (event.key === "Enter" && !event.shiftKey) {
         event.preventDefault();
         handleSend();
       }
+      // Shift+Enter allows default behavior (new line) - no need to handle explicitly
     },
     [handleSend],
   );
@@ -53,6 +56,5 @@ export function useChatInput({
     setValue,
     handleKeyDown,
     handleSend,
-    textareaRef,
   };
 }

autogpt_platform/frontend/src/app/(platform)/chat/components/Chat/components/ChatLoadingState/ChatLoadingState.tsx

@@ -0,0 +1,19 @@
+import { LoadingSpinner } from "@/components/atoms/LoadingSpinner/LoadingSpinner";
+import { cn } from "@/lib/utils";
+
+export interface ChatLoadingStateProps {
+  message?: string;
+  className?: string;
+}
+
+export function ChatLoadingState({ className }: ChatLoadingStateProps) {
+  return (
+    <div
+      className={cn("flex flex-1 items-center justify-center p-6", className)}
+    >
+      <div className="flex flex-col items-center gap-4 text-center">
+        <LoadingSpinner />
+      </div>
+    </div>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/chat/components/Chat/components/ChatMessage/ChatMessage.tsx

@@ -0,0 +1,341 @@
+"use client";
+
+import { useGetV2GetUserProfile } from "@/app/api/__generated__/endpoints/store/store";
+import Avatar, {
+  AvatarFallback,
+  AvatarImage,
+} from "@/components/atoms/Avatar/Avatar";
+import { Button } from "@/components/atoms/Button/Button";
+import { useSupabase } from "@/lib/supabase/hooks/useSupabase";
+import { cn } from "@/lib/utils";
+import {
+  ArrowClockwise,
+  CheckCircleIcon,
+  CheckIcon,
+  CopyIcon,
+  RobotIcon,
+} from "@phosphor-icons/react";
+import { useRouter } from "next/navigation";
+import { useCallback, useState } from "react";
+import { getToolActionPhrase } from "../../helpers";
+import { AgentCarouselMessage } from "../AgentCarouselMessage/AgentCarouselMessage";
+import { AuthPromptWidget } from "../AuthPromptWidget/AuthPromptWidget";
+import { ChatCredentialsSetup } from "../ChatCredentialsSetup/ChatCredentialsSetup";
+import { ExecutionStartedMessage } from "../ExecutionStartedMessage/ExecutionStartedMessage";
+import { MarkdownContent } from "../MarkdownContent/MarkdownContent";
+import { MessageBubble } from "../MessageBubble/MessageBubble";
+import { NoResultsMessage } from "../NoResultsMessage/NoResultsMessage";
+import { ToolCallMessage } from "../ToolCallMessage/ToolCallMessage";
+import { ToolResponseMessage } from "../ToolResponseMessage/ToolResponseMessage";
+import { useChatMessage, type ChatMessageData } from "./useChatMessage";
+export interface ChatMessageProps {
+  message: ChatMessageData;
+  className?: string;
+  onDismissLogin?: () => void;
+  onDismissCredentials?: () => void;
+  onSendMessage?: (content: string, isUserMessage?: boolean) => void;
+  agentOutput?: ChatMessageData;
+}
+
+export function ChatMessage({
+  message,
+  className,
+  onDismissCredentials,
+  onSendMessage,
+  agentOutput,
+}: ChatMessageProps) {
+  const { user } = useSupabase();
+  const router = useRouter();
+  const [copied, setCopied] = useState(false);
+  const {
+    isUser,
+    isToolCall,
+    isToolResponse,
+    isLoginNeeded,
+    isCredentialsNeeded,
+  } = useChatMessage(message);
+
+  const { data: profile } = useGetV2GetUserProfile({
+    query: {
+      select: (res) => (res.status === 200 ? res.data : null),
+      enabled: isUser && !!user,
+      queryKey: ["/api/store/profile", user?.id],
+    },
+  });
+
+  const handleAllCredentialsComplete = useCallback(
+    function handleAllCredentialsComplete() {
+      // Send a user message that explicitly asks to retry the setup
+      // This ensures the LLM calls get_required_setup_info again and proceeds with execution
+      if (onSendMessage) {
+        onSendMessage(
+          "I've configured the required credentials. Please check if everything is ready and proceed with setting up the agent.",
+        );
+      }
+      // Optionally dismiss the credentials prompt
+      if (onDismissCredentials) {
+        onDismissCredentials();
+      }
+    },
+    [onSendMessage, onDismissCredentials],
+  );
+
+  function handleCancelCredentials() {
+    // Dismiss the credentials prompt
+    if (onDismissCredentials) {
+      onDismissCredentials();
+    }
+  }
+
+  const handleCopy = useCallback(async () => {
+    if (message.type !== "message") return;
+
+    try {
+      await navigator.clipboard.writeText(message.content);
+      setCopied(true);
+      setTimeout(() => setCopied(false), 2000);
+    } catch (error) {
+      console.error("Failed to copy:", error);
+    }
+  }, [message]);
+
+  const handleTryAgain = useCallback(() => {
+    if (message.type !== "message" || !onSendMessage) return;
+    onSendMessage(message.content, message.role === "user");
+  }, [message, onSendMessage]);
+
+  const handleViewExecution = useCallback(() => {
+    if (message.type === "execution_started" && message.libraryAgentLink) {
+      router.push(message.libraryAgentLink);
+    }
+  }, [message, router]);
+
+  // Render credentials needed messages
+  if (isCredentialsNeeded && message.type === "credentials_needed") {
+    return (
+      <ChatCredentialsSetup
+        credentials={message.credentials}
+        agentName={message.agentName}
+        message={message.message}
+        onAllCredentialsComplete={handleAllCredentialsComplete}
+        onCancel={handleCancelCredentials}
+        className={className}
+      />
+    );
+  }
+
+  // Render login needed messages
+  if (isLoginNeeded && message.type === "login_needed") {
+    // If user is already logged in, show success message instead of auth prompt
+    if (user) {
+      return (
+        <div className={cn("px-4 py-2", className)}>
+          <div className="my-4 overflow-hidden rounded-lg border border-green-200 bg-gradient-to-br from-green-50 to-emerald-50">
+            <div className="px-6 py-4">
+              <div className="flex items-center gap-3">
+                <div className="flex h-10 w-10 items-center justify-center rounded-full bg-green-600">
+                  <CheckCircleIcon
+                    size={20}
+                    weight="fill"
+                    className="text-white"
+                  />
+                </div>
+                <div>
+                  <h3 className="text-lg font-semibold text-neutral-900">
+                    Successfully Authenticated
+                  </h3>
+                  <p className="text-sm text-neutral-600">
+                    You&apos;re now signed in and ready to continue
+                  </p>
+                </div>
+              </div>
+            </div>
+          </div>
+        </div>
+      );
+    }
+
+    // Show auth prompt if not logged in
+    return (
+      <div className={cn("px-4 py-2", className)}>
+        <AuthPromptWidget
+          message={message.message}
+          sessionId={message.sessionId}
+          agentInfo={message.agentInfo}
+        />
+      </div>
+    );
+  }
+
+  // Render tool call messages
+  if (isToolCall && message.type === "tool_call") {
+    return (
+      <div className={cn("px-4 py-2", className)}>
+        <ToolCallMessage toolName={message.toolName} />
+      </div>
+    );
+  }
+
+  // Render no_results messages - use dedicated component, not ToolResponseMessage
+  if (message.type === "no_results") {
+    return (
+      <div className={cn("px-4 py-2", className)}>
+        <NoResultsMessage
+          message={message.message}
+          suggestions={message.suggestions}
+        />
+      </div>
+    );
+  }
+
+  // Render agent_carousel messages - use dedicated component, not ToolResponseMessage
+  if (message.type === "agent_carousel") {
+    return (
+      <div className={cn("px-4 py-2", className)}>
+        <AgentCarouselMessage
+          agents={message.agents}
+          totalCount={message.totalCount}
+        />
+      </div>
+    );
+  }
+
+  // Render execution_started messages - use dedicated component, not ToolResponseMessage
+  if (message.type === "execution_started") {
+    return (
+      <div className={cn("px-4 py-2", className)}>
+        <ExecutionStartedMessage
+          executionId={message.executionId}
+          agentName={message.agentName}
+          message={message.message}
+          onViewExecution={
+            message.libraryAgentLink ? handleViewExecution : undefined
+          }
+        />
+      </div>
+    );
+  }
+
+  // Render tool response messages (but skip agent_output if it's being rendered inside assistant message)
+  if (isToolResponse && message.type === "tool_response") {
+    // Check if this is an agent_output that should be rendered inside assistant message
+    if (message.result) {
+      let parsedResult: Record<string, unknown> | null = null;
+      try {
+        parsedResult =
+          typeof message.result === "string"
+            ? JSON.parse(message.result)
+            : (message.result as Record<string, unknown>);
+      } catch {
+        parsedResult = null;
+      }
+      if (parsedResult?.type === "agent_output") {
+        // Skip rendering - this will be rendered inside the assistant message
+        return null;
+      }
+    }
+
+    return (
+      <div className={cn("px-4 py-2", className)}>
+        <ToolResponseMessage
+          toolName={getToolActionPhrase(message.toolName)}
+          result={message.result}
+        />
+      </div>
+    );
+  }
+
+  // Render regular chat messages
+  if (message.type === "message") {
+    return (
+      <div
+        className={cn(
+          "group relative flex w-full gap-3 px-4 py-3",
+          isUser ? "justify-end" : "justify-start",
+          className,
+        )}
+      >
+        <div className="flex w-full max-w-3xl gap-3">
+          {!isUser && (
+            <div className="flex-shrink-0">
+              <div className="flex h-7 w-7 items-center justify-center rounded-lg bg-indigo-500">
+                <RobotIcon className="h-4 w-4 text-indigo-50" />
+              </div>
+            </div>
+          )}
+
+          <div
+            className={cn(
+              "flex min-w-0 flex-1 flex-col",
+              isUser && "items-end",
+            )}
+          >
+            <MessageBubble variant={isUser ? "user" : "assistant"}>
+              <MarkdownContent content={message.content} />
+              {agentOutput &&
+                agentOutput.type === "tool_response" &&
+                !isUser && (
+                  <div className="mt-4">
+                    <ToolResponseMessage
+                      toolName={
+                        agentOutput.toolName
+                          ? getToolActionPhrase(agentOutput.toolName)
+                          : "Agent Output"
+                      }
+                      result={agentOutput.result}
+                    />
+                  </div>
+                )}
+            </MessageBubble>
+            <div
+              className={cn(
+                "mt-1 flex gap-1",
+                isUser ? "justify-end" : "justify-start",
+              )}
+            >
+              {isUser && onSendMessage && (
+                <Button
+                  variant="ghost"
+                  size="icon"
+                  onClick={handleTryAgain}
+                  aria-label="Try again"
+                >
+                  <ArrowClockwise className="size-3 text-neutral-500" />
+                </Button>
+              )}
+              <Button
+                variant="ghost"
+                size="icon"
+                onClick={handleCopy}
+                aria-label="Copy message"
+              >
+                {copied ? (
+                  <CheckIcon className="size-3 text-green-600" />
+                ) : (
+                  <CopyIcon className="size-3 text-neutral-500" />
+                )}
+              </Button>
+            </div>
+          </div>
+
+          {isUser && (
+            <div className="flex-shrink-0">
+              <Avatar className="h-7 w-7">
+                <AvatarImage
+                  src={profile?.avatar_url ?? ""}
+                  alt={profile?.username ?? "User"}
+                />
+                <AvatarFallback className="rounded-lg bg-neutral-200 text-neutral-600">
+                  {profile?.username?.charAt(0)?.toUpperCase() || "U"}
+                </AvatarFallback>
+              </Avatar>
+            </div>
+          )}
+        </div>
+      </div>
+    );
+  }
+
+  // Fallback for unknown message types
+  return null;
+}

autogpt_platform/frontend/src/app/(platform)/chat/components/Chat/components/ChatMessage/useChatMessage.ts

@@ -1,5 +1,5 @@
-import { formatDistanceToNow } from "date-fns";
 import type { ToolArguments, ToolResult } from "@/types/chat";
+import { formatDistanceToNow } from "date-fns";
 
 export type ChatMessageData =
   | {
@@ -65,6 +65,7 @@ export type ChatMessageData =
         name: string;
         description: string;
         version?: number;
+        image_url?: string;
       }>;
       totalCount?: number;
       timestamp?: string | Date;
@@ -77,6 +78,17 @@ export type ChatMessageData =
       message?: string;
       libraryAgentLink?: string;
       timestamp?: string | Date;
+    }
+  | {
+      type: "inputs_needed";
+      toolName: string;
+      agentName?: string;
+      agentId?: string;
+      graphVersion?: number;
+      inputSchema: Record<string, any>;
+      credentialsSchema?: Record<string, any>;
+      message: string;
+      timestamp?: string | Date;
     };
 
 export function useChatMessage(message: ChatMessageData) {
@@ -96,5 +108,6 @@ export function useChatMessage(message: ChatMessageData) {
     isNoResults: message.type === "no_results",
     isAgentCarousel: message.type === "agent_carousel",
     isExecutionStarted: message.type === "execution_started",
+    isInputsNeeded: message.type === "inputs_needed",
   };
 }

autogpt_platform/frontend/src/app/(platform)/chat/components/Chat/components/ExecutionStartedMessage/ExecutionStartedMessage.tsx

