revert: remove screenshots from repo

docs: add PR testing screenshots
style: format
2026-02-05 20:35:10 -05:00 · 2026-02-03 12:25:26 +00:00 · 2026-02-03 12:22:11 +00:00 · 2026-02-03 11:57:07 +00:00 · 2026-02-03 11:50:06 +00:00 · 2026-02-03 11:44:21 +00:00
136 changed files with 1183 additions and 7715 deletions
--- a/.gitignore
+++ b/.gitignore
@@ -180,4 +180,3 @@ autogpt_platform/backend/settings.py
 .claude/settings.local.json
 CLAUDE.local.md
 /autogpt_platform/backend/logs
 .next
--- a/autogpt_platform/backend/.env.default
+++ b/autogpt_platform/backend/.env.default
@@ -152,7 +152,6 @@ REPLICATE_API_KEY=
 REVID_API_KEY=
 SCREENSHOTONE_API_KEY=
 UNREAL_SPEECH_API_KEY=
 ELEVENLABS_API_KEY=
 # Data & Search Services
 E2B_API_KEY=
--- a/autogpt_platform/backend/.gitignore
+++ b/autogpt_platform/backend/.gitignore
@@ -19,6 +19,3 @@ load-tests/*.json
 load-tests/*.log
 load-tests/node_modules/*
 migrations/*/rollback*.sql
 # Workspace files
 workspaces/
--- a/autogpt_platform/backend/Dockerfile
+++ b/autogpt_platform/backend/Dockerfile
@@ -62,12 +62,10 @@ ENV POETRY_HOME=/opt/poetry \
    DEBIAN_FRONTEND=noninteractive
 ENV PATH=/opt/poetry/bin:$PATH
-# Install Python, FFmpeg, and ImageMagick (required for video processing blocks)
+# Install Python without upgrading system-managed packages
 RUN apt-get update && apt-get install -y \
    python3.13 \
    python3-pip \
    ffmpeg \
    imagemagick \
    && rm -rf /var/lib/apt/lists/*
 # Copy only necessary files from builder
--- a/autogpt_platform/backend/backend/api/features/chat/completion_consumer.py
+++ b/autogpt_platform/backend/backend/api/features/chat/completion_consumer.py
@@ -1,368 +0,0 @@
 """Redis Streams consumer for operation completion messages.
 This module provides a consumer (ChatCompletionConsumer) that listens for
 completion notifications (OperationCompleteMessage) from external services
 (like Agent Generator) and triggers the appropriate stream registry and
 chat service updates via process_operation_success/process_operation_failure.
 Why Redis Streams instead of RabbitMQ?
 --------------------------------------
 While the project typically uses RabbitMQ for async task queues (e.g., execution
 queue), Redis Streams was chosen for chat completion notifications because:
 1. **Unified Infrastructure**: The SSE reconnection feature already uses Redis
   Streams (via stream_registry) for message persistence and replay. Using Redis
   Streams for completion notifications keeps all chat streaming infrastructure
   in one system, simplifying operations and reducing cross-system coordination.
 2. **Message Replay**: Redis Streams support XREAD with arbitrary message IDs,
   allowing consumers to replay missed messages after reconnection. This aligns
   with the SSE reconnection pattern where clients can resume from last_message_id.
 3. **Consumer Groups with XAUTOCLAIM**: Redis consumer groups provide automatic
   load balancing across pods with explicit message claiming (XAUTOCLAIM) for
   recovering from dead consumers - ideal for the completion callback pattern.
 4. **Lower Latency**: For real-time SSE updates, Redis (already in-memory for
   stream_registry) provides lower latency than an additional RabbitMQ hop.
 5. **Atomicity with Task State**: Completion processing often needs to update
   task metadata stored in Redis. Keeping both in Redis enables simpler
   transactional semantics without distributed coordination.
 The consumer uses Redis Streams with consumer groups for reliable message
 processing across multiple platform pods, with XAUTOCLAIM for reclaiming
 stale pending messages from dead consumers.
 """
 import asyncio
 import logging
 import os
 import uuid
 from typing import Any
 import orjson
 from prisma import Prisma
 from pydantic import BaseModel
 from redis.exceptions import ResponseError
 from backend.data.redis_client import get_redis_async
 from . import stream_registry
 from .completion_handler import process_operation_failure, process_operation_success
 from .config import ChatConfig
 logger = logging.getLogger(__name__)
 config = ChatConfig()
 class OperationCompleteMessage(BaseModel):
    """Message format for operation completion notifications."""
    operation_id: str
    task_id: str
    success: bool
    result: dict | str | None = None
    error: str | None = None
 class ChatCompletionConsumer:
    """Consumer for chat operation completion messages from Redis Streams.
    This consumer initializes its own Prisma client in start() to ensure
    database operations work correctly within this async context.
    Uses Redis consumer groups to allow multiple platform pods to consume
    messages reliably with automatic redelivery on failure.
    """
    def __init__(self):
        self._consumer_task: asyncio.Task | None = None
        self._running = False
        self._prisma: Prisma | None = None
        self._consumer_name = f"consumer-{uuid.uuid4().hex[:8]}"
    async def start(self) -> None:
        """Start the completion consumer."""
        if self._running:
            logger.warning("Completion consumer already running")
            return
        # Create consumer group if it doesn't exist
        try:
            redis = await get_redis_async()
            await redis.xgroup_create(
                config.stream_completion_name,
                config.stream_consumer_group,
                id="0",
                mkstream=True,
            )
            logger.info(
                f"Created consumer group '{config.stream_consumer_group}' "
                f"on stream '{config.stream_completion_name}'"
            )
        except ResponseError as e:
            if "BUSYGROUP" in str(e):
                logger.debug(
                    f"Consumer group '{config.stream_consumer_group}' already exists"
                )
            else:
                raise
        self._running = True
        self._consumer_task = asyncio.create_task(self._consume_messages())
        logger.info(
            f"Chat completion consumer started (consumer: {self._consumer_name})"
        )
    async def _ensure_prisma(self) -> Prisma:
        """Lazily initialize Prisma client on first use."""
        if self._prisma is None:
            database_url = os.getenv("DATABASE_URL", "postgresql://localhost:5432")
            self._prisma = Prisma(datasource={"url": database_url})
            await self._prisma.connect()
            logger.info("[COMPLETION] Consumer Prisma client connected (lazy init)")
        return self._prisma
    async def stop(self) -> None:
        """Stop the completion consumer."""
        self._running = False
        if self._consumer_task:
            self._consumer_task.cancel()
            try:
                await self._consumer_task
            except asyncio.CancelledError:
                pass
            self._consumer_task = None
        if self._prisma:
            await self._prisma.disconnect()
            self._prisma = None
            logger.info("[COMPLETION] Consumer Prisma client disconnected")
        logger.info("Chat completion consumer stopped")
    async def _consume_messages(self) -> None:
        """Main message consumption loop with retry logic."""
        max_retries = 10
        retry_delay = 5  # seconds
        retry_count = 0
        block_timeout = 5000  # milliseconds
        while self._running and retry_count < max_retries:
            try:
                redis = await get_redis_async()
                # Reset retry count on successful connection
                retry_count = 0
                while self._running:
                    # First, claim any stale pending messages from dead consumers
                    # Redis does NOT auto-redeliver pending messages; we must explicitly
                    # claim them using XAUTOCLAIM
                    try:
                        claimed_result = await redis.xautoclaim(
                            name=config.stream_completion_name,
                            groupname=config.stream_consumer_group,
                            consumername=self._consumer_name,
                            min_idle_time=config.stream_claim_min_idle_ms,
                            start_id="0-0",
                            count=10,
                        )
                        # xautoclaim returns: (next_start_id, [(id, data), ...], [deleted_ids])
                        if claimed_result and len(claimed_result) >= 2:
                            claimed_entries = claimed_result[1]
                            if claimed_entries:
                                logger.info(
                                    f"Claimed {len(claimed_entries)} stale pending messages"
                                )
                                for entry_id, data in claimed_entries:
                                    if not self._running:
                                        return
                                    await self._process_entry(redis, entry_id, data)
                    except Exception as e:
                        logger.warning(f"XAUTOCLAIM failed (non-fatal): {e}")
                    # Read new messages from the stream
                    messages = await redis.xreadgroup(
                        groupname=config.stream_consumer_group,
                        consumername=self._consumer_name,
                        streams={config.stream_completion_name: ">"},
                        block=block_timeout,
                        count=10,
                    )
                    if not messages:
                        continue
                    for stream_name, entries in messages:
                        for entry_id, data in entries:
                            if not self._running:
                                return
                            await self._process_entry(redis, entry_id, data)
            except asyncio.CancelledError:
                logger.info("Consumer cancelled")
                return
            except Exception as e:
                retry_count += 1
                logger.error(
                    f"Consumer error (retry {retry_count}/{max_retries}): {e}",
                    exc_info=True,
                )
                if self._running and retry_count < max_retries:
                    await asyncio.sleep(retry_delay)
                else:
                    logger.error("Max retries reached, stopping consumer")
                    return
    async def _process_entry(
        self, redis: Any, entry_id: str, data: dict[str, Any]
    ) -> None:
        """Process a single stream entry and acknowledge it on success.
        Args:
            redis: Redis client connection
            entry_id: The stream entry ID
            data: The entry data dict
        """
        try:
            # Handle the message
            message_data = data.get("data")
            if message_data:
                await self._handle_message(
                    message_data.encode()
                    if isinstance(message_data, str)
                    else message_data
                )
            # Acknowledge the message after successful processing
            await redis.xack(
                config.stream_completion_name,
                config.stream_consumer_group,
                entry_id,
            )
        except Exception as e:
            logger.error(
                f"Error processing completion message {entry_id}: {e}",
                exc_info=True,
            )
            # Message remains in pending state and will be claimed by
            # XAUTOCLAIM after min_idle_time expires
    async def _handle_message(self, body: bytes) -> None:
        """Handle a completion message using our own Prisma client."""
        try:
            data = orjson.loads(body)
            message = OperationCompleteMessage(**data)
        except Exception as e:
            logger.error(f"Failed to parse completion message: {e}")
            return
        logger.info(
            f"[COMPLETION] Received completion for operation {message.operation_id} "
            f"(task_id={message.task_id}, success={message.success})"
        )
        # Find task in registry
        task = await stream_registry.find_task_by_operation_id(message.operation_id)
        if task is None:
            task = await stream_registry.get_task(message.task_id)
        if task is None:
            logger.warning(
                f"[COMPLETION] Task not found for operation {message.operation_id} "
                f"(task_id={message.task_id})"
            )
            return
        logger.info(
            f"[COMPLETION] Found task: task_id={task.task_id}, "
            f"session_id={task.session_id}, tool_call_id={task.tool_call_id}"
        )
        # Guard against empty task fields
        if not task.task_id or not task.session_id or not task.tool_call_id:
            logger.error(
                f"[COMPLETION] Task has empty critical fields! "
                f"task_id={task.task_id!r}, session_id={task.session_id!r}, "
                f"tool_call_id={task.tool_call_id!r}"
            )
            return
        if message.success:
            await self._handle_success(task, message)
        else:
            await self._handle_failure(task, message)
    async def _handle_success(
        self,
        task: stream_registry.ActiveTask,
        message: OperationCompleteMessage,
    ) -> None:
        """Handle successful operation completion."""
        prisma = await self._ensure_prisma()
        await process_operation_success(task, message.result, prisma)
    async def _handle_failure(
        self,
        task: stream_registry.ActiveTask,
        message: OperationCompleteMessage,
    ) -> None:
        """Handle failed operation completion."""
        prisma = await self._ensure_prisma()
        await process_operation_failure(task, message.error, prisma)
 # Module-level consumer instance
 _consumer: ChatCompletionConsumer | None = None
 async def start_completion_consumer() -> None:
    """Start the global completion consumer."""
    global _consumer
    if _consumer is None:
        _consumer = ChatCompletionConsumer()
    await _consumer.start()
 async def stop_completion_consumer() -> None:
    """Stop the global completion consumer."""
    global _consumer
    if _consumer:
        await _consumer.stop()
        _consumer = None
 async def publish_operation_complete(
    operation_id: str,
    task_id: str,
    success: bool,
    result: dict | str | None = None,
    error: str | None = None,
 ) -> None:
    """Publish an operation completion message to Redis Streams.
    Args:
        operation_id: The operation ID that completed.
        task_id: The task ID associated with the operation.
        success: Whether the operation succeeded.
        result: The result data (for success).
        error: The error message (for failure).
    """
    message = OperationCompleteMessage(
        operation_id=operation_id,
        task_id=task_id,
        success=success,
        result=result,
        error=error,
    )
    redis = await get_redis_async()
    await redis.xadd(
        config.stream_completion_name,
        {"data": message.model_dump_json()},
        maxlen=config.stream_max_length,
    )
    logger.info(f"Published completion for operation {operation_id}")
--- a/autogpt_platform/backend/backend/api/features/chat/completion_handler.py
+++ b/autogpt_platform/backend/backend/api/features/chat/completion_handler.py
@@ -1,344 +0,0 @@
 """Shared completion handling for operation success and failure.
 This module provides common logic for handling operation completion from both:
 - The Redis Streams consumer (completion_consumer.py)
 - The HTTP webhook endpoint (routes.py)
 """
 import logging
 from typing import Any
 import orjson
 from prisma import Prisma
 from . import service as chat_service
 from . import stream_registry
 from .response_model import StreamError, StreamToolOutputAvailable
 from .tools.models import ErrorResponse
 logger = logging.getLogger(__name__)
 # Tools that produce agent_json that needs to be saved to library
 AGENT_GENERATION_TOOLS = {"create_agent", "edit_agent"}
 # Keys that should be stripped from agent_json when returning in error responses
 SENSITIVE_KEYS = frozenset(
    {
        "api_key",
        "apikey",
        "api_secret",
        "password",
        "secret",
        "credentials",
        "credential",
        "token",
        "access_token",
        "refresh_token",
        "private_key",
        "privatekey",
        "auth",
        "authorization",
    }
 )
 def _sanitize_agent_json(obj: Any) -> Any:
    """Recursively sanitize agent_json by removing sensitive keys.
    Args:
        obj: The object to sanitize (dict, list, or primitive)
    Returns:
        Sanitized copy with sensitive keys removed/redacted
    """
    if isinstance(obj, dict):
        return {
            k: "[REDACTED]" if k.lower() in SENSITIVE_KEYS else _sanitize_agent_json(v)
            for k, v in obj.items()
        }
    elif isinstance(obj, list):
        return [_sanitize_agent_json(item) for item in obj]
    else:
        return obj
 class ToolMessageUpdateError(Exception):
    """Raised when updating a tool message in the database fails."""
    pass
 async def _update_tool_message(
    session_id: str,
    tool_call_id: str,
    content: str,
    prisma_client: Prisma | None,
 ) -> None:
    """Update tool message in database.
    Args:
        session_id: The session ID
        tool_call_id: The tool call ID to update
        content: The new content for the message
        prisma_client: Optional Prisma client. If None, uses chat_service.
    Raises:
        ToolMessageUpdateError: If the database update fails. The caller should
            handle this to avoid marking the task as completed with inconsistent state.
    """
    try:
        if prisma_client:
            # Use provided Prisma client (for consumer with its own connection)
            updated_count = await prisma_client.chatmessage.update_many(
                where={
                    "sessionId": session_id,
                    "toolCallId": tool_call_id,
                },
                data={"content": content},
            )
            # Check if any rows were updated - 0 means message not found
            if updated_count == 0:
                raise ToolMessageUpdateError(
                    f"No message found with tool_call_id={tool_call_id} in session {session_id}"
                )
        else:
            # Use service function (for webhook endpoint)
            await chat_service._update_pending_operation(
                session_id=session_id,
                tool_call_id=tool_call_id,
                result=content,
            )
    except ToolMessageUpdateError:
        raise
    except Exception as e:
        logger.error(f"[COMPLETION] Failed to update tool message: {e}", exc_info=True)
        raise ToolMessageUpdateError(
            f"Failed to update tool message for tool_call_id={tool_call_id}: {e}"
        ) from e
 def serialize_result(result: dict | list | str | int | float | bool | None) -> str:
    """Serialize result to JSON string with sensible defaults.
    Args:
        result: The result to serialize. Can be a dict, list, string,
            number, boolean, or None.
    Returns:
        JSON string representation of the result. Returns '{"status": "completed"}'
        only when result is explicitly None.
    """
    if isinstance(result, str):
        return result
    if result is None:
        return '{"status": "completed"}'
    return orjson.dumps(result).decode("utf-8")
 async def _save_agent_from_result(
    result: dict[str, Any],
    user_id: str | None,
    tool_name: str,
 ) -> dict[str, Any]:
    """Save agent to library if result contains agent_json.
    Args:
        result: The result dict that may contain agent_json
        user_id: The user ID to save the agent for
        tool_name: The tool name (create_agent or edit_agent)
    Returns:
        Updated result dict with saved agent details, or original result if no agent_json
    """
    if not user_id:
        logger.warning("[COMPLETION] Cannot save agent: no user_id in task")
        return result
    agent_json = result.get("agent_json")
    if not agent_json:
        logger.warning(
            f"[COMPLETION] {tool_name} completed but no agent_json in result"
        )
        return result
    try:
        from .tools.agent_generator import save_agent_to_library
        is_update = tool_name == "edit_agent"
        created_graph, library_agent = await save_agent_to_library(
            agent_json, user_id, is_update=is_update
        )
        logger.info(
            f"[COMPLETION] Saved agent '{created_graph.name}' to library "
            f"(graph_id={created_graph.id}, library_agent_id={library_agent.id})"
        )
        # Return a response similar to AgentSavedResponse
        return {
            "type": "agent_saved",
            "message": f"Agent '{created_graph.name}' has been saved to your library!",
            "agent_id": created_graph.id,
            "agent_name": created_graph.name,
            "library_agent_id": library_agent.id,
            "library_agent_link": f"/library/agents/{library_agent.id}",
            "agent_page_link": f"/build?flowID={created_graph.id}",
        }
    except Exception as e:
        logger.error(
            f"[COMPLETION] Failed to save agent to library: {e}",
            exc_info=True,
        )
        # Return error but don't fail the whole operation
        # Sanitize agent_json to remove sensitive keys before returning
        return {
            "type": "error",
            "message": f"Agent was generated but failed to save: {str(e)}",
            "error": str(e),
            "agent_json": _sanitize_agent_json(agent_json),
        }
 async def process_operation_success(
    task: stream_registry.ActiveTask,
    result: dict | str | None,
    prisma_client: Prisma | None = None,
 ) -> None:
    """Handle successful operation completion.
    Publishes the result to the stream registry, updates the database,
    generates LLM continuation, and marks the task as completed.
    Args:
        task: The active task that completed
        result: The result data from the operation
        prisma_client: Optional Prisma client for database operations.
            If None, uses chat_service._update_pending_operation instead.
    Raises:
        ToolMessageUpdateError: If the database update fails. The task will be
            marked as failed instead of completed to avoid inconsistent state.
    """
    # For agent generation tools, save the agent to library
    if task.tool_name in AGENT_GENERATION_TOOLS and isinstance(result, dict):
        result = await _save_agent_from_result(result, task.user_id, task.tool_name)
    # Serialize result for output (only substitute default when result is exactly None)
    result_output = result if result is not None else {"status": "completed"}
    output_str = (
        result_output
        if isinstance(result_output, str)
        else orjson.dumps(result_output).decode("utf-8")
    )
    # Publish result to stream registry
    await stream_registry.publish_chunk(
        task.task_id,
        StreamToolOutputAvailable(
            toolCallId=task.tool_call_id,
            toolName=task.tool_name,
            output=output_str,
            success=True,
        ),
    )
    # Update pending operation in database
    # If this fails, we must not continue to mark the task as completed
    result_str = serialize_result(result)
    try:
        await _update_tool_message(
            session_id=task.session_id,
            tool_call_id=task.tool_call_id,
            content=result_str,
            prisma_client=prisma_client,
        )
    except ToolMessageUpdateError:
        # DB update failed - mark task as failed to avoid inconsistent state
        logger.error(
            f"[COMPLETION] DB update failed for task {task.task_id}, "
            "marking as failed instead of completed"
        )
        await stream_registry.publish_chunk(
            task.task_id,
            StreamError(errorText="Failed to save operation result to database"),
        )
        await stream_registry.mark_task_completed(task.task_id, status="failed")
        raise
    # Generate LLM continuation with streaming
    try:
        await chat_service._generate_llm_continuation_with_streaming(
            session_id=task.session_id,
            user_id=task.user_id,
            task_id=task.task_id,
        )
    except Exception as e:
        logger.error(
            f"[COMPLETION] Failed to generate LLM continuation: {e}",
            exc_info=True,
        )
    # Mark task as completed and release Redis lock
    await stream_registry.mark_task_completed(task.task_id, status="completed")
    try:
        await chat_service._mark_operation_completed(task.tool_call_id)
    except Exception as e:
        logger.error(f"[COMPLETION] Failed to mark operation completed: {e}")
    logger.info(
        f"[COMPLETION] Successfully processed completion for task {task.task_id}"
    )
 async def process_operation_failure(
    task: stream_registry.ActiveTask,
    error: str | None,
    prisma_client: Prisma | None = None,
 ) -> None:
    """Handle failed operation completion.
    Publishes the error to the stream registry, updates the database with
    the error response, and marks the task as failed.
    Args:
        task: The active task that failed
        error: The error message from the operation
        prisma_client: Optional Prisma client for database operations.
            If None, uses chat_service._update_pending_operation instead.
    """
    error_msg = error or "Operation failed"
    # Publish error to stream registry
    await stream_registry.publish_chunk(
        task.task_id,
        StreamError(errorText=error_msg),
    )
    # Update pending operation with error
    # If this fails, we still continue to mark the task as failed
    error_response = ErrorResponse(
        message=error_msg,
        error=error,
    )
    try:
        await _update_tool_message(
            session_id=task.session_id,
            tool_call_id=task.tool_call_id,
            content=error_response.model_dump_json(),
            prisma_client=prisma_client,
        )
    except ToolMessageUpdateError:
        # DB update failed - log but continue with cleanup
        logger.error(
            f"[COMPLETION] DB update failed while processing failure for task {task.task_id}, "
            "continuing with cleanup"
        )
    # Mark task as failed and release Redis lock
    await stream_registry.mark_task_completed(task.task_id, status="failed")
    try:
        await chat_service._mark_operation_completed(task.tool_call_id)
    except Exception as e:
        logger.error(f"[COMPLETION] Failed to mark operation completed: {e}")
    logger.info(f"[COMPLETION] Processed failure for task {task.task_id}: {error_msg}")
--- a/autogpt_platform/backend/backend/api/features/chat/config.py
+++ b/autogpt_platform/backend/backend/api/features/chat/config.py
@@ -11,7 +11,7 @@ class ChatConfig(BaseSettings):
    # OpenAI API Configuration
    model: str = Field(
-        default="anthropic/claude-opus-4.6", description="Default model to use"
+        default="anthropic/claude-opus-4.5", description="Default model to use"
    )
    title_model: str = Field(
        default="openai/gpt-4o-mini",
@@ -44,48 +44,6 @@ class ChatConfig(BaseSettings):
        description="TTL in seconds for long-running operation tracking in Redis (safety net if pod dies)",
    )
    # Stream registry configuration for SSE reconnection
    stream_ttl: int = Field(
        default=3600,
        description="TTL in seconds for stream data in Redis (1 hour)",
    )
    stream_max_length: int = Field(
        default=10000,
        description="Maximum number of messages to store per stream",
    )
    # Redis Streams configuration for completion consumer
    stream_completion_name: str = Field(
        default="chat:completions",
        description="Redis Stream name for operation completions",
    )
    stream_consumer_group: str = Field(
        default="chat_consumers",
        description="Consumer group name for completion stream",
    )
    stream_claim_min_idle_ms: int = Field(
        default=60000,
        description="Minimum idle time in milliseconds before claiming pending messages from dead consumers",
    )
    # Redis key prefixes for stream registry
    task_meta_prefix: str = Field(
        default="chat:task:meta:",
        description="Prefix for task metadata hash keys",
    )
    task_stream_prefix: str = Field(
        default="chat:stream:",
        description="Prefix for task message stream keys",
    )
    task_op_prefix: str = Field(
        default="chat:task:op:",
        description="Prefix for operation ID to task ID mapping keys",
    )
    internal_api_key: str | None = Field(
        default=None,
        description="API key for internal webhook callbacks (env: CHAT_INTERNAL_API_KEY)",
    )
    # Langfuse Prompt Management Configuration
    # Note: Langfuse credentials are in Settings().secrets (settings.py)
    langfuse_prompt_name: str = Field(
@@ -124,14 +82,6 @@ class ChatConfig(BaseSettings):
                v = "https://openrouter.ai/api/v1"
        return v
    @field_validator("internal_api_key", mode="before")
    @classmethod
    def get_internal_api_key(cls, v):
        """Get internal API key from environment if not provided."""
        if v is None:
            v = os.getenv("CHAT_INTERNAL_API_KEY")
        return v
    # Prompt paths for different contexts
    PROMPT_PATHS: dict[str, str] = {
        "default": "prompts/chat_system.md",
--- a/autogpt_platform/backend/backend/api/features/chat/response_model.py
+++ b/autogpt_platform/backend/backend/api/features/chat/response_model.py
@@ -52,10 +52,6 @@ class StreamStart(StreamBaseResponse):
    type: ResponseType = ResponseType.START
    messageId: str = Field(..., description="Unique message ID")
    taskId: str | None = Field(
        default=None,
        description="Task ID for SSE reconnection. Clients can reconnect using GET /tasks/{taskId}/stream",
    )
 class StreamFinish(StreamBaseResponse):
--- a/autogpt_platform/backend/backend/api/features/chat/routes.py
+++ b/autogpt_platform/backend/backend/api/features/chat/routes.py
@@ -1,23 +1,19 @@
 """Chat API routes for chat session management and streaming via SSE."""
 import logging
 import uuid as uuid_module
 from collections.abc import AsyncGenerator
 from typing import Annotated
 from autogpt_libs import auth
-from fastapi import APIRouter, Depends, Header, HTTPException, Query, Security
+from fastapi import APIRouter, Depends, Query, Security
 from fastapi.responses import StreamingResponse
 from pydantic import BaseModel
 from backend.util.exceptions import NotFoundError
 from . import service as chat_service
 from . import stream_registry
 from .completion_handler import process_operation_failure, process_operation_success
 from .config import ChatConfig
 from .model import ChatSession, create_chat_session, get_chat_session, get_user_sessions
 from .response_model import StreamFinish, StreamHeartbeat, StreamStart
 config = ChatConfig()
@@ -59,15 +55,6 @@ class CreateSessionResponse(BaseModel):
    user_id: str | None
 class ActiveStreamInfo(BaseModel):
    """Information about an active stream for reconnection."""
    task_id: str
    last_message_id: str  # Redis Stream message ID for resumption
    operation_id: str  # Operation ID for completion tracking
    tool_name: str  # Name of the tool being executed
 class SessionDetailResponse(BaseModel):
    """Response model providing complete details for a chat session, including messages."""