@@ -1,8 +1,7 @@
-import React from "react";
-import { Text } from "@/components/atoms/Text/Text";
 import { Button } from "@/components/atoms/Button/Button";
-import { CheckCircle, Play, ArrowSquareOut } from "@phosphor-icons/react";
+import { Text } from "@/components/atoms/Text/Text";
 import { cn } from "@/lib/utils";
+import { ArrowSquareOut, CheckCircle, Play } from "@phosphor-icons/react";
 
 export interface ExecutionStartedMessageProps {
   executionId: string;
@@ -22,7 +21,7 @@ export function ExecutionStartedMessage({
   return (
     <div
       className={cn(
-        "mx-4 my-2 flex flex-col gap-4 rounded-lg border border-green-200 bg-green-50 p-6 dark:border-green-900 dark:bg-green-950",
+        "mx-4 my-2 flex flex-col gap-4 rounded-lg border border-green-200 bg-green-50 p-6",
         className,
       )}
     >
@@ -32,48 +31,33 @@ export function ExecutionStartedMessage({
           <CheckCircle size={24} weight="bold" className="text-white" />
         </div>
         <div className="flex-1">
-          <Text
-            variant="h3"
-            className="mb-1 text-green-900 dark:text-green-100"
-          >
+          <Text variant="h3" className="mb-1 text-green-900">
             Execution Started
           </Text>
-          <Text variant="body" className="text-green-700 dark:text-green-300">
+          <Text variant="body" className="text-green-700">
             {message}
           </Text>
         </div>
       </div>
 
       {/* Details */}
-      <div className="rounded-md bg-green-100 p-4 dark:bg-green-900">
+      <div className="rounded-md bg-green-100 p-4">
         <div className="space-y-2">
           {agentName && (
             <div className="flex items-center justify-between">
-              <Text
-                variant="small"
-                className="font-semibold text-green-900 dark:text-green-100"
-              >
+              <Text variant="small" className="font-semibold text-green-900">
                 Agent:
               </Text>
-              <Text
-                variant="body"
-                className="text-green-800 dark:text-green-200"
-              >
+              <Text variant="body" className="text-green-800">
                 {agentName}
               </Text>
             </div>
           )}
           <div className="flex items-center justify-between">
-            <Text
-              variant="small"
-              className="font-semibold text-green-900 dark:text-green-100"
-            >
+            <Text variant="small" className="font-semibold text-green-900">
               Execution ID:
             </Text>
-            <Text
-              variant="small"
-              className="font-mono text-green-800 dark:text-green-200"
-            >
+            <Text variant="small" className="font-mono text-green-800">
               {executionId.slice(0, 16)}...
             </Text>
           </div>
@@ -94,7 +78,7 @@ export function ExecutionStartedMessage({
         </div>
       )}
 
-      <div className="flex items-center gap-2 text-green-600 dark:text-green-400">
+      <div className="flex items-center gap-2 text-green-600">
         <Play size={16} weight="fill" />
         <Text variant="small">
           Your agent is now running. You can monitor its progress in the monitor

autogpt_platform/frontend/src/app/(platform)/chat/components/Chat/components/MarkdownContent/MarkdownContent.tsx

@@ -1,9 +1,9 @@
 "use client";
 
+import { cn } from "@/lib/utils";
 import React from "react";
 import ReactMarkdown from "react-markdown";
 import remarkGfm from "remark-gfm";
-import { cn } from "@/lib/utils";
 
 interface MarkdownContentProps {
   content: string;
@@ -41,7 +41,7 @@ export function MarkdownContent({ content, className }: MarkdownContentProps) {
             if (isInline) {
               return (
                 <code
-                  className="rounded bg-zinc-100 px-1.5 py-0.5 font-mono text-sm text-zinc-800 dark:bg-zinc-800 dark:text-zinc-200"
+                  className="rounded bg-zinc-100 px-1.5 py-0.5 font-mono text-sm text-zinc-800"
                   {...props}
                 >
                   {children}
@@ -49,17 +49,14 @@ export function MarkdownContent({ content, className }: MarkdownContentProps) {
               );
             }
             return (
-              <code
-                className="font-mono text-sm text-zinc-100 dark:text-zinc-200"
-                {...props}
-              >
+              <code className="font-mono text-sm text-zinc-100" {...props}>
                 {children}
               </code>
             );
           },
           pre: ({ children, ...props }) => (
             <pre
-              className="my-2 overflow-x-auto rounded-md bg-zinc-900 p-3 dark:bg-zinc-950"
+              className="my-2 overflow-x-auto rounded-md bg-zinc-900 p-3"
               {...props}
             >
               {children}
@@ -70,7 +67,7 @@ export function MarkdownContent({ content, className }: MarkdownContentProps) {
               href={href}
               target="_blank"
               rel="noopener noreferrer"
-              className="text-purple-600 underline decoration-1 underline-offset-2 hover:text-purple-700 dark:text-purple-400 dark:hover:text-purple-300"
+              className="text-purple-600 underline decoration-1 underline-offset-2 hover:text-purple-700"
               {...props}
             >
               {children}
@@ -126,7 +123,7 @@ export function MarkdownContent({ content, className }: MarkdownContentProps) {
               return (
                 <input
                   type="checkbox"
-                  className="mr-2 h-4 w-4 rounded border-zinc-300 text-purple-600 focus:ring-purple-500 disabled:cursor-not-allowed disabled:opacity-70 dark:border-zinc-600"
+                  className="mr-2 h-4 w-4 rounded border-zinc-300 text-purple-600 focus:ring-purple-500 disabled:cursor-not-allowed disabled:opacity-70"
                   disabled
                   {...props}
                 />
@@ -136,57 +133,42 @@ export function MarkdownContent({ content, className }: MarkdownContentProps) {
           },
           blockquote: ({ children, ...props }) => (
             <blockquote
-              className="my-2 border-l-4 border-zinc-300 pl-3 italic text-zinc-700 dark:border-zinc-600 dark:text-zinc-300"
+              className="my-2 border-l-4 border-zinc-300 pl-3 italic text-zinc-700"
               {...props}
             >
               {children}
             </blockquote>
           ),
           h1: ({ children, ...props }) => (
-            <h1
-              className="my-2 text-xl font-bold text-zinc-900 dark:text-zinc-100"
-              {...props}
-            >
+            <h1 className="my-2 text-xl font-bold text-zinc-900" {...props}>
               {children}
             </h1>
           ),
           h2: ({ children, ...props }) => (
-            <h2
-              className="my-2 text-lg font-semibold text-zinc-800 dark:text-zinc-200"
-              {...props}
-            >
+            <h2 className="my-2 text-lg font-semibold text-zinc-800" {...props}>
               {children}
             </h2>
           ),
           h3: ({ children, ...props }) => (
             <h3
-              className="my-1 text-base font-semibold text-zinc-800 dark:text-zinc-200"
+              className="my-1 text-base font-semibold text-zinc-800"
               {...props}
             >
               {children}
             </h3>
           ),
           h4: ({ children, ...props }) => (
-            <h4
-              className="my-1 text-sm font-medium text-zinc-700 dark:text-zinc-300"
-              {...props}
-            >
+            <h4 className="my-1 text-sm font-medium text-zinc-700" {...props}>
               {children}
             </h4>
           ),
           h5: ({ children, ...props }) => (
-            <h5
-              className="my-1 text-sm font-medium text-zinc-700 dark:text-zinc-300"
-              {...props}
-            >
+            <h5 className="my-1 text-sm font-medium text-zinc-700" {...props}>
               {children}
             </h5>
           ),
           h6: ({ children, ...props }) => (
-            <h6
-              className="my-1 text-xs font-medium text-zinc-600 dark:text-zinc-400"
-              {...props}
-            >
+            <h6 className="my-1 text-xs font-medium text-zinc-600" {...props}>
               {children}
             </h6>
           ),
@@ -196,15 +178,12 @@ export function MarkdownContent({ content, className }: MarkdownContentProps) {
             </p>
           ),
           hr: ({ ...props }) => (
-            <hr
-              className="my-3 border-zinc-300 dark:border-zinc-700"
-              {...props}
-            />
+            <hr className="my-3 border-zinc-300" {...props} />
           ),
           table: ({ children, ...props }) => (
             <div className="my-2 overflow-x-auto">
               <table
-                className="min-w-full divide-y divide-zinc-200 rounded border border-zinc-200 dark:divide-zinc-700 dark:border-zinc-700"
+                className="min-w-full divide-y divide-zinc-200 rounded border border-zinc-200"
                 {...props}
               >
                 {children}
@@ -213,7 +192,7 @@ export function MarkdownContent({ content, className }: MarkdownContentProps) {
           ),
           th: ({ children, ...props }) => (
             <th
-              className="bg-zinc-50 px-3 py-2 text-left text-xs font-semibold text-zinc-700 dark:bg-zinc-800 dark:text-zinc-300"
+              className="bg-zinc-50 px-3 py-2 text-left text-xs font-semibold text-zinc-700"
               {...props}
             >
               {children}
@@ -221,7 +200,7 @@ export function MarkdownContent({ content, className }: MarkdownContentProps) {
           ),
           td: ({ children, ...props }) => (
             <td
-              className="border-t border-zinc-200 px-3 py-2 text-sm dark:border-zinc-700"
+              className="border-t border-zinc-200 px-3 py-2 text-sm"
               {...props}
             >
               {children}

autogpt_platform/frontend/src/app/(platform)/chat/components/Chat/components/MessageBubble/MessageBubble.tsx

@@ -0,0 +1,56 @@
+import { cn } from "@/lib/utils";
+import { ReactNode } from "react";
+
+export interface MessageBubbleProps {
+  children: ReactNode;
+  variant: "user" | "assistant";
+  className?: string;
+}
+
+export function MessageBubble({
+  children,
+  variant,
+  className,
+}: MessageBubbleProps) {
+  const userTheme = {
+    bg: "bg-slate-900",
+    border: "border-slate-800",
+    gradient: "from-slate-900/30 via-slate-800/20 to-transparent",
+    text: "text-slate-50",
+  };
+
+  const assistantTheme = {
+    bg: "bg-slate-50/20",
+    border: "border-slate-100",
+    gradient: "from-slate-200/20 via-slate-300/10 to-transparent",
+    text: "text-slate-900",
+  };
+
+  const theme = variant === "user" ? userTheme : assistantTheme;
+
+  return (
+    <div
+      className={cn(
+        "group relative min-w-20 overflow-hidden rounded-xl border px-6 py-2.5 text-sm leading-relaxed backdrop-blur-xl transition-all duration-500 ease-in-out",
+        theme.bg,
+        theme.border,
+        variant === "user" && "text-right",
+        variant === "assistant" && "text-left",
+        className,
+      )}
+    >
+      {/* Gradient flare background */}
+      <div
+        className={cn("absolute inset-0 bg-gradient-to-br", theme.gradient)}
+      />
+      <div
+        className={cn(
+          "relative z-10 transition-all duration-500 ease-in-out",
+          theme.text,
+        )}
+      >
+        {children}
+      </div>
+    </div>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/chat/components/Chat/components/MessageList/MessageList.tsx

@@ -0,0 +1,121 @@
+"use client";
+
+import { cn } from "@/lib/utils";
+import { ChatMessage } from "../ChatMessage/ChatMessage";
+import type { ChatMessageData } from "../ChatMessage/useChatMessage";
+import { StreamingMessage } from "../StreamingMessage/StreamingMessage";
+import { ThinkingMessage } from "../ThinkingMessage/ThinkingMessage";
+import { useMessageList } from "./useMessageList";
+
+export interface MessageListProps {
+  messages: ChatMessageData[];
+  streamingChunks?: string[];
+  isStreaming?: boolean;
+  className?: string;
+  onStreamComplete?: () => void;
+  onSendMessage?: (content: string) => void;
+}
+
+export function MessageList({
+  messages,
+  streamingChunks = [],
+  isStreaming = false,
+  className,
+  onStreamComplete,
+  onSendMessage,
+}: MessageListProps) {
+  const { messagesEndRef, messagesContainerRef } = useMessageList({
+    messageCount: messages.length,
+    isStreaming,
+  });
+
+  return (
+    <div
+      ref={messagesContainerRef}
+      className={cn(
+        "flex-1 overflow-y-auto",
+        "scrollbar-thin scrollbar-track-transparent scrollbar-thumb-zinc-300",
+        className,
+      )}
+    >
+      <div className="mx-auto flex max-w-3xl flex-col py-4">
+        {/* Render all persisted messages */}
+        {messages.map((message, index) => {
+          // Check if current message is an agent_output tool_response
+          // and if previous message is an assistant message
+          let agentOutput: ChatMessageData | undefined;
+
+          if (message.type === "tool_response" && message.result) {
+            let parsedResult: Record<string, unknown> | null = null;
+            try {
+              parsedResult =
+                typeof message.result === "string"
+                  ? JSON.parse(message.result)
+                  : (message.result as Record<string, unknown>);
+            } catch {
+              parsedResult = null;
+            }
+            if (parsedResult?.type === "agent_output") {
+              const prevMessage = messages[index - 1];
+              if (
+                prevMessage &&
+                prevMessage.type === "message" &&
+                prevMessage.role === "assistant"
+              ) {
+                // This agent output will be rendered inside the previous assistant message
+                // Skip rendering this message separately
+                return null;
+              }
+            }
+          }
+
+          // Check if next message is an agent_output tool_response to include in current assistant message
+          if (message.type === "message" && message.role === "assistant") {
+            const nextMessage = messages[index + 1];
+            if (
+              nextMessage &&
+              nextMessage.type === "tool_response" &&
+              nextMessage.result
+            ) {
+              let parsedResult: Record<string, unknown> | null = null;
+              try {
+                parsedResult =
+                  typeof nextMessage.result === "string"
+                    ? JSON.parse(nextMessage.result)
+                    : (nextMessage.result as Record<string, unknown>);
+              } catch {
+                parsedResult = null;
+              }
+              if (parsedResult?.type === "agent_output") {
+                agentOutput = nextMessage;
+              }
+            }
+          }
+
+          return (
+            <ChatMessage
+              key={index}
+              message={message}
+              onSendMessage={onSendMessage}
+              agentOutput={agentOutput}
+            />
+          );
+        })}
+
+        {/* Render thinking message when streaming but no chunks yet */}
+        {isStreaming && streamingChunks.length === 0 && <ThinkingMessage />}
+
+        {/* Render streaming message if active */}
+        {isStreaming && streamingChunks.length > 0 && (
+          <StreamingMessage
+            chunks={streamingChunks}
+            onComplete={onStreamComplete}
+          />
+        )}
+
+        {/* Invisible div to scroll to */}
+        <div ref={messagesEndRef} />
+      </div>
+    </div>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/chat/components/Chat/components/NoResultsMessage/NoResultsMessage.tsx

@@ -1,7 +1,6 @@
-import React from "react";
 import { Text } from "@/components/atoms/Text/Text";
-import { MagnifyingGlass, X } from "@phosphor-icons/react";
 import { cn } from "@/lib/utils";
+import { MagnifyingGlass, X } from "@phosphor-icons/react";
 
 export interface NoResultsMessageProps {
   message: string;
@@ -17,26 +16,26 @@ export function NoResultsMessage({
   return (
     <div
       className={cn(
-        "mx-4 my-2 flex flex-col items-center gap-4 rounded-lg border border-gray-200 bg-gray-50 p-6 dark:border-gray-800 dark:bg-gray-900",
+        "mx-4 my-2 flex flex-col items-center gap-4 rounded-lg border border-gray-200 bg-gray-50 p-6",
         className,
       )}
     >
       {/* Icon */}
       <div className="relative flex h-16 w-16 items-center justify-center">
-        <div className="flex h-16 w-16 items-center justify-center rounded-full bg-gray-200 dark:bg-gray-700">
+        <div className="flex h-16 w-16 items-center justify-center rounded-full bg-gray-200">
           <MagnifyingGlass size={32} weight="bold" className="text-gray-500" />
         </div>
-        <div className="absolute -right-1 -top-1 flex h-8 w-8 items-center justify-center rounded-full bg-gray-400 dark:bg-gray-600">
+        <div className="absolute -right-1 -top-1 flex h-8 w-8 items-center justify-center rounded-full bg-gray-400">
           <X size={20} weight="bold" className="text-white" />
         </div>
       </div>
 
       {/* Content */}
       <div className="text-center">
-        <Text variant="h3" className="mb-2 text-gray-900 dark:text-gray-100">
+        <Text variant="h3" className="mb-2 text-gray-900">
           No Results Found
         </Text>
-        <Text variant="body" className="text-gray-700 dark:text-gray-300">
+        <Text variant="body" className="text-gray-700">
           {message}
         </Text>
       </div>
@@ -44,17 +43,14 @@ export function NoResultsMessage({
       {/* Suggestions */}
       {suggestions.length > 0 && (
         <div className="w-full space-y-2">
-          <Text
-            variant="small"
-            className="font-semibold text-gray-900 dark:text-gray-100"
-          >
+          <Text variant="small" className="font-semibold text-gray-900">
             Try these suggestions:
           </Text>
-          <ul className="space-y-1 rounded-md bg-gray-100 p-4 dark:bg-gray-800">
+          <ul className="space-y-1 rounded-md bg-gray-100 p-4">
             {suggestions.map((suggestion, index) => (
               <li
                 key={index}
-                className="flex items-start gap-2 text-sm text-gray-700 dark:text-gray-300"
+                className="flex items-start gap-2 text-sm text-gray-700"
               >
                 <span className="mt-1 text-gray-500">•</span>
                 <span>{suggestion}</span>

autogpt_platform/frontend/src/app/(platform)/chat/components/Chat/components/QuickActionsWelcome/QuickActionsWelcome.tsx

@@ -0,0 +1,94 @@
+"use client";
+
+import { Text } from "@/components/atoms/Text/Text";
+import { cn } from "@/lib/utils";
+
+export interface QuickActionsWelcomeProps {
+  title: string;
+  description: string;
+  actions: string[];
+  onActionClick: (action: string) => void;
+  disabled?: boolean;
+  className?: string;
+}
+
+export function QuickActionsWelcome({
+  title,
+  description,
+  actions,
+  onActionClick,
+  disabled = false,
+  className,
+}: QuickActionsWelcomeProps) {
+  return (
+    <div
+      className={cn("flex flex-1 items-center justify-center p-8", className)}
+    >
+      <div className="w-full max-w-3xl">
+        <div className="mb-12 text-center">
+          <Text
+            variant="h2"
+            className="mb-3 text-2xl font-semibold text-zinc-900"
+          >
+            {title}
+          </Text>
+          <Text variant="body" className="text-zinc-500">
+            {description}
+          </Text>
+        </div>
+        <div className="grid gap-3 sm:grid-cols-2">
+          {actions.map((action) => {
+            // Use slate theme for all cards
+            const theme = {
+              bg: "bg-slate-50/10",
+              border: "border-slate-100",
+              hoverBg: "hover:bg-slate-50/20",
+              hoverBorder: "hover:border-slate-200",
+              gradient: "from-slate-200/20 via-slate-300/10 to-transparent",
+              text: "text-slate-900",
+              hoverText: "group-hover:text-slate-900",
+            };
+
+            return (
+              <button
+                key={action}
+                onClick={() => onActionClick(action)}
+                disabled={disabled}
+                className={cn(
+                  "group relative overflow-hidden rounded-xl border p-5 text-left backdrop-blur-xl",
+                  "transition-all duration-200",
+                  theme.bg,
+                  theme.border,
+                  theme.hoverBg,
+                  theme.hoverBorder,
+                  "hover:shadow-sm",
+                  "focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-white/50 focus-visible:ring-offset-2",
+                  "disabled:cursor-not-allowed disabled:opacity-50 disabled:hover:shadow-none",
+                )}
+              >
+                {/* Gradient flare background */}
+                <div
+                  className={cn(
+                    "absolute inset-0 bg-gradient-to-br",
+                    theme.gradient,
+                  )}
+                />
+
+                <Text
+                  variant="body"
+                  className={cn(
+                    "relative z-10 font-medium",
+                    theme.text,
+                    theme.hoverText,
+                  )}
+                >
+                  {action}
+                </Text>
+              </button>
+            );
+          })}
+        </div>
+      </div>
+    </div>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/chat/components/Chat/components/SessionsDrawer/SessionsDrawer.tsx

@@ -0,0 +1,136 @@
+"use client";
+
+import { useGetV2ListSessions } from "@/app/api/__generated__/endpoints/chat/chat";
+import { Text } from "@/components/atoms/Text/Text";
+import { scrollbarStyles } from "@/components/styles/scrollbars";
+import { cn } from "@/lib/utils";
+import { X } from "@phosphor-icons/react";
+import { formatDistanceToNow } from "date-fns";
+import { Drawer } from "vaul";
+
+interface SessionsDrawerProps {
+  isOpen: boolean;
+  onClose: () => void;
+  onSelectSession: (sessionId: string) => void;
+  currentSessionId?: string | null;
+}
+
+export function SessionsDrawer({
+  isOpen,
+  onClose,
+  onSelectSession,
+  currentSessionId,
+}: SessionsDrawerProps) {
+  const { data, isLoading } = useGetV2ListSessions(
+    { limit: 100 },
+    {
+      query: {
+        enabled: isOpen,
+      },
+    },
+  );
+
+  const sessions =
+    data?.status === 200
+      ? data.data.sessions.filter((session) => {
+          // Filter out sessions without messages (sessions that were never updated)
+          // If updated_at equals created_at, the session was created but never had messages
+          return session.updated_at !== session.created_at;
+        })
+      : [];
+
+  function handleSelectSession(sessionId: string) {
+    onSelectSession(sessionId);
+    onClose();
+  }
+
+  return (
+    <Drawer.Root
+      open={isOpen}
+      onOpenChange={(open) => !open && onClose()}
+      direction="right"
+    >
+      <Drawer.Portal>
+        <Drawer.Overlay className="fixed inset-0 z-[60] bg-black/10 backdrop-blur-sm" />
+        <Drawer.Content
+          className={cn(
+            "fixed right-0 top-0 z-[70] flex h-full w-96 flex-col border-l border-zinc-200 bg-white",
+            scrollbarStyles,
+          )}
+        >
+          <div className="shrink-0 p-4">
+            <div className="flex items-center justify-between">
+              <Drawer.Title className="text-lg font-semibold">
+                Chat Sessions
+              </Drawer.Title>
+              <button
+                aria-label="Close"
+                onClick={onClose}
+                className="flex size-8 items-center justify-center rounded hover:bg-zinc-100"
+              >
+                <X width="1.25rem" height="1.25rem" />
+              </button>
+            </div>
+          </div>
+
+          <div className="flex-1 overflow-y-auto p-4">
+            {isLoading ? (
+              <div className="flex items-center justify-center py-8">
+                <Text variant="body" className="text-zinc-500">
+                  Loading sessions...
+                </Text>
+              </div>
+            ) : sessions.length === 0 ? (
+              <div className="flex items-center justify-center py-8">
+                <Text variant="body" className="text-zinc-500">
+                  No sessions found
+                </Text>
+              </div>
+            ) : (
+              <div className="space-y-2">
+                {sessions.map((session) => {
+                  const isActive = session.id === currentSessionId;
+                  const updatedAt = session.updated_at
+                    ? formatDistanceToNow(new Date(session.updated_at), {
+                        addSuffix: true,
+                      })
+                    : "";
+
+                  return (
+                    <button
+                      key={session.id}
+                      onClick={() => handleSelectSession(session.id)}
+                      className={cn(
+                        "w-full rounded-lg border p-3 text-left transition-colors",
+                        isActive
+                          ? "border-indigo-500 bg-zinc-50"
+                          : "border-zinc-200 bg-zinc-100/50 hover:border-zinc-300 hover:bg-zinc-50",
+                      )}
+                    >
+                      <div className="flex flex-col gap-1">
+                        <Text
+                          variant="body"
+                          className={cn(
+                            "font-medium",
+                            isActive ? "text-indigo-900" : "text-zinc-900",
+                          )}
+                        >
+                          {session.title || "Untitled Chat"}
+                        </Text>
+                        <div className="flex items-center gap-2 text-xs text-zinc-500">
+                          <span>{session.id.slice(0, 8)}...</span>
+                          {updatedAt && <span>•</span>}
+                          <span>{updatedAt}</span>
+                        </div>
+                      </div>
+                    </button>
+                  );
+                })}
+              </div>
+            )}
+          </div>
+        </Drawer.Content>
+      </Drawer.Portal>
+    </Drawer.Root>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/chat/components/Chat/components/StreamingMessage/StreamingMessage.tsx

@@ -0,0 +1,42 @@
+import { cn } from "@/lib/utils";
+import { RobotIcon } from "@phosphor-icons/react";
+import { MarkdownContent } from "../MarkdownContent/MarkdownContent";
+import { MessageBubble } from "../MessageBubble/MessageBubble";
+import { useStreamingMessage } from "./useStreamingMessage";
+
+export interface StreamingMessageProps {
+  chunks: string[];
+  className?: string;
+  onComplete?: () => void;
+}
+
+export function StreamingMessage({
+  chunks,
+  className,
+  onComplete,
+}: StreamingMessageProps) {
+  const { displayText } = useStreamingMessage({ chunks, onComplete });
+
+  return (
+    <div
+      className={cn(
+        "group relative flex w-full justify-start gap-3 px-4 py-3",
+        className,
+      )}
+    >
+      <div className="flex w-full max-w-3xl gap-3">
+        <div className="flex-shrink-0">
+          <div className="flex h-7 w-7 items-center justify-center rounded-lg bg-indigo-600">
+            <RobotIcon className="h-4 w-4 text-indigo-50" />
+          </div>
+        </div>
+
+        <div className="flex min-w-0 flex-1 flex-col">
+          <MessageBubble variant="assistant">
+            <MarkdownContent content={displayText} />
+          </MessageBubble>
+        </div>
+      </div>
+    </div>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/chat/components/Chat/components/ThinkingMessage/ThinkingMessage.tsx