@@ -76,7 +63,6 @@ class SessionDetailResponse(BaseModel):
    updated_at: str
    user_id: str | None
    messages: list[dict]
    active_stream: ActiveStreamInfo | None = None  # Present if stream is still active
 class SessionSummaryResponse(BaseModel):
@@ -95,14 +81,6 @@ class ListSessionsResponse(BaseModel):
    total: int
 class OperationCompleteRequest(BaseModel):
    """Request model for external completion webhook."""
    success: bool
    result: dict | str | None = None
    error: str | None = None
 # ========== Routes ==========
@@ -188,14 +166,13 @@ async def get_session(
    Retrieve the details of a specific chat session.
    Looks up a chat session by ID for the given user (if authenticated) and returns all session data including messages.
    If there's an active stream for this session, returns the task_id for reconnection.
    Args:
        session_id: The unique identifier for the desired chat session.
        user_id: The optional authenticated user ID, or None for anonymous access.
    Returns:
-        SessionDetailResponse: Details for the requested session, including active_stream info if applicable.
+        SessionDetailResponse: Details for the requested session, or None if not found.
    """
    session = await get_chat_session(session_id, user_id)
@@ -203,28 +180,11 @@ async def get_session(
        raise NotFoundError(f"Session {session_id} not found.")
    messages = [message.model_dump() for message in session.messages]
-
+    logger.info(
-    # Check if there's an active stream for this session
+        f"Returning session {session_id}: "
-    active_stream_info = None
+        f"message_count={len(messages)}, "
-    active_task, last_message_id = await stream_registry.get_active_task_for_session(
+        f"roles={[m.get('role') for m in messages]}"
        session_id, user_id
    )
    if active_task:
        # Filter out the in-progress assistant message from the session response.
        # The client will receive the complete assistant response through the SSE
        # stream replay instead, preventing duplicate content.
        if messages and messages[-1].get("role") == "assistant":
            messages = messages[:-1]
        # Use "0-0" as last_message_id to replay the stream from the beginning.
        # Since we filtered out the cached assistant message, the client needs
        # the full stream to reconstruct the response.
        active_stream_info = ActiveStreamInfo(
            task_id=active_task.task_id,
            last_message_id="0-0",
            operation_id=active_task.operation_id,
            tool_name=active_task.tool_name,
        )
    return SessionDetailResponse(
        id=session.session_id,
@@ -232,7 +192,6 @@ async def get_session(
        updated_at=session.updated_at.isoformat(),
        user_id=session.user_id or None,
        messages=messages,
        active_stream=active_stream_info,
    )
@@ -252,112 +211,49 @@ async def stream_chat_post(
      - Tool call UI elements (if invoked)
      - Tool execution results
    The AI generation runs in a background task that continues even if the client disconnects.
    All chunks are written to Redis for reconnection support. If the client disconnects,
    they can reconnect using GET /tasks/{task_id}/stream to resume from where they left off.
    Args:
        session_id: The chat session identifier to associate with the streamed messages.
        request: Request body containing message, is_user_message, and optional context.
        user_id: Optional authenticated user ID.
    Returns:
-        StreamingResponse: SSE-formatted response chunks. First chunk is a "start" event
+        StreamingResponse: SSE-formatted response chunks.
        containing the task_id for reconnection.
    """
    import asyncio
    session = await _validate_and_get_session(session_id, user_id)
    # Create a task in the stream registry for reconnection support
    task_id = str(uuid_module.uuid4())
    operation_id = str(uuid_module.uuid4())
    await stream_registry.create_task(
        task_id=task_id,
        session_id=session_id,
        user_id=user_id,
        tool_call_id="chat_stream",  # Not a tool call, but needed for the model
        tool_name="chat",
        operation_id=operation_id,
    )
    # Background task that runs the AI generation independently of SSE connection
    async def run_ai_generation():
        try:
            # Emit a start event with task_id for reconnection
            start_chunk = StreamStart(messageId=task_id, taskId=task_id)
            await stream_registry.publish_chunk(task_id, start_chunk)
            async for chunk in chat_service.stream_chat_completion(
                session_id,
                request.message,
                is_user_message=request.is_user_message,
                user_id=user_id,
                session=session,  # Pass pre-fetched session to avoid double-fetch
                context=request.context,
            ):
                # Write to Redis (subscribers will receive via XREAD)
                await stream_registry.publish_chunk(task_id, chunk)
            # Mark task as completed
            await stream_registry.mark_task_completed(task_id, "completed")
        except Exception as e:
            logger.error(
                f"Error in background AI generation for session {session_id}: {e}"
            )
            await stream_registry.mark_task_completed(task_id, "failed")
    # Start the AI generation in a background task
    bg_task = asyncio.create_task(run_ai_generation())
    await stream_registry.set_task_asyncio_task(task_id, bg_task)
    # SSE endpoint that subscribes to the task's stream
    async def event_generator() -> AsyncGenerator[str, None]:
-        subscriber_queue = None
+        chunk_count = 0
-        try:
+        first_chunk_type: str | None = None
-            # Subscribe to the task stream (this replays existing messages + live updates)
+        async for chunk in chat_service.stream_chat_completion(
-            subscriber_queue = await stream_registry.subscribe_to_task(
+            session_id,
-                task_id=task_id,
+            request.message,
-                user_id=user_id,
+            is_user_message=request.is_user_message,
-                last_message_id="0-0",  # Get all messages from the beginning
+            user_id=user_id,
-            )
+            session=session,  # Pass pre-fetched session to avoid double-fetch
-
+            context=request.context,
-            if subscriber_queue is None:
+        ):
-                yield StreamFinish().to_sse()
+            if chunk_count < 3:
-                yield "data: [DONE]\n\n"
+                logger.info(
-                return
+                    "Chat stream chunk",
-
+                    extra={
-            # Read from the subscriber queue and yield to SSE
+                        "session_id": session_id,
-            while True:
+                        "chunk_type": str(chunk.type),
-                try:
+                    },
-                    chunk = await asyncio.wait_for(subscriber_queue.get(), timeout=30.0)
+                )
-                    yield chunk.to_sse()
+            if not first_chunk_type:
-
+                first_chunk_type = str(chunk.type)
-                    # Check for finish signal
+            chunk_count += 1
-                    if isinstance(chunk, StreamFinish):
+            yield chunk.to_sse()
-                        break
+        logger.info(
-                except asyncio.TimeoutError:
+            "Chat stream completed",
-                    # Send heartbeat to keep connection alive
+            extra={
-                    yield StreamHeartbeat().to_sse()
+                "session_id": session_id,
-
+                "chunk_count": chunk_count,
-        except GeneratorExit:
+                "first_chunk_type": first_chunk_type,
-            pass  # Client disconnected - background task continues
+            },
-        except Exception as e:
+        )
-            logger.error(f"Error in SSE stream for task {task_id}: {e}")
+        # AI SDK protocol termination
-        finally:
+        yield "data: [DONE]\n\n"
            # Unsubscribe when client disconnects or stream ends to prevent resource leak
            if subscriber_queue is not None:
                try:
                    await stream_registry.unsubscribe_from_task(
                        task_id, subscriber_queue
                    )
                except Exception as unsub_err:
                    logger.error(
                        f"Error unsubscribing from task {task_id}: {unsub_err}",
                        exc_info=True,
                    )
            # AI SDK protocol termination - always yield even if unsubscribe fails
            yield "data: [DONE]\n\n"
    return StreamingResponse(
        event_generator(),
@@ -470,251 +366,6 @@ async def session_assign_user(
    return {"status": "ok"}
 # ========== Task Streaming (SSE Reconnection) ==========
@router.get(
    "/tasks/{task_id}/stream",
 )
 async def stream_task(
    task_id: str,
    user_id: str | None = Depends(auth.get_user_id),
    last_message_id: str = Query(
        default="0-0",
        description="Last Redis Stream message ID received (e.g., '1706540123456-0'). Use '0-0' for full replay.",
    ),
 ):
    """
    Reconnect to a long-running task's SSE stream.
    When a long-running operation (like agent generation) starts, the client
    receives a task_id. If the connection drops, the client can reconnect
    using this endpoint to resume receiving updates.
    Args:
        task_id: The task ID from the operation_started response.
        user_id: Authenticated user ID for ownership validation.
        last_message_id: Last Redis Stream message ID received ("0-0" for full replay).
    Returns:
        StreamingResponse: SSE-formatted response chunks starting after last_message_id.
    Raises:
        HTTPException: 404 if task not found, 410 if task expired, 403 if access denied.
    """
    # Check task existence and expiry before subscribing
    task, error_code = await stream_registry.get_task_with_expiry_info(task_id)
    if error_code == "TASK_EXPIRED":
        raise HTTPException(
            status_code=410,
            detail={
                "code": "TASK_EXPIRED",
                "message": "This operation has expired. Please try again.",
            },
        )
    if error_code == "TASK_NOT_FOUND":
        raise HTTPException(
            status_code=404,
            detail={
                "code": "TASK_NOT_FOUND",
                "message": f"Task {task_id} not found.",
            },
        )
    # Validate ownership if task has an owner
    if task and task.user_id and user_id != task.user_id:
        raise HTTPException(
            status_code=403,
            detail={
                "code": "ACCESS_DENIED",
                "message": "You do not have access to this task.",
            },
        )
    # Get subscriber queue from stream registry
    subscriber_queue = await stream_registry.subscribe_to_task(
        task_id=task_id,
        user_id=user_id,
        last_message_id=last_message_id,
    )
    if subscriber_queue is None:
        raise HTTPException(
            status_code=404,
            detail={
                "code": "TASK_NOT_FOUND",
                "message": f"Task {task_id} not found or access denied.",
            },
        )
    async def event_generator() -> AsyncGenerator[str, None]:
        import asyncio
        heartbeat_interval = 15.0  # Send heartbeat every 15 seconds
        try:
            while True:
                try:
                    # Wait for next chunk with timeout for heartbeats
                    chunk = await asyncio.wait_for(
                        subscriber_queue.get(), timeout=heartbeat_interval
                    )
                    yield chunk.to_sse()
                    # Check for finish signal
                    if isinstance(chunk, StreamFinish):
                        break
                except asyncio.TimeoutError:
                    # Send heartbeat to keep connection alive
                    yield StreamHeartbeat().to_sse()
        except Exception as e:
            logger.error(f"Error in task stream {task_id}: {e}", exc_info=True)
        finally:
            # Unsubscribe when client disconnects or stream ends
            try:
                await stream_registry.unsubscribe_from_task(task_id, subscriber_queue)
            except Exception as unsub_err:
                logger.error(
                    f"Error unsubscribing from task {task_id}: {unsub_err}",
                    exc_info=True,
                )
            # AI SDK protocol termination - always yield even if unsubscribe fails
            yield "data: [DONE]\n\n"
    return StreamingResponse(
        event_generator(),
        media_type="text/event-stream",
        headers={
            "Cache-Control": "no-cache",
            "Connection": "keep-alive",
            "X-Accel-Buffering": "no",
            "x-vercel-ai-ui-message-stream": "v1",
        },
    )
@router.get(
    "/tasks/{task_id}",
 )
 async def get_task_status(
    task_id: str,
    user_id: str | None = Depends(auth.get_user_id),
 ) -> dict:
    """
    Get the status of a long-running task.
    Args:
        task_id: The task ID to check.
        user_id: Authenticated user ID for ownership validation.
    Returns:
        dict: Task status including task_id, status, tool_name, and operation_id.
    Raises:
        NotFoundError: If task_id is not found or user doesn't have access.
    """
    task = await stream_registry.get_task(task_id)
    if task is None:
        raise NotFoundError(f"Task {task_id} not found.")
    # Validate ownership - if task has an owner, requester must match
    if task.user_id and user_id != task.user_id:
        raise NotFoundError(f"Task {task_id} not found.")
    return {
        "task_id": task.task_id,
        "session_id": task.session_id,
        "status": task.status,
        "tool_name": task.tool_name,
        "operation_id": task.operation_id,
        "created_at": task.created_at.isoformat(),
    }
 # ========== External Completion Webhook ==========
@router.post(
    "/operations/{operation_id}/complete",
    status_code=200,
 )
 async def complete_operation(
    operation_id: str,
    request: OperationCompleteRequest,
    x_api_key: str | None = Header(default=None),
 ) -> dict:
    """
    External completion webhook for long-running operations.
    Called by Agent Generator (or other services) when an operation completes.
    This triggers the stream registry to publish completion and continue LLM generation.
    Args:
        operation_id: The operation ID to complete.
        request: Completion payload with success status and result/error.
        x_api_key: Internal API key for authentication.
    Returns:
        dict: Status of the completion.
    Raises:
        HTTPException: If API key is invalid or operation not found.
    """
    # Validate internal API key - reject if not configured or invalid
    if not config.internal_api_key:
        logger.error(
            "Operation complete webhook rejected: CHAT_INTERNAL_API_KEY not configured"
        )
        raise HTTPException(
            status_code=503,
            detail="Webhook not available: internal API key not configured",
        )
    if x_api_key != config.internal_api_key:
        raise HTTPException(status_code=401, detail="Invalid API key")
    # Find task by operation_id
    task = await stream_registry.find_task_by_operation_id(operation_id)
    if task is None:
        raise HTTPException(
            status_code=404,
            detail=f"Operation {operation_id} not found",
        )
    logger.info(
        f"Received completion webhook for operation {operation_id} "
        f"(task_id={task.task_id}, success={request.success})"
    )
    if request.success:
        await process_operation_success(task, request.result)
    else:
        await process_operation_failure(task, request.error)
    return {"status": "ok", "task_id": task.task_id}
 # ========== Configuration ==========
@router.get("/config/ttl", status_code=200)
 async def get_ttl_config() -> dict:
    """
    Get the stream TTL configuration.
    Returns the Time-To-Live settings for chat streams, which determines
    how long clients can reconnect to an active stream.
    Returns:
        dict: TTL configuration with seconds and milliseconds values.
    """
    return {
        "stream_ttl_seconds": config.stream_ttl,
        "stream_ttl_ms": config.stream_ttl * 1000,
    }
 # ========== Health Check ==========
--- a/autogpt_platform/backend/backend/api/features/chat/service.py
+++ b/autogpt_platform/backend/backend/api/features/chat/service.py
@@ -33,10 +33,9 @@ from backend.data.understanding import (
    get_business_understanding,
 )
 from backend.util.exceptions import NotFoundError
-from backend.util.settings import AppEnvironment, Settings
+from backend.util.settings import Settings
 from . import db as chat_db
 from . import stream_registry
 from .config import ChatConfig
 from .model import (
    ChatMessage,
@@ -222,18 +221,8 @@ async def _get_system_prompt_template(context: str) -> str:
        try:
            # cache_ttl_seconds=0 disables SDK caching to always get the latest prompt
            # Use asyncio.to_thread to avoid blocking the event loop
            # In non-production environments, fetch the latest prompt version
            # instead of the production-labeled version for easier testing
            label = (
                None
                if settings.config.app_env == AppEnvironment.PRODUCTION
                else "latest"
            )
            prompt = await asyncio.to_thread(
-                langfuse.get_prompt,
+                langfuse.get_prompt, config.langfuse_prompt_name, cache_ttl_seconds=0
                config.langfuse_prompt_name,
                label=label,
                cache_ttl_seconds=0,
            )
            return prompt.compile(users_information=context)
        except Exception as e:
@@ -628,9 +617,6 @@ async def stream_chat_completion(
                        total_tokens=chunk.totalTokens,
                    )
                )
            elif isinstance(chunk, StreamHeartbeat):
                # Pass through heartbeat to keep SSE connection alive
                yield chunk
            else:
                logger.error(f"Unknown chunk type: {type(chunk)}", exc_info=True)
@@ -1198,9 +1184,8 @@ async def _yield_tool_call(
            )
            return
-        # Generate operation ID and task ID
+        # Generate operation ID
        operation_id = str(uuid_module.uuid4())
        task_id = str(uuid_module.uuid4())
        # Build a user-friendly message based on tool and arguments
        if tool_name == "create_agent":
@@ -1243,16 +1228,6 @@ async def _yield_tool_call(
        # Wrap session save and task creation in try-except to release lock on failure
        try:
            # Create task in stream registry for SSE reconnection support
            await stream_registry.create_task(
                task_id=task_id,
                session_id=session.session_id,
                user_id=session.user_id,
                tool_call_id=tool_call_id,
                tool_name=tool_name,
                operation_id=operation_id,
            )
            # Save assistant message with tool_call FIRST (required by LLM)
            assistant_message = ChatMessage(
                role="assistant",
@@ -1274,27 +1249,23 @@ async def _yield_tool_call(
            session.messages.append(pending_message)
            await upsert_chat_session(session)
            logger.info(
-                f"Saved pending operation {operation_id} (task_id={task_id}) "
+                f"Saved pending operation {operation_id} for tool {tool_name} "
-                f"for tool {tool_name} in session {session.session_id}"
+                f"in session {session.session_id}"
            )
            # Store task reference in module-level set to prevent GC before completion
-            bg_task = asyncio.create_task(
+            task = asyncio.create_task(
-                _execute_long_running_tool_with_streaming(
+                _execute_long_running_tool(
                    tool_name=tool_name,
                    parameters=arguments,
                    tool_call_id=tool_call_id,
                    operation_id=operation_id,
                    task_id=task_id,
                    session_id=session.session_id,
                    user_id=session.user_id,
                )
            )
-            _background_tasks.add(bg_task)
+            _background_tasks.add(task)
-            bg_task.add_done_callback(_background_tasks.discard)
+            task.add_done_callback(_background_tasks.discard)
            # Associate the asyncio task with the stream registry task
            await stream_registry.set_task_asyncio_task(task_id, bg_task)
        except Exception as e:
            # Roll back appended messages to prevent data corruption on subsequent saves
            if (
@@ -1312,11 +1283,6 @@ async def _yield_tool_call(
            # Release the Redis lock since the background task won't be spawned
            await _mark_operation_completed(tool_call_id)
            # Mark stream registry task as failed if it was created
            try:
                await stream_registry.mark_task_completed(task_id, status="failed")
            except Exception:
                pass
            logger.error(
                f"Failed to setup long-running tool {tool_name}: {e}", exc_info=True
            )
@@ -1330,7 +1296,6 @@ async def _yield_tool_call(
                message=started_msg,
                operation_id=operation_id,
                tool_name=tool_name,
                task_id=task_id,  # Include task_id for SSE reconnection
            ).model_dump_json(),
            success=True,
        )
@@ -1400,9 +1365,6 @@ async def _execute_long_running_tool(
    This function runs independently of the SSE connection, so the operation
    survives if the user closes their browser tab.
    NOTE: This is the legacy function without stream registry support.
    Use _execute_long_running_tool_with_streaming for new implementations.
    """
    try:
        # Load fresh session (not stale reference)
@@ -1455,133 +1417,6 @@ async def _execute_long_running_tool(
        await _mark_operation_completed(tool_call_id)
 async def _execute_long_running_tool_with_streaming(
    tool_name: str,
    parameters: dict[str, Any],
    tool_call_id: str,
    operation_id: str,
    task_id: str,
    session_id: str,
    user_id: str | None,
 ) -> None:
    """Execute a long-running tool with stream registry support for SSE reconnection.
    This function runs independently of the SSE connection, publishes progress
    to the stream registry, and survives if the user closes their browser tab.
    Clients can reconnect via GET /chat/tasks/{task_id}/stream to resume streaming.
    If the external service returns a 202 Accepted (async), this function exits
    early and lets the Redis Streams completion consumer handle the rest.
    """
    # Track whether we delegated to async processing - if so, the Redis Streams
    # completion consumer (stream_registry / completion_consumer) will handle cleanup, not us
    delegated_to_async = False
    try:
        # Load fresh session (not stale reference)
        session = await get_chat_session(session_id, user_id)
        if not session:
            logger.error(f"Session {session_id} not found for background tool")
            await stream_registry.mark_task_completed(task_id, status="failed")
            return
        # Pass operation_id and task_id to the tool for async processing
        enriched_parameters = {
            **parameters,
            "_operation_id": operation_id,
            "_task_id": task_id,
        }
        # Execute the actual tool
        result = await execute_tool(
            tool_name=tool_name,
            parameters=enriched_parameters,
            tool_call_id=tool_call_id,
            user_id=user_id,
            session=session,
        )
        # Check if the tool result indicates async processing
        # (e.g., Agent Generator returned 202 Accepted)
        try:
            if isinstance(result.output, dict):
                result_data = result.output
            elif result.output:
                result_data = orjson.loads(result.output)
            else:
                result_data = {}
            if result_data.get("status") == "accepted":
                logger.info(
                    f"Tool {tool_name} delegated to async processing "
                    f"(operation_id={operation_id}, task_id={task_id}). "
                    f"Redis Streams completion consumer will handle the rest."
                )
                # Don't publish result, don't continue with LLM, and don't cleanup
                # The Redis Streams consumer (completion_consumer) will handle
                # everything when the external service completes via webhook
                delegated_to_async = True
                return
        except (orjson.JSONDecodeError, TypeError):
            pass  # Not JSON or not async - continue normally
        # Publish tool result to stream registry
        await stream_registry.publish_chunk(task_id, result)
        # Update the pending message with result
        result_str = (
            result.output
            if isinstance(result.output, str)
            else orjson.dumps(result.output).decode("utf-8")
        )
        await _update_pending_operation(
            session_id=session_id,
            tool_call_id=tool_call_id,
            result=result_str,
        )
        logger.info(
            f"Background tool {tool_name} completed for session {session_id} "
            f"(task_id={task_id})"
        )
        # Generate LLM continuation and stream chunks to registry
        await _generate_llm_continuation_with_streaming(
            session_id=session_id,
            user_id=user_id,
            task_id=task_id,
        )
        # Mark task as completed in stream registry
        await stream_registry.mark_task_completed(task_id, status="completed")
    except Exception as e:
        logger.error(f"Background tool {tool_name} failed: {e}", exc_info=True)
        error_response = ErrorResponse(
            message=f"Tool {tool_name} failed: {str(e)}",
        )
        # Publish error to stream registry followed by finish event
        await stream_registry.publish_chunk(
            task_id,
            StreamError(errorText=str(e)),
        )
        await stream_registry.publish_chunk(task_id, StreamFinish())
        await _update_pending_operation(
            session_id=session_id,
            tool_call_id=tool_call_id,
            result=error_response.model_dump_json(),
        )
        # Mark task as failed in stream registry
        await stream_registry.mark_task_completed(task_id, status="failed")
    finally:
        # Only cleanup if we didn't delegate to async processing
        # For async path, the Redis Streams completion consumer handles cleanup
        if not delegated_to_async:
            await _mark_operation_completed(tool_call_id)
 async def _update_pending_operation(
    session_id: str,
    tool_call_id: str,
@@ -1762,128 +1597,3 @@ async def _generate_llm_continuation(
    except Exception as e:
        logger.error(f"Failed to generate LLM continuation: {e}", exc_info=True)
 async def _generate_llm_continuation_with_streaming(
    session_id: str,
    user_id: str | None,
    task_id: str,
 ) -> None:
    """Generate an LLM response with streaming to the stream registry.
    This is called by background tasks to continue the conversation
    after a tool result is saved. Chunks are published to the stream registry
    so reconnecting clients can receive them.
    """
    import uuid as uuid_module
    try:
        # Load fresh session from DB (bypass cache to get the updated tool result)
        await invalidate_session_cache(session_id)
        session = await get_chat_session(session_id, user_id)
        if not session:
            logger.error(f"Session {session_id} not found for LLM continuation")
            return
        # Build system prompt
        system_prompt, _ = await _build_system_prompt(user_id)
        # Build messages in OpenAI format
        messages = session.to_openai_messages()
        if system_prompt:
            from openai.types.chat import ChatCompletionSystemMessageParam
            system_message = ChatCompletionSystemMessageParam(
                role="system",
                content=system_prompt,
            )
            messages = [system_message] + messages
        # Build extra_body for tracing
        extra_body: dict[str, Any] = {
            "posthogProperties": {
                "environment": settings.config.app_env.value,
            },
        }
        if user_id:
            extra_body["user"] = user_id[:128]
            extra_body["posthogDistinctId"] = user_id
        if session_id:
            extra_body["session_id"] = session_id[:128]
        # Make streaming LLM call (no tools - just text response)
        from typing import cast
        from openai.types.chat import ChatCompletionMessageParam
        # Generate unique IDs for AI SDK protocol
        message_id = str(uuid_module.uuid4())
        text_block_id = str(uuid_module.uuid4())
        # Publish start event
        await stream_registry.publish_chunk(task_id, StreamStart(messageId=message_id))
        await stream_registry.publish_chunk(task_id, StreamTextStart(id=text_block_id))
        # Stream the response
        stream = await client.chat.completions.create(
            model=config.model,
            messages=cast(list[ChatCompletionMessageParam], messages),
            extra_body=extra_body,
            stream=True,
        )
        assistant_content = ""
        async for chunk in stream:
            if chunk.choices and chunk.choices[0].delta.content:
                delta = chunk.choices[0].delta.content
                assistant_content += delta
                # Publish delta to stream registry
                await stream_registry.publish_chunk(
                    task_id,
                    StreamTextDelta(id=text_block_id, delta=delta),
                )
        # Publish end events
        await stream_registry.publish_chunk(task_id, StreamTextEnd(id=text_block_id))
        if assistant_content:
            # Reload session from DB to avoid race condition with user messages
            fresh_session = await get_chat_session(session_id, user_id)
            if not fresh_session:
                logger.error(
                    f"Session {session_id} disappeared during LLM continuation"
                )
                return
            # Save assistant message to database
            assistant_message = ChatMessage(
                role="assistant",
                content=assistant_content,
            )
            fresh_session.messages.append(assistant_message)
            # Save to database (not cache) to persist the response
            await upsert_chat_session(fresh_session)
            # Invalidate cache so next poll/refresh gets fresh data
            await invalidate_session_cache(session_id)
            logger.info(
                f"Generated streaming LLM continuation for session {session_id} "
                f"(task_id={task_id}), response length: {len(assistant_content)}"
            )
        else:
            logger.warning(
                f"Streaming LLM continuation returned empty response for {session_id}"
            )
    except Exception as e:
        logger.error(
            f"Failed to generate streaming LLM continuation: {e}", exc_info=True
        )
        # Publish error to stream registry followed by finish event
        await stream_registry.publish_chunk(
            task_id,
            StreamError(errorText=f"Failed to generate response: {e}"),
        )
        await stream_registry.publish_chunk(task_id, StreamFinish())
--- a/autogpt_platform/backend/backend/api/features/chat/stream_registry.py
+++ b/autogpt_platform/backend/backend/api/features/chat/stream_registry.py
@@ -1,704 +0,0 @@
 """Stream registry for managing reconnectable SSE streams.