@@ -0,0 +1,70 @@
+import { cn } from "@/lib/utils";
+import { RobotIcon } from "@phosphor-icons/react";
+import { useEffect, useRef, useState } from "react";
+import { MessageBubble } from "../MessageBubble/MessageBubble";
+
+export interface ThinkingMessageProps {
+  className?: string;
+}
+
+export function ThinkingMessage({ className }: ThinkingMessageProps) {
+  const [showSlowLoader, setShowSlowLoader] = useState(false);
+  const timerRef = useRef<NodeJS.Timeout | null>(null);
+
+  useEffect(() => {
+    if (timerRef.current === null) {
+      timerRef.current = setTimeout(() => {
+        setShowSlowLoader(true);
+      }, 8000);
+    }
+
+    return () => {
+      if (timerRef.current) {
+        clearTimeout(timerRef.current);
+        timerRef.current = null;
+      }
+    };
+  }, []);
+
+  return (
+    <div
+      className={cn(
+        "group relative flex w-full justify-start gap-3 px-4 py-3",
+        className,
+      )}
+    >
+      <div className="flex w-full max-w-3xl gap-3">
+        <div className="flex-shrink-0">
+          <div className="flex h-7 w-7 items-center justify-center rounded-lg bg-indigo-500">
+            <RobotIcon className="h-4 w-4 text-indigo-50" />
+          </div>
+        </div>
+
+        <div className="flex min-w-0 flex-1 flex-col">
+          <MessageBubble variant="assistant">
+            <div className="transition-all duration-500 ease-in-out">
+              {showSlowLoader ? (
+                <div className="flex flex-col items-center gap-3 py-2">
+                  <div className="loader" style={{ flexShrink: 0 }} />
+                  <p className="text-sm text-slate-700">
+                    Taking a bit longer to think, wait a moment please
+                  </p>
+                </div>
+              ) : (
+                <span
+                  className="inline-block bg-gradient-to-r from-neutral-400 via-neutral-600 to-neutral-400 bg-clip-text text-transparent"
+                  style={{
+                    backgroundSize: "200% 100%",
+                    animation: "shimmer 2s ease-in-out infinite",
+                  }}
+                >
+                  Thinking...
+                </span>
+              )}
+            </div>
+          </MessageBubble>
+        </div>
+      </div>
+    </div>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/chat/components/Chat/components/ToolCallMessage/ToolCallMessage.tsx

@@ -0,0 +1,24 @@
+import { Text } from "@/components/atoms/Text/Text";
+import { cn } from "@/lib/utils";
+import { WrenchIcon } from "@phosphor-icons/react";
+import { getToolActionPhrase } from "../../helpers";
+
+export interface ToolCallMessageProps {
+  toolName: string;
+  className?: string;
+}
+
+export function ToolCallMessage({ toolName, className }: ToolCallMessageProps) {
+  return (
+    <div className={cn("flex items-center justify-center gap-2", className)}>
+      <WrenchIcon
+        size={14}
+        weight="bold"
+        className="flex-shrink-0 text-neutral-500"
+      />
+      <Text variant="small" className="text-neutral-500">
+        {getToolActionPhrase(toolName)}...
+      </Text>
+    </div>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/chat/components/Chat/components/ToolResponseMessage/ToolResponseMessage.tsx

@@ -0,0 +1,260 @@
+import { Text } from "@/components/atoms/Text/Text";
+import "@/components/contextual/OutputRenderers";
+import {
+  globalRegistry,
+  OutputItem,
+} from "@/components/contextual/OutputRenderers";
+import { cn } from "@/lib/utils";
+import type { ToolResult } from "@/types/chat";
+import { WrenchIcon } from "@phosphor-icons/react";
+import { getToolActionPhrase } from "../../helpers";
+
+export interface ToolResponseMessageProps {
+  toolName: string;
+  result?: ToolResult;
+  success?: boolean;
+  className?: string;
+}
+
+export function ToolResponseMessage({
+  toolName,
+  result,
+  success: _success = true,
+  className,
+}: ToolResponseMessageProps) {
+  if (!result) {
+    return (
+      <div className={cn("flex items-center justify-center gap-2", className)}>
+        <WrenchIcon
+          size={14}
+          weight="bold"
+          className="flex-shrink-0 text-neutral-500"
+        />
+        <Text variant="small" className="text-neutral-500">
+          {getToolActionPhrase(toolName)}...
+        </Text>
+      </div>
+    );
+  }
+
+  let parsedResult: Record<string, unknown> | null = null;
+  try {
+    parsedResult =
+      typeof result === "string"
+        ? JSON.parse(result)
+        : (result as Record<string, unknown>);
+  } catch {
+    parsedResult = null;
+  }
+
+  if (parsedResult && typeof parsedResult === "object") {
+    const responseType = parsedResult.type as string | undefined;
+
+    if (responseType === "agent_output") {
+      const execution = parsedResult.execution as
+        | {
+            outputs?: Record<string, unknown[]>;
+          }
+        | null
+        | undefined;
+      const outputs = execution?.outputs || {};
+      const message = parsedResult.message as string | undefined;
+
+      return (
+        <div className={cn("space-y-4 px-4 py-2", className)}>
+          <div className="flex items-center gap-2">
+            <WrenchIcon
+              size={14}
+              weight="bold"
+              className="flex-shrink-0 text-neutral-500"
+            />
+            <Text variant="small" className="text-neutral-500">
+              {getToolActionPhrase(toolName)}
+            </Text>
+          </div>
+          {message && (
+            <div className="rounded border p-4">
+              <Text variant="small" className="text-neutral-600">
+                {message}
+              </Text>
+            </div>
+          )}
+          {Object.keys(outputs).length > 0 && (
+            <div className="space-y-4">
+              {Object.entries(outputs).map(([outputName, values]) =>
+                values.map((value, index) => {
+                  const renderer = globalRegistry.getRenderer(value);
+                  if (renderer) {
+                    return (
+                      <OutputItem
+                        key={`${outputName}-${index}`}
+                        value={value}
+                        renderer={renderer}
+                        label={outputName}
+                      />
+                    );
+                  }
+                  return (
+                    <div
+                      key={`${outputName}-${index}`}
+                      className="rounded border p-4"
+                    >
+                      <Text variant="large-medium" className="mb-2 capitalize">
+                        {outputName}
+                      </Text>
+                      <pre className="overflow-auto text-sm">
+                        {JSON.stringify(value, null, 2)}
+                      </pre>
+                    </div>
+                  );
+                }),
+              )}
+            </div>
+          )}
+        </div>
+      );
+    }
+
+    if (responseType === "block_output" && parsedResult.outputs) {
+      const outputs = parsedResult.outputs as Record<string, unknown[]>;
+
+      return (
+        <div className={cn("space-y-4 px-4 py-2", className)}>
+          <div className="flex items-center gap-2">
+            <WrenchIcon
+              size={14}
+              weight="bold"
+              className="flex-shrink-0 text-neutral-500"
+            />
+            <Text variant="small" className="text-neutral-500">
+              {getToolActionPhrase(toolName)}
+            </Text>
+          </div>
+          <div className="space-y-4">
+            {Object.entries(outputs).map(([outputName, values]) =>
+              values.map((value, index) => {
+                const renderer = globalRegistry.getRenderer(value);
+                if (renderer) {
+                  return (
+                    <OutputItem
+                      key={`${outputName}-${index}`}
+                      value={value}
+                      renderer={renderer}
+                      label={outputName}
+                    />
+                  );
+                }
+                return (
+                  <div
+                    key={`${outputName}-${index}`}
+                    className="rounded border p-4"
+                  >
+                    <Text variant="large-medium" className="mb-2 capitalize">
+                      {outputName}
+                    </Text>
+                    <pre className="overflow-auto text-sm">
+                      {JSON.stringify(value, null, 2)}
+                    </pre>
+                  </div>
+                );
+              }),
+            )}
+          </div>
+        </div>
+      );
+    }
+
+    // Handle other response types with a message field (e.g., understanding_updated)
+    if (parsedResult.message && typeof parsedResult.message === "string") {
+      // Format tool name from snake_case to Title Case
+      const formattedToolName = toolName
+        .split("_")
+        .map((word) => word.charAt(0).toUpperCase() + word.slice(1))
+        .join(" ");
+
+      // Clean up message - remove incomplete user_name references
+      let cleanedMessage = parsedResult.message;
+      // Remove "Updated understanding with: user_name" pattern if user_name is just a placeholder
+      cleanedMessage = cleanedMessage.replace(
+        /Updated understanding with:\s*user_name\.?\s*/gi,
+        "",
+      );
+      // Remove standalone user_name references
+      cleanedMessage = cleanedMessage.replace(/\buser_name\b\.?\s*/gi, "");
+      cleanedMessage = cleanedMessage.trim();
+
+      // Only show message if it has content after cleaning
+      if (!cleanedMessage) {
+        return (
+          <div
+            className={cn(
+              "flex items-center justify-center gap-2 px-4 py-2",
+              className,
+            )}
+          >
+            <WrenchIcon
+              size={14}
+              weight="bold"
+              className="flex-shrink-0 text-neutral-500"
+            />
+            <Text variant="small" className="text-neutral-500">
+              {formattedToolName}
+            </Text>
+          </div>
+        );
+      }
+
+      return (
+        <div className={cn("space-y-2 px-4 py-2", className)}>
+          <div className="flex items-center justify-center gap-2">
+            <WrenchIcon
+              size={14}
+              weight="bold"
+              className="flex-shrink-0 text-neutral-500"
+            />
+            <Text variant="small" className="text-neutral-500">
+              {formattedToolName}
+            </Text>
+          </div>
+          <div className="rounded border p-4">
+            <Text variant="small" className="text-neutral-600">
+              {cleanedMessage}
+            </Text>
+          </div>
+        </div>
+      );
+    }
+  }
+
+  const renderer = globalRegistry.getRenderer(result);
+  if (renderer) {
+    return (
+      <div className={cn("px-4 py-2", className)}>
+        <div className="mb-2 flex items-center gap-2">
+          <WrenchIcon
+            size={14}
+            weight="bold"
+            className="flex-shrink-0 text-neutral-500"
+          />
+          <Text variant="small" className="text-neutral-500">
+            {getToolActionPhrase(toolName)}
+          </Text>
+        </div>
+        <OutputItem value={result} renderer={renderer} />
+      </div>
+    );
+  }
+
+  return (
+    <div className={cn("flex items-center justify-center gap-2", className)}>
+      <WrenchIcon
+        size={14}
+        weight="bold"
+        className="flex-shrink-0 text-neutral-500"
+      />
+      <Text variant="small" className="text-neutral-500">
+        {getToolActionPhrase(toolName)}...
+      </Text>
+    </div>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/chat/components/Chat/helpers.ts

@@ -64,10 +64,3 @@ export function getToolCompletionPhrase(toolName: string): string {
     `Finished ${toolName.replace(/_/g, " ").replace("...", "")}`
   );
 }
-
-/** Validate UUID v4 format */
-export function isValidUUID(value: string): boolean {
-  const uuidRegex =
-    /^[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}$/i;
-  return uuidRegex.test(value);
-}

autogpt_platform/frontend/src/app/(platform)/chat/components/Chat/useChat.ts

@@ -1,17 +1,12 @@
 "use client";
 
-import { useEffect, useRef } from "react";
-import { useRouter, useSearchParams } from "next/navigation";
-import { toast } from "sonner";
-import { useChatSession } from "@/app/(platform)/chat/useChatSession";
 import { useSupabase } from "@/lib/supabase/hooks/useSupabase";
-import { useChatStream } from "@/app/(platform)/chat/useChatStream";
+import { useEffect, useRef } from "react";
+import { toast } from "sonner";
+import { useChatSession } from "./useChatSession";
+import { useChatStream } from "./useChatStream";
 
-export function useChatPage() {
-  const router = useRouter();
-  const searchParams = useSearchParams();
-  const urlSessionId =
-    searchParams.get("session_id") || searchParams.get("session");
+export function useChat() {
   const hasCreatedSessionRef = useRef(false);
   const hasClaimedSessionRef = useRef(false);
   const { user } = useSupabase();
@@ -25,29 +20,24 @@ export function useChatPage() {
     isCreating,
     error,
     createSession,
-    refreshSession,
     claimSession,
     clearSession: clearSessionBase,
+    loadSession,
   } = useChatSession({
-    urlSessionId,
+    urlSessionId: null,
     autoCreate: false,
   });
 
   useEffect(
     function autoCreateSession() {
-      if (
-        !urlSessionId &&
-        !hasCreatedSessionRef.current &&
-        !isCreating &&
-        !sessionIdFromHook
-      ) {
+      if (!hasCreatedSessionRef.current && !isCreating && !sessionIdFromHook) {
         hasCreatedSessionRef.current = true;
         createSession().catch((_err) => {
           hasCreatedSessionRef.current = false;
         });
       }
     },
-    [urlSessionId, isCreating, sessionIdFromHook, createSession],
+    [isCreating, sessionIdFromHook, createSession],
   );
 
   useEffect(
@@ -111,7 +101,6 @@ export function useChatPage() {
     clearSessionBase();
     hasCreatedSessionRef.current = false;
     hasClaimedSessionRef.current = false;
-    router.push("/chat");
   }
 
   return {
@@ -121,8 +110,8 @@ export function useChatPage() {
     isCreating,
     error,
     createSession,
-    refreshSession,
     clearSession,
+    loadSession,
     sessionId: sessionIdFromHook,
   };
 }

autogpt_platform/frontend/src/app/(platform)/chat/components/Chat/useChatDrawer.ts

@@ -0,0 +1,17 @@
+"use client";
+
+import { create } from "zustand";
+
+interface ChatDrawerState {
+  isOpen: boolean;
+  open: () => void;
+  close: () => void;
+  toggle: () => void;
+}
+
+export const useChatDrawer = create<ChatDrawerState>((set) => ({
+  isOpen: false,
+  open: () => set({ isOpen: true }),
+  close: () => set({ isOpen: false }),
+  toggle: () => set((state) => ({ isOpen: !state.isOpen })),
+}));

autogpt_platform/frontend/src/app/(platform)/chat/components/Chat/useChatSession.ts