 This module provides a registry for tracking active streaming tasks and their
 messages. It uses Redis for all state management (no in-memory state), making
 pods stateless and horizontally scalable.
 Architecture:
 - Redis Stream: Persists all messages for replay and real-time delivery
 - Redis Hash: Task metadata (status, session_id, etc.)
 Subscribers:
 1. Replay missed messages from Redis Stream (XREAD)
 2. Listen for live updates via blocking XREAD
 3. No in-memory state required on the subscribing pod
 """
 import asyncio
 import logging
 from dataclasses import dataclass, field
 from datetime import datetime, timezone
 from typing import Any, Literal
 import orjson
 from backend.data.redis_client import get_redis_async
 from .config import ChatConfig
 from .response_model import StreamBaseResponse, StreamError, StreamFinish
 logger = logging.getLogger(__name__)
 config = ChatConfig()
 # Track background tasks for this pod (just the asyncio.Task reference, not subscribers)
 _local_tasks: dict[str, asyncio.Task] = {}
 # Track listener tasks per subscriber queue for cleanup
 # Maps queue id() to (task_id, asyncio.Task) for proper cleanup on unsubscribe
 _listener_tasks: dict[int, tuple[str, asyncio.Task]] = {}
 # Timeout for putting chunks into subscriber queues (seconds)
 # If the queue is full and doesn't drain within this time, send an overflow error
 QUEUE_PUT_TIMEOUT = 5.0
 # Lua script for atomic compare-and-swap status update (idempotent completion)
 # Returns 1 if status was updated, 0 if already completed/failed
 COMPLETE_TASK_SCRIPT = """
 local current = redis.call("HGET", KEYS[1], "status")
 if current == "running" then
    redis.call("HSET", KEYS[1], "status", ARGV[1])
    return 1
 end
 return 0
 """
@dataclass
 class ActiveTask:
    """Represents an active streaming task (metadata only, no in-memory queues)."""
    task_id: str
    session_id: str
    user_id: str | None
    tool_call_id: str
    tool_name: str
    operation_id: str
    status: Literal["running", "completed", "failed"] = "running"
    created_at: datetime = field(default_factory=lambda: datetime.now(timezone.utc))
    asyncio_task: asyncio.Task | None = None
 def _get_task_meta_key(task_id: str) -> str:
    """Get Redis key for task metadata."""
    return f"{config.task_meta_prefix}{task_id}"
 def _get_task_stream_key(task_id: str) -> str:
    """Get Redis key for task message stream."""
    return f"{config.task_stream_prefix}{task_id}"
 def _get_operation_mapping_key(operation_id: str) -> str:
    """Get Redis key for operation_id to task_id mapping."""
    return f"{config.task_op_prefix}{operation_id}"
 async def create_task(
    task_id: str,
    session_id: str,
    user_id: str | None,
    tool_call_id: str,
    tool_name: str,
    operation_id: str,
 ) -> ActiveTask:
    """Create a new streaming task in Redis.
    Args:
        task_id: Unique identifier for the task
        session_id: Chat session ID
        user_id: User ID (may be None for anonymous)
        tool_call_id: Tool call ID from the LLM
        tool_name: Name of the tool being executed
        operation_id: Operation ID for webhook callbacks
    Returns:
        The created ActiveTask instance (metadata only)
    """
    task = ActiveTask(
        task_id=task_id,
        session_id=session_id,
        user_id=user_id,
        tool_call_id=tool_call_id,
        tool_name=tool_name,
        operation_id=operation_id,
    )
    # Store metadata in Redis
    redis = await get_redis_async()
    meta_key = _get_task_meta_key(task_id)
    op_key = _get_operation_mapping_key(operation_id)
    await redis.hset(  # type: ignore[misc]
        meta_key,
        mapping={
            "task_id": task_id,
            "session_id": session_id,
            "user_id": user_id or "",
            "tool_call_id": tool_call_id,
            "tool_name": tool_name,
            "operation_id": operation_id,
            "status": task.status,
            "created_at": task.created_at.isoformat(),
        },
    )
    await redis.expire(meta_key, config.stream_ttl)
    # Create operation_id -> task_id mapping for webhook lookups
    await redis.set(op_key, task_id, ex=config.stream_ttl)
    logger.debug(f"Created task {task_id} for session {session_id}")
    return task
 async def publish_chunk(
    task_id: str,
    chunk: StreamBaseResponse,
 ) -> str:
    """Publish a chunk to Redis Stream.
    All delivery is via Redis Streams - no in-memory state.
    Args:
        task_id: Task ID to publish to
        chunk: The stream response chunk to publish
    Returns:
        The Redis Stream message ID
    """
    chunk_json = chunk.model_dump_json()
    message_id = "0-0"
    try:
        redis = await get_redis_async()
        stream_key = _get_task_stream_key(task_id)
        # Write to Redis Stream for persistence and real-time delivery
        raw_id = await redis.xadd(
            stream_key,
            {"data": chunk_json},
            maxlen=config.stream_max_length,
        )
        message_id = raw_id if isinstance(raw_id, str) else raw_id.decode()
        # Set TTL on stream to match task metadata TTL
        await redis.expire(stream_key, config.stream_ttl)
    except Exception as e:
        logger.error(
            f"Failed to publish chunk for task {task_id}: {e}",
            exc_info=True,
        )
    return message_id
 async def subscribe_to_task(
    task_id: str,
    user_id: str | None,
    last_message_id: str = "0-0",
 ) -> asyncio.Queue[StreamBaseResponse] | None:
    """Subscribe to a task's stream with replay of missed messages.
    This is fully stateless - uses Redis Stream for replay and pub/sub for live updates.
    Args:
        task_id: Task ID to subscribe to
        user_id: User ID for ownership validation
        last_message_id: Last Redis Stream message ID received ("0-0" for full replay)
    Returns:
        An asyncio Queue that will receive stream chunks, or None if task not found
        or user doesn't have access
    """
    redis = await get_redis_async()
    meta_key = _get_task_meta_key(task_id)
    meta: dict[Any, Any] = await redis.hgetall(meta_key)  # type: ignore[misc]
    if not meta:
        logger.debug(f"Task {task_id} not found in Redis")
        return None
    # Note: Redis client uses decode_responses=True, so keys are strings
    task_status = meta.get("status", "")
    task_user_id = meta.get("user_id", "") or None
    # Validate ownership - if task has an owner, requester must match
    if task_user_id:
        if user_id != task_user_id:
            logger.warning(
                f"User {user_id} denied access to task {task_id} "
                f"owned by {task_user_id}"
            )
            return None
    subscriber_queue: asyncio.Queue[StreamBaseResponse] = asyncio.Queue()
    stream_key = _get_task_stream_key(task_id)
    # Step 1: Replay messages from Redis Stream
    messages = await redis.xread({stream_key: last_message_id}, block=0, count=1000)
    replayed_count = 0
    replay_last_id = last_message_id
    if messages:
        for _stream_name, stream_messages in messages:
            for msg_id, msg_data in stream_messages:
                replay_last_id = msg_id if isinstance(msg_id, str) else msg_id.decode()
                # Note: Redis client uses decode_responses=True, so keys are strings
                if "data" in msg_data:
                    try:
                        chunk_data = orjson.loads(msg_data["data"])
                        chunk = _reconstruct_chunk(chunk_data)
                        if chunk:
                            await subscriber_queue.put(chunk)
                            replayed_count += 1
                    except Exception as e:
                        logger.warning(f"Failed to replay message: {e}")
    logger.debug(f"Task {task_id}: replayed {replayed_count} messages")
    # Step 2: If task is still running, start stream listener for live updates
    if task_status == "running":
        listener_task = asyncio.create_task(
            _stream_listener(task_id, subscriber_queue, replay_last_id)
        )
        # Track listener task for cleanup on unsubscribe
        _listener_tasks[id(subscriber_queue)] = (task_id, listener_task)
    else:
        # Task is completed/failed - add finish marker
        await subscriber_queue.put(StreamFinish())
    return subscriber_queue
 async def _stream_listener(
    task_id: str,
    subscriber_queue: asyncio.Queue[StreamBaseResponse],
    last_replayed_id: str,
 ) -> None:
    """Listen to Redis Stream for new messages using blocking XREAD.
    This approach avoids the duplicate message issue that can occur with pub/sub
    when messages are published during the gap between replay and subscription.
    Args:
        task_id: Task ID to listen for
        subscriber_queue: Queue to deliver messages to
        last_replayed_id: Last message ID from replay (continue from here)
    """
    queue_id = id(subscriber_queue)
    # Track the last successfully delivered message ID for recovery hints
    last_delivered_id = last_replayed_id
    try:
        redis = await get_redis_async()
        stream_key = _get_task_stream_key(task_id)
        current_id = last_replayed_id
        while True:
            # Block for up to 30 seconds waiting for new messages
            # This allows periodic checking if task is still running
            messages = await redis.xread(
                {stream_key: current_id}, block=30000, count=100
            )
            if not messages:
                # Timeout - check if task is still running
                meta_key = _get_task_meta_key(task_id)
                status = await redis.hget(meta_key, "status")  # type: ignore[misc]
                if status and status != "running":
                    try:
                        await asyncio.wait_for(
                            subscriber_queue.put(StreamFinish()),
                            timeout=QUEUE_PUT_TIMEOUT,
                        )
                    except asyncio.TimeoutError:
                        logger.warning(
                            f"Timeout delivering finish event for task {task_id}"
                        )
                    break
                continue
            for _stream_name, stream_messages in messages:
                for msg_id, msg_data in stream_messages:
                    current_id = msg_id if isinstance(msg_id, str) else msg_id.decode()
                    if "data" not in msg_data:
                        continue
                    try:
                        chunk_data = orjson.loads(msg_data["data"])
                        chunk = _reconstruct_chunk(chunk_data)
                        if chunk:
                            try:
                                await asyncio.wait_for(
                                    subscriber_queue.put(chunk),
                                    timeout=QUEUE_PUT_TIMEOUT,
                                )
                                # Update last delivered ID on successful delivery
                                last_delivered_id = current_id
                            except asyncio.TimeoutError:
                                logger.warning(
                                    f"Subscriber queue full for task {task_id}, "
                                    f"message delivery timed out after {QUEUE_PUT_TIMEOUT}s"
                                )
                                # Send overflow error with recovery info
                                try:
                                    overflow_error = StreamError(
                                        errorText="Message delivery timeout - some messages may have been missed",
                                        code="QUEUE_OVERFLOW",
                                        details={
                                            "last_delivered_id": last_delivered_id,
                                            "recovery_hint": f"Reconnect with last_message_id={last_delivered_id}",
                                        },
                                    )
                                    subscriber_queue.put_nowait(overflow_error)
                                except asyncio.QueueFull:
                                    # Queue is completely stuck, nothing more we can do
                                    logger.error(
                                        f"Cannot deliver overflow error for task {task_id}, "
                                        "queue completely blocked"
                                    )
                            # Stop listening on finish
                            if isinstance(chunk, StreamFinish):
                                return
                    except Exception as e:
                        logger.warning(f"Error processing stream message: {e}")
    except asyncio.CancelledError:
        logger.debug(f"Stream listener cancelled for task {task_id}")
        raise  # Re-raise to propagate cancellation
    except Exception as e:
        logger.error(f"Stream listener error for task {task_id}: {e}")
        # On error, send finish to unblock subscriber
        try:
            await asyncio.wait_for(
                subscriber_queue.put(StreamFinish()),
                timeout=QUEUE_PUT_TIMEOUT,
            )
        except (asyncio.TimeoutError, asyncio.QueueFull):
            logger.warning(
                f"Could not deliver finish event for task {task_id} after error"
            )
    finally:
        # Clean up listener task mapping on exit
        _listener_tasks.pop(queue_id, None)
 async def mark_task_completed(
    task_id: str,
    status: Literal["completed", "failed"] = "completed",
 ) -> bool:
    """Mark a task as completed and publish finish event.
    This is idempotent - calling multiple times with the same task_id is safe.
    Uses atomic compare-and-swap via Lua script to prevent race conditions.
    Status is updated first (source of truth), then finish event is published (best-effort).
    Args:
        task_id: Task ID to mark as completed
        status: Final status ("completed" or "failed")
    Returns:
        True if task was newly marked completed, False if already completed/failed
    """
    redis = await get_redis_async()
    meta_key = _get_task_meta_key(task_id)
    # Atomic compare-and-swap: only update if status is "running"
    # This prevents race conditions when multiple callers try to complete simultaneously
    result = await redis.eval(COMPLETE_TASK_SCRIPT, 1, meta_key, status)  # type: ignore[misc]
    if result == 0:
        logger.debug(f"Task {task_id} already completed/failed, skipping")
        return False
    # THEN publish finish event (best-effort - listeners can detect via status polling)
    try:
        await publish_chunk(task_id, StreamFinish())
    except Exception as e:
        logger.error(
            f"Failed to publish finish event for task {task_id}: {e}. "
            "Listeners will detect completion via status polling."
        )
    # Clean up local task reference if exists
    _local_tasks.pop(task_id, None)
    return True
 async def find_task_by_operation_id(operation_id: str) -> ActiveTask | None:
    """Find a task by its operation ID.
    Used by webhook callbacks to locate the task to update.
    Args:
        operation_id: Operation ID to search for
    Returns:
        ActiveTask if found, None otherwise
    """
    redis = await get_redis_async()
    op_key = _get_operation_mapping_key(operation_id)
    task_id = await redis.get(op_key)
    if not task_id:
        return None
    task_id_str = task_id.decode() if isinstance(task_id, bytes) else task_id
    return await get_task(task_id_str)
 async def get_task(task_id: str) -> ActiveTask | None:
    """Get a task by its ID from Redis.
    Args:
        task_id: Task ID to look up
    Returns:
        ActiveTask if found, None otherwise
    """
    redis = await get_redis_async()
    meta_key = _get_task_meta_key(task_id)
    meta: dict[Any, Any] = await redis.hgetall(meta_key)  # type: ignore[misc]
    if not meta:
        return None
    # Note: Redis client uses decode_responses=True, so keys/values are strings
    return ActiveTask(
        task_id=meta.get("task_id", ""),
        session_id=meta.get("session_id", ""),
        user_id=meta.get("user_id", "") or None,
        tool_call_id=meta.get("tool_call_id", ""),
        tool_name=meta.get("tool_name", ""),
        operation_id=meta.get("operation_id", ""),
        status=meta.get("status", "running"),  # type: ignore[arg-type]
    )
 async def get_task_with_expiry_info(
    task_id: str,
 ) -> tuple[ActiveTask | None, str | None]:
    """Get a task by its ID with expiration detection.
    Returns (task, error_code) where error_code is:
    - None if task found
    - "TASK_EXPIRED" if stream exists but metadata is gone (TTL expired)
    - "TASK_NOT_FOUND" if neither exists
    Args:
        task_id: Task ID to look up
    Returns:
        Tuple of (ActiveTask or None, error_code or None)
    """
    redis = await get_redis_async()
    meta_key = _get_task_meta_key(task_id)
    stream_key = _get_task_stream_key(task_id)
    meta: dict[Any, Any] = await redis.hgetall(meta_key)  # type: ignore[misc]
    if not meta:
        # Check if stream still has data (metadata expired but stream hasn't)
        stream_len = await redis.xlen(stream_key)
        if stream_len > 0:
            return None, "TASK_EXPIRED"
        return None, "TASK_NOT_FOUND"
    # Note: Redis client uses decode_responses=True, so keys/values are strings
    return (
        ActiveTask(
            task_id=meta.get("task_id", ""),
            session_id=meta.get("session_id", ""),
            user_id=meta.get("user_id", "") or None,
            tool_call_id=meta.get("tool_call_id", ""),
            tool_name=meta.get("tool_name", ""),
            operation_id=meta.get("operation_id", ""),
            status=meta.get("status", "running"),  # type: ignore[arg-type]
        ),
        None,
    )
 async def get_active_task_for_session(
    session_id: str,
    user_id: str | None = None,
 ) -> tuple[ActiveTask | None, str]:
    """Get the active (running) task for a session, if any.
    Scans Redis for tasks matching the session_id with status="running".
    Args:
        session_id: Session ID to look up
        user_id: User ID for ownership validation (optional)
    Returns:
        Tuple of (ActiveTask if found and running, last_message_id from Redis Stream)
    """
    redis = await get_redis_async()
    # Scan Redis for task metadata keys
    cursor = 0
    tasks_checked = 0
    while True:
        cursor, keys = await redis.scan(
            cursor, match=f"{config.task_meta_prefix}*", count=100
        )
        for key in keys:
            tasks_checked += 1
            meta: dict[Any, Any] = await redis.hgetall(key)  # type: ignore[misc]
            if not meta:
                continue
            # Note: Redis client uses decode_responses=True, so keys/values are strings
            task_session_id = meta.get("session_id", "")
            task_status = meta.get("status", "")
            task_user_id = meta.get("user_id", "") or None
            task_id = meta.get("task_id", "")
            if task_session_id == session_id and task_status == "running":
                # Validate ownership - if task has an owner, requester must match
                if task_user_id and user_id != task_user_id:
                    continue
                # Get the last message ID from Redis Stream
                stream_key = _get_task_stream_key(task_id)
                last_id = "0-0"
                try:
                    messages = await redis.xrevrange(stream_key, count=1)
                    if messages:
                        msg_id = messages[0][0]
                        last_id = msg_id if isinstance(msg_id, str) else msg_id.decode()
                except Exception as e:
                    logger.warning(f"Failed to get last message ID: {e}")
                return (
                    ActiveTask(
                        task_id=task_id,
                        session_id=task_session_id,
                        user_id=task_user_id,
                        tool_call_id=meta.get("tool_call_id", ""),
                        tool_name=meta.get("tool_name", ""),
                        operation_id=meta.get("operation_id", ""),
                        status="running",
                    ),
                    last_id,
                )
        if cursor == 0:
            break
    return None, "0-0"
 def _reconstruct_chunk(chunk_data: dict) -> StreamBaseResponse | None:
    """Reconstruct a StreamBaseResponse from JSON data.
    Args:
        chunk_data: Parsed JSON data from Redis
    Returns:
        Reconstructed response object, or None if unknown type
    """
    from .response_model import (
        ResponseType,
        StreamError,
        StreamFinish,
        StreamHeartbeat,
        StreamStart,
        StreamTextDelta,
        StreamTextEnd,
        StreamTextStart,
        StreamToolInputAvailable,
        StreamToolInputStart,
        StreamToolOutputAvailable,
        StreamUsage,
    )
    # Map response types to their corresponding classes
    type_to_class: dict[str, type[StreamBaseResponse]] = {
        ResponseType.START.value: StreamStart,
        ResponseType.FINISH.value: StreamFinish,
        ResponseType.TEXT_START.value: StreamTextStart,
        ResponseType.TEXT_DELTA.value: StreamTextDelta,
        ResponseType.TEXT_END.value: StreamTextEnd,
        ResponseType.TOOL_INPUT_START.value: StreamToolInputStart,
        ResponseType.TOOL_INPUT_AVAILABLE.value: StreamToolInputAvailable,
        ResponseType.TOOL_OUTPUT_AVAILABLE.value: StreamToolOutputAvailable,
        ResponseType.ERROR.value: StreamError,
        ResponseType.USAGE.value: StreamUsage,
        ResponseType.HEARTBEAT.value: StreamHeartbeat,
    }
    chunk_type = chunk_data.get("type")
    chunk_class = type_to_class.get(chunk_type)  # type: ignore[arg-type]
    if chunk_class is None:
        logger.warning(f"Unknown chunk type: {chunk_type}")
        return None
    try:
        return chunk_class(**chunk_data)
    except Exception as e:
        logger.warning(f"Failed to reconstruct chunk of type {chunk_type}: {e}")
        return None
 async def set_task_asyncio_task(task_id: str, asyncio_task: asyncio.Task) -> None:
    """Track the asyncio.Task for a task (local reference only).
    This is just for cleanup purposes - the task state is in Redis.
    Args:
        task_id: Task ID
        asyncio_task: The asyncio Task to track
    """
    _local_tasks[task_id] = asyncio_task
 async def unsubscribe_from_task(
    task_id: str,
    subscriber_queue: asyncio.Queue[StreamBaseResponse],
 ) -> None:
    """Clean up when a subscriber disconnects.
    Cancels the XREAD-based listener task associated with this subscriber queue
    to prevent resource leaks.
    Args:
        task_id: Task ID
        subscriber_queue: The subscriber's queue used to look up the listener task
    """
    queue_id = id(subscriber_queue)
    listener_entry = _listener_tasks.pop(queue_id, None)
    if listener_entry is None:
        logger.debug(
            f"No listener task found for task {task_id} queue {queue_id} "
            "(may have already completed)"
        )
        return
    stored_task_id, listener_task = listener_entry
    if stored_task_id != task_id:
        logger.warning(
            f"Task ID mismatch in unsubscribe: expected {task_id}, "
            f"found {stored_task_id}"
        )
    if listener_task.done():
        logger.debug(f"Listener task for task {task_id} already completed")
        return
    # Cancel the listener task
    listener_task.cancel()
    try:
        # Wait for the task to be cancelled with a timeout
        await asyncio.wait_for(listener_task, timeout=5.0)
    except asyncio.CancelledError:
        # Expected - the task was successfully cancelled
        pass
    except asyncio.TimeoutError:
        logger.warning(
            f"Timeout waiting for listener task cancellation for task {task_id}"
        )
    except Exception as e:
        logger.error(f"Error during listener task cancellation for task {task_id}: {e}")
    logger.debug(f"Successfully unsubscribed from task {task_id}")
--- a/autogpt_platform/backend/backend/api/features/chat/tools/init.py
+++ b/autogpt_platform/backend/backend/api/features/chat/tools/init.py
@@ -10,7 +10,6 @@ from .add_understanding import AddUnderstandingTool
 from .agent_output import AgentOutputTool
 from .base import BaseTool
 from .create_agent import CreateAgentTool
 from .customize_agent import CustomizeAgentTool
 from .edit_agent import EditAgentTool
 from .find_agent import FindAgentTool
 from .find_block import FindBlockTool
@@ -35,7 +34,6 @@ logger = logging.getLogger(__name__)
 TOOL_REGISTRY: dict[str, BaseTool] = {
    "add_understanding": AddUnderstandingTool(),
    "create_agent": CreateAgentTool(),
    "customize_agent": CustomizeAgentTool(),
    "edit_agent": EditAgentTool(),
    "find_agent": FindAgentTool(),
    "find_block": FindBlockTool(),
--- a/autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/init.py
+++ b/autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/init.py
@@ -8,7 +8,6 @@ from .core import (
    DecompositionStep,
    LibraryAgentSummary,
    MarketplaceAgentSummary,
    customize_template,
    decompose_goal,
    enrich_library_agents_from_steps,
    extract_search_terms_from_steps,
@@ -20,7 +19,6 @@ from .core import (
    get_library_agent_by_graph_id,
    get_library_agent_by_id,
    get_library_agents_for_generation,
    graph_to_json,
    json_to_graph,
    save_agent_to_library,
    search_marketplace_agents_for_generation,
@@ -38,7 +36,6 @@ __all__ = [
    "LibraryAgentSummary",
    "MarketplaceAgentSummary",
    "check_external_service_health",
    "customize_template",
    "decompose_goal",
    "enrich_library_agents_from_steps",
    "extract_search_terms_from_steps",
@@ -51,7 +48,6 @@ __all__ = [
    "get_library_agent_by_id",
    "get_library_agents_for_generation",
    "get_user_message_for_error",
    "graph_to_json",
    "is_external_service_configured",
    "json_to_graph",
    "save_agent_to_library",
--- a/autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/core.py
+++ b/autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/core.py
@@ -7,11 +7,18 @@ from typing import Any, NotRequired, TypedDict
 from backend.api.features.library import db as library_db
 from backend.api.features.store import db as store_db
-from backend.data.graph import Graph, Link, Node, get_graph, get_store_listed_graphs
+from backend.data.graph import (
    Graph,
    Link,
    Node,
    create_graph,
    get_graph,
    get_graph_all_versions,
    get_store_listed_graphs,
 )
 from backend.util.exceptions import DatabaseError, NotFoundError
 from .service import (
    customize_template_external,
    decompose_goal_external,
    generate_agent_external,
    generate_agent_patch_external,
@@ -20,6 +27,8 @@ from .service import (
 logger = logging.getLogger(__name__)
 AGENT_EXECUTOR_BLOCK_ID = "e189baac-8c20-45a1-94a7-55177ea42565"
 class ExecutionSummary(TypedDict):
    """Summary of a single execution for quality assessment."""
@@ -540,21 +549,15 @@ async def decompose_goal(
 async def generate_agent(
    instructions: DecompositionResult | dict[str, Any],
    library_agents: list[AgentSummary] | list[dict[str, Any]] | None = None,
    operation_id: str | None = None,
    task_id: str | None = None,
 ) -> dict[str, Any] | None:
    """Generate agent JSON from instructions.
    Args:
        instructions: Structured instructions from decompose_goal
        library_agents: User's library agents available for sub-agent composition
        operation_id: Operation ID for async processing (enables Redis Streams
            completion notification)
        task_id: Task ID for async processing (enables Redis Streams persistence
            and SSE delivery)
    Returns:
-        Agent JSON dict, {"status": "accepted"} for async, error dict {"type": "error", ...}, or None on error
+        Agent JSON dict, error dict {"type": "error", ...}, or None on error
    Raises:
        AgentGeneratorNotConfiguredError: If the external service is not configured.