@@ -1,17 +1,18 @@
-import { useCallback, useEffect, useState, useRef, useMemo } from "react";
-import { useQueryClient } from "@tanstack/react-query";
-import { toast } from "sonner";
 import {
-  usePostV2CreateSession,
+  getGetV2GetSessionQueryKey,
+  getGetV2GetSessionQueryOptions,
   postV2CreateSession,
   useGetV2GetSession,
   usePatchV2SessionAssignUser,
-  getGetV2GetSessionQueryKey,
+  usePostV2CreateSession,
 } from "@/app/api/__generated__/endpoints/chat/chat";
 import type { SessionDetailResponse } from "@/app/api/__generated__/models/sessionDetailResponse";
-import { storage, Key } from "@/services/storage/local-storage";
-import { isValidUUID } from "@/app/(platform)/chat/helpers";
 import { okData } from "@/app/api/helpers";
+import { isValidUUID } from "@/lib/utils";
+import { Key, storage } from "@/services/storage/local-storage";
+import { useQueryClient } from "@tanstack/react-query";
+import { useCallback, useEffect, useMemo, useRef, useState } from "react";
+import { toast } from "sonner";
 
 interface UseChatSessionArgs {
   urlSessionId?: string | null;
@@ -155,10 +156,22 @@ export function useChatSession({
     async function loadSession(id: string) {
       try {
         setError(null);
+        // Invalidate the query cache for this session to force a fresh fetch
+        await queryClient.invalidateQueries({
+          queryKey: getGetV2GetSessionQueryKey(id),
+        });
+        // Set sessionId after invalidation to ensure the hook refetches
         setSessionId(id);
         storage.set(Key.CHAT_SESSION_ID, id);
-        const result = await refetch();
-        if (!result.data || result.isError) {
+        // Force fetch with fresh data (bypass cache)
+        const queryOptions = getGetV2GetSessionQueryOptions(id, {
+          query: {
+            staleTime: 0, // Force fresh fetch
+            retry: 1,
+          },
+        });
+        const result = await queryClient.fetchQuery(queryOptions);
+        if (!result || ("status" in result && result.status !== 200)) {
           console.warn("Session not found on server, clearing local state");
           storage.clean(Key.CHAT_SESSION_ID);
           setSessionId(null);
@@ -171,7 +184,7 @@ export function useChatSession({
         throw error;
       }
     },
-    [refetch],
+    [queryClient],
   );
 
   const refreshSession = useCallback(

autogpt_platform/frontend/src/app/(platform)/chat/components/Chat/useChatStream.ts

@@ -0,0 +1,371 @@
+import type { ToolArguments, ToolResult } from "@/types/chat";
+import { useCallback, useEffect, useRef, useState } from "react";
+import { toast } from "sonner";
+
+const MAX_RETRIES = 3;
+const INITIAL_RETRY_DELAY = 1000;
+
+export interface StreamChunk {
+  type:
+    | "text_chunk"
+    | "text_ended"
+    | "tool_call"
+    | "tool_call_start"
+    | "tool_response"
+    | "login_needed"
+    | "need_login"
+    | "credentials_needed"
+    | "error"
+    | "usage"
+    | "stream_end";
+  timestamp?: string;
+  content?: string;
+  message?: string;
+  tool_id?: string;
+  tool_name?: string;
+  arguments?: ToolArguments;
+  result?: ToolResult;
+  success?: boolean;
+  idx?: number;
+  session_id?: string;
+  agent_info?: {
+    graph_id: string;
+    name: string;
+    trigger_type: string;
+  };
+  provider?: string;
+  provider_name?: string;
+  credential_type?: string;
+  scopes?: string[];
+  title?: string;
+  [key: string]: unknown;
+}
+
+type VercelStreamChunk =
+  | { type: "start"; messageId: string }
+  | { type: "finish" }
+  | { type: "text-start"; id: string }
+  | { type: "text-delta"; id: string; delta: string }
+  | { type: "text-end"; id: string }
+  | { type: "tool-input-start"; toolCallId: string; toolName: string }
+  | {
+      type: "tool-input-available";
+      toolCallId: string;
+      toolName: string;
+      input: ToolArguments;
+    }
+  | {
+      type: "tool-output-available";
+      toolCallId: string;
+      toolName?: string;
+      output: ToolResult;
+      success?: boolean;
+    }
+  | {
+      type: "usage";
+      promptTokens: number;
+      completionTokens: number;
+      totalTokens: number;
+    }
+  | {
+      type: "error";
+      errorText: string;
+      code?: string;
+      details?: Record<string, unknown>;
+    };
+
+const LEGACY_STREAM_TYPES = new Set<StreamChunk["type"]>([
+  "text_chunk",
+  "text_ended",
+  "tool_call",
+  "tool_call_start",
+  "tool_response",
+  "login_needed",
+  "need_login",
+  "credentials_needed",
+  "error",
+  "usage",
+  "stream_end",
+]);
+
+function isLegacyStreamChunk(
+  chunk: StreamChunk | VercelStreamChunk,
+): chunk is StreamChunk {
+  return LEGACY_STREAM_TYPES.has(chunk.type as StreamChunk["type"]);
+}
+
+function normalizeStreamChunk(
+  chunk: StreamChunk | VercelStreamChunk,
+): StreamChunk | null {
+  if (isLegacyStreamChunk(chunk)) {
+    return chunk;
+  }
+  switch (chunk.type) {
+    case "text-delta":
+      return { type: "text_chunk", content: chunk.delta };
+    case "text-end":
+      return { type: "text_ended" };
+    case "tool-input-available":
+      return {
+        type: "tool_call_start",
+        tool_id: chunk.toolCallId,
+        tool_name: chunk.toolName,
+        arguments: chunk.input,
+      };
+    case "tool-output-available":
+      return {
+        type: "tool_response",
+        tool_id: chunk.toolCallId,
+        tool_name: chunk.toolName,
+        result: chunk.output,
+        success: chunk.success ?? true,
+      };
+    case "usage":
+      return {
+        type: "usage",
+        promptTokens: chunk.promptTokens,
+        completionTokens: chunk.completionTokens,
+        totalTokens: chunk.totalTokens,
+      };
+    case "error":
+      return {
+        type: "error",
+        message: chunk.errorText,
+        code: chunk.code,
+        details: chunk.details,
+      };
+    case "finish":
+      return { type: "stream_end" };
+    case "start":
+    case "text-start":
+    case "tool-input-start":
+      return null;
+  }
+}
+
+export function useChatStream() {
+  const [isStreaming, setIsStreaming] = useState(false);
+  const [error, setError] = useState<Error | null>(null);
+  const retryCountRef = useRef<number>(0);
+  const retryTimeoutRef = useRef<NodeJS.Timeout | null>(null);
+  const abortControllerRef = useRef<AbortController | null>(null);
+
+  const stopStreaming = useCallback(() => {
+    if (abortControllerRef.current) {
+      abortControllerRef.current.abort();
+      abortControllerRef.current = null;
+    }
+    if (retryTimeoutRef.current) {
+      clearTimeout(retryTimeoutRef.current);
+      retryTimeoutRef.current = null;
+    }
+    setIsStreaming(false);
+  }, []);
+
+  useEffect(() => {
+    return () => {
+      stopStreaming();
+    };
+  }, [stopStreaming]);
+
+  const sendMessage = useCallback(
+    async (
+      sessionId: string,
+      message: string,
+      onChunk: (chunk: StreamChunk) => void,
+      isUserMessage: boolean = true,
+      context?: { url: string; content: string },
+      isRetry: boolean = false,
+    ) => {
+      stopStreaming();
+
+      const abortController = new AbortController();
+      abortControllerRef.current = abortController;
+
+      if (abortController.signal.aborted) {
+        return Promise.reject(new Error("Request aborted"));
+      }
+
+      if (!isRetry) {
+        retryCountRef.current = 0;
+      }
+      setIsStreaming(true);
+      setError(null);
+
+      try {
+        const url = `/api/chat/sessions/${sessionId}/stream`;
+        const body = JSON.stringify({
+          message,
+          is_user_message: isUserMessage,
+          context: context || null,
+        });
+
+        const response = await fetch(url, {
+          method: "POST",
+          headers: {
+            "Content-Type": "application/json",
+            Accept: "text/event-stream",
+          },
+          body,
+          signal: abortController.signal,
+        });
+
+        if (!response.ok) {
+          const errorText = await response.text();
+          throw new Error(errorText || `HTTP ${response.status}`);
+        }
+
+        if (!response.body) {
+          throw new Error("Response body is null");
+        }
+
+        const reader = response.body.getReader();
+        const decoder = new TextDecoder();
+        let buffer = "";
+
+        return new Promise<void>((resolve, reject) => {
+          let didDispatchStreamEnd = false;
+
+          function dispatchStreamEnd() {
+            if (didDispatchStreamEnd) return;
+            didDispatchStreamEnd = true;
+            onChunk({ type: "stream_end" });
+          }
+
+          const cleanup = () => {
+            reader.cancel().catch(() => {
+              // Ignore cancel errors
+            });
+          };
+
+          async function readStream() {
+            try {
+              while (true) {
+                const { done, value } = await reader.read();
+
+                if (done) {
+                  cleanup();
+                  dispatchStreamEnd();
+                  retryCountRef.current = 0;
+                  stopStreaming();
+                  resolve();
+                  return;
+                }
+
+                buffer += decoder.decode(value, { stream: true });
+                const lines = buffer.split("\n");
+                buffer = lines.pop() || "";
+
+                for (const line of lines) {
+                  if (line.startsWith("data: ")) {
+                    const data = line.slice(6);
+                    if (data === "[DONE]") {
+                      cleanup();
+                      dispatchStreamEnd();
+                      retryCountRef.current = 0;
+                      stopStreaming();
+                      resolve();
+                      return;
+                    }
+
+                    try {
+                      const rawChunk = JSON.parse(data) as
+                        | StreamChunk
+                        | VercelStreamChunk;
+                      const chunk = normalizeStreamChunk(rawChunk);
+                      if (!chunk) {
+                        continue;
+                      }
+
+                      // Call the chunk handler
+                      onChunk(chunk);
+
+                      // Handle stream lifecycle
+                      if (chunk.type === "stream_end") {
+                        didDispatchStreamEnd = true;
+                        cleanup();
+                        retryCountRef.current = 0;
+                        stopStreaming();
+                        resolve();
+                        return;
+                      } else if (chunk.type === "error") {
+                        cleanup();
+                        reject(
+                          new Error(
+                            chunk.message || chunk.content || "Stream error",
+                          ),
+                        );
+                        return;
+                      }
+                    } catch (err) {
+                      // Skip invalid JSON lines
+                      console.warn("Failed to parse SSE chunk:", err, data);
+                    }
+                  }
+                }
+              }
+            } catch (err) {
+              if (err instanceof Error && err.name === "AbortError") {
+                cleanup();
+                return;
+              }
+
+              const streamError =
+                err instanceof Error ? err : new Error("Failed to read stream");
+
+              if (retryCountRef.current < MAX_RETRIES) {
+                retryCountRef.current += 1;
+                const retryDelay =
+                  INITIAL_RETRY_DELAY * Math.pow(2, retryCountRef.current - 1);
+
+                toast.info("Connection interrupted", {
+                  description: `Retrying in ${retryDelay / 1000} seconds...`,
+                });
+
+                retryTimeoutRef.current = setTimeout(() => {
+                  sendMessage(
+                    sessionId,
+                    message,
+                    onChunk,
+                    isUserMessage,
+                    context,
+                    true,
+                  ).catch((_err) => {
+                    // Retry failed
+                  });
+                }, retryDelay);
+              } else {
+                setError(streamError);
+                toast.error("Connection Failed", {
+                  description:
+                    "Unable to connect to chat service. Please try again.",
+                });
+                cleanup();
+                dispatchStreamEnd();
+                retryCountRef.current = 0;
+                stopStreaming();
+                reject(streamError);
+              }
+            }
+          }
+
+          readStream();
+        });
+      } catch (err) {
+        const streamError =
+          err instanceof Error ? err : new Error("Failed to start stream");
+        setError(streamError);
+        setIsStreaming(false);
+        throw streamError;
+      }
+    },
+    [stopStreaming],
+  );
+
+  return {
+    isStreaming,
+    error,
+    sendMessage,
+    stopStreaming,
+  };
+}

autogpt_platform/frontend/src/app/(platform)/chat/components/Chat/usePageContext.ts

@@ -0,0 +1,98 @@
+import { useCallback } from "react";
+
+export interface PageContext {
+  url: string;
+  content: string;
+}
+
+const MAX_CONTENT_CHARS = 10000;
+
+/**
+ * Hook to capture the current page context (URL + full page content)
+ * Privacy-hardened: removes sensitive inputs and enforces content size limits
+ */
+export function usePageContext() {
+  const capturePageContext = useCallback((): PageContext => {
+    if (typeof window === "undefined" || typeof document === "undefined") {
+      return { url: "", content: "" };
+    }
+
+    const url = window.location.href;
+
+    // Clone document to avoid modifying the original
+    const clone = document.cloneNode(true) as Document;
+
+    // Remove script, style, and noscript elements
+    const scripts = clone.querySelectorAll("script, style, noscript");
+    scripts.forEach((el) => el.remove());
+
+    // Remove sensitive elements and their content
+    const sensitiveSelectors = [
+      "input",
+      "textarea",
+      "[contenteditable]",
+      'input[type="password"]',
+      'input[type="email"]',
+      'input[type="tel"]',
+      'input[type="search"]',
+      'input[type="hidden"]',
+      "form",
+      "[data-sensitive]",
+      "[data-sensitive='true']",
+    ];
+
+    sensitiveSelectors.forEach((selector) => {
+      const elements = clone.querySelectorAll(selector);
+      elements.forEach((el) => {
+        // For form elements, remove the entire element
+        if (el.tagName === "FORM") {
+          el.remove();
+        } else {
+          // For inputs and textareas, clear their values but keep the element structure
+          if (
+            el instanceof HTMLInputElement ||
+            el instanceof HTMLTextAreaElement
+          ) {
+            el.value = "";
+            el.textContent = "";
+          } else {
+            // For other sensitive elements, remove them entirely
+            el.remove();
+          }
+        }
+      });
+    });
+
+    // Strip any remaining input values that might have been missed
+    const allInputs = clone.querySelectorAll("input, textarea");
+    allInputs.forEach((el) => {
+      if (el instanceof HTMLInputElement || el instanceof HTMLTextAreaElement) {
+        el.value = "";
+        el.textContent = "";
+      }
+    });
+
+    // Get text content from body
+    const body = clone.body;
+    const content = body?.textContent || body?.innerText || "";
+
+    // Clean up whitespace
+    let cleanedContent = content
+      .replace(/\s+/g, " ")
+      .replace(/\n\s*\n/g, "\n")
+      .trim();
+
+    // Enforce maximum content size
+    if (cleanedContent.length > MAX_CONTENT_CHARS) {
+      cleanedContent =
+        cleanedContent.substring(0, MAX_CONTENT_CHARS) + "... [truncated]";
+    }
+
+    return {
+      url,
+      content: cleanedContent,
+    };
+  }, []);
+
+  return { capturePageContext };
+}

autogpt_platform/frontend/src/app/(platform)/chat/components/ChatContainer/ChatContainer.tsx

@@ -1,68 +0,0 @@
-import { cn } from "@/lib/utils";
-import { ChatInput } from "@/app/(platform)/chat/components/ChatInput/ChatInput";
-import { MessageList } from "@/app/(platform)/chat/components/MessageList/MessageList";
-import { QuickActionsWelcome } from "@/app/(platform)/chat/components/QuickActionsWelcome/QuickActionsWelcome";
-import { useChatContainer } from "./useChatContainer";
-import type { SessionDetailResponse } from "@/app/api/__generated__/models/sessionDetailResponse";
-
-export interface ChatContainerProps {
-  sessionId: string | null;
-  initialMessages: SessionDetailResponse["messages"];
-  onRefreshSession: () => Promise<void>;
-  className?: string;
-}
-
-export function ChatContainer({
-  sessionId,
-  initialMessages,
-  onRefreshSession,
-  className,
-}: ChatContainerProps) {
-  const { messages, streamingChunks, isStreaming, sendMessage } =
-    useChatContainer({
-      sessionId,
-      initialMessages,
-      onRefreshSession,
-    });
-
-  const quickActions = [
-    "Find agents for social media management",
-    "Show me agents for content creation",
-    "Help me automate my business",
-    "What can you help me with?",
-  ];
-
-  return (
-    <div className={cn("flex h-full flex-col", className)}>
-      {/* Messages or Welcome Screen */}
-      {messages.length === 0 ? (
-        <QuickActionsWelcome
-          title="Welcome to AutoGPT Chat"
-          description="Start a conversation to discover and run AI agents."
-          actions={quickActions}
-          onActionClick={sendMessage}
-          disabled={isStreaming || !sessionId}
-        />
-      ) : (
-        <MessageList
-          messages={messages}
-          streamingChunks={streamingChunks}
-          isStreaming={isStreaming}
-          onSendMessage={sendMessage}
-          className="flex-1"
-        />
-      )}
-
-      {/* Input - Always visible */}
-      <div className="border-t border-zinc-200 p-4 dark:border-zinc-800">
-        <ChatInput
-          onSend={sendMessage}
-          disabled={isStreaming || !sessionId}
-          placeholder={
-            sessionId ? "Type your message..." : "Creating session..."
-          }
-        />
-      </div>
-    </div>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/chat/components/ChatContainer/useChatContainer.ts

@@ -1,130 +0,0 @@
-import { useState, useCallback, useRef, useMemo } from "react";
-import { toast } from "sonner";
-import { useChatStream } from "@/app/(platform)/chat/useChatStream";
-import type { SessionDetailResponse } from "@/app/api/__generated__/models/sessionDetailResponse";
-import type { ChatMessageData } from "@/app/(platform)/chat/components/ChatMessage/useChatMessage";
-import {
-  parseToolResponse,
-  isValidMessage,
-  isToolCallArray,
-  createUserMessage,
-  filterAuthMessages,
-} from "./helpers";
-import { createStreamEventDispatcher } from "./createStreamEventDispatcher";
-
-interface UseChatContainerArgs {
-  sessionId: string | null;
-  initialMessages: SessionDetailResponse["messages"];
-  onRefreshSession: () => Promise<void>;
-}
-
-export function useChatContainer({
-  sessionId,
-  initialMessages,
-}: UseChatContainerArgs) {
-  const [messages, setMessages] = useState<ChatMessageData[]>([]);
-  const [streamingChunks, setStreamingChunks] = useState<string[]>([]);
-  const [hasTextChunks, setHasTextChunks] = useState(false);
-  const streamingChunksRef = useRef<string[]>([]);
-  const { error, sendMessage: sendStreamMessage } = useChatStream();
-  const isStreaming = hasTextChunks;
-
-  const allMessages = useMemo(() => {
-    const processedInitialMessages = initialMessages
-      .filter((msg: Record<string, unknown>) => {
-        if (!isValidMessage(msg)) {
-          console.warn("Invalid message structure from backend:", msg);
-          return false;
-        }
-        const content = String(msg.content || "").trim();
-        const toolCalls = msg.tool_calls;
-        return (
-          content.length > 0 ||
-          (toolCalls && Array.isArray(toolCalls) && toolCalls.length > 0)
-        );
-      })
-      .map((msg: Record<string, unknown>) => {
-        const content = String(msg.content || "");
-        const role = String(msg.role || "assistant").toLowerCase();
-        const toolCalls = msg.tool_calls;
-        if (
-          role === "assistant" &&
-          toolCalls &&
-          isToolCallArray(toolCalls) &&
-          toolCalls.length > 0
-        ) {
-          return null;
-        }
-        if (role === "tool") {
-          const timestamp = msg.timestamp
-            ? new Date(msg.timestamp as string)
-            : undefined;
-          const toolResponse = parseToolResponse(
-            content,
-            (msg.tool_call_id as string) || "",
-            "unknown",
-            timestamp,
-          );
-          if (!toolResponse) {
-            return null;
-          }
-          return toolResponse;
-        }
-        return {
-          type: "message",
-          role: role as "user" | "assistant" | "system",
-          content,
-          timestamp: msg.timestamp
-            ? new Date(msg.timestamp as string)
-            : undefined,
-        };
-      })
-      .filter((msg): msg is ChatMessageData => msg !== null);
-
-    return [...processedInitialMessages, ...messages];
-  }, [initialMessages, messages]);
-
-  const sendMessage = useCallback(
-    async function sendMessage(content: string, isUserMessage: boolean = true) {
-      if (!sessionId) {
-        console.error("Cannot send message: no session ID");
-        return;
-      }
-      if (isUserMessage) {
-        const userMessage = createUserMessage(content);
-        setMessages((prev) => [...filterAuthMessages(prev), userMessage]);
-      } else {
-        setMessages((prev) => filterAuthMessages(prev));
-      }
-      setStreamingChunks([]);
-      streamingChunksRef.current = [];
-      setHasTextChunks(false);
-      const dispatcher = createStreamEventDispatcher({
-        setHasTextChunks,
-        setStreamingChunks,
-        streamingChunksRef,
-        setMessages,
-        sessionId,
-      });
-      try {
-        await sendStreamMessage(sessionId, content, dispatcher, isUserMessage);
-      } catch (err) {
-        console.error("Failed to send message:", err);
-        const errorMessage =
-          err instanceof Error ? err.message : "Failed to send message";
-        toast.error("Failed to send message", {
-          description: errorMessage,
-        });
-      }
-    },
-    [sessionId, sendStreamMessage],
-  );
-
-  return {
-    messages: allMessages,
-    streamingChunks,
-    isStreaming,
-    error,
-    sendMessage,
-  };
-}

autogpt_platform/frontend/src/app/(platform)/chat/components/ChatCredentialsSetup/ChatCredentialsSetup.tsx

@@ -1,153 +0,0 @@
-import { CredentialsInput } from "@/app/(platform)/library/agents/[id]/components/NewAgentLibraryView/components/modals/CredentialsInputs/CredentialsInput";
-import { Card } from "@/components/atoms/Card/Card";
-import { Text } from "@/components/atoms/Text/Text";
-import type { BlockIOCredentialsSubSchema } from "@/lib/autogpt-server-api";
-import { cn } from "@/lib/utils";
-import { CheckIcon, KeyIcon, WarningIcon } from "@phosphor-icons/react";
-import { useEffect, useRef } from "react";
-import { useChatCredentialsSetup } from "./useChatCredentialsSetup";
-
-export interface CredentialInfo {
-  provider: string;
-  providerName: string;
-  credentialType: "api_key" | "oauth2" | "user_password" | "host_scoped";
-  title: string;
-  scopes?: string[];
-}
-
-interface Props {
-  credentials: CredentialInfo[];
-  agentName?: string;
-  message: string;
-  onAllCredentialsComplete: () => void;
-  onCancel: () => void;
-  className?: string;
-}
-
-function createSchemaFromCredentialInfo(
-  credential: CredentialInfo,
-): BlockIOCredentialsSubSchema {
-  return {
-    type: "object",
-    properties: {},
-    credentials_provider: [credential.provider],
-    credentials_types: [credential.credentialType],
-    credentials_scopes: credential.scopes,
-    discriminator: undefined,
-    discriminator_mapping: undefined,
-    discriminator_values: undefined,
-  };
-}
-
-export function ChatCredentialsSetup({
-  credentials,
-  agentName: _agentName,
-  message,
-  onAllCredentialsComplete,
-  onCancel: _onCancel,
-  className,
-}: Props) {
-  const { selectedCredentials, isAllComplete, handleCredentialSelect } =
-    useChatCredentialsSetup(credentials);
-
-  // Track if we've already called completion to prevent double calls
-  const hasCalledCompleteRef = useRef(false);
-
-  // Reset the completion flag when credentials change (new credential setup flow)
-  useEffect(
-    function resetCompletionFlag() {
-      hasCalledCompleteRef.current = false;
-    },
-    [credentials],
-  );
-
-  // Auto-call completion when all credentials are configured
-  useEffect(
-    function autoCompleteWhenReady() {
-      if (isAllComplete && !hasCalledCompleteRef.current) {
-        hasCalledCompleteRef.current = true;
-        onAllCredentialsComplete();
-      }
-    },
-    [isAllComplete, onAllCredentialsComplete],
-  );
-
-  return (
-    <Card
-      className={cn(
-        "mx-4 my-2 overflow-hidden border-orange-200 bg-orange-50 dark:border-orange-900 dark:bg-orange-950",
-        className,
-      )}
-    >
-      <div className="flex items-start gap-4 p-6">
-        <div className="flex h-12 w-12 flex-shrink-0 items-center justify-center rounded-full bg-orange-500">
-          <KeyIcon size={24} weight="bold" className="text-white" />
-        </div>
-        <div className="flex-1">
-          <Text
-            variant="h3"
-            className="mb-2 text-orange-900 dark:text-orange-100"
-          >
-            Credentials Required
-          </Text>
-          <Text
-            variant="body"
-            className="mb-4 text-orange-700 dark:text-orange-300"
-          >
-            {message}
-          </Text>
-
-          <div className="space-y-3">
-            {credentials.map((cred, index) => {
-              const schema = createSchemaFromCredentialInfo(cred);
-              const isSelected = !!selectedCredentials[cred.provider];
-
-              return (
-                <div
-                  key={`${cred.provider}-${index}`}
-                  className={cn(
-                    "relative rounded-lg border border-orange-200 bg-white p-4 dark:border-orange-800 dark:bg-orange-900/20",
-                    isSelected &&
-                      "border-green-500 bg-green-50 dark:border-green-700 dark:bg-green-950/30",
-                  )}
-                >
-                  <div className="mb-2 flex items-center justify-between">
-                    <div className="flex items-center gap-2">
-                      {isSelected ? (
-                        <CheckIcon
-                          size={20}
-                          className="text-green-500"
-                          weight="bold"
-                        />
-                      ) : (
-                        <WarningIcon
-                          size={20}
-                          className="text-orange-500"
-                          weight="bold"
-                        />
-                      )}
-                      <Text
-                        variant="body"
-                        className="font-semibold text-orange-900 dark:text-orange-100"
-                      >
-                        {cred.providerName}
-                      </Text>
-                    </div>
-                  </div>
-
-                  <CredentialsInput
-                    schema={schema}
-                    selectedCredentials={selectedCredentials[cred.provider]}
-                    onSelectCredentials={(credMeta) =>
-                      handleCredentialSelect(cred.provider, credMeta)
-                    }
-                  />
-                </div>
-              );
-            })}
-          </div>
-        </div>
-      </div>
-    </Card>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/chat/components/ChatInput/ChatInput.tsx