@@ -562,13 +565,8 @@ async def generate_agent(
    _check_service_configured()
    logger.info("Calling external Agent Generator service for generate_agent")
    result = await generate_agent_external(
-        dict(instructions), _to_dict_list(library_agents), operation_id, task_id
+        dict(instructions), _to_dict_list(library_agents)
    )
    # Don't modify async response
    if result and result.get("status") == "accepted":
        return result
    if result:
        if isinstance(result, dict) and result.get("type") == "error":
            return result
@@ -659,6 +657,45 @@ def json_to_graph(agent_json: dict[str, Any]) -> Graph:
    )
 def _reassign_node_ids(graph: Graph) -> None:
    """Reassign all node and link IDs to new UUIDs.
    This is needed when creating a new version to avoid unique constraint violations.
    """
    id_map = {node.id: str(uuid.uuid4()) for node in graph.nodes}
    for node in graph.nodes:
        node.id = id_map[node.id]
    for link in graph.links:
        link.id = str(uuid.uuid4())
        if link.source_id in id_map:
            link.source_id = id_map[link.source_id]
        if link.sink_id in id_map:
            link.sink_id = id_map[link.sink_id]
 def _populate_agent_executor_user_ids(agent_json: dict[str, Any], user_id: str) -> None:
    """Populate user_id in AgentExecutorBlock nodes.
    The external agent generator creates AgentExecutorBlock nodes with empty user_id.
    This function fills in the actual user_id so sub-agents run with correct permissions.
    Args:
        agent_json: Agent JSON dict (modified in place)
        user_id: User ID to set
    """
    for node in agent_json.get("nodes", []):
        if node.get("block_id") == AGENT_EXECUTOR_BLOCK_ID:
            input_default = node.get("input_default") or {}
            if not input_default.get("user_id"):
                input_default["user_id"] = user_id
                node["input_default"] = input_default
                logger.debug(
                    f"Set user_id for AgentExecutorBlock node {node.get('id')}"
                )
 async def save_agent_to_library(
    agent_json: dict[str, Any], user_id: str, is_update: bool = False
 ) -> tuple[Graph, Any]:
@@ -672,21 +709,63 @@ async def save_agent_to_library(
    Returns:
        Tuple of (created Graph, LibraryAgent)
    """
    # Populate user_id in AgentExecutorBlock nodes before conversion
    _populate_agent_executor_user_ids(agent_json, user_id)
    graph = json_to_graph(agent_json)
    if is_update:
-        return await library_db.update_graph_in_library(graph, user_id)
+        if graph.id:
-    return await library_db.create_graph_in_library(graph, user_id)
+            existing_versions = await get_graph_all_versions(graph.id, user_id)
            if existing_versions:
                latest_version = max(v.version for v in existing_versions)
                graph.version = latest_version + 1
                _reassign_node_ids(graph)
                logger.info(f"Updating agent {graph.id} to version {graph.version}")
    else:
        graph.id = str(uuid.uuid4())
        graph.version = 1
        _reassign_node_ids(graph)
        logger.info(f"Creating new agent with ID {graph.id}")
    created_graph = await create_graph(graph, user_id)
    library_agents = await library_db.create_library_agent(
        graph=created_graph,
        user_id=user_id,
        sensitive_action_safe_mode=True,
        create_library_agents_for_sub_graphs=False,
    )
    return created_graph, library_agents[0]
-def graph_to_json(graph: Graph) -> dict[str, Any]:
+async def get_agent_as_json(
-    """Convert a Graph object to JSON format for the agent generator.
+    agent_id: str, user_id: str | None
 ) -> dict[str, Any] | None:
    """Fetch an agent and convert to JSON format for editing.
    Args:
-        graph: Graph object to convert
+        agent_id: Graph ID or library agent ID
        user_id: User ID
    Returns:
-        Agent as JSON dict
+        Agent as JSON dict or None if not found
    """
    graph = await get_graph(agent_id, version=None, user_id=user_id)
    if not graph and user_id:
        try:
            library_agent = await library_db.get_library_agent(agent_id, user_id)
            graph = await get_graph(
                library_agent.graph_id, version=None, user_id=user_id
            )
        except NotFoundError:
            pass
    if not graph:
        return None
    nodes = []
    for node in graph.nodes:
        nodes.append(
@@ -723,41 +802,10 @@ def graph_to_json(graph: Graph) -> dict[str, Any]:
    }
 async def get_agent_as_json(
    agent_id: str, user_id: str | None
 ) -> dict[str, Any] | None:
    """Fetch an agent and convert to JSON format for editing.
    Args:
        agent_id: Graph ID or library agent ID
        user_id: User ID
    Returns:
        Agent as JSON dict or None if not found
    """
    graph = await get_graph(agent_id, version=None, user_id=user_id)
    if not graph and user_id:
        try:
            library_agent = await library_db.get_library_agent(agent_id, user_id)
            graph = await get_graph(
                library_agent.graph_id, version=None, user_id=user_id
            )
        except NotFoundError:
            pass
    if not graph:
        return None
    return graph_to_json(graph)
 async def generate_agent_patch(
    update_request: str,
    current_agent: dict[str, Any],
    library_agents: list[AgentSummary] | None = None,
    operation_id: str | None = None,
    task_id: str | None = None,
 ) -> dict[str, Any] | None:
    """Update an existing agent using natural language.
@@ -770,12 +818,10 @@ async def generate_agent_patch(
        update_request: Natural language description of changes
        current_agent: Current agent JSON
        library_agents: User's library agents available for sub-agent composition
        operation_id: Operation ID for async processing (enables Redis Streams callback)
        task_id: Task ID for async processing (enables Redis Streams callback)
    Returns:
        Updated agent JSON, clarifying questions dict {"type": "clarifying_questions", ...},
-        {"status": "accepted"} for async, error dict {"type": "error", ...}, or None on error
+        error dict {"type": "error", ...}, or None on unexpected error
    Raises:
        AgentGeneratorNotConfiguredError: If the external service is not configured.
@@ -783,43 +829,5 @@ async def generate_agent_patch(
    _check_service_configured()
    logger.info("Calling external Agent Generator service for generate_agent_patch")
    return await generate_agent_patch_external(
-        update_request,
+        update_request, current_agent, _to_dict_list(library_agents)
        current_agent,
        _to_dict_list(library_agents),
        operation_id,
        task_id,
    )
 async def customize_template(
    template_agent: dict[str, Any],
    modification_request: str,
    context: str = "",
 ) -> dict[str, Any] | None:
    """Customize a template/marketplace agent using natural language.
    This is used when users want to modify a template or marketplace agent
    to fit their specific needs before adding it to their library.
    The external Agent Generator service handles:
    - Understanding the modification request
    - Applying changes to the template
    - Fixing and validating the result
    Args:
        template_agent: The template agent JSON to customize
        modification_request: Natural language description of customizations
        context: Additional context (e.g., answers to previous questions)
    Returns:
        Customized agent JSON, clarifying questions dict {"type": "clarifying_questions", ...},
        error dict {"type": "error", ...}, or None on unexpected error
    Raises:
        AgentGeneratorNotConfiguredError: If the external service is not configured.
    """
    _check_service_configured()
    logger.info("Calling external Agent Generator service for customize_template")
    return await customize_template_external(
        template_agent, modification_request, context
    )
--- a/autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/service.py
+++ b/autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/service.py
@@ -212,45 +212,24 @@ async def decompose_goal_external(
 async def generate_agent_external(
    instructions: dict[str, Any],
    library_agents: list[dict[str, Any]] | None = None,
    operation_id: str | None = None,
    task_id: str | None = None,
 ) -> dict[str, Any] | None:
    """Call the external service to generate an agent from instructions.
    Args:
        instructions: Structured instructions from decompose_goal
        library_agents: User's library agents available for sub-agent composition
        operation_id: Operation ID for async processing (enables Redis Streams callback)
        task_id: Task ID for async processing (enables Redis Streams callback)
    Returns:
-        Agent JSON dict, {"status": "accepted"} for async, or error dict {"type": "error", ...} on error
+        Agent JSON dict on success, or error dict {"type": "error", ...} on error
    """
    client = _get_client()
    # Build request payload
    payload: dict[str, Any] = {"instructions": instructions}
    if library_agents:
        payload["library_agents"] = library_agents
    if operation_id and task_id:
        payload["operation_id"] = operation_id
        payload["task_id"] = task_id
    try:
        response = await client.post("/api/generate-agent", json=payload)
        # Handle 202 Accepted for async processing
        if response.status_code == 202:
            logger.info(
                f"Agent Generator accepted async request "
                f"(operation_id={operation_id}, task_id={task_id})"
            )
            return {
                "status": "accepted",
                "operation_id": operation_id,
                "task_id": task_id,
            }
        response.raise_for_status()
        data = response.json()
@@ -282,8 +261,6 @@ async def generate_agent_patch_external(
    update_request: str,
    current_agent: dict[str, Any],
    library_agents: list[dict[str, Any]] | None = None,
    operation_id: str | None = None,
    task_id: str | None = None,
 ) -> dict[str, Any] | None:
    """Call the external service to generate a patch for an existing agent.
@@ -291,40 +268,21 @@ async def generate_agent_patch_external(
        update_request: Natural language description of changes
        current_agent: Current agent JSON
        library_agents: User's library agents available for sub-agent composition
        operation_id: Operation ID for async processing (enables Redis Streams callback)
        task_id: Task ID for async processing (enables Redis Streams callback)
    Returns:
-        Updated agent JSON, clarifying questions dict, {"status": "accepted"} for async, or error dict on error
+        Updated agent JSON, clarifying questions dict, or error dict on error
    """
    client = _get_client()
    # Build request payload
    payload: dict[str, Any] = {
        "update_request": update_request,
        "current_agent_json": current_agent,
    }
    if library_agents:
        payload["library_agents"] = library_agents
    if operation_id and task_id:
        payload["operation_id"] = operation_id
        payload["task_id"] = task_id
    try:
        response = await client.post("/api/update-agent", json=payload)
        # Handle 202 Accepted for async processing
        if response.status_code == 202:
            logger.info(
                f"Agent Generator accepted async update request "
                f"(operation_id={operation_id}, task_id={task_id})"
            )
            return {
                "status": "accepted",
                "operation_id": operation_id,
                "task_id": task_id,
            }
        response.raise_for_status()
        data = response.json()
@@ -368,77 +326,6 @@ async def generate_agent_patch_external(
        return _create_error_response(error_msg, "unexpected_error")
 async def customize_template_external(
    template_agent: dict[str, Any],
    modification_request: str,
    context: str = "",
 ) -> dict[str, Any] | None:
    """Call the external service to customize a template/marketplace agent.
    Args:
        template_agent: The template agent JSON to customize
        modification_request: Natural language description of customizations
        context: Additional context (e.g., answers to previous questions)
    Returns:
        Customized agent JSON, clarifying questions dict, or error dict on error
    """
    client = _get_client()
    request = modification_request
    if context:
        request = f"{modification_request}\n\nAdditional context from user:\n{context}"
    payload: dict[str, Any] = {
        "template_agent_json": template_agent,
        "modification_request": request,
    }
    try:
        response = await client.post("/api/template-modification", json=payload)
        response.raise_for_status()
        data = response.json()
        if not data.get("success"):
            error_msg = data.get("error", "Unknown error from Agent Generator")
            error_type = data.get("error_type", "unknown")
            logger.error(
                f"Agent Generator template customization failed: {error_msg} "
                f"(type: {error_type})"
            )
            return _create_error_response(error_msg, error_type)
        # Check if it's clarifying questions
        if data.get("type") == "clarifying_questions":
            return {
                "type": "clarifying_questions",
                "questions": data.get("questions", []),
            }
        # Check if it's an error passed through
        if data.get("type") == "error":
            return _create_error_response(
                data.get("error", "Unknown error"),
                data.get("error_type", "unknown"),
            )
        # Otherwise return the customized agent JSON
        return data.get("agent_json")
    except httpx.HTTPStatusError as e:
        error_type, error_msg = _classify_http_error(e)
        logger.error(error_msg)
        return _create_error_response(error_msg, error_type)
    except httpx.RequestError as e:
        error_type, error_msg = _classify_request_error(e)
        logger.error(error_msg)
        return _create_error_response(error_msg, error_type)
    except Exception as e:
        error_msg = f"Unexpected error calling Agent Generator: {e}"
        logger.error(error_msg)
        return _create_error_response(error_msg, "unexpected_error")
 async def get_blocks_external() -> list[dict[str, Any]] | None:
    """Get available blocks from the external service.
--- a/autogpt_platform/backend/backend/api/features/chat/tools/agent_search.py
+++ b/autogpt_platform/backend/backend/api/features/chat/tools/agent_search.py
@@ -206,9 +206,9 @@ async def search_agents(
            ]
        )
        no_results_msg = (
-            f"No agents found matching '{query}'. Let the user know they can try different keywords or browse the marketplace. Also let them know you can create a custom agent for them based on their needs."
+            f"No agents found matching '{query}'. Try different keywords or browse the marketplace."
            if source == "marketplace"
-            else f"No agents matching '{query}' found in your library. Let the user know you can create a custom agent for them based on their needs."
+            else f"No agents matching '{query}' found in your library."
        )
        return NoResultsResponse(
            message=no_results_msg, session_id=session_id, suggestions=suggestions
@@ -224,10 +224,10 @@ async def search_agents(
    message = (
        "Now you have found some options for the user to choose from. "
        "You can add a link to a recommended agent at: /marketplace/agent/agent_id "
-        "Please ask the user if they would like to use any of these agents. Let the user know we can create a custom agent for them based on their needs."
+        "Please ask the user if they would like to use any of these agents."
        if source == "marketplace"
        else "Found agents in the user's library. You can provide a link to view an agent at: "
-        "/library/agents/{agent_id}. Use agent_output to get execution results, or run_agent to execute. Let the user know we can create a custom agent for them based on their needs."
+        "/library/agents/{agent_id}. Use agent_output to get execution results, or run_agent to execute."
    )
    return AgentsFoundResponse(
--- a/autogpt_platform/backend/backend/api/features/chat/tools/create_agent.py
+++ b/autogpt_platform/backend/backend/api/features/chat/tools/create_agent.py
@@ -18,7 +18,6 @@ from .base import BaseTool
 from .models import (
    AgentPreviewResponse,
    AgentSavedResponse,
    AsyncProcessingResponse,
    ClarificationNeededResponse,
    ClarifyingQuestion,
    ErrorResponse,
@@ -99,10 +98,6 @@ class CreateAgentTool(BaseTool):
        save = kwargs.get("save", True)
        session_id = session.session_id if session else None
        # Extract async processing params (passed by long-running tool handler)
        operation_id = kwargs.get("_operation_id")
        task_id = kwargs.get("_task_id")
        if not description:
            return ErrorResponse(
                message="Please provide a description of what the agent should do.",
@@ -224,12 +219,7 @@ class CreateAgentTool(BaseTool):
                logger.warning(f"Failed to enrich library agents from steps: {e}")
        try:
-            agent_json = await generate_agent(
+            agent_json = await generate_agent(decomposition_result, library_agents)
                decomposition_result,
                library_agents,
                operation_id=operation_id,
                task_id=task_id,
            )
        except AgentGeneratorNotConfiguredError:
            return ErrorResponse(
                message=(
@@ -273,19 +263,6 @@ class CreateAgentTool(BaseTool):
                session_id=session_id,
            )
        # Check if Agent Generator accepted for async processing
        if agent_json.get("status") == "accepted":
            logger.info(
                f"Agent generation delegated to async processing "
                f"(operation_id={operation_id}, task_id={task_id})"
            )
            return AsyncProcessingResponse(
                message="Agent generation started. You'll be notified when it's complete.",
                operation_id=operation_id,
                task_id=task_id,
                session_id=session_id,
            )
        agent_name = agent_json.get("name", "Generated Agent")
        agent_description = agent_json.get("description", "")
        node_count = len(agent_json.get("nodes", []))
--- a/autogpt_platform/backend/backend/api/features/chat/tools/customize_agent.py
+++ b/autogpt_platform/backend/backend/api/features/chat/tools/customize_agent.py
@@ -1,337 +0,0 @@
 """CustomizeAgentTool - Customizes marketplace/template agents using natural language."""
 import logging
 from typing import Any
 from backend.api.features.chat.model import ChatSession
 from backend.api.features.store import db as store_db
 from backend.api.features.store.exceptions import AgentNotFoundError
 from .agent_generator import (
    AgentGeneratorNotConfiguredError,
    customize_template,
    get_user_message_for_error,
    graph_to_json,
    save_agent_to_library,
 )
 from .base import BaseTool
 from .models import (
    AgentPreviewResponse,
    AgentSavedResponse,
    ClarificationNeededResponse,
    ClarifyingQuestion,
    ErrorResponse,
    ToolResponseBase,
 )
 logger = logging.getLogger(__name__)
 class CustomizeAgentTool(BaseTool):
    """Tool for customizing marketplace/template agents using natural language."""
    @property
    def name(self) -> str:
        return "customize_agent"
    @property
    def description(self) -> str:
        return (
            "Customize a marketplace or template agent using natural language. "
            "Takes an existing agent from the marketplace and modifies it based on "
            "the user's requirements before adding to their library."
        )
    @property
    def requires_auth(self) -> bool:
        return True
    @property
    def is_long_running(self) -> bool:
        return True
    @property
    def parameters(self) -> dict[str, Any]:
        return {
            "type": "object",
            "properties": {
                "agent_id": {
                    "type": "string",
                    "description": (
                        "The marketplace agent ID in format 'creator/slug' "
                        "(e.g., 'autogpt/newsletter-writer'). "
                        "Get this from find_agent results."
                    ),
                },
                "modifications": {
                    "type": "string",
                    "description": (
                        "Natural language description of how to customize the agent. "
                        "Be specific about what changes you want to make."
                    ),
                },
                "context": {
                    "type": "string",
                    "description": (
                        "Additional context or answers to previous clarifying questions."
                    ),
                },
                "save": {
                    "type": "boolean",
                    "description": (
                        "Whether to save the customized agent to the user's library. "
                        "Default is true. Set to false for preview only."
                    ),
                    "default": True,
                },
            },
            "required": ["agent_id", "modifications"],
        }
    async def _execute(
        self,
        user_id: str | None,
        session: ChatSession,
        **kwargs,
    ) -> ToolResponseBase:
        """Execute the customize_agent tool.
        Flow:
        1. Parse the agent ID to get creator/slug
        2. Fetch the template agent from the marketplace
        3. Call customize_template with the modification request
        4. Preview or save based on the save parameter
        """
        agent_id = kwargs.get("agent_id", "").strip()
        modifications = kwargs.get("modifications", "").strip()
        context = kwargs.get("context", "")
        save = kwargs.get("save", True)
        session_id = session.session_id if session else None
        if not agent_id:
            return ErrorResponse(
                message="Please provide the marketplace agent ID (e.g., 'creator/agent-name').",
                error="missing_agent_id",
                session_id=session_id,
            )
        if not modifications:
            return ErrorResponse(
                message="Please describe how you want to customize this agent.",
                error="missing_modifications",
                session_id=session_id,
            )
        # Parse agent_id in format "creator/slug"
        parts = [p.strip() for p in agent_id.split("/")]
        if len(parts) != 2 or not parts[0] or not parts[1]:
            return ErrorResponse(
                message=(
                    f"Invalid agent ID format: '{agent_id}'. "
                    "Expected format is 'creator/agent-name' "
                    "(e.g., 'autogpt/newsletter-writer')."
                ),
                error="invalid_agent_id_format",
                session_id=session_id,
            )
        creator_username, agent_slug = parts
        # Fetch the marketplace agent details
        try:
            agent_details = await store_db.get_store_agent_details(
                username=creator_username, agent_name=agent_slug
            )
        except AgentNotFoundError:
            return ErrorResponse(
                message=(
                    f"Could not find marketplace agent '{agent_id}'. "
                    "Please check the agent ID and try again."
                ),
                error="agent_not_found",
                session_id=session_id,
            )
        except Exception as e:
            logger.error(f"Error fetching marketplace agent {agent_id}: {e}")
            return ErrorResponse(
                message="Failed to fetch the marketplace agent. Please try again.",
                error="fetch_error",
                session_id=session_id,
            )
        if not agent_details.store_listing_version_id:
            return ErrorResponse(
                message=(
                    f"The agent '{agent_id}' does not have an available version. "
                    "Please try a different agent."
                ),
                error="no_version_available",
                session_id=session_id,
            )
        # Get the full agent graph
        try:
            graph = await store_db.get_agent(agent_details.store_listing_version_id)
            template_agent = graph_to_json(graph)
        except Exception as e:
            logger.error(f"Error fetching agent graph for {agent_id}: {e}")
            return ErrorResponse(
                message="Failed to fetch the agent configuration. Please try again.",
                error="graph_fetch_error",
                session_id=session_id,
            )
        # Call customize_template
        try:
            result = await customize_template(
                template_agent=template_agent,
                modification_request=modifications,
                context=context,
            )
        except AgentGeneratorNotConfiguredError:
            return ErrorResponse(
                message=(
                    "Agent customization is not available. "
                    "The Agent Generator service is not configured."
                ),
                error="service_not_configured",
                session_id=session_id,
            )
        except Exception as e:
            logger.error(f"Error calling customize_template for {agent_id}: {e}")
            return ErrorResponse(
                message=(
                    "Failed to customize the agent due to a service error. "
                    "Please try again."
                ),
                error="customization_service_error",
                session_id=session_id,
            )
        if result is None:
            return ErrorResponse(
                message=(
                    "Failed to customize the agent. "
                    "The agent generation service may be unavailable or timed out. "
                    "Please try again."
                ),
                error="customization_failed",
                session_id=session_id,
            )
        # Handle error response
        if isinstance(result, dict) and result.get("type") == "error":
            error_msg = result.get("error", "Unknown error")
            error_type = result.get("error_type", "unknown")
            user_message = get_user_message_for_error(
                error_type,
                operation="customize the agent",
                llm_parse_message=(
                    "The AI had trouble customizing the agent. "
                    "Please try again or simplify your request."
                ),
                validation_message=(
                    "The customized agent failed validation. "
                    "Please try rephrasing your request."
                ),
                error_details=error_msg,
            )
            return ErrorResponse(
                message=user_message,
                error=f"customization_failed:{error_type}",
                session_id=session_id,
            )
        # Handle clarifying questions
        if isinstance(result, dict) and result.get("type") == "clarifying_questions":
            questions = result.get("questions") or []
            if not isinstance(questions, list):
                logger.error(
                    f"Unexpected clarifying questions format: {type(questions)}"
                )
                questions = []
            return ClarificationNeededResponse(
                message=(
                    "I need some more information to customize this agent. "
                    "Please answer the following questions:"
                ),
                questions=[
                    ClarifyingQuestion(
                        question=q.get("question", ""),
                        keyword=q.get("keyword", ""),
                        example=q.get("example"),
                    )
                    for q in questions
                    if isinstance(q, dict)
                ],
                session_id=session_id,
            )
        # Result should be the customized agent JSON
        if not isinstance(result, dict):
            logger.error(f"Unexpected customize_template response type: {type(result)}")
            return ErrorResponse(
                message="Failed to customize the agent due to an unexpected response.",
                error="unexpected_response_type",
                session_id=session_id,
            )
        customized_agent = result
        agent_name = customized_agent.get(
            "name", f"Customized {agent_details.agent_name}"
        )
        agent_description = customized_agent.get("description", "")
        nodes = customized_agent.get("nodes")
        links = customized_agent.get("links")
        node_count = len(nodes) if isinstance(nodes, list) else 0
        link_count = len(links) if isinstance(links, list) else 0
        if not save:
            return AgentPreviewResponse(
                message=(
                    f"I've customized the agent '{agent_details.agent_name}'. "
                    f"The customized agent has {node_count} blocks. "
                    f"Review it and call customize_agent with save=true to save it."
                ),
                agent_json=customized_agent,
                agent_name=agent_name,
                description=agent_description,
                node_count=node_count,
                link_count=link_count,
                session_id=session_id,
            )
        if not user_id:
            return ErrorResponse(
                message="You must be logged in to save agents.",
                error="auth_required",
                session_id=session_id,
            )
        # Save to user's library
        try:
            created_graph, library_agent = await save_agent_to_library(
                customized_agent, user_id, is_update=False
            )
            return AgentSavedResponse(
                message=(
                    f"Customized agent '{created_graph.name}' "
                    f"(based on '{agent_details.agent_name}') "
                    f"has been saved to your library!"