@@ -1,63 +0,0 @@
-import { cn } from "@/lib/utils";
-import { PaperPlaneRightIcon } from "@phosphor-icons/react";
-import { Button } from "@/components/atoms/Button/Button";
-import { useChatInput } from "./useChatInput";
-
-export interface ChatInputProps {
-  onSend: (message: string) => void;
-  disabled?: boolean;
-  placeholder?: string;
-  className?: string;
-}
-
-export function ChatInput({
-  onSend,
-  disabled = false,
-  placeholder = "Type your message...",
-  className,
-}: ChatInputProps) {
-  const { value, setValue, handleKeyDown, handleSend, textareaRef } =
-    useChatInput({
-      onSend,
-      disabled,
-      maxRows: 5,
-    });
-
-  return (
-    <div className={cn("flex gap-2", className)}>
-      <textarea
-        ref={textareaRef}
-        value={value}
-        onChange={(e) => setValue(e.target.value)}
-        onKeyDown={handleKeyDown}
-        placeholder={placeholder}
-        disabled={disabled}
-        rows={1}
-        autoComplete="off"
-        aria-label="Chat message input"
-        aria-describedby="chat-input-hint"
-        className={cn(
-          "flex-1 resize-none rounded-lg border border-neutral-200 bg-white px-4 py-2 text-sm",
-          "placeholder:text-neutral-400",
-          "focus:border-violet-600 focus:outline-none focus:ring-2 focus:ring-violet-600/20",
-          "dark:border-neutral-800 dark:bg-neutral-900 dark:text-neutral-100 dark:placeholder:text-neutral-500",
-          "disabled:cursor-not-allowed disabled:opacity-50",
-        )}
-      />
-      <span id="chat-input-hint" className="sr-only">
-        Press Enter to send, Shift+Enter for new line
-      </span>
-
-      <Button
-        variant="primary"
-        size="small"
-        onClick={handleSend}
-        disabled={disabled || !value.trim()}
-        className="self-end"
-        aria-label="Send message"
-      >
-        <PaperPlaneRightIcon className="h-4 w-4" weight="fill" />
-      </Button>
-    </div>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/chat/components/ChatLoadingState/ChatLoadingState.tsx

@@ -1,31 +0,0 @@
-import React from "react";
-import { Text } from "@/components/atoms/Text/Text";
-import { ArrowClockwiseIcon } from "@phosphor-icons/react";
-import { cn } from "@/lib/utils";
-
-export interface ChatLoadingStateProps {
-  message?: string;
-  className?: string;
-}
-
-export function ChatLoadingState({
-  message = "Loading...",
-  className,
-}: ChatLoadingStateProps) {
-  return (
-    <div
-      className={cn("flex flex-1 items-center justify-center p-6", className)}
-    >
-      <div className="flex flex-col items-center gap-4 text-center">
-        <ArrowClockwiseIcon
-          size={32}
-          weight="bold"
-          className="animate-spin text-purple-500"
-        />
-        <Text variant="body" className="text-zinc-600 dark:text-zinc-400">
-          {message}
-        </Text>
-      </div>
-    </div>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/chat/components/ChatMessage/ChatMessage.tsx

@@ -1,194 +0,0 @@
-"use client";
-
-import { cn } from "@/lib/utils";
-import { RobotIcon, UserIcon, CheckCircleIcon } from "@phosphor-icons/react";
-import { useCallback } from "react";
-import { MessageBubble } from "@/app/(platform)/chat/components/MessageBubble/MessageBubble";
-import { MarkdownContent } from "@/app/(platform)/chat/components/MarkdownContent/MarkdownContent";
-import { ToolCallMessage } from "@/app/(platform)/chat/components/ToolCallMessage/ToolCallMessage";
-import { ToolResponseMessage } from "@/app/(platform)/chat/components/ToolResponseMessage/ToolResponseMessage";
-import { AuthPromptWidget } from "@/app/(platform)/chat/components/AuthPromptWidget/AuthPromptWidget";
-import { ChatCredentialsSetup } from "@/app/(platform)/chat/components/ChatCredentialsSetup/ChatCredentialsSetup";
-import { useSupabase } from "@/lib/supabase/hooks/useSupabase";
-import { useChatMessage, type ChatMessageData } from "./useChatMessage";
-import { getToolActionPhrase } from "@/app/(platform)/chat/helpers";
-export interface ChatMessageProps {
-  message: ChatMessageData;
-  className?: string;
-  onDismissLogin?: () => void;
-  onDismissCredentials?: () => void;
-  onSendMessage?: (content: string, isUserMessage?: boolean) => void;
-}
-
-export function ChatMessage({
-  message,
-  className,
-  onDismissCredentials,
-  onSendMessage,
-}: ChatMessageProps) {
-  const { user } = useSupabase();
-  const {
-    formattedTimestamp,
-    isUser,
-    isAssistant,
-    isToolCall,
-    isToolResponse,
-    isLoginNeeded,
-    isCredentialsNeeded,
-  } = useChatMessage(message);
-
-  const handleAllCredentialsComplete = useCallback(
-    function handleAllCredentialsComplete() {
-      // Send a user message that explicitly asks to retry the setup
-      // This ensures the LLM calls get_required_setup_info again and proceeds with execution
-      if (onSendMessage) {
-        onSendMessage(
-          "I've configured the required credentials. Please check if everything is ready and proceed with setting up the agent.",
-        );
-      }
-      // Optionally dismiss the credentials prompt
-      if (onDismissCredentials) {
-        onDismissCredentials();
-      }
-    },
-    [onSendMessage, onDismissCredentials],
-  );
-
-  function handleCancelCredentials() {
-    // Dismiss the credentials prompt
-    if (onDismissCredentials) {
-      onDismissCredentials();
-    }
-  }
-
-  // Render credentials needed messages
-  if (isCredentialsNeeded && message.type === "credentials_needed") {
-    return (
-      <ChatCredentialsSetup
-        credentials={message.credentials}
-        agentName={message.agentName}
-        message={message.message}
-        onAllCredentialsComplete={handleAllCredentialsComplete}
-        onCancel={handleCancelCredentials}
-        className={className}
-      />
-    );
-  }
-
-  // Render login needed messages
-  if (isLoginNeeded && message.type === "login_needed") {
-    // If user is already logged in, show success message instead of auth prompt
-    if (user) {
-      return (
-        <div className={cn("px-4 py-2", className)}>
-          <div className="my-4 overflow-hidden rounded-lg border border-green-200 bg-gradient-to-br from-green-50 to-emerald-50 dark:border-green-800 dark:from-green-950/30 dark:to-emerald-950/30">
-            <div className="px-6 py-4">
-              <div className="flex items-center gap-3">
-                <div className="flex h-10 w-10 items-center justify-center rounded-full bg-green-600">
-                  <CheckCircleIcon
-                    size={20}
-                    weight="fill"
-                    className="text-white"
-                  />
-                </div>
-                <div>
-                  <h3 className="text-lg font-semibold text-neutral-900 dark:text-neutral-100">
-                    Successfully Authenticated
-                  </h3>
-                  <p className="text-sm text-neutral-600 dark:text-neutral-400">
-                    You&apos;re now signed in and ready to continue
-                  </p>
-                </div>
-              </div>
-            </div>
-          </div>
-        </div>
-      );
-    }
-
-    // Show auth prompt if not logged in
-    return (
-      <div className={cn("px-4 py-2", className)}>
-        <AuthPromptWidget
-          message={message.message}
-          sessionId={message.sessionId}
-          agentInfo={message.agentInfo}
-          returnUrl="/chat"
-        />
-      </div>
-    );
-  }
-
-  // Render tool call messages
-  if (isToolCall && message.type === "tool_call") {
-    return (
-      <div className={cn("px-4 py-2", className)}>
-        <ToolCallMessage toolName={message.toolName} />
-      </div>
-    );
-  }
-
-  // Render tool response messages
-  if (
-    (isToolResponse && message.type === "tool_response") ||
-    message.type === "no_results" ||
-    message.type === "agent_carousel" ||
-    message.type === "execution_started"
-  ) {
-    return (
-      <div className={cn("px-4 py-2", className)}>
-        <ToolResponseMessage toolName={getToolActionPhrase(message.toolName)} />
-      </div>
-    );
-  }
-
-  // Render regular chat messages
-  if (message.type === "message") {
-    return (
-      <div
-        className={cn(
-          "flex gap-3 px-4 py-4",
-          isUser && "flex-row-reverse",
-          className,
-        )}
-      >
-        {/* Avatar */}
-        <div className="flex-shrink-0">
-          <div
-            className={cn(
-              "flex h-8 w-8 items-center justify-center rounded-full",
-              isUser && "bg-zinc-200 dark:bg-zinc-700",
-              isAssistant && "bg-purple-600 dark:bg-purple-500",
-            )}
-          >
-            {isUser ? (
-              <UserIcon className="h-5 w-5 text-zinc-700 dark:text-zinc-200" />
-            ) : (
-              <RobotIcon className="h-5 w-5 text-white" />
-            )}
-          </div>
-        </div>
-
-        {/* Message Content */}
-        <div className={cn("flex max-w-[70%] flex-col", isUser && "items-end")}>
-          <MessageBubble variant={isUser ? "user" : "assistant"}>
-            <MarkdownContent content={message.content} />
-          </MessageBubble>
-
-          {/* Timestamp */}
-          <span
-            className={cn(
-              "mt-1 text-xs text-zinc-500 dark:text-zinc-400",
-              isUser && "text-right",
-            )}
-          >
-            {formattedTimestamp}
-          </span>
-        </div>
-      </div>
-    );
-  }
-
-  // Fallback for unknown message types
-  return null;
-}

autogpt_platform/frontend/src/app/(platform)/chat/components/MessageBubble/MessageBubble.tsx

@@ -1,28 +0,0 @@
-import { cn } from "@/lib/utils";
-import { ReactNode } from "react";
-
-export interface MessageBubbleProps {
-  children: ReactNode;
-  variant: "user" | "assistant";
-  className?: string;
-}
-
-export function MessageBubble({
-  children,
-  variant,
-  className,
-}: MessageBubbleProps) {
-  return (
-    <div
-      className={cn(
-        "rounded-lg px-4 py-3 text-sm",
-        variant === "user" && "bg-violet-600 text-white dark:bg-violet-500",
-        variant === "assistant" &&
-          "border border-neutral-200 bg-white dark:border-neutral-700 dark:bg-neutral-900 dark:text-neutral-100",
-        className,
-      )}
-    >
-      {children}
-    </div>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/chat/components/MessageList/MessageList.tsx

@@ -1,61 +0,0 @@
-import { cn } from "@/lib/utils";
-import { ChatMessage } from "../ChatMessage/ChatMessage";
-import type { ChatMessageData } from "../ChatMessage/useChatMessage";
-import { StreamingMessage } from "../StreamingMessage/StreamingMessage";
-import { useMessageList } from "./useMessageList";
-
-export interface MessageListProps {
-  messages: ChatMessageData[];
-  streamingChunks?: string[];
-  isStreaming?: boolean;
-  className?: string;
-  onStreamComplete?: () => void;
-  onSendMessage?: (content: string) => void;
-}
-
-export function MessageList({
-  messages,
-  streamingChunks = [],
-  isStreaming = false,
-  className,
-  onStreamComplete,
-  onSendMessage,
-}: MessageListProps) {
-  const { messagesEndRef, messagesContainerRef } = useMessageList({
-    messageCount: messages.length,
-    isStreaming,
-  });
-
-  return (
-    <div
-      ref={messagesContainerRef}
-      className={cn(
-        "flex-1 overflow-y-auto",
-        "scrollbar-thin scrollbar-track-transparent scrollbar-thumb-zinc-300 dark:scrollbar-thumb-zinc-700",
-        className,
-      )}
-    >
-      <div className="space-y-0">
-        {/* Render all persisted messages */}
-        {messages.map((message, index) => (
-          <ChatMessage
-            key={index}
-            message={message}
-            onSendMessage={onSendMessage}
-          />
-        ))}
-
-        {/* Render streaming message if active */}
-        {isStreaming && streamingChunks.length > 0 && (
-          <StreamingMessage
-            chunks={streamingChunks}
-            onComplete={onStreamComplete}
-          />
-        )}
-
-        {/* Invisible div to scroll to */}
-        <div ref={messagesEndRef} />
-      </div>
-    </div>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/chat/components/QuickActionsWelcome/QuickActionsWelcome.tsx

@@ -1,51 +0,0 @@
-import React from "react";
-import { Text } from "@/components/atoms/Text/Text";
-import { cn } from "@/lib/utils";
-
-export interface QuickActionsWelcomeProps {
-  title: string;
-  description: string;
-  actions: string[];
-  onActionClick: (action: string) => void;
-  disabled?: boolean;
-  className?: string;
-}
-
-export function QuickActionsWelcome({
-  title,
-  description,
-  actions,
-  onActionClick,
-  disabled = false,
-  className,
-}: QuickActionsWelcomeProps) {
-  return (
-    <div
-      className={cn("flex flex-1 items-center justify-center p-4", className)}
-    >
-      <div className="max-w-2xl text-center">
-        <Text
-          variant="h2"
-          className="mb-4 text-3xl font-bold text-zinc-900 dark:text-zinc-100"
-        >
-          {title}
-        </Text>
-        <Text variant="body" className="mb-8 text-zinc-600 dark:text-zinc-400">
-          {description}
-        </Text>
-        <div className="grid gap-2 sm:grid-cols-2">
-          {actions.map((action) => (
-            <button
-              key={action}
-              onClick={() => onActionClick(action)}
-              disabled={disabled}
-              className="rounded-lg border border-zinc-200 bg-white p-4 text-left text-sm hover:bg-zinc-50 disabled:cursor-not-allowed disabled:opacity-50 dark:border-zinc-800 dark:bg-zinc-900 dark:hover:bg-zinc-800"
-            >
-              {action}
-            </button>
-          ))}
-        </div>
-      </div>
-    </div>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/chat/components/StreamingMessage/StreamingMessage.tsx