                ),
                agent_id=created_graph.id,
                agent_name=created_graph.name,
                library_agent_id=library_agent.id,
                library_agent_link=f"/library/agents/{library_agent.id}",
                agent_page_link=f"/build?flowID={created_graph.id}",
                session_id=session_id,
            )
        except Exception as e:
            logger.error(f"Error saving customized agent: {e}")
            return ErrorResponse(
                message="Failed to save the customized agent. Please try again.",
                error="save_failed",
                session_id=session_id,
            )
--- a/autogpt_platform/backend/backend/api/features/chat/tools/edit_agent.py
+++ b/autogpt_platform/backend/backend/api/features/chat/tools/edit_agent.py
@@ -17,7 +17,6 @@ from .base import BaseTool
 from .models import (
    AgentPreviewResponse,
    AgentSavedResponse,
    AsyncProcessingResponse,
    ClarificationNeededResponse,
    ClarifyingQuestion,
    ErrorResponse,
@@ -105,10 +104,6 @@ class EditAgentTool(BaseTool):
        save = kwargs.get("save", True)
        session_id = session.session_id if session else None
        # Extract async processing params (passed by long-running tool handler)
        operation_id = kwargs.get("_operation_id")
        task_id = kwargs.get("_task_id")
        if not agent_id:
            return ErrorResponse(
                message="Please provide the agent ID to edit.",
@@ -154,11 +149,7 @@ class EditAgentTool(BaseTool):
        try:
            result = await generate_agent_patch(
-                update_request,
+                update_request, current_agent, library_agents
                current_agent,
                library_agents,
                operation_id=operation_id,
                task_id=task_id,
            )
        except AgentGeneratorNotConfiguredError:
            return ErrorResponse(
@@ -178,20 +169,6 @@ class EditAgentTool(BaseTool):
                session_id=session_id,
            )
        # Check if Agent Generator accepted for async processing
        if result.get("status") == "accepted":
            logger.info(
                f"Agent edit delegated to async processing "
                f"(operation_id={operation_id}, task_id={task_id})"
            )
            return AsyncProcessingResponse(
                message="Agent edit started. You'll be notified when it's complete.",
                operation_id=operation_id,
                task_id=task_id,
                session_id=session_id,
            )
        # Check if the result is an error from the external service
        if isinstance(result, dict) and result.get("type") == "error":
            error_msg = result.get("error", "Unknown error")
            error_type = result.get("error_type", "unknown")
--- a/autogpt_platform/backend/backend/api/features/chat/tools/models.py
+++ b/autogpt_platform/backend/backend/api/features/chat/tools/models.py
@@ -38,8 +38,6 @@ class ResponseType(str, Enum):
    OPERATION_STARTED = "operation_started"
    OPERATION_PENDING = "operation_pending"
    OPERATION_IN_PROGRESS = "operation_in_progress"
    # Input validation
    INPUT_VALIDATION_ERROR = "input_validation_error"
 # Base response model
@@ -70,10 +68,6 @@ class AgentInfo(BaseModel):
    has_external_trigger: bool | None = None
    new_output: bool | None = None
    graph_id: str | None = None
    inputs: dict[str, Any] | None = Field(
        default=None,
        description="Input schema for the agent, including field names, types, and defaults",
    )
 class AgentsFoundResponse(ToolResponseBase):
@@ -200,20 +194,6 @@ class ErrorResponse(ToolResponseBase):
    details: dict[str, Any] | None = None
 class InputValidationErrorResponse(ToolResponseBase):
    """Response when run_agent receives unknown input fields."""
    type: ResponseType = ResponseType.INPUT_VALIDATION_ERROR
    unrecognized_fields: list[str] = Field(
        description="List of input field names that were not recognized"
    )
    inputs: dict[str, Any] = Field(
        description="The agent's valid input schema for reference"
    )
    graph_id: str | None = None
    graph_version: int | None = None
 # Agent output models
 class ExecutionOutputInfo(BaseModel):
    """Summary of a single execution's outputs."""
@@ -372,15 +352,11 @@ class OperationStartedResponse(ToolResponseBase):
    This is returned immediately to the client while the operation continues
    to execute. The user can close the tab and check back later.
    The task_id can be used to reconnect to the SSE stream via
    GET /chat/tasks/{task_id}/stream?last_idx=0
    """
    type: ResponseType = ResponseType.OPERATION_STARTED
    operation_id: str
    tool_name: str
    task_id: str | None = None  # For SSE reconnection
 class OperationPendingResponse(ToolResponseBase):
@@ -404,20 +380,3 @@ class OperationInProgressResponse(ToolResponseBase):
    type: ResponseType = ResponseType.OPERATION_IN_PROGRESS
    tool_call_id: str
 class AsyncProcessingResponse(ToolResponseBase):
    """Response when an operation has been delegated to async processing.
    This is returned by tools when the external service accepts the request
    for async processing (HTTP 202 Accepted). The Redis Streams completion
    consumer will handle the result when the external service completes.
    The status field is specifically "accepted" to allow the long-running tool
    handler to detect this response and skip LLM continuation.
    """
    type: ResponseType = ResponseType.OPERATION_STARTED
    status: str = "accepted"  # Must be "accepted" for detection
    operation_id: str | None = None
    task_id: str | None = None
--- a/autogpt_platform/backend/backend/api/features/chat/tools/run_agent.py
+++ b/autogpt_platform/backend/backend/api/features/chat/tools/run_agent.py
@@ -30,7 +30,6 @@ from .models import (
    ErrorResponse,
    ExecutionOptions,
    ExecutionStartedResponse,
    InputValidationErrorResponse,
    SetupInfo,
    SetupRequirementsResponse,
    ToolResponseBase,
@@ -274,22 +273,6 @@ class RunAgentTool(BaseTool):
            input_properties = graph.input_schema.get("properties", {})
            required_fields = set(graph.input_schema.get("required", []))
            provided_inputs = set(params.inputs.keys())
            valid_fields = set(input_properties.keys())
            # Check for unknown input fields
            unrecognized_fields = provided_inputs - valid_fields
            if unrecognized_fields:
                return InputValidationErrorResponse(
                    message=(
                        f"Unknown input field(s) provided: {', '.join(sorted(unrecognized_fields))}. "
                        f"Agent was not executed. Please use the correct field names from the schema."
                    ),
                    session_id=session_id,
                    unrecognized_fields=sorted(unrecognized_fields),
                    inputs=graph.input_schema,
                    graph_id=graph.id,
                    graph_version=graph.version,
                )
            # If agent has inputs but none were provided AND use_defaults is not set,
            # always show what's available first so user can decide
--- a/autogpt_platform/backend/backend/api/features/chat/tools/run_agent_test.py
+++ b/autogpt_platform/backend/backend/api/features/chat/tools/run_agent_test.py
@@ -402,42 +402,3 @@ async def test_run_agent_schedule_without_name(setup_test_data):
    # Should return error about missing schedule_name
    assert result_data.get("type") == "error"
    assert "schedule_name" in result_data["message"].lower()
@pytest.mark.asyncio(loop_scope="session")
 async def test_run_agent_rejects_unknown_input_fields(setup_test_data):
    """Test that run_agent returns input_validation_error for unknown input fields."""
    user = setup_test_data["user"]
    store_submission = setup_test_data["store_submission"]
    tool = RunAgentTool()
    agent_marketplace_id = f"{user.email.split('@')[0]}/{store_submission.slug}"
    session = make_session(user_id=user.id)
    # Execute with unknown input field names
    response = await tool.execute(
        user_id=user.id,
        session_id=str(uuid.uuid4()),
        tool_call_id=str(uuid.uuid4()),
        username_agent_slug=agent_marketplace_id,
        inputs={
            "unknown_field": "some value",
            "another_unknown": "another value",
        },
        session=session,
    )
    assert response is not None
    assert hasattr(response, "output")
    assert isinstance(response.output, str)
    result_data = orjson.loads(response.output)
    # Should return input_validation_error type with unrecognized fields
    assert result_data.get("type") == "input_validation_error"
    assert "unrecognized_fields" in result_data
    assert set(result_data["unrecognized_fields"]) == {
        "another_unknown",
        "unknown_field",
    }
    assert "inputs" in result_data  # Contains the valid schema
    assert "Agent was not executed" in result_data["message"]
--- a/autogpt_platform/backend/backend/api/features/chat/tools/run_block.py
+++ b/autogpt_platform/backend/backend/api/features/chat/tools/run_block.py
@@ -5,8 +5,6 @@ import uuid
 from collections import defaultdict
 from typing import Any
 from pydantic_core import PydanticUndefined
 from backend.api.features.chat.model import ChatSession
 from backend.data.block import get_block
 from backend.data.execution import ExecutionContext
@@ -77,22 +75,15 @@ class RunBlockTool(BaseTool):
        self,
        user_id: str,
        block: Any,
        input_data: dict[str, Any] | None = None,
    ) -> tuple[dict[str, CredentialsMetaInput], list[CredentialsMetaInput]]:
        """
        Check if user has required credentials for a block.
        Args:
            user_id: User ID
            block: Block to check credentials for
            input_data: Input data for the block (used to determine provider via discriminator)
        Returns:
            tuple[matched_credentials, missing_credentials]
        """
        matched_credentials: dict[str, CredentialsMetaInput] = {}
        missing_credentials: list[CredentialsMetaInput] = []
        input_data = input_data or {}
        # Get credential field info from block's input schema
        credentials_fields_info = block.input_schema.get_credentials_fields_info()
@@ -105,33 +96,14 @@ class RunBlockTool(BaseTool):
        available_creds = await creds_manager.store.get_all_creds(user_id)
        for field_name, field_info in credentials_fields_info.items():
-            effective_field_info = field_info
+            # field_info.provider is a frozenset of acceptable providers
-            if field_info.discriminator and field_info.discriminator_mapping:
+            # field_info.supported_types is a frozenset of acceptable types
                # Get discriminator from input, falling back to schema default
                discriminator_value = input_data.get(field_info.discriminator)
                if discriminator_value is None:
                    field = block.input_schema.model_fields.get(
                        field_info.discriminator
                    )
                    if field and field.default is not PydanticUndefined:
                        discriminator_value = field.default
                if (
                    discriminator_value
                    and discriminator_value in field_info.discriminator_mapping
                ):
                    effective_field_info = field_info.discriminate(discriminator_value)
                    logger.debug(
                        f"Discriminated provider for {field_name}: "
                        f"{discriminator_value} -> {effective_field_info.provider}"
                    )
            matching_cred = next(
                (
                    cred
                    for cred in available_creds
-                    if cred.provider in effective_field_info.provider
+                    if cred.provider in field_info.provider
-                    and cred.type in effective_field_info.supported_types
+                    and cred.type in field_info.supported_types
                ),
                None,
            )
@@ -145,8 +117,8 @@ class RunBlockTool(BaseTool):
                )
            else:
                # Create a placeholder for the missing credential
-                provider = next(iter(effective_field_info.provider), "unknown")
+                provider = next(iter(field_info.provider), "unknown")
-                cred_type = next(iter(effective_field_info.supported_types), "api_key")
+                cred_type = next(iter(field_info.supported_types), "api_key")
                missing_credentials.append(
                    CredentialsMetaInput(
                        id=field_name,
@@ -214,9 +186,10 @@ class RunBlockTool(BaseTool):
        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, input_data
+            user_id, block
        )
        if missing_credentials:
--- a/autogpt_platform/backend/backend/api/features/chat/tools/utils.py
+++ b/autogpt_platform/backend/backend/api/features/chat/tools/utils.py
@@ -8,12 +8,7 @@ from backend.api.features.library import model as library_model
 from backend.api.features.store import db as store_db
 from backend.data import graph as graph_db
 from backend.data.graph import GraphModel
-from backend.data.model import (
+from backend.data.model import Credentials, CredentialsFieldInfo, CredentialsMetaInput
    CredentialsFieldInfo,
    CredentialsMetaInput,
    HostScopedCredentials,
    OAuth2Credentials,
 )
 from backend.integrations.creds_manager import IntegrationCredentialsManager
 from backend.util.exceptions import NotFoundError
@@ -278,14 +273,7 @@ async def match_user_credentials_to_graph(
                for cred in available_creds
                if cred.provider in credential_requirements.provider
                and cred.type in credential_requirements.supported_types
-                and (
+                and _credential_has_required_scopes(cred, credential_requirements)
                    cred.type != "oauth2"
                    or _credential_has_required_scopes(cred, credential_requirements)
                )
                and (
                    cred.type != "host_scoped"
                    or _credential_is_for_host(cred, credential_requirements)
                )
            ),
            None,
        )
@@ -330,10 +318,19 @@ async def match_user_credentials_to_graph(
 def _credential_has_required_scopes(
-    credential: OAuth2Credentials,
+    credential: Credentials,
    requirements: CredentialsFieldInfo,
 ) -> bool:
-    """Check if an OAuth2 credential has all the scopes required by the input."""
+    """
    Check if a credential has all the scopes required by the block.
    For OAuth2 credentials, verifies that the credential's scopes are a superset
    of the required scopes. For other credential types, returns True (no scope check).
    """
    # Only OAuth2 credentials have scopes to check
    if credential.type != "oauth2":
        return True
    # If no scopes are required, any credential matches
    if not requirements.required_scopes:
        return True
@@ -342,22 +339,6 @@ def _credential_has_required_scopes(
    return set(credential.scopes).issuperset(requirements.required_scopes)
 def _credential_is_for_host(
    credential: HostScopedCredentials,
    requirements: CredentialsFieldInfo,
 ) -> bool:
    """Check if a host-scoped credential matches the host required by the input."""
    # We need to know the host to match host-scoped credentials to.
    # Graph.aggregate_credentials_inputs() adds the node's set URL value (if any)
    # to discriminator_values. No discriminator_values -> no host to match against.
    if not requirements.discriminator_values:
        return True
    # Check that credential host matches required host.
    # Host-scoped credential inputs are grouped by host, so any item from the set works.
    return credential.matches_url(list(requirements.discriminator_values)[0])
 async def check_user_has_required_credentials(
    user_id: str,
    required_credentials: list[CredentialsMetaInput],
--- a/autogpt_platform/backend/backend/api/features/library/db.py
+++ b/autogpt_platform/backend/backend/api/features/library/db.py
@@ -19,10 +19,7 @@ from backend.data.graph import GraphSettings
 from backend.data.includes import AGENT_PRESET_INCLUDE, library_agent_include
 from backend.data.model import CredentialsMetaInput
 from backend.integrations.creds_manager import IntegrationCredentialsManager
-from backend.integrations.webhooks.graph_lifecycle_hooks import (
+from backend.integrations.webhooks.graph_lifecycle_hooks import on_graph_activate
    on_graph_activate,
    on_graph_deactivate,
 )
 from backend.util.clients import get_scheduler_client
 from backend.util.exceptions import DatabaseError, InvalidInputError, NotFoundError
 from backend.util.json import SafeJson
@@ -540,92 +537,6 @@ async def update_agent_version_in_library(
    return library_model.LibraryAgent.from_db(lib)
 async def create_graph_in_library(
    graph: graph_db.Graph,
    user_id: str,
 ) -> tuple[graph_db.GraphModel, library_model.LibraryAgent]:
    """Create a new graph and add it to the user's library."""
    graph.version = 1
    graph_model = graph_db.make_graph_model(graph, user_id)
    graph_model.reassign_ids(user_id=user_id, reassign_graph_id=True)
    created_graph = await graph_db.create_graph(graph_model, user_id)
    library_agents = await create_library_agent(
        graph=created_graph,
        user_id=user_id,
        sensitive_action_safe_mode=True,
        create_library_agents_for_sub_graphs=False,
    )
    if created_graph.is_active:
        created_graph = await on_graph_activate(created_graph, user_id=user_id)
    return created_graph, library_agents[0]
 async def update_graph_in_library(
    graph: graph_db.Graph,
    user_id: str,
 ) -> tuple[graph_db.GraphModel, library_model.LibraryAgent]:
    """Create a new version of an existing graph and update the library entry."""
    existing_versions = await graph_db.get_graph_all_versions(graph.id, user_id)
    current_active_version = (
        next((v for v in existing_versions if v.is_active), None)
        if existing_versions
        else None
    )
    graph.version = (
        max(v.version for v in existing_versions) + 1 if existing_versions else 1
    )
    graph_model = graph_db.make_graph_model(graph, user_id)
    graph_model.reassign_ids(user_id=user_id, reassign_graph_id=False)
    created_graph = await graph_db.create_graph(graph_model, user_id)
    library_agent = await get_library_agent_by_graph_id(user_id, created_graph.id)
    if not library_agent:
        raise NotFoundError(f"Library agent not found for graph {created_graph.id}")
    library_agent = await update_library_agent_version_and_settings(
        user_id, created_graph
    )
    if created_graph.is_active:
        created_graph = await on_graph_activate(created_graph, user_id=user_id)
        await graph_db.set_graph_active_version(
            graph_id=created_graph.id,
            version=created_graph.version,
            user_id=user_id,
        )
        if current_active_version:
            await on_graph_deactivate(current_active_version, user_id=user_id)
    return created_graph, library_agent
 async def update_library_agent_version_and_settings(
    user_id: str, agent_graph: graph_db.GraphModel
 ) -> library_model.LibraryAgent:
    """Update library agent to point to new graph version and sync settings."""
    library = await update_agent_version_in_library(
        user_id, agent_graph.id, agent_graph.version
    )
    updated_settings = GraphSettings.from_graph(
        graph=agent_graph,
        hitl_safe_mode=library.settings.human_in_the_loop_safe_mode,
        sensitive_action_safe_mode=library.settings.sensitive_action_safe_mode,
    )
    if updated_settings != library.settings:
        library = await update_library_agent(
            library_agent_id=library.id,
            user_id=user_id,
            settings=updated_settings,
        )
    return library
 async def update_library_agent(
    library_agent_id: str,
    user_id: str,
--- a/autogpt_platform/backend/backend/api/features/store/embeddings_e2e_test.py
+++ b/autogpt_platform/backend/backend/api/features/store/embeddings_e2e_test.py
@@ -454,9 +454,6 @@ async def test_unified_hybrid_search_pagination(
    cleanup_embeddings: list,
 ):
    """Test unified search pagination works correctly."""
    # Use a unique search term to avoid matching other test data
    unique_term = f"xyzpagtest{uuid.uuid4().hex[:8]}"
    # Create multiple items
    content_ids = []
    for i in range(5):
@@ -468,14 +465,14 @@ async def test_unified_hybrid_search_pagination(
            content_type=ContentType.BLOCK,
            content_id=content_id,
            embedding=mock_embedding,
-            searchable_text=f"{unique_term} item number {i}",
+            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=unique_term,
+        query="pagination test",
        content_types=[ContentType.BLOCK],
        page=1,
        page_size=2,
@@ -483,7 +480,7 @@ async def test_unified_hybrid_search_pagination(
    # Get second page
    page2_results, total2 = await unified_hybrid_search(
-        query=unique_term,
+        query="pagination test",
        content_types=[ContentType.BLOCK],
        page=2,
        page_size=2,
--- a/autogpt_platform/backend/backend/api/features/v1.py
+++ b/autogpt_platform/backend/backend/api/features/v1.py
@@ -101,6 +101,7 @@ from backend.util.timezone_utils import (
 from backend.util.virus_scanner import scan_content_safe
 from .library import db as library_db
 from .library import model as library_model
 from .store.model import StoreAgentDetails
@@ -822,16 +823,18 @@ async def update_graph(
    graph: graph_db.Graph,
    user_id: Annotated[str, Security(get_user_id)],
 ) -> graph_db.GraphModel:
    # Sanity check
    if graph.id and graph.id != graph_id:
        raise HTTPException(400, detail="Graph ID does not match ID in URI")
    # Determine new version
    existing_versions = await graph_db.get_graph_all_versions(graph_id, user_id=user_id)
    if not existing_versions:
        raise HTTPException(404, detail=f"Graph #{graph_id} not found")
    latest_version_number = max(g.version for g in existing_versions)
    graph.version = latest_version_number + 1
    graph.version = max(g.version for g in existing_versions) + 1
    current_active_version = next((v for v in existing_versions if v.is_active), None)
    graph = graph_db.make_graph_model(graph, user_id)
    graph.reassign_ids(user_id=user_id, reassign_graph_id=False)
    graph.validate_graph(for_run=False)
@@ -839,23 +842,27 @@ async def update_graph(
    new_graph_version = await graph_db.create_graph(graph, user_id=user_id)
    if new_graph_version.is_active:
-        await library_db.update_library_agent_version_and_settings(
+        # Keep the library agent up to date with the new active version
-            user_id, new_graph_version
+        await _update_library_agent_version_and_settings(user_id, new_graph_version)
-        )
+
        # Handle activation of the new graph first to ensure continuity
        new_graph_version = await on_graph_activate(new_graph_version, user_id=user_id)
        # Ensure new version is the only active version
        await graph_db.set_graph_active_version(
            graph_id=graph_id, version=new_graph_version.version, user_id=user_id
        )
        if current_active_version:
            # Handle deactivation of the previously active version
            await on_graph_deactivate(current_active_version, user_id=user_id)
    # Fetch new graph version *with sub-graphs* (needed for credentials input schema)
    new_graph_version_with_subgraphs = await graph_db.get_graph(
        graph_id,
        new_graph_version.version,
        user_id=user_id,
        include_subgraphs=True,
    )
-    assert new_graph_version_with_subgraphs
+    assert new_graph_version_with_subgraphs  # make type checker happy
    return new_graph_version_with_subgraphs
@@ -893,15 +900,33 @@ async def set_graph_active_version(
    )
    # Keep the library agent up to date with the new active version
-    await library_db.update_library_agent_version_and_settings(
+    await _update_library_agent_version_and_settings(user_id, new_active_graph)
        user_id, new_active_graph
    )
    if current_active_graph and current_active_graph.version != new_active_version:
        # Handle deactivation of the previously active version
        await on_graph_deactivate(current_active_graph, user_id=user_id)
 async def _update_library_agent_version_and_settings(
    user_id: str, agent_graph: graph_db.GraphModel
 ) -> library_model.LibraryAgent:
    library = await library_db.update_agent_version_in_library(
        user_id, agent_graph.id, agent_graph.version
    )
    updated_settings = GraphSettings.from_graph(
        graph=agent_graph,
        hitl_safe_mode=library.settings.human_in_the_loop_safe_mode,
        sensitive_action_safe_mode=library.settings.sensitive_action_safe_mode,
    )
    if updated_settings != library.settings:
        library = await library_db.update_library_agent(
            library_agent_id=library.id,
            user_id=user_id,
            settings=updated_settings,
        )
    return library
@v1_router.patch(
    path="/graphs/{graph_id}/settings",
    summary="Update graph settings",
--- a/autogpt_platform/backend/backend/api/rest_api.py
+++ b/autogpt_platform/backend/backend/api/rest_api.py
@@ -40,10 +40,6 @@ import backend.data.user
 import backend.integrations.webhooks.utils
 import backend.util.service
 import backend.util.settings
 from backend.api.features.chat.completion_consumer import (
    start_completion_consumer,
    stop_completion_consumer,
 )
 from backend.blocks.llm import DEFAULT_LLM_MODEL
 from backend.data.model import Credentials
 from backend.integrations.providers import ProviderName
@@ -122,21 +118,9 @@ async def lifespan_context(app: fastapi.FastAPI):
    await backend.data.graph.migrate_llm_models(DEFAULT_LLM_MODEL)
    await backend.integrations.webhooks.utils.migrate_legacy_triggered_graphs()
    # Start chat completion consumer for Redis Streams notifications
    try:
        await start_completion_consumer()
    except Exception as e:
        logger.warning(f"Could not start chat completion consumer: {e}")
    with launch_darkly_context():
        yield
    # Stop chat completion consumer
    try:
        await stop_completion_consumer()
    except Exception as e:
        logger.warning(f"Error stopping chat completion consumer: {e}")
    try:
        await shutdown_cloud_storage_handler()
    except Exception as e:
--- a/autogpt_platform/backend/backend/blocks/elevenlabs/_auth.py
+++ b/autogpt_platform/backend/backend/blocks/elevenlabs/_auth.py
@@ -1,28 +0,0 @@
 """ElevenLabs integration blocks - test credentials and shared utilities."""
 from typing import Literal
 from pydantic import SecretStr
 from backend.data.model import APIKeyCredentials, CredentialsMetaInput
 from backend.integrations.providers import ProviderName
 TEST_CREDENTIALS = APIKeyCredentials(
    id="01234567-89ab-cdef-0123-456789abcdef",
    provider="elevenlabs",
    api_key=SecretStr("mock-elevenlabs-api-key"),
    title="Mock ElevenLabs API key",
    expires_at=None,
 )
 TEST_CREDENTIALS_INPUT = {
    "provider": TEST_CREDENTIALS.provider,
    "id": TEST_CREDENTIALS.id,
    "type": TEST_CREDENTIALS.type,
    "title": TEST_CREDENTIALS.title,
 }
 ElevenLabsCredentials = APIKeyCredentials
 ElevenLabsCredentialsInput = CredentialsMetaInput[
    Literal[ProviderName.ELEVENLABS], Literal["api_key"]
 ]
--- a/autogpt_platform/backend/backend/blocks/encoder_block.py
+++ b/autogpt_platform/backend/backend/blocks/encoder_block.py
@@ -1,77 +0,0 @@
 """Text encoding block for converting special characters to escape sequences."""
 import codecs
 from backend.data.block import (
    Block,
    BlockCategory,
    BlockOutput,
    BlockSchemaInput,
    BlockSchemaOutput,
 )
 from backend.data.model import SchemaField
 class TextEncoderBlock(Block):
    """
    Encodes a string by converting special characters into escape sequences.
    This block is the inverse of TextDecoderBlock. It takes text containing
    special characters (like newlines, tabs, etc.) and converts them into
    their escape sequence representations (e.g., newline becomes \\n).
    """
    class Input(BlockSchemaInput):
        """Input schema for TextEncoderBlock."""
        text: str = SchemaField(
            description="A string containing special characters to be encoded",
            placeholder="Your text with newlines and quotes to encode",
        )
    class Output(BlockSchemaOutput):
        """Output schema for TextEncoderBlock."""
        encoded_text: str = SchemaField(
            description="The encoded text with special characters converted to escape sequences"
        )
        error: str = SchemaField(description="Error message if encoding fails")
    def __init__(self):
        super().__init__(
            id="5185f32e-4b65-4ecf-8fbb-873f003f09d6",
            description="Encodes a string by converting special characters into escape sequences",
            categories={BlockCategory.TEXT},
            input_schema=TextEncoderBlock.Input,
            output_schema=TextEncoderBlock.Output,
            test_input={
                "text": """Hello
 World!
 This is a "quoted" string."""
            },
            test_output=[
                (
                    "encoded_text",
                    """Hello\\nWorld!\\nThis is a "quoted" string.""",
                )
            ],
        )
    async def run(self, input_data: Input, **kwargs) -> BlockOutput:
        """
        Encode the input text by converting special characters to escape sequences.
        Args:
            input_data: The input containing the text to encode.
            **kwargs: Additional keyword arguments (unused).
        Yields:
            The encoded text with escape sequences, or an error message if encoding fails.
        """
        try:
            encoded_text = codecs.encode(input_data.text, "unicode_escape").decode(
                "utf-8"
            )
            yield "encoded_text", encoded_text
        except Exception as e:
            yield "error", f"Encoding error: {str(e)}"
--- a/autogpt_platform/backend/backend/blocks/linear/_api.py
+++ b/autogpt_platform/backend/backend/blocks/linear/_api.py
@@ -162,16 +162,8 @@ class LinearClient:
                "searchTerm": team_name,
            }
-            result = await self.query(query, variables)
+            team_id = await self.query(query, variables)
-            nodes = result["teams"]["nodes"]
+            return team_id["teams"]["nodes"][0]["id"]
            if not nodes:
                raise LinearAPIException(
                    f"Team '{team_name}' not found. Check the team name or key and try again.",
                    status_code=404,
                )
            return nodes[0]["id"]
        except LinearAPIException as e:
            raise e
@@ -248,44 +240,17 @@ class LinearClient:
        except LinearAPIException as e:
            raise e
-    async def try_search_issues(
+    async def try_search_issues(self, term: str) -> list[Issue]:
        self,
        term: str,
        max_results: int = 10,
        team_id: str | None = None,
    ) -> list[Issue]:
        try:
            query = """
-                query SearchIssues(
+                query SearchIssues($term: String!, $includeComments: Boolean!) {
-                    $term: String!,
+                    searchIssues(term: $term, includeComments: $includeComments) {
                    $first: Int,
                    $teamId: String
                ) {
                    searchIssues(
                        term: $term,
                        first: $first,
                        teamId: $teamId
                    ) {
                        nodes {
                            id
                            identifier
                            title
                            description
                            priority
                            createdAt
                            state {
                                id
                                name
                                type
                            }
                            project {
                                id
                                name
                            }
                            assignee {
                                id
                                name
                            }
                        }
                    }
                }
@@ -293,8 +258,7 @@ class LinearClient:
            variables: dict[str, Any] = {
                "term": term,
-                "first": max_results,
+                "includeComments": True,
                "teamId": team_id,
            }
            issues = await self.query(query, variables)
--- a/autogpt_platform/backend/backend/blocks/linear/issues.py
+++ b/autogpt_platform/backend/backend/blocks/linear/issues.py
@@ -17,7 +17,7 @@ from ._config import (
    LinearScope,
    linear,
 )
-from .models import CreateIssueResponse, Issue, State
+from .models import CreateIssueResponse, Issue
 class LinearCreateIssueBlock(Block):
@@ -135,20 +135,9 @@ class LinearSearchIssuesBlock(Block):
            description="Linear credentials with read permissions",
            required_scopes={LinearScope.READ},
        )
        max_results: int = SchemaField(
            description="Maximum number of results to return",
            default=10,
            ge=1,
            le=100,
        )
        team_name: str | None = SchemaField(
            description="Optional team name to filter results (e.g., 'Internal', 'Open Source')",
            default=None,
        )
    class Output(BlockSchemaOutput):
        issues: list[Issue] = SchemaField(description="List of issues")
        error: str = SchemaField(description="Error message if the search failed")
    def __init__(self):
        super().__init__(
@@ -156,11 +145,8 @@ class LinearSearchIssuesBlock(Block):
            description="Searches for issues on Linear",
            input_schema=self.Input,
            output_schema=self.Output,
            categories={BlockCategory.PRODUCTIVITY, BlockCategory.ISSUE_TRACKING},
            test_input={
                "term": "Test issue",
                "max_results": 10,
                "team_name": None,
                "credentials": TEST_CREDENTIALS_INPUT_OAUTH,
            },
            test_credentials=TEST_CREDENTIALS_OAUTH,
@@ -170,14 +156,10 @@ class LinearSearchIssuesBlock(Block):
                    [
                        Issue(
                            id="abc123",
-                            identifier="TST-123",
+                            identifier="abc123",
                            title="Test issue",
                            description="Test description",
                            priority=1,
                            state=State(
                                id="state1", name="In Progress", type="started"
                            ),
                            createdAt="2026-01-15T10:00:00.000Z",
                        )
                    ],
                )
@@ -186,12 +168,10 @@ class LinearSearchIssuesBlock(Block):
                "search_issues": lambda *args, **kwargs: [
                    Issue(
                        id="abc123",
-                        identifier="TST-123",
+                        identifier="abc123",
                        title="Test issue",
                        description="Test description",
                        priority=1,
                        state=State(id="state1", name="In Progress", type="started"),
                        createdAt="2026-01-15T10:00:00.000Z",
                    )
                ]
            },
@@ -201,22 +181,10 @@ class LinearSearchIssuesBlock(Block):
    async def search_issues(
        credentials: OAuth2Credentials | APIKeyCredentials,
        term: str,
        max_results: int = 10,
        team_name: str | None = None,
    ) -> list[Issue]:
        client = LinearClient(credentials=credentials)
-
+        response: list[Issue] = await client.try_search_issues(term=term)
-        # Resolve team name to ID if provided
+        return response
        # Raises LinearAPIException with descriptive message if team not found
        team_id: str | None = None
        if team_name:
            team_id = await client.try_get_team_by_name(team_name=team_name)
        return await client.try_search_issues(
            term=term,
            max_results=max_results,
            team_id=team_id,
        )
    async def run(
        self,
@@ -228,10 +196,7 @@ class LinearSearchIssuesBlock(Block):
        """Execute the issue search"""
        try:
            issues = await self.search_issues(
-                credentials=credentials,
+                credentials=credentials, term=input_data.term
                term=input_data.term,
                max_results=input_data.max_results,
                team_name=input_data.team_name,
            )
            yield "issues", issues
        except LinearAPIException as e:
--- a/autogpt_platform/backend/backend/blocks/linear/models.py
+++ b/autogpt_platform/backend/backend/blocks/linear/models.py
@@ -36,21 +36,12 @@ class Project(BaseModel):
    content: str | None = None
 class State(BaseModel):
    id: str
    name: str
    type: str | None = (
        None  # Workflow state type (e.g., "triage", "backlog", "started", "completed", "canceled")
    )
 class Issue(BaseModel):
    id: str
    identifier: str
    title: str
    description: str | None
    priority: int
    state: State | None = None
    project: Project | None = None
    createdAt: str | None = None
    comments: list[Comment] | None = None
--- a/autogpt_platform/backend/backend/blocks/llm.py
+++ b/autogpt_platform/backend/backend/blocks/llm.py
@@ -115,7 +115,6 @@ class LlmModel(str, Enum, metaclass=LlmModelMeta):
    CLAUDE_4_5_OPUS = "claude-opus-4-5-20251101"
    CLAUDE_4_5_SONNET = "claude-sonnet-4-5-20250929"
    CLAUDE_4_5_HAIKU = "claude-haiku-4-5-20251001"
    CLAUDE_4_6_OPUS = "claude-opus-4-6"
    CLAUDE_3_HAIKU = "claude-3-haiku-20240307"
    # AI/ML API models
    AIML_API_QWEN2_5_72B = "Qwen/Qwen2.5-72B-Instruct-Turbo"
@@ -271,9 +270,6 @@ MODEL_METADATA = {
    LlmModel.CLAUDE_4_SONNET: ModelMetadata(
        "anthropic", 200000, 64000, "Claude Sonnet 4", "Anthropic", "Anthropic", 2
    ),  # claude-4-sonnet-20250514
    LlmModel.CLAUDE_4_6_OPUS: ModelMetadata(
        "anthropic", 200000, 128000, "Claude Opus 4.6", "Anthropic", "Anthropic", 3
    ),  # claude-opus-4-6
    LlmModel.CLAUDE_4_5_OPUS: ModelMetadata(
        "anthropic", 200000, 64000, "Claude Opus 4.5", "Anthropic", "Anthropic", 3
    ),  # claude-opus-4-5-20251101
--- a/autogpt_platform/backend/backend/blocks/media.py
+++ b/autogpt_platform/backend/backend/blocks/media.py
@@ -0,0 +1,246 @@
 import os
 import tempfile
 from typing import Optional
 from moviepy.audio.io.AudioFileClip import AudioFileClip
 from moviepy.video.fx.Loop import Loop
 from moviepy.video.io.VideoFileClip import VideoFileClip
 from backend.data.block import (
    Block,
    BlockCategory,
    BlockOutput,
    BlockSchemaInput,
    BlockSchemaOutput,
 )
 from backend.data.execution import ExecutionContext
 from backend.data.model import SchemaField
 from backend.util.file import MediaFileType, get_exec_file_path, store_media_file
 class MediaDurationBlock(Block):
    class Input(BlockSchemaInput):
        media_in: MediaFileType = SchemaField(
            description="Media input (URL, data URI, or local path)."
        )
        is_video: bool = SchemaField(
            description="Whether the media is a video (True) or audio (False).",
            default=True,
        )
    class Output(BlockSchemaOutput):
        duration: float = SchemaField(
            description="Duration of the media file (in seconds)."
        )
    def __init__(self):
        super().__init__(
            id="d8b91fd4-da26-42d4-8ecb-8b196c6d84b6",
            description="Block to get the duration of a media file.",
            categories={BlockCategory.MULTIMEDIA},
            input_schema=MediaDurationBlock.Input,
            output_schema=MediaDurationBlock.Output,
        )
    async def run(
        self,
        input_data: Input,
        *,
        execution_context: ExecutionContext,
        **kwargs,
    ) -> BlockOutput:
        # 1) Store the input media locally
        local_media_path = await store_media_file(
            file=input_data.media_in,
            execution_context=execution_context,
            return_format="for_local_processing",
        )
        assert execution_context.graph_exec_id is not None
        media_abspath = get_exec_file_path(
            execution_context.graph_exec_id, local_media_path
        )
        # 2) Load the clip
        if input_data.is_video:
            clip = VideoFileClip(media_abspath)
        else:
            clip = AudioFileClip(media_abspath)
        yield "duration", clip.duration
 class LoopVideoBlock(Block):
    """
    Block for looping (repeating) a video clip until a given duration or number of loops.
    """
    class Input(BlockSchemaInput):
        video_in: MediaFileType = SchemaField(
            description="The input video (can be a URL, data URI, or local path)."
        )
        # Provide EITHER a `duration` or `n_loops` or both. We'll demonstrate `duration`.
        duration: Optional[float] = SchemaField(
            description="Target duration (in seconds) to loop the video to. If omitted, defaults to no looping.",
            default=None,
            ge=0.0,
        )
        n_loops: Optional[int] = SchemaField(
            description="Number of times to repeat the video. If omitted, defaults to 1 (no repeat).",
            default=None,
            ge=1,
        )
    class Output(BlockSchemaOutput):
        video_out: str = SchemaField(
            description="Looped video returned either as a relative path or a data URI."
        )
    def __init__(self):
        super().__init__(
            id="8bf9eef6-5451-4213-b265-25306446e94b",
            description="Block to loop a video to a given duration or number of repeats.",
            categories={BlockCategory.MULTIMEDIA},
            input_schema=LoopVideoBlock.Input,
            output_schema=LoopVideoBlock.Output,
        )
    async def run(
        self,
        input_data: Input,
        *,
        execution_context: ExecutionContext,
        **kwargs,
    ) -> BlockOutput:
        assert execution_context.graph_exec_id is not None
        assert execution_context.node_exec_id is not None
        graph_exec_id = execution_context.graph_exec_id
        node_exec_id = execution_context.node_exec_id
        # 1) Store the input video locally
        local_video_path = await store_media_file(
            file=input_data.video_in,
            execution_context=execution_context,
            return_format="for_local_processing",
        )
        input_abspath = get_exec_file_path(graph_exec_id, local_video_path)
        # 2) Load the clip
        clip = VideoFileClip(input_abspath)
        # 3) Apply the loop effect
        looped_clip = clip
        if input_data.duration:
            # Loop until we reach the specified duration
            looped_clip = looped_clip.with_effects([Loop(duration=input_data.duration)])
        elif input_data.n_loops:
            looped_clip = looped_clip.with_effects([Loop(n=input_data.n_loops)])
        else:
            raise ValueError("Either 'duration' or 'n_loops' must be provided.")
        assert isinstance(looped_clip, VideoFileClip)
        # 4) Save the looped output
        output_filename = MediaFileType(
            f"{node_exec_id}_looped_{os.path.basename(local_video_path)}"
        )
        output_abspath = get_exec_file_path(graph_exec_id, output_filename)
        looped_clip = looped_clip.with_audio(clip.audio)
        looped_clip.write_videofile(output_abspath, codec="libx264", audio_codec="aac")
        # Return output - for_block_output returns workspace:// if available, else data URI
        video_out = await store_media_file(
            file=output_filename,
            execution_context=execution_context,
            return_format="for_block_output",
        )
        yield "video_out", video_out
 class AddAudioToVideoBlock(Block):
    """
    Block that adds (attaches) an audio track to an existing video.
    Optionally scale the volume of the new track.
    """
    class Input(BlockSchemaInput):
        video_in: MediaFileType = SchemaField(
            description="Video input (URL, data URI, or local path)."
        )
        audio_in: MediaFileType = SchemaField(
            description="Audio input (URL, data URI, or local path)."
        )
        volume: float = SchemaField(
            description="Volume scale for the newly attached audio track (1.0 = original).",
            default=1.0,
        )
    class Output(BlockSchemaOutput):
        video_out: MediaFileType = SchemaField(
            description="Final video (with attached audio), as a path or data URI."
        )
    def __init__(self):
        super().__init__(
            id="3503748d-62b6-4425-91d6-725b064af509",
            description="Block to attach an audio file to a video file using moviepy.",
            categories={BlockCategory.MULTIMEDIA},
            input_schema=AddAudioToVideoBlock.Input,
            output_schema=AddAudioToVideoBlock.Output,
        )
    async def run(
        self,
        input_data: Input,
        *,
        execution_context: ExecutionContext,
        **kwargs,
    ) -> BlockOutput:
        assert execution_context.graph_exec_id is not None
        assert execution_context.node_exec_id is not None
        graph_exec_id = execution_context.graph_exec_id
        node_exec_id = execution_context.node_exec_id
        # 1) Store the inputs locally
        local_video_path = await store_media_file(
            file=input_data.video_in,
            execution_context=execution_context,
            return_format="for_local_processing",
        )
        local_audio_path = await store_media_file(
            file=input_data.audio_in,
            execution_context=execution_context,
            return_format="for_local_processing",
        )
        abs_temp_dir = os.path.join(tempfile.gettempdir(), "exec_file", graph_exec_id)
        video_abspath = os.path.join(abs_temp_dir, local_video_path)
        audio_abspath = os.path.join(abs_temp_dir, local_audio_path)
        # 2) Load video + audio with moviepy
        video_clip = VideoFileClip(video_abspath)
        audio_clip = AudioFileClip(audio_abspath)
        # Optionally scale volume
        if input_data.volume != 1.0:
            audio_clip = audio_clip.with_volume_scaled(input_data.volume)
        # 3) Attach the new audio track
        final_clip = video_clip.with_audio(audio_clip)
        # 4) Write to output file
        output_filename = MediaFileType(
            f"{node_exec_id}_audio_attached_{os.path.basename(local_video_path)}"
        )
        output_abspath = os.path.join(abs_temp_dir, output_filename)
        final_clip.write_videofile(output_abspath, codec="libx264", audio_codec="aac")
        # 5) Return output - for_block_output returns workspace:// if available, else data URI
        video_out = await store_media_file(
            file=output_filename,
            execution_context=execution_context,
            return_format="for_block_output",
        )
        yield "video_out", video_out
--- a/autogpt_platform/backend/backend/blocks/stagehand/blocks.py
+++ b/autogpt_platform/backend/backend/blocks/stagehand/blocks.py
@@ -182,7 +182,10 @@ class StagehandObserveBlock(Block):
        **kwargs,
    ) -> BlockOutput:
-        logger.debug(f"OBSERVE: Using model provider {model_credentials.provider}")
+        logger.info(f"OBSERVE: Stagehand credentials: {stagehand_credentials}")
        logger.info(
            f"OBSERVE: Model credentials: {model_credentials} for provider {model_credentials.provider} secret: {model_credentials.api_key.get_secret_value()}"
        )
        with disable_signal_handling():
            stagehand = Stagehand(
@@ -279,7 +282,10 @@ class StagehandActBlock(Block):
        **kwargs,
    ) -> BlockOutput:
-        logger.debug(f"ACT: Using model provider {model_credentials.provider}")
+        logger.info(f"ACT: Stagehand credentials: {stagehand_credentials}")
        logger.info(
            f"ACT: Model credentials: {model_credentials} for provider {model_credentials.provider} secret: {model_credentials.api_key.get_secret_value()}"
        )
        with disable_signal_handling():
            stagehand = Stagehand(
@@ -364,7 +370,10 @@ class StagehandExtractBlock(Block):
        **kwargs,
    ) -> BlockOutput:
-        logger.debug(f"EXTRACT: Using model provider {model_credentials.provider}")
+        logger.info(f"EXTRACT: Stagehand credentials: {stagehand_credentials}")
        logger.info(
            f"EXTRACT: Model credentials: {model_credentials} for provider {model_credentials.provider} secret: {model_credentials.api_key.get_secret_value()}"
        )
        with disable_signal_handling():
            stagehand = Stagehand(
--- a/autogpt_platform/backend/backend/blocks/test/test_text_encoder.py
+++ b/autogpt_platform/backend/backend/blocks/test/test_text_encoder.py
@@ -1,77 +0,0 @@
 import pytest
 from backend.blocks.encoder_block import TextEncoderBlock
@pytest.mark.asyncio
 async def test_text_encoder_basic():
    """Test basic encoding of newlines and special characters."""
    block = TextEncoderBlock()
    result = []
    async for output in block.run(TextEncoderBlock.Input(text="Hello\nWorld")):
        result.append(output)
    assert len(result) == 1
    assert result[0][0] == "encoded_text"
    assert result[0][1] == "Hello\\nWorld"
@pytest.mark.asyncio
 async def test_text_encoder_multiple_escapes():
    """Test encoding of multiple escape sequences."""
    block = TextEncoderBlock()
    result = []
    async for output in block.run(
        TextEncoderBlock.Input(text="Line1\nLine2\tTabbed\rCarriage")
    ):
        result.append(output)
    assert len(result) == 1
    assert result[0][0] == "encoded_text"
    assert "\\n" in result[0][1]
    assert "\\t" in result[0][1]
    assert "\\r" in result[0][1]
@pytest.mark.asyncio
 async def test_text_encoder_unicode():
    """Test that unicode characters are handled correctly."""
    block = TextEncoderBlock()
    result = []
    async for output in block.run(TextEncoderBlock.Input(text="Hello 世界\n")):
        result.append(output)
    assert len(result) == 1
    assert result[0][0] == "encoded_text"
    # Unicode characters should be escaped as \uXXXX sequences
    assert "\\n" in result[0][1]
@pytest.mark.asyncio
 async def test_text_encoder_empty_string():
    """Test encoding of an empty string."""
    block = TextEncoderBlock()
    result = []
    async for output in block.run(TextEncoderBlock.Input(text="")):
        result.append(output)
    assert len(result) == 1
    assert result[0][0] == "encoded_text"
    assert result[0][1] == ""
@pytest.mark.asyncio
 async def test_text_encoder_error_handling():
    """Test that encoding errors are handled gracefully."""
    from unittest.mock import patch
    block = TextEncoderBlock()
    result = []
    with patch("codecs.encode", side_effect=Exception("Mocked encoding error")):
        async for output in block.run(TextEncoderBlock.Input(text="test")):
            result.append(output)
    assert len(result) == 1
    assert result[0][0] == "error"
    assert "Mocked encoding error" in result[0][1]
--- a/autogpt_platform/backend/backend/blocks/video/init.py
+++ b/autogpt_platform/backend/backend/blocks/video/init.py
@@ -1,37 +0,0 @@
 """Video editing blocks for AutoGPT Platform.
 This module provides blocks for:
 - Downloading videos from URLs (YouTube, Vimeo, news sites, direct links)
 - Clipping/trimming video segments
 - Concatenating multiple videos
 - Adding text overlays
 - Adding AI-generated narration
 - Getting media duration
 - Looping videos
 - Adding audio to videos
 Dependencies:
 - yt-dlp: For video downloading
 - moviepy: For video editing operations
 - elevenlabs: For AI narration (optional)
 """
 from backend.blocks.video.add_audio import AddAudioToVideoBlock
 from backend.blocks.video.clip import VideoClipBlock
 from backend.blocks.video.concat import VideoConcatBlock
 from backend.blocks.video.download import VideoDownloadBlock
 from backend.blocks.video.duration import MediaDurationBlock
 from backend.blocks.video.loop import LoopVideoBlock
 from backend.blocks.video.narration import VideoNarrationBlock
 from backend.blocks.video.text_overlay import VideoTextOverlayBlock
 __all__ = [
    "AddAudioToVideoBlock",
    "LoopVideoBlock",
    "MediaDurationBlock",
    "VideoClipBlock",
    "VideoConcatBlock",
    "VideoDownloadBlock",
    "VideoNarrationBlock",
    "VideoTextOverlayBlock",
 ]
--- a/autogpt_platform/backend/backend/blocks/video/_utils.py
+++ b/autogpt_platform/backend/backend/blocks/video/_utils.py
@@ -1,131 +0,0 @@
 """Shared utilities for video blocks."""
 from __future__ import annotations
 import logging
 import os
 import re
 import subprocess
 from pathlib import Path
 logger = logging.getLogger(__name__)
 # Known operation tags added by video blocks
 _VIDEO_OPS = (
    r"(?:clip|overlay|narrated|looped|concat|audio_attached|with_audio|narration)"
 )
 # Matches: {node_exec_id}_{operation}_ where node_exec_id contains a UUID
 _BLOCK_PREFIX_RE = re.compile(
    r"^[a-zA-Z0-9_-]*"
    r"[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}"
    r"[a-zA-Z0-9_-]*"
    r"_" + _VIDEO_OPS + r"_"
 )
 # Matches: a lone {node_exec_id}_ prefix (no operation keyword, e.g. download output)
 _UUID_PREFIX_RE = re.compile(
    r"^[a-zA-Z0-9_-]*"
    r"[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}"
    r"[a-zA-Z0-9_-]*_"
 )
 def extract_source_name(input_path: str, max_length: int = 50) -> str:
    """Extract the original source filename by stripping block-generated prefixes.
    Iteratively removes {node_exec_id}_{operation}_ prefixes that accumulate
    when chaining video blocks, recovering the original human-readable name.
    Safe for plain filenames (no UUID -> no stripping).
    Falls back to "video" if everything is stripped.
    """
    stem = Path(input_path).stem
    # Pass 1: strip {node_exec_id}_{operation}_ prefixes iteratively
    while _BLOCK_PREFIX_RE.match(stem):
        stem = _BLOCK_PREFIX_RE.sub("", stem, count=1)
    # Pass 2: strip a lone {node_exec_id}_ prefix (e.g. from download block)
    if _UUID_PREFIX_RE.match(stem):
        stem = _UUID_PREFIX_RE.sub("", stem, count=1)
    if not stem:
        return "video"
    return stem[:max_length]
 def get_video_codecs(output_path: str) -> tuple[str, str]:
    """Get appropriate video and audio codecs based on output file extension.
    Args:
        output_path: Path to the output file (used to determine extension)
    Returns:
        Tuple of (video_codec, audio_codec)
    Codec mappings:
        - .mp4: H.264 + AAC (universal compatibility)
        - .webm: VP8 + Vorbis (web streaming)
        - .mkv: H.264 + AAC (container supports many codecs)
        - .mov: H.264 + AAC (Apple QuickTime, widely compatible)
        - .m4v: H.264 + AAC (Apple iTunes/devices)
        - .avi: MPEG-4 + MP3 (legacy Windows)
    """
    ext = os.path.splitext(output_path)[1].lower()
    codec_map: dict[str, tuple[str, str]] = {
        ".mp4": ("libx264", "aac"),
        ".webm": ("libvpx", "libvorbis"),
        ".mkv": ("libx264", "aac"),
        ".mov": ("libx264", "aac"),
        ".m4v": ("libx264", "aac"),
        ".avi": ("mpeg4", "libmp3lame"),
    }
    return codec_map.get(ext, ("libx264", "aac"))
 def strip_chapters_inplace(video_path: str) -> None:
    """Strip chapter metadata from a media file in-place using ffmpeg.
    MoviePy 2.x crashes with IndexError when parsing files with embedded
    chapter metadata (https://github.com/Zulko/moviepy/issues/2419).
    This strips chapters without re-encoding.
    Args:
        video_path: Absolute path to the media file to strip chapters from.
    """
    base, ext = os.path.splitext(video_path)
    tmp_path = base + ".tmp" + ext
    try:
        result = subprocess.run(
            [
                "ffmpeg",
                "-y",
                "-i",
                video_path,
                "-map_chapters",
                "-1",
                "-codec",
                "copy",
                tmp_path,
            ],
            capture_output=True,
            text=True,
            timeout=300,
        )
        if result.returncode != 0:
            logger.warning(
                "ffmpeg chapter strip failed (rc=%d): %s",
                result.returncode,
                result.stderr,
            )
            return
        os.replace(tmp_path, video_path)
    except FileNotFoundError:
        logger.warning("ffmpeg not found; skipping chapter strip")
    finally:
        if os.path.exists(tmp_path):
            os.unlink(tmp_path)
--- a/autogpt_platform/backend/backend/blocks/video/add_audio.py
+++ b/autogpt_platform/backend/backend/blocks/video/add_audio.py
@@ -1,113 +0,0 @@
 """AddAudioToVideoBlock - Attach an audio track to a video file."""
 from moviepy.audio.io.AudioFileClip import AudioFileClip
 from moviepy.video.io.VideoFileClip import VideoFileClip
 from backend.blocks.video._utils import extract_source_name, strip_chapters_inplace
 from backend.data.block import (
    Block,
    BlockCategory,
    BlockOutput,
    BlockSchemaInput,
    BlockSchemaOutput,
 )
 from backend.data.execution import ExecutionContext
 from backend.data.model import SchemaField
 from backend.util.file import MediaFileType, get_exec_file_path, store_media_file
 class AddAudioToVideoBlock(Block):
    """Add (attach) an audio track to an existing video."""
    class Input(BlockSchemaInput):
        video_in: MediaFileType = SchemaField(
            description="Video input (URL, data URI, or local path)."
        )
        audio_in: MediaFileType = SchemaField(
            description="Audio input (URL, data URI, or local path)."
        )
        volume: float = SchemaField(
            description="Volume scale for the newly attached audio track (1.0 = original).",
            default=1.0,
        )
    class Output(BlockSchemaOutput):
        video_out: MediaFileType = SchemaField(
            description="Final video (with attached audio), as a path or data URI."