@@ -1,42 +0,0 @@
-import { cn } from "@/lib/utils";
-import { Robot } from "@phosphor-icons/react";
-import { MessageBubble } from "@/app/(platform)/chat/components/MessageBubble/MessageBubble";
-import { MarkdownContent } from "@/app/(platform)/chat/components/MarkdownContent/MarkdownContent";
-import { useStreamingMessage } from "./useStreamingMessage";
-
-export interface StreamingMessageProps {
-  chunks: string[];
-  className?: string;
-  onComplete?: () => void;
-}
-
-export function StreamingMessage({
-  chunks,
-  className,
-  onComplete,
-}: StreamingMessageProps) {
-  const { displayText } = useStreamingMessage({ chunks, onComplete });
-
-  return (
-    <div className={cn("flex gap-3 px-4 py-4", className)}>
-      {/* Avatar */}
-      <div className="flex-shrink-0">
-        <div className="flex h-8 w-8 items-center justify-center rounded-full bg-purple-600 dark:bg-purple-500">
-          <Robot className="h-5 w-5 text-white" />
-        </div>
-      </div>
-
-      {/* Message Content */}
-      <div className="flex max-w-[70%] flex-col">
-        <MessageBubble variant="assistant">
-          <MarkdownContent content={displayText} />
-        </MessageBubble>
-
-        {/* Timestamp */}
-        <span className="mt-1 text-xs text-neutral-500 dark:text-neutral-400">
-          Typing...
-        </span>
-      </div>
-    </div>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/chat/components/ToolCallMessage/ToolCallMessage.tsx

@@ -1,49 +0,0 @@
-import React from "react";
-import { WrenchIcon } from "@phosphor-icons/react";
-import { cn } from "@/lib/utils";
-import { getToolActionPhrase } from "@/app/(platform)/chat/helpers";
-
-export interface ToolCallMessageProps {
-  toolName: string;
-  className?: string;
-}
-
-export function ToolCallMessage({ toolName, className }: ToolCallMessageProps) {
-  return (
-    <div
-      className={cn(
-        "mx-10 max-w-[70%] overflow-hidden rounded-lg border transition-all duration-200",
-        "border-neutral-200 dark:border-neutral-700",
-        "bg-white dark:bg-neutral-900",
-        "animate-in fade-in-50 slide-in-from-top-1",
-        className,
-      )}
-    >
-      {/* Header */}
-      <div
-        className={cn(
-          "flex items-center justify-between px-3 py-2",
-          "bg-gradient-to-r from-neutral-50 to-neutral-100 dark:from-neutral-800/20 dark:to-neutral-700/20",
-        )}
-      >
-        <div className="flex items-center gap-2 overflow-hidden">
-          <WrenchIcon
-            size={16}
-            weight="bold"
-            className="flex-shrink-0 text-neutral-500 dark:text-neutral-400"
-          />
-          <span className="relative inline-block overflow-hidden text-sm font-medium text-neutral-700 dark:text-neutral-300">
-            {getToolActionPhrase(toolName)}...
-            <span
-              className={cn(
-                "absolute inset-0 bg-gradient-to-r from-transparent via-white/50 to-transparent",
-                "dark:via-white/20",
-                "animate-shimmer",
-              )}
-            />
-          </span>
-        </div>
-      </div>
-    </div>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/chat/components/ToolResponseMessage/ToolResponseMessage.tsx

@@ -1,52 +0,0 @@
-import React from "react";
-import { WrenchIcon } from "@phosphor-icons/react";
-import { cn } from "@/lib/utils";
-import { getToolActionPhrase } from "@/app/(platform)/chat/helpers";
-
-export interface ToolResponseMessageProps {
-  toolName: string;
-  success?: boolean;
-  className?: string;
-}
-
-export function ToolResponseMessage({
-  toolName,
-  success = true,
-  className,
-}: ToolResponseMessageProps) {
-  return (
-    <div
-      className={cn(
-        "mx-10 max-w-[70%] overflow-hidden rounded-lg border transition-all duration-200",
-        success
-          ? "border-neutral-200 dark:border-neutral-700"
-          : "border-red-200 dark:border-red-800",
-        "bg-white dark:bg-neutral-900",
-        "animate-in fade-in-50 slide-in-from-top-1",
-        className,
-      )}
-    >
-      {/* Header */}
-      <div
-        className={cn(
-          "flex items-center justify-between px-3 py-2",
-          "bg-gradient-to-r",
-          success
-            ? "from-neutral-50 to-neutral-100 dark:from-neutral-800/20 dark:to-neutral-700/20"
-            : "from-red-50 to-red-100 dark:from-red-900/20 dark:to-red-800/20",
-        )}
-      >
-        <div className="flex items-center gap-2">
-          <WrenchIcon
-            size={16}
-            weight="bold"
-            className="text-neutral-500 dark:text-neutral-400"
-          />
-          <span className="text-sm font-medium text-neutral-700 dark:text-neutral-300">
-            {getToolActionPhrase(toolName)}...
-          </span>
-        </div>
-      </div>
-    </div>
-  );
-}

autogpt_platform/frontend/src/app/(platform)/chat/page.tsx

@@ -1,30 +1,17 @@
 "use client";
 
-import { useChatPage } from "./useChatPage";
-import { ChatContainer } from "./components/ChatContainer/ChatContainer";
-import { ChatErrorState } from "./components/ChatErrorState/ChatErrorState";
-import { ChatLoadingState } from "./components/ChatLoadingState/ChatLoadingState";
-import { useGetFlag, Flag } from "@/services/feature-flags/use-get-flag";
+import { Flag, useGetFlag } from "@/services/feature-flags/use-get-flag";
 import { useRouter } from "next/navigation";
 import { useEffect } from "react";
+import { Chat } from "./components/Chat/Chat";
 
 export default function ChatPage() {
   const isChatEnabled = useGetFlag(Flag.CHAT);
   const router = useRouter();
-  const {
-    messages,
-    isLoading,
-    isCreating,
-    error,
-    sessionId,
-    createSession,
-    clearSession,
-    refreshSession,
-  } = useChatPage();
 
   useEffect(() => {
     if (isChatEnabled === false) {
-      router.push("/404");
+      router.push("/marketplace");
     }
   }, [isChatEnabled, router]);
 
@@ -34,50 +21,7 @@ export default function ChatPage() {
 
   return (
     <div className="flex h-full flex-col">
-      {/* Header */}
-      <header className="border-b border-zinc-200 bg-white p-4 dark:border-zinc-800 dark:bg-zinc-900">
-        <div className="container mx-auto flex items-center justify-between">
-          <h1 className="text-xl font-semibold">Chat</h1>
-          {sessionId && (
-            <div className="flex items-center gap-4">
-              <span className="text-sm text-zinc-600 dark:text-zinc-400">
-                Session: {sessionId.slice(0, 8)}...
-              </span>
-              <button
-                onClick={clearSession}
-                className="text-sm text-zinc-600 hover:text-zinc-900 dark:text-zinc-400 dark:hover:text-zinc-100"
-              >
-                New Chat
-              </button>
-            </div>
-          )}
-        </div>
-      </header>
-
-      {/* Main Content */}
-      <main className="container mx-auto flex flex-1 flex-col overflow-hidden">
-        {/* Loading State - show when explicitly loading/creating OR when we don't have a session yet and no error */}
-        {(isLoading || isCreating || (!sessionId && !error)) && (
-          <ChatLoadingState
-            message={isCreating ? "Creating session..." : "Loading..."}
-          />
-        )}
-
-        {/* Error State */}
-        {error && !isLoading && (
-          <ChatErrorState error={error} onRetry={createSession} />
-        )}
-
-        {/* Session Content */}
-        {sessionId && !isLoading && !error && (
-          <ChatContainer
-            sessionId={sessionId}
-            initialMessages={messages}
-            onRefreshSession={refreshSession}
-            className="flex-1"
-          />
-        )}
-      </main>
+      <Chat className="flex-1" />
     </div>
   );
 }

autogpt_platform/frontend/src/app/(platform)/chat/useChatStream.ts

@@ -1,204 +0,0 @@
-import { useState, useCallback, useRef, useEffect } from "react";
-import { toast } from "sonner";
-import type { ToolArguments, ToolResult } from "@/types/chat";
-
-const MAX_RETRIES = 3;
-const INITIAL_RETRY_DELAY = 1000;
-
-export interface StreamChunk {
-  type:
-    | "text_chunk"
-    | "text_ended"
-    | "tool_call"
-    | "tool_call_start"
-    | "tool_response"
-    | "login_needed"
-    | "need_login"
-    | "credentials_needed"
-    | "error"
-    | "usage"
-    | "stream_end";
-  timestamp?: string;
-  content?: string;
-  message?: string;
-  tool_id?: string;
-  tool_name?: string;
-  arguments?: ToolArguments;
-  result?: ToolResult;
-  success?: boolean;
-  idx?: number;
-  session_id?: string;
-  agent_info?: {
-    graph_id: string;
-    name: string;
-    trigger_type: string;
-  };
-  provider?: string;
-  provider_name?: string;
-  credential_type?: string;
-  scopes?: string[];
-  title?: string;
-  [key: string]: unknown;
-}
-
-export function useChatStream() {
-  const [isStreaming, setIsStreaming] = useState(false);
-  const [error, setError] = useState<Error | null>(null);
-  const eventSourceRef = useRef<EventSource | null>(null);
-  const retryCountRef = useRef<number>(0);
-  const retryTimeoutRef = useRef<NodeJS.Timeout | null>(null);
-  const abortControllerRef = useRef<AbortController | null>(null);
-
-  const stopStreaming = useCallback(() => {
-    if (abortControllerRef.current) {
-      abortControllerRef.current.abort();
-      abortControllerRef.current = null;
-    }
-    if (eventSourceRef.current) {
-      eventSourceRef.current.close();
-      eventSourceRef.current = null;
-    }
-    if (retryTimeoutRef.current) {
-      clearTimeout(retryTimeoutRef.current);
-      retryTimeoutRef.current = null;
-    }
-    retryCountRef.current = 0;
-    setIsStreaming(false);
-  }, []);
-
-  useEffect(() => {
-    return () => {
-      stopStreaming();
-    };
-  }, [stopStreaming]);
-
-  const sendMessage = useCallback(
-    async (
-      sessionId: string,
-      message: string,
-      onChunk: (chunk: StreamChunk) => void,
-      isUserMessage: boolean = true,
-    ) => {
-      stopStreaming();
-
-      const abortController = new AbortController();
-      abortControllerRef.current = abortController;
-
-      if (abortController.signal.aborted) {
-        return Promise.reject(new Error("Request aborted"));
-      }
-
-      retryCountRef.current = 0;
-      setIsStreaming(true);
-      setError(null);
-
-      try {
-        const url = `/api/chat/sessions/${sessionId}/stream?message=${encodeURIComponent(
-          message,
-        )}&is_user_message=${isUserMessage}`;
-
-        const eventSource = new EventSource(url);
-        eventSourceRef.current = eventSource;
-
-        abortController.signal.addEventListener("abort", () => {
-          eventSource.close();
-          eventSourceRef.current = null;
-        });
-
-        return new Promise<void>((resolve, reject) => {
-          const cleanup = () => {
-            eventSource.removeEventListener("message", messageHandler);
-            eventSource.removeEventListener("error", errorHandler);
-          };
-
-          const messageHandler = (event: MessageEvent) => {
-            try {
-              const chunk = JSON.parse(event.data) as StreamChunk;
-
-              if (retryCountRef.current > 0) {
-                retryCountRef.current = 0;
-              }
-
-              // Call the chunk handler
-              onChunk(chunk);
-
-              // Handle stream lifecycle
-              if (chunk.type === "stream_end") {
-                cleanup();
-                stopStreaming();
-                resolve();
-              } else if (chunk.type === "error") {
-                cleanup();
-                reject(
-                  new Error(chunk.message || chunk.content || "Stream error"),
-                );
-              }
-            } catch (err) {
-              const parseError =
-                err instanceof Error
-                  ? err
-                  : new Error("Failed to parse stream chunk");
-              setError(parseError);
-              cleanup();
-              reject(parseError);
-            }
-          };
-
-          const errorHandler = () => {
-            if (eventSourceRef.current) {
-              eventSourceRef.current.close();
-              eventSourceRef.current = null;
-            }
-
-            if (retryCountRef.current < MAX_RETRIES) {
-              retryCountRef.current += 1;
-              const retryDelay =
-                INITIAL_RETRY_DELAY * Math.pow(2, retryCountRef.current - 1);
-
-              toast.info("Connection interrupted", {
-                description: `Retrying in ${retryDelay / 1000} seconds...`,
-              });
-
-              retryTimeoutRef.current = setTimeout(() => {
-                sendMessage(sessionId, message, onChunk, isUserMessage).catch(
-                  (_err) => {
-                    // Retry failed
-                  },
-                );
-              }, retryDelay);
-            } else {
-              const streamError = new Error(
-                "Stream connection failed after multiple retries",
-              );
-              setError(streamError);
-              toast.error("Connection Failed", {
-                description:
-                  "Unable to connect to chat service. Please try again.",
-              });
-              cleanup();
-              stopStreaming();
-              reject(streamError);
-            }
-          };
-
-          eventSource.addEventListener("message", messageHandler);
-          eventSource.addEventListener("error", errorHandler);
-        });
-      } catch (err) {
-        const streamError =
-          err instanceof Error ? err : new Error("Failed to start stream");
-        setError(streamError);
-        setIsStreaming(false);
-        throw streamError;
-      }
-    },
-    [stopStreaming],
-  );
-
-  return {
-    isStreaming,
-    error,
-    sendMessage,
-    stopStreaming,
-  };
-}