        )
    def __init__(self):
        super().__init__(
            id="3503748d-62b6-4425-91d6-725b064af509",
            description="Block to attach an audio file to a video file using moviepy.",
            categories={BlockCategory.MULTIMEDIA},
            input_schema=AddAudioToVideoBlock.Input,
            output_schema=AddAudioToVideoBlock.Output,
        )
    async def run(
        self,
        input_data: Input,
        *,
        execution_context: ExecutionContext,
        **kwargs,
    ) -> BlockOutput:
        assert execution_context.graph_exec_id is not None
        assert execution_context.node_exec_id is not None
        graph_exec_id = execution_context.graph_exec_id
        node_exec_id = execution_context.node_exec_id
        # 1) Store the inputs locally
        local_video_path = await store_media_file(
            file=input_data.video_in,
            execution_context=execution_context,
            return_format="for_local_processing",
        )
        local_audio_path = await store_media_file(
            file=input_data.audio_in,
            execution_context=execution_context,
            return_format="for_local_processing",
        )
        video_abspath = get_exec_file_path(graph_exec_id, local_video_path)
        audio_abspath = get_exec_file_path(graph_exec_id, local_audio_path)
        # 2) Load video + audio with moviepy
        strip_chapters_inplace(video_abspath)
        strip_chapters_inplace(audio_abspath)
        video_clip = None
        audio_clip = None
        final_clip = None
        try:
            video_clip = VideoFileClip(video_abspath)
            audio_clip = AudioFileClip(audio_abspath)
            # Optionally scale volume
            if input_data.volume != 1.0:
                audio_clip = audio_clip.with_volume_scaled(input_data.volume)
            # 3) Attach the new audio track
            final_clip = video_clip.with_audio(audio_clip)
            # 4) Write to output file
            source = extract_source_name(local_video_path)
            output_filename = MediaFileType(f"{node_exec_id}_with_audio_{source}.mp4")
            output_abspath = get_exec_file_path(graph_exec_id, output_filename)
            final_clip.write_videofile(
                output_abspath, codec="libx264", audio_codec="aac"
            )
        finally:
            if final_clip:
                final_clip.close()
            if audio_clip:
                audio_clip.close()
            if video_clip:
                video_clip.close()
        # 5) Return output - for_block_output returns workspace:// if available, else data URI
        video_out = await store_media_file(
            file=output_filename,
            execution_context=execution_context,
            return_format="for_block_output",
        )
        yield "video_out", video_out
--- a/autogpt_platform/backend/backend/blocks/video/clip.py
+++ b/autogpt_platform/backend/backend/blocks/video/clip.py
@@ -1,167 +0,0 @@
 """VideoClipBlock - Extract a segment from a video file."""
 from typing import Literal
 from moviepy.video.io.VideoFileClip import VideoFileClip
 from backend.blocks.video._utils import (
    extract_source_name,
    get_video_codecs,
    strip_chapters_inplace,
 )
 from backend.data.block import (
    Block,
    BlockCategory,
    BlockOutput,
    BlockSchemaInput,
    BlockSchemaOutput,
 )
 from backend.data.execution import ExecutionContext
 from backend.data.model import SchemaField
 from backend.util.exceptions import BlockExecutionError
 from backend.util.file import MediaFileType, get_exec_file_path, store_media_file
 class VideoClipBlock(Block):
    """Extract a time segment from a video."""
    class Input(BlockSchemaInput):
        video_in: MediaFileType = SchemaField(
            description="Input video (URL, data URI, or local path)"
        )
        start_time: float = SchemaField(description="Start time in seconds", ge=0.0)
        end_time: float = SchemaField(description="End time in seconds", ge=0.0)
        output_format: Literal["mp4", "webm", "mkv", "mov"] = SchemaField(
            description="Output format", default="mp4", advanced=True
        )
    class Output(BlockSchemaOutput):
        video_out: MediaFileType = SchemaField(
            description="Clipped video file (path or data URI)"
        )
        duration: float = SchemaField(description="Clip duration in seconds")
    def __init__(self):
        super().__init__(
            id="8f539119-e580-4d86-ad41-86fbcb22abb1",
            description="Extract a time segment from a video",
            categories={BlockCategory.MULTIMEDIA},
            input_schema=self.Input,
            output_schema=self.Output,
            test_input={
                "video_in": "/tmp/test.mp4",
                "start_time": 0.0,
                "end_time": 10.0,
            },
            test_output=[("video_out", str), ("duration", float)],
            test_mock={
                "_clip_video": lambda *args: 10.0,
                "_store_input_video": lambda *args, **kwargs: "test.mp4",
                "_store_output_video": lambda *args, **kwargs: "clip_test.mp4",
            },
        )
    async def _store_input_video(
        self, execution_context: ExecutionContext, file: MediaFileType
    ) -> MediaFileType:
        """Store input video. Extracted for testability."""
        return await store_media_file(
            file=file,
            execution_context=execution_context,
            return_format="for_local_processing",
        )
    async def _store_output_video(
        self, execution_context: ExecutionContext, file: MediaFileType
    ) -> MediaFileType:
        """Store output video. Extracted for testability."""
        return await store_media_file(
            file=file,
            execution_context=execution_context,
            return_format="for_block_output",
        )
    def _clip_video(
        self,
        video_abspath: str,
        output_abspath: str,
        start_time: float,
        end_time: float,
    ) -> float:
        """Extract a clip from a video. Extracted for testability."""
        clip = None
        subclip = None
        try:
            strip_chapters_inplace(video_abspath)
            clip = VideoFileClip(video_abspath)
            subclip = clip.subclipped(start_time, end_time)
            video_codec, audio_codec = get_video_codecs(output_abspath)
            subclip.write_videofile(
                output_abspath, codec=video_codec, audio_codec=audio_codec
            )
            return subclip.duration
        finally:
            if subclip:
                subclip.close()
            if clip:
                clip.close()
    async def run(
        self,
        input_data: Input,
        *,
        execution_context: ExecutionContext,
        node_exec_id: str,
        **kwargs,
    ) -> BlockOutput:
        # Validate time range
        if input_data.end_time <= input_data.start_time:
            raise BlockExecutionError(
                message=f"end_time ({input_data.end_time}) must be greater than start_time ({input_data.start_time})",
                block_name=self.name,
                block_id=str(self.id),
            )
        try:
            assert execution_context.graph_exec_id is not None
            # Store the input video locally
            local_video_path = await self._store_input_video(
                execution_context, input_data.video_in
            )
            video_abspath = get_exec_file_path(
                execution_context.graph_exec_id, local_video_path
            )
            # Build output path
            source = extract_source_name(local_video_path)
            output_filename = MediaFileType(
                f"{node_exec_id}_clip_{source}.{input_data.output_format}"
            )
            output_abspath = get_exec_file_path(
                execution_context.graph_exec_id, output_filename
            )
            duration = self._clip_video(
                video_abspath,
                output_abspath,
                input_data.start_time,
                input_data.end_time,
            )
            # Return as workspace path or data URI based on context
            video_out = await self._store_output_video(
                execution_context, output_filename
            )
            yield "video_out", video_out
            yield "duration", duration
        except BlockExecutionError:
            raise
        except Exception as e:
            raise BlockExecutionError(
                message=f"Failed to clip video: {e}",
                block_name=self.name,
                block_id=str(self.id),
            ) from e
--- a/autogpt_platform/backend/backend/blocks/video/concat.py
+++ b/autogpt_platform/backend/backend/blocks/video/concat.py
@@ -1,227 +0,0 @@
 """VideoConcatBlock - Concatenate multiple video clips into one."""
 from typing import Literal
 from moviepy import concatenate_videoclips
 from moviepy.video.fx import CrossFadeIn, CrossFadeOut, FadeIn, FadeOut
 from moviepy.video.io.VideoFileClip import VideoFileClip
 from backend.blocks.video._utils import (
    extract_source_name,
    get_video_codecs,
    strip_chapters_inplace,
 )
 from backend.data.block import (
    Block,
    BlockCategory,
    BlockOutput,
    BlockSchemaInput,
    BlockSchemaOutput,
 )
 from backend.data.execution import ExecutionContext
 from backend.data.model import SchemaField
 from backend.util.exceptions import BlockExecutionError
 from backend.util.file import MediaFileType, get_exec_file_path, store_media_file
 class VideoConcatBlock(Block):
    """Merge multiple video clips into one continuous video."""
    class Input(BlockSchemaInput):
        videos: list[MediaFileType] = SchemaField(
            description="List of video files to concatenate (in order)"
        )
        transition: Literal["none", "crossfade", "fade_black"] = SchemaField(
            description="Transition between clips", default="none"
        )
        transition_duration: int = SchemaField(
            description="Transition duration in seconds",
            default=1,
            ge=0,
            advanced=True,
        )
        output_format: Literal["mp4", "webm", "mkv", "mov"] = SchemaField(
            description="Output format", default="mp4", advanced=True
        )
    class Output(BlockSchemaOutput):
        video_out: MediaFileType = SchemaField(
            description="Concatenated video file (path or data URI)"
        )
        total_duration: float = SchemaField(description="Total duration in seconds")
    def __init__(self):
        super().__init__(
            id="9b0f531a-1118-487f-aeec-3fa63ea8900a",
            description="Merge multiple video clips into one continuous video",
            categories={BlockCategory.MULTIMEDIA},
            input_schema=self.Input,
            output_schema=self.Output,
            test_input={
                "videos": ["/tmp/a.mp4", "/tmp/b.mp4"],
            },
            test_output=[
                ("video_out", str),
                ("total_duration", float),
            ],
            test_mock={
                "_concat_videos": lambda *args: 20.0,
                "_store_input_video": lambda *args, **kwargs: "test.mp4",
                "_store_output_video": lambda *args, **kwargs: "concat_test.mp4",
            },
        )
    async def _store_input_video(
        self, execution_context: ExecutionContext, file: MediaFileType
    ) -> MediaFileType:
        """Store input video. Extracted for testability."""
        return await store_media_file(
            file=file,
            execution_context=execution_context,
            return_format="for_local_processing",
        )
    async def _store_output_video(
        self, execution_context: ExecutionContext, file: MediaFileType
    ) -> MediaFileType:
        """Store output video. Extracted for testability."""
        return await store_media_file(
            file=file,
            execution_context=execution_context,
            return_format="for_block_output",
        )
    def _concat_videos(
        self,
        video_abspaths: list[str],
        output_abspath: str,
        transition: str,
        transition_duration: int,
    ) -> float:
        """Concatenate videos. Extracted for testability.
        Returns:
            Total duration of the concatenated video.
        """
        clips = []
        faded_clips = []
        final = None
        try:
            # Load clips
            for v in video_abspaths:
                strip_chapters_inplace(v)
                clips.append(VideoFileClip(v))
            # Validate transition_duration against shortest clip
            if transition in {"crossfade", "fade_black"} and transition_duration > 0:
                min_duration = min(c.duration for c in clips)
                if transition_duration >= min_duration:
                    raise BlockExecutionError(
                        message=(
                            f"transition_duration ({transition_duration}s) must be "
                            f"shorter than the shortest clip ({min_duration:.2f}s)"
                        ),
                        block_name=self.name,
                        block_id=str(self.id),
                    )
            if transition == "crossfade":
                for i, clip in enumerate(clips):
                    effects = []
                    if i > 0:
                        effects.append(CrossFadeIn(transition_duration))
                    if i < len(clips) - 1:
                        effects.append(CrossFadeOut(transition_duration))
                    if effects:
                        clip = clip.with_effects(effects)
                    faded_clips.append(clip)
                final = concatenate_videoclips(
                    faded_clips,
                    method="compose",
                    padding=-transition_duration,
                )
            elif transition == "fade_black":
                for clip in clips:
                    faded = clip.with_effects(
                        [FadeIn(transition_duration), FadeOut(transition_duration)]
                    )
                    faded_clips.append(faded)
                final = concatenate_videoclips(faded_clips)
            else:
                final = concatenate_videoclips(clips)
            video_codec, audio_codec = get_video_codecs(output_abspath)
            final.write_videofile(
                output_abspath, codec=video_codec, audio_codec=audio_codec
            )
            return final.duration
        finally:
            if final:
                final.close()
            for clip in faded_clips:
                clip.close()
            for clip in clips:
                clip.close()
    async def run(
        self,
        input_data: Input,
        *,
        execution_context: ExecutionContext,
        node_exec_id: str,
        **kwargs,
    ) -> BlockOutput:
        # Validate minimum clips
        if len(input_data.videos) < 2:
            raise BlockExecutionError(
                message="At least 2 videos are required for concatenation",
                block_name=self.name,
                block_id=str(self.id),
            )
        try:
            assert execution_context.graph_exec_id is not None
            # Store all input videos locally
            video_abspaths = []
            for video in input_data.videos:
                local_path = await self._store_input_video(execution_context, video)
                video_abspaths.append(
                    get_exec_file_path(execution_context.graph_exec_id, local_path)
                )
            # Build output path
            source = (
                extract_source_name(video_abspaths[0]) if video_abspaths else "video"
            )
            output_filename = MediaFileType(
                f"{node_exec_id}_concat_{source}.{input_data.output_format}"
            )
            output_abspath = get_exec_file_path(
                execution_context.graph_exec_id, output_filename
            )
            total_duration = self._concat_videos(
                video_abspaths,
                output_abspath,
                input_data.transition,
                input_data.transition_duration,
            )
            # Return as workspace path or data URI based on context
            video_out = await self._store_output_video(
                execution_context, output_filename
            )
            yield "video_out", video_out
            yield "total_duration", total_duration
        except BlockExecutionError:
            raise
        except Exception as e:
            raise BlockExecutionError(
                message=f"Failed to concatenate videos: {e}",
                block_name=self.name,
                block_id=str(self.id),
            ) from e
--- a/autogpt_platform/backend/backend/blocks/video/download.py
+++ b/autogpt_platform/backend/backend/blocks/video/download.py
@@ -1,172 +0,0 @@
 """VideoDownloadBlock - Download video from URL (YouTube, Vimeo, news sites, direct links)."""
 import os
 import typing
 from typing import Literal
 import yt_dlp
 if typing.TYPE_CHECKING:
    from yt_dlp import _Params
 from backend.data.block import (
    Block,
    BlockCategory,
    BlockOutput,
    BlockSchemaInput,
    BlockSchemaOutput,
 )
 from backend.data.execution import ExecutionContext
 from backend.data.model import SchemaField
 from backend.util.exceptions import BlockExecutionError
 from backend.util.file import MediaFileType, get_exec_file_path, store_media_file
 class VideoDownloadBlock(Block):
    """Download video from URL using yt-dlp."""
    class Input(BlockSchemaInput):
        url: str = SchemaField(
            description="URL of the video to download (YouTube, Vimeo, direct link, etc.)",
            placeholder="https://www.youtube.com/watch?v=...",
        )
        quality: Literal["best", "1080p", "720p", "480p", "audio_only"] = SchemaField(
            description="Video quality preference", default="720p"
        )
        output_format: Literal["mp4", "webm", "mkv"] = SchemaField(
            description="Output video format", default="mp4", advanced=True
        )
    class Output(BlockSchemaOutput):
        video_file: MediaFileType = SchemaField(
            description="Downloaded video (path or data URI)"
        )
        duration: float = SchemaField(description="Video duration in seconds")
        title: str = SchemaField(description="Video title from source")
        source_url: str = SchemaField(description="Original source URL")
    def __init__(self):
        super().__init__(
            id="c35daabb-cd60-493b-b9ad-51f1fe4b50c4",
            description="Download video from URL (YouTube, Vimeo, news sites, direct links)",
            categories={BlockCategory.MULTIMEDIA},
            input_schema=self.Input,
            output_schema=self.Output,
            disabled=True,  # Disable until we can sandbox yt-dlp and handle security implications
            test_input={
                "url": "https://www.youtube.com/watch?v=dQw4w9WgXcQ",
                "quality": "480p",
            },
            test_output=[
                ("video_file", str),
                ("duration", float),
                ("title", str),
                ("source_url", str),
            ],
            test_mock={
                "_download_video": lambda *args: (
                    "video.mp4",
                    212.0,
                    "Test Video",
                ),
                "_store_output_video": lambda *args, **kwargs: "video.mp4",
            },
        )
    async def _store_output_video(
        self, execution_context: ExecutionContext, file: MediaFileType
    ) -> MediaFileType:
        """Store output video. Extracted for testability."""
        return await store_media_file(
            file=file,
            execution_context=execution_context,
            return_format="for_block_output",
        )
    def _get_format_string(self, quality: str) -> str:
        formats = {
            "best": "bestvideo+bestaudio/best",
            "1080p": "bestvideo[height<=1080]+bestaudio/best[height<=1080]",
            "720p": "bestvideo[height<=720]+bestaudio/best[height<=720]",
            "480p": "bestvideo[height<=480]+bestaudio/best[height<=480]",
            "audio_only": "bestaudio/best",
        }
        return formats.get(quality, formats["720p"])
    def _download_video(
        self,
        url: str,
        quality: str,
        output_format: str,
        output_dir: str,
        node_exec_id: str,
    ) -> tuple[str, float, str]:
        """Download video. Extracted for testability."""
        output_template = os.path.join(
            output_dir, f"{node_exec_id}_%(title).50s.%(ext)s"
        )
        ydl_opts: "_Params" = {
            "format": f"{self._get_format_string(quality)}/best",
            "outtmpl": output_template,
            "merge_output_format": output_format,
            "quiet": True,
            "no_warnings": True,
        }
        with yt_dlp.YoutubeDL(ydl_opts) as ydl:
            info = ydl.extract_info(url, download=True)
            video_path = ydl.prepare_filename(info)
            # Handle format conversion in filename
            if not video_path.endswith(f".{output_format}"):
                video_path = video_path.rsplit(".", 1)[0] + f".{output_format}"
            # Return just the filename, not the full path
            filename = os.path.basename(video_path)
            return (
                filename,
                info.get("duration") or 0.0,
                info.get("title") or "Unknown",
            )
    async def run(
        self,
        input_data: Input,
        *,
        execution_context: ExecutionContext,
        node_exec_id: str,
        **kwargs,
    ) -> BlockOutput:
        try:
            assert execution_context.graph_exec_id is not None
            # Get the exec file directory
            output_dir = get_exec_file_path(execution_context.graph_exec_id, "")
            os.makedirs(output_dir, exist_ok=True)
            filename, duration, title = self._download_video(
                input_data.url,
                input_data.quality,
                input_data.output_format,
                output_dir,
                node_exec_id,
            )
            # Return as workspace path or data URI based on context
            video_out = await self._store_output_video(
                execution_context, MediaFileType(filename)
            )
            yield "video_file", video_out
            yield "duration", duration
            yield "title", title
            yield "source_url", input_data.url
        except Exception as e:
            raise BlockExecutionError(
                message=f"Failed to download video: {e}",
                block_name=self.name,
                block_id=str(self.id),
            ) from e
--- a/autogpt_platform/backend/backend/blocks/video/duration.py
+++ b/autogpt_platform/backend/backend/blocks/video/duration.py
@@ -1,77 +0,0 @@
 """MediaDurationBlock - Get the duration of a media file."""
 from moviepy.audio.io.AudioFileClip import AudioFileClip
 from moviepy.video.io.VideoFileClip import VideoFileClip
 from backend.blocks.video._utils import strip_chapters_inplace
 from backend.data.block import (
    Block,
    BlockCategory,
    BlockOutput,
    BlockSchemaInput,
    BlockSchemaOutput,
 )
 from backend.data.execution import ExecutionContext
 from backend.data.model import SchemaField
 from backend.util.file import MediaFileType, get_exec_file_path, store_media_file
 class MediaDurationBlock(Block):
    """Get the duration of a media file (video or audio)."""
    class Input(BlockSchemaInput):
        media_in: MediaFileType = SchemaField(
            description="Media input (URL, data URI, or local path)."
        )
        is_video: bool = SchemaField(
            description="Whether the media is a video (True) or audio (False).",
            default=True,
        )
    class Output(BlockSchemaOutput):
        duration: float = SchemaField(
            description="Duration of the media file (in seconds)."
        )
    def __init__(self):
        super().__init__(
            id="d8b91fd4-da26-42d4-8ecb-8b196c6d84b6",
            description="Block to get the duration of a media file.",
            categories={BlockCategory.MULTIMEDIA},
            input_schema=MediaDurationBlock.Input,
            output_schema=MediaDurationBlock.Output,
        )
    async def run(
        self,
        input_data: Input,
        *,
        execution_context: ExecutionContext,
        **kwargs,
    ) -> BlockOutput:
        # 1) Store the input media locally
        local_media_path = await store_media_file(
            file=input_data.media_in,
            execution_context=execution_context,
            return_format="for_local_processing",
        )
        assert execution_context.graph_exec_id is not None
        media_abspath = get_exec_file_path(
            execution_context.graph_exec_id, local_media_path
        )
        # 2) Strip chapters to avoid MoviePy crash, then load the clip
        strip_chapters_inplace(media_abspath)
        clip = None
        try:
            if input_data.is_video:
                clip = VideoFileClip(media_abspath)
            else:
                clip = AudioFileClip(media_abspath)
            duration = clip.duration
        finally:
            if clip:
                clip.close()
        yield "duration", duration
--- a/autogpt_platform/backend/backend/blocks/video/loop.py
+++ b/autogpt_platform/backend/backend/blocks/video/loop.py
@@ -1,115 +0,0 @@
 """LoopVideoBlock - Loop a video to a given duration or number of repeats."""
 from typing import Optional
 from moviepy.video.fx.Loop import Loop
 from moviepy.video.io.VideoFileClip import VideoFileClip
 from backend.blocks.video._utils import extract_source_name, strip_chapters_inplace
 from backend.data.block import (
    Block,
    BlockCategory,
    BlockOutput,
    BlockSchemaInput,
    BlockSchemaOutput,
 )
 from backend.data.execution import ExecutionContext
 from backend.data.model import SchemaField
 from backend.util.file import MediaFileType, get_exec_file_path, store_media_file
 class LoopVideoBlock(Block):
    """Loop (repeat) a video clip until a given duration or number of loops."""
    class Input(BlockSchemaInput):
        video_in: MediaFileType = SchemaField(
            description="The input video (can be a URL, data URI, or local path)."
        )
        duration: Optional[float] = SchemaField(
            description="Target duration (in seconds) to loop the video to. Either duration or n_loops must be provided.",
            default=None,
            ge=0.0,
            le=3600.0,  # Max 1 hour to prevent disk exhaustion
        )
        n_loops: Optional[int] = SchemaField(
            description="Number of times to repeat the video. Either n_loops or duration must be provided.",
            default=None,
            ge=1,
            le=10,  # Max 10 loops to prevent disk exhaustion
        )
    class Output(BlockSchemaOutput):
        video_out: MediaFileType = SchemaField(
            description="Looped video returned either as a relative path or a data URI."
        )
    def __init__(self):
        super().__init__(
            id="8bf9eef6-5451-4213-b265-25306446e94b",
            description="Block to loop a video to a given duration or number of repeats.",
            categories={BlockCategory.MULTIMEDIA},
            input_schema=LoopVideoBlock.Input,
            output_schema=LoopVideoBlock.Output,
        )
    async def run(
        self,
        input_data: Input,
        *,
        execution_context: ExecutionContext,
        **kwargs,
    ) -> BlockOutput:
        assert execution_context.graph_exec_id is not None
        assert execution_context.node_exec_id is not None
        graph_exec_id = execution_context.graph_exec_id
        node_exec_id = execution_context.node_exec_id
        # 1) Store the input video locally
        local_video_path = await store_media_file(
            file=input_data.video_in,
            execution_context=execution_context,
            return_format="for_local_processing",
        )
        input_abspath = get_exec_file_path(graph_exec_id, local_video_path)
        # 2) Load the clip
        strip_chapters_inplace(input_abspath)
        clip = None
        looped_clip = None
        try:
            clip = VideoFileClip(input_abspath)
            # 3) Apply the loop effect
            if input_data.duration:
                # Loop until we reach the specified duration
                looped_clip = clip.with_effects([Loop(duration=input_data.duration)])
            elif input_data.n_loops:
                looped_clip = clip.with_effects([Loop(n=input_data.n_loops)])
            else:
                raise ValueError("Either 'duration' or 'n_loops' must be provided.")
            assert isinstance(looped_clip, VideoFileClip)
            # 4) Save the looped output
            source = extract_source_name(local_video_path)
            output_filename = MediaFileType(f"{node_exec_id}_looped_{source}.mp4")
            output_abspath = get_exec_file_path(graph_exec_id, output_filename)
            looped_clip = looped_clip.with_audio(clip.audio)
            looped_clip.write_videofile(
                output_abspath, codec="libx264", audio_codec="aac"
            )
        finally:
            if looped_clip:
                looped_clip.close()
            if clip:
                clip.close()
        # Return output - for_block_output returns workspace:// if available, else data URI
        video_out = await store_media_file(
            file=output_filename,
            execution_context=execution_context,
            return_format="for_block_output",
        )
        yield "video_out", video_out
--- a/autogpt_platform/backend/backend/blocks/video/narration.py
+++ b/autogpt_platform/backend/backend/blocks/video/narration.py
@@ -1,267 +0,0 @@
 """VideoNarrationBlock - Generate AI voice narration and add to video."""
 import os
 from typing import Literal
 from elevenlabs import ElevenLabs
 from moviepy import CompositeAudioClip
 from moviepy.audio.io.AudioFileClip import AudioFileClip
 from moviepy.video.io.VideoFileClip import VideoFileClip
 from backend.blocks.elevenlabs._auth import (
    TEST_CREDENTIALS,
    TEST_CREDENTIALS_INPUT,
    ElevenLabsCredentials,
    ElevenLabsCredentialsInput,
 )
 from backend.blocks.video._utils import (
    extract_source_name,
    get_video_codecs,
    strip_chapters_inplace,
 )
 from backend.data.block import (
    Block,
    BlockCategory,
    BlockOutput,
    BlockSchemaInput,
    BlockSchemaOutput,
 )
 from backend.data.execution import ExecutionContext
 from backend.data.model import CredentialsField, SchemaField
 from backend.util.exceptions import BlockExecutionError
 from backend.util.file import MediaFileType, get_exec_file_path, store_media_file
 class VideoNarrationBlock(Block):
    """Generate AI narration and add to video."""
    class Input(BlockSchemaInput):
        credentials: ElevenLabsCredentialsInput = CredentialsField(
            description="ElevenLabs API key for voice synthesis"
        )
        video_in: MediaFileType = SchemaField(
            description="Input video (URL, data URI, or local path)"
        )
        script: str = SchemaField(description="Narration script text")
        voice_id: str = SchemaField(
            description="ElevenLabs voice ID", default="21m00Tcm4TlvDq8ikWAM"  # Rachel
        )
        model_id: Literal[
            "eleven_multilingual_v2",
            "eleven_flash_v2_5",
            "eleven_turbo_v2_5",
            "eleven_turbo_v2",
        ] = SchemaField(
            description="ElevenLabs TTS model",
            default="eleven_multilingual_v2",
        )
        mix_mode: Literal["replace", "mix", "ducking"] = SchemaField(
            description="How to combine with original audio. 'ducking' applies stronger attenuation than 'mix'.",
            default="ducking",
        )
        narration_volume: float = SchemaField(
            description="Narration volume (0.0 to 2.0)",
            default=1.0,
            ge=0.0,
            le=2.0,
            advanced=True,
        )
        original_volume: float = SchemaField(
            description="Original audio volume when mixing (0.0 to 1.0)",
            default=0.3,
            ge=0.0,
            le=1.0,
            advanced=True,
        )
    class Output(BlockSchemaOutput):
        video_out: MediaFileType = SchemaField(
            description="Video with narration (path or data URI)"
        )
        audio_file: MediaFileType = SchemaField(
            description="Generated audio file (path or data URI)"
        )
    def __init__(self):
        super().__init__(
            id="3d036b53-859c-4b17-9826-ca340f736e0e",
            description="Generate AI narration and add to video",
            categories={BlockCategory.MULTIMEDIA, BlockCategory.AI},
            input_schema=self.Input,
            output_schema=self.Output,
            test_input={
                "video_in": "/tmp/test.mp4",
                "script": "Hello world",
                "credentials": TEST_CREDENTIALS_INPUT,
            },
            test_credentials=TEST_CREDENTIALS,
            test_output=[("video_out", str), ("audio_file", str)],
            test_mock={
                "_generate_narration_audio": lambda *args: b"mock audio content",
                "_add_narration_to_video": lambda *args: None,
                "_store_input_video": lambda *args, **kwargs: "test.mp4",
                "_store_output_video": lambda *args, **kwargs: "narrated_test.mp4",
            },
        )
    async def _store_input_video(
        self, execution_context: ExecutionContext, file: MediaFileType
    ) -> MediaFileType:
        """Store input video. Extracted for testability."""
        return await store_media_file(
            file=file,
            execution_context=execution_context,
            return_format="for_local_processing",
        )
    async def _store_output_video(
        self, execution_context: ExecutionContext, file: MediaFileType
    ) -> MediaFileType:
        """Store output video. Extracted for testability."""
        return await store_media_file(
            file=file,
            execution_context=execution_context,
            return_format="for_block_output",
        )
    def _generate_narration_audio(
        self, api_key: str, script: str, voice_id: str, model_id: str
    ) -> bytes:
        """Generate narration audio via ElevenLabs API."""
        client = ElevenLabs(api_key=api_key)
        audio_generator = client.text_to_speech.convert(
            voice_id=voice_id,
            text=script,
            model_id=model_id,
        )
        # The SDK returns a generator, collect all chunks
        return b"".join(audio_generator)
    def _add_narration_to_video(
        self,
        video_abspath: str,
        audio_abspath: str,
        output_abspath: str,
        mix_mode: str,
        narration_volume: float,
        original_volume: float,
    ) -> None:
        """Add narration audio to video. Extracted for testability."""
        video = None
        final = None
        narration_original = None
        narration_scaled = None
        original = None
        try:
            strip_chapters_inplace(video_abspath)
            video = VideoFileClip(video_abspath)
            narration_original = AudioFileClip(audio_abspath)
            narration_scaled = narration_original.with_volume_scaled(narration_volume)
            narration = narration_scaled
            if mix_mode == "replace":
                final_audio = narration
            elif mix_mode == "mix":
                if video.audio:
                    original = video.audio.with_volume_scaled(original_volume)
                    final_audio = CompositeAudioClip([original, narration])
                else:
                    final_audio = narration
            else:  # ducking - apply stronger attenuation
                if video.audio:
                    # Ducking uses a much lower volume for original audio
                    ducking_volume = original_volume * 0.3
                    original = video.audio.with_volume_scaled(ducking_volume)
                    final_audio = CompositeAudioClip([original, narration])
                else:
                    final_audio = narration
            final = video.with_audio(final_audio)
            video_codec, audio_codec = get_video_codecs(output_abspath)
            final.write_videofile(
                output_abspath, codec=video_codec, audio_codec=audio_codec
            )
        finally:
            if original:
                original.close()
            if narration_scaled:
                narration_scaled.close()
            if narration_original:
                narration_original.close()
            if final:
                final.close()
            if video:
                video.close()
    async def run(
        self,
        input_data: Input,
        *,
        credentials: ElevenLabsCredentials,
        execution_context: ExecutionContext,
        node_exec_id: str,
        **kwargs,
    ) -> BlockOutput:
        try:
            assert execution_context.graph_exec_id is not None
            # Store the input video locally
            local_video_path = await self._store_input_video(
                execution_context, input_data.video_in
            )
            video_abspath = get_exec_file_path(
                execution_context.graph_exec_id, local_video_path
            )
            # Generate narration audio via ElevenLabs
            audio_content = self._generate_narration_audio(
                credentials.api_key.get_secret_value(),
                input_data.script,
                input_data.voice_id,
                input_data.model_id,
            )
            # Save audio to exec file path
            audio_filename = MediaFileType(f"{node_exec_id}_narration.mp3")
            audio_abspath = get_exec_file_path(
                execution_context.graph_exec_id, audio_filename
            )
            os.makedirs(os.path.dirname(audio_abspath), exist_ok=True)
            with open(audio_abspath, "wb") as f:
                f.write(audio_content)
            # Add narration to video
            source = extract_source_name(local_video_path)
            output_filename = MediaFileType(f"{node_exec_id}_narrated_{source}.mp4")
            output_abspath = get_exec_file_path(
                execution_context.graph_exec_id, output_filename
            )
            self._add_narration_to_video(
                video_abspath,
                audio_abspath,
                output_abspath,
                input_data.mix_mode,
                input_data.narration_volume,
                input_data.original_volume,
            )
            # Return as workspace path or data URI based on context
            video_out = await self._store_output_video(
                execution_context, output_filename
            )
            audio_out = await self._store_output_video(
                execution_context, audio_filename
            )
            yield "video_out", video_out
            yield "audio_file", audio_out
        except Exception as e:
            raise BlockExecutionError(
                message=f"Failed to add narration: {e}",
                block_name=self.name,
                block_id=str(self.id),
            ) from e
--- a/autogpt_platform/backend/backend/blocks/video/text_overlay.py
+++ b/autogpt_platform/backend/backend/blocks/video/text_overlay.py
@@ -1,231 +0,0 @@
 """VideoTextOverlayBlock - Add text overlay to video."""
 from typing import Literal
 from moviepy import CompositeVideoClip, TextClip
 from moviepy.video.io.VideoFileClip import VideoFileClip
 from backend.blocks.video._utils import (
    extract_source_name,
    get_video_codecs,
    strip_chapters_inplace,
 )
 from backend.data.block import (
    Block,
    BlockCategory,
    BlockOutput,
    BlockSchemaInput,
    BlockSchemaOutput,
 )
 from backend.data.execution import ExecutionContext
 from backend.data.model import SchemaField
 from backend.util.exceptions import BlockExecutionError
 from backend.util.file import MediaFileType, get_exec_file_path, store_media_file
 class VideoTextOverlayBlock(Block):
    """Add text overlay/caption to video."""
    class Input(BlockSchemaInput):
        video_in: MediaFileType = SchemaField(
            description="Input video (URL, data URI, or local path)"
        )
        text: str = SchemaField(description="Text to overlay on video")
        position: Literal[
            "top",
            "center",
            "bottom",
            "top-left",
            "top-right",
            "bottom-left",
            "bottom-right",
        ] = SchemaField(description="Position of text on screen", default="bottom")
        start_time: float | None = SchemaField(
            description="When to show text (seconds). None = entire video",
            default=None,
            advanced=True,
        )
        end_time: float | None = SchemaField(
            description="When to hide text (seconds). None = until end",
            default=None,
            advanced=True,
        )
        font_size: int = SchemaField(
            description="Font size", default=48, ge=12, le=200, advanced=True
        )
        font_color: str = SchemaField(
            description="Font color (hex or name)", default="white", advanced=True
        )
        bg_color: str | None = SchemaField(
            description="Background color behind text (None for transparent)",
            default=None,
            advanced=True,
        )
    class Output(BlockSchemaOutput):
        video_out: MediaFileType = SchemaField(
            description="Video with text overlay (path or data URI)"
        )
    def __init__(self):
        super().__init__(
            id="8ef14de6-cc90-430a-8cfa-3a003be92454",
            description="Add text overlay/caption to video",
            categories={BlockCategory.MULTIMEDIA},
            input_schema=self.Input,
            output_schema=self.Output,
            disabled=True,  # Disable until we can lockdown imagemagick security policy
            test_input={"video_in": "/tmp/test.mp4", "text": "Hello World"},
            test_output=[("video_out", str)],
            test_mock={
                "_add_text_overlay": lambda *args: None,
                "_store_input_video": lambda *args, **kwargs: "test.mp4",
                "_store_output_video": lambda *args, **kwargs: "overlay_test.mp4",
            },
        )
    async def _store_input_video(
        self, execution_context: ExecutionContext, file: MediaFileType
    ) -> MediaFileType:
        """Store input video. Extracted for testability."""
        return await store_media_file(
            file=file,
            execution_context=execution_context,
            return_format="for_local_processing",
        )
    async def _store_output_video(
        self, execution_context: ExecutionContext, file: MediaFileType
    ) -> MediaFileType:
        """Store output video. Extracted for testability."""
        return await store_media_file(
            file=file,
            execution_context=execution_context,
            return_format="for_block_output",
        )
    def _add_text_overlay(
        self,
        video_abspath: str,
        output_abspath: str,
        text: str,
        position: str,
        start_time: float | None,
        end_time: float | None,
        font_size: int,
        font_color: str,
        bg_color: str | None,
    ) -> None:
        """Add text overlay to video. Extracted for testability."""
        video = None
        final = None
        txt_clip = None
        try:
            strip_chapters_inplace(video_abspath)
            video = VideoFileClip(video_abspath)
            txt_clip = TextClip(
                text=text,
                font_size=font_size,
                color=font_color,
                bg_color=bg_color,
            )
            # Position mapping
            pos_map = {
                "top": ("center", "top"),
                "center": ("center", "center"),
                "bottom": ("center", "bottom"),
                "top-left": ("left", "top"),
                "top-right": ("right", "top"),
                "bottom-left": ("left", "bottom"),
                "bottom-right": ("right", "bottom"),
            }
            txt_clip = txt_clip.with_position(pos_map[position])
            # Set timing
            start = start_time or 0
            end = end_time or video.duration
            duration = max(0, end - start)
            txt_clip = txt_clip.with_start(start).with_end(end).with_duration(duration)
            final = CompositeVideoClip([video, txt_clip])
            video_codec, audio_codec = get_video_codecs(output_abspath)
            final.write_videofile(
                output_abspath, codec=video_codec, audio_codec=audio_codec
            )
        finally:
            if txt_clip:
                txt_clip.close()
            if final:
                final.close()
            if video:
                video.close()
    async def run(
        self,
        input_data: Input,
        *,
        execution_context: ExecutionContext,
        node_exec_id: str,
        **kwargs,
    ) -> BlockOutput:
        # Validate time range if both are provided
        if (
            input_data.start_time is not None
            and input_data.end_time is not None
            and input_data.end_time <= input_data.start_time
        ):
            raise BlockExecutionError(
                message=f"end_time ({input_data.end_time}) must be greater than start_time ({input_data.start_time})",
                block_name=self.name,
                block_id=str(self.id),
            )
        try:
            assert execution_context.graph_exec_id is not None
            # Store the input video locally
            local_video_path = await self._store_input_video(
                execution_context, input_data.video_in
            )
            video_abspath = get_exec_file_path(
                execution_context.graph_exec_id, local_video_path
            )
            # Build output path
            source = extract_source_name(local_video_path)
            output_filename = MediaFileType(f"{node_exec_id}_overlay_{source}.mp4")
            output_abspath = get_exec_file_path(
                execution_context.graph_exec_id, output_filename
            )
            self._add_text_overlay(
                video_abspath,
                output_abspath,
                input_data.text,
                input_data.position,
                input_data.start_time,
                input_data.end_time,
                input_data.font_size,
                input_data.font_color,
                input_data.bg_color,
            )
            # Return as workspace path or data URI based on context
            video_out = await self._store_output_video(
                execution_context, output_filename
            )
            yield "video_out", video_out
        except BlockExecutionError:
            raise
        except Exception as e:
            raise BlockExecutionError(
                message=f"Failed to add text overlay: {e}",
                block_name=self.name,
                block_id=str(self.id),
            ) from e
--- a/autogpt_platform/backend/backend/blocks/youtube.py
+++ b/autogpt_platform/backend/backend/blocks/youtube.py
@@ -165,13 +165,10 @@ class TranscribeYoutubeVideoBlock(Block):
        credentials: WebshareProxyCredentials,
        **kwargs,
    ) -> BlockOutput:
-        try:
+        video_id = self.extract_video_id(input_data.youtube_url)
-            video_id = self.extract_video_id(input_data.youtube_url)
+        yield "video_id", video_id
            transcript = self.get_transcript(video_id, credentials)
            transcript_text = self.format_transcript(transcript=transcript)
-            # Only yield after all operations succeed
+        transcript = self.get_transcript(video_id, credentials)
-            yield "video_id", video_id
+        transcript_text = self.format_transcript(transcript=transcript)
-            yield "transcript", transcript_text
+
-        except Exception as e:
+        yield "transcript", transcript_text
            yield "error", str(e)
--- a/autogpt_platform/backend/backend/data/block.py
+++ b/autogpt_platform/backend/backend/data/block.py
@@ -873,13 +873,14 @@ def is_block_auth_configured(
 async def initialize_blocks() -> None:
    # First, sync all provider costs to blocks
    # Imported here to avoid circular import
    from backend.sdk.cost_integration import sync_all_provider_costs
    from backend.util.retry import func_retry
    sync_all_provider_costs()
-    @func_retry
+    for cls in get_blocks().values():
-    async def sync_block_to_db(block: Block) -> None:
+        block = cls()
        existing_block = await AgentBlock.prisma().find_first(
            where={"OR": [{"id": block.id}, {"name": block.name}]}
        )
@@ -892,7 +893,7 @@ async def initialize_blocks() -> None:
                    outputSchema=json.dumps(block.output_schema.jsonschema()),
                )
            )
-            return
+            continue
        input_schema = json.dumps(block.input_schema.jsonschema())
        output_schema = json.dumps(block.output_schema.jsonschema())
@@ -912,25 +913,6 @@ async def initialize_blocks() -> None:
                },
            )
    failed_blocks: list[str] = []
    for cls in get_blocks().values():
        block = cls()
        try:
            await sync_block_to_db(block)
        except Exception as e:
            logger.warning(
                f"Failed to sync block {block.name} to database: {e}. "
                "Block is still available in memory.",
                exc_info=True,
            )
            failed_blocks.append(block.name)
    if failed_blocks:
        logger.error(
            f"Failed to sync {len(failed_blocks)} block(s) to database: "
            f"{', '.join(failed_blocks)}. These blocks are still available in memory."
        )
 # Note on the return type annotation: https://github.com/microsoft/pyright/issues/10281
 def get_block(block_id: str) -> AnyBlockSchema | None:
--- a/autogpt_platform/backend/backend/data/block_cost_config.py
+++ b/autogpt_platform/backend/backend/data/block_cost_config.py
@@ -36,14 +36,12 @@ from backend.blocks.replicate.replicate_block import ReplicateModelBlock
 from backend.blocks.smart_decision_maker import SmartDecisionMakerBlock
 from backend.blocks.talking_head import CreateTalkingAvatarVideoBlock
 from backend.blocks.text_to_speech_block import UnrealTextToSpeechBlock
 from backend.blocks.video.narration import VideoNarrationBlock
 from backend.data.block import Block, BlockCost, BlockCostType
 from backend.integrations.credentials_store import (
    aiml_api_credentials,
    anthropic_credentials,
    apollo_credentials,
    did_credentials,
    elevenlabs_credentials,
    enrichlayer_credentials,
    groq_credentials,
    ideogram_credentials,
@@ -80,7 +78,6 @@ MODEL_COST: dict[LlmModel, int] = {
    LlmModel.CLAUDE_4_1_OPUS: 21,
    LlmModel.CLAUDE_4_OPUS: 21,
    LlmModel.CLAUDE_4_SONNET: 5,
    LlmModel.CLAUDE_4_6_OPUS: 14,
    LlmModel.CLAUDE_4_5_HAIKU: 4,
    LlmModel.CLAUDE_4_5_OPUS: 14,
    LlmModel.CLAUDE_4_5_SONNET: 9,
@@ -642,16 +639,4 @@ BLOCK_COSTS: dict[Type[Block], list[BlockCost]] = {
            },
        ),
    ],
    VideoNarrationBlock: [
        BlockCost(
            cost_amount=5,  # ElevenLabs TTS cost
            cost_filter={
                "credentials": {
                    "id": elevenlabs_credentials.id,
                    "provider": elevenlabs_credentials.provider,
                    "type": elevenlabs_credentials.type,
                }
            },
        )
    ],
 }
--- a/autogpt_platform/backend/backend/data/credit_test.py
+++ b/autogpt_platform/backend/backend/data/credit_test.py
@@ -134,16 +134,6 @@ async def test_block_credit_reset(server: SpinTestServer):
        month1 = datetime.now(timezone.utc).replace(month=1, day=1)
        user_credit.time_now = lambda: month1
        # IMPORTANT: Set updatedAt to December of previous year to ensure it's
        # in a different month than month1 (January). This fixes a timing bug
        # where if the test runs in early February, 35 days ago would be January,
        # matching the mocked month1 and preventing the refill from triggering.
        dec_previous_year = month1.replace(year=month1.year - 1, month=12, day=15)
        await UserBalance.prisma().update(
            where={"userId": DEFAULT_USER_ID},
            data={"updatedAt": dec_previous_year},
        )
        # First call in month 1 should trigger refill
        balance = await user_credit.get_credits(DEFAULT_USER_ID)
        assert balance == REFILL_VALUE  # Should get 1000 credits
--- a/autogpt_platform/backend/backend/data/model.py
+++ b/autogpt_platform/backend/backend/data/model.py
@@ -19,6 +19,7 @@ from typing import (
    cast,
    get_args,
 )
 from urllib.parse import urlparse
 from uuid import uuid4
 from prisma.enums import CreditTransactionType, OnboardingStep
@@ -41,7 +42,6 @@ from typing_extensions import TypedDict
 from backend.integrations.providers import ProviderName
 from backend.util.json import loads as json_loads
 from backend.util.request import parse_url
 from backend.util.settings import Secrets
 # Type alias for any provider name (including custom ones)
@@ -397,25 +397,19 @@ class HostScopedCredentials(_BaseCredentials):
    def matches_url(self, url: str) -> bool:
        """Check if this credential should be applied to the given URL."""
-        request_host, request_port = _extract_host_from_url(url)
+        parsed_url = urlparse(url)
-        cred_scope_host, cred_scope_port = _extract_host_from_url(self.host)
+        # Extract hostname without port
        request_host = parsed_url.hostname
        if not request_host:
            return False
-        # If a port is specified in credential host, the request host port must match
+        # Simple host matching - exact match or wildcard subdomain match
-        if cred_scope_port is not None and request_port != cred_scope_port:
+        if self.host == request_host:
            return False
        # Non-standard ports are only allowed if explicitly specified in credential host
        elif cred_scope_port is None and request_port not in (80, 443, None):
            return False
        # Simple host matching
        if cred_scope_host == request_host:
            return True
        # Support wildcard matching (e.g., "*.example.com" matches "api.example.com")
-        if cred_scope_host.startswith("*."):
+        if self.host.startswith("*."):
-            domain = cred_scope_host[2:]  # Remove "*."
+            domain = self.host[2:]  # Remove "*."
            return request_host.endswith(f".{domain}") or request_host == domain
        return False
@@ -557,13 +551,13 @@ class CredentialsMetaInput(BaseModel, Generic[CP, CT]):
    )
-def _extract_host_from_url(url: str) -> tuple[str, int | None]:
+def _extract_host_from_url(url: str) -> str:
-    """Extract host and port from URL for grouping host-scoped credentials."""
+    """Extract host from URL for grouping host-scoped credentials."""
    try:
-        parsed = parse_url(url)
+        parsed = urlparse(url)
-        return parsed.hostname or url, parsed.port
+        return parsed.hostname or url
    except Exception:
-        return "", None
+        return ""
 class CredentialsFieldInfo(BaseModel, Generic[CP, CT]):
@@ -612,7 +606,7 @@ class CredentialsFieldInfo(BaseModel, Generic[CP, CT]):
                providers = frozenset(
                    [cast(CP, "http")]
                    + [
-                        cast(CP, parse_url(str(value)).netloc)
+                        cast(CP, _extract_host_from_url(str(value)))
                        for value in field.discriminator_values
                    ]
                )
--- a/autogpt_platform/backend/backend/data/model_test.py
+++ b/autogpt_platform/backend/backend/data/model_test.py
@@ -79,23 +79,10 @@ class TestHostScopedCredentials:
            headers={"Authorization": SecretStr("Bearer token")},
        )
-        # Non-standard ports require explicit port in credential host
+        assert creds.matches_url("http://localhost:8080/api/v1")
        assert not creds.matches_url("http://localhost:8080/api/v1")
        assert creds.matches_url("https://localhost:443/secure/endpoint")
        assert creds.matches_url("http://localhost/simple")
    def test_matches_url_with_explicit_port(self):
        """Test URL matching with explicit port in credential host."""
        creds = HostScopedCredentials(
            provider="custom",
            host="localhost:8080",
            headers={"Authorization": SecretStr("Bearer token")},
        )
        assert creds.matches_url("http://localhost:8080/api/v1")
        assert not creds.matches_url("http://localhost:3000/api/v1")
        assert not creds.matches_url("http://localhost/simple")
    def test_empty_headers_dict(self):
        """Test HostScopedCredentials with empty headers."""
        creds = HostScopedCredentials(
@@ -141,20 +128,8 @@ class TestHostScopedCredentials:
            ("*.example.com", "https://sub.api.example.com/test", True),
            ("*.example.com", "https://example.com/test", True),
            ("*.example.com", "https://example.org/test", False),
-            # Non-standard ports require explicit port in credential host
+            ("localhost", "http://localhost:3000/test", True),
            ("localhost", "http://localhost:3000/test", False),
            ("localhost:3000", "http://localhost:3000/test", True),
            ("localhost", "http://127.0.0.1:3000/test", False),
            # IPv6 addresses (frontend stores with brackets via URL.hostname)
            ("[::1]", "http://[::1]/test", True),
            ("[::1]", "http://[::1]:80/test", True),
            ("[::1]", "https://[::1]:443/test", True),
            ("[::1]", "http://[::1]:8080/test", False),  # Non-standard port
            ("[::1]:8080", "http://[::1]:8080/test", True),
            ("[::1]:8080", "http://[::1]:9090/test", False),
            ("[2001:db8::1]", "http://[2001:db8::1]/path", True),
            ("[2001:db8::1]", "https://[2001:db8::1]:443/path", True),
            ("[2001:db8::1]", "http://[2001:db8::ff]/path", False),
        ],
    )
    def test_url_matching_parametrized(self, host: str, test_url: str, expected: bool):
--- a/autogpt_platform/backend/backend/integrations/credentials_store.py
+++ b/autogpt_platform/backend/backend/integrations/credentials_store.py
@@ -224,14 +224,6 @@ openweathermap_credentials = APIKeyCredentials(
    expires_at=None,
 )
 elevenlabs_credentials = APIKeyCredentials(
    id="f4a8b6c2-3d1e-4f5a-9b8c-7d6e5f4a3b2c",
    provider="elevenlabs",
    api_key=SecretStr(settings.secrets.elevenlabs_api_key),
    title="Use Credits for ElevenLabs",
    expires_at=None,
 )
 DEFAULT_CREDENTIALS = [
    ollama_credentials,
    revid_credentials,
@@ -260,7 +252,6 @@ DEFAULT_CREDENTIALS = [
    v0_credentials,
    webshare_proxy_credentials,
    openweathermap_credentials,
    elevenlabs_credentials,
 ]
 SYSTEM_CREDENTIAL_IDS = {cred.id for cred in DEFAULT_CREDENTIALS}
@@ -375,8 +366,6 @@ class IntegrationCredentialsStore:
            all_credentials.append(webshare_proxy_credentials)
        if settings.secrets.openweathermap_api_key:
            all_credentials.append(openweathermap_credentials)
        if settings.secrets.elevenlabs_api_key:
            all_credentials.append(elevenlabs_credentials)
        return all_credentials
    async def get_creds_by_id(
--- a/autogpt_platform/backend/backend/integrations/providers.py
+++ b/autogpt_platform/backend/backend/integrations/providers.py
@@ -18,7 +18,6 @@ class ProviderName(str, Enum):
    DISCORD = "discord"
    D_ID = "d_id"
    E2B = "e2b"
    ELEVENLABS = "elevenlabs"
    FAL = "fal"
    GITHUB = "github"
    GOOGLE = "google"
--- a/autogpt_platform/backend/backend/util/file.py
+++ b/autogpt_platform/backend/backend/util/file.py
@@ -8,8 +8,6 @@ from pathlib import Path
 from typing import TYPE_CHECKING, Literal
 from urllib.parse import urlparse
 from pydantic import BaseModel
 from backend.util.cloud_storage import get_cloud_storage_handler
 from backend.util.request import Requests
 from backend.util.settings import Config
@@ -19,35 +17,6 @@ from backend.util.virus_scanner import scan_content_safe
 if TYPE_CHECKING:
    from backend.data.execution import ExecutionContext
 class WorkspaceUri(BaseModel):
    """Parsed workspace:// URI."""
    file_ref: str  # File ID or path (e.g. "abc123" or "/path/to/file.txt")
    mime_type: str | None = None  # MIME type from fragment (e.g. "video/mp4")
    is_path: bool = False  # True if file_ref is a path (starts with "/")
 def parse_workspace_uri(uri: str) -> WorkspaceUri:
    """Parse a workspace:// URI into its components.
    Examples:
        "workspace://abc123"            → WorkspaceUri(file_ref="abc123", mime_type=None, is_path=False)
        "workspace://abc123#video/mp4"  → WorkspaceUri(file_ref="abc123", mime_type="video/mp4", is_path=False)
        "workspace:///path/to/file.txt" → WorkspaceUri(file_ref="/path/to/file.txt", mime_type=None, is_path=True)
    """
    raw = uri.removeprefix("workspace://")
    mime_type: str | None = None
    if "#" in raw:
        raw, fragment = raw.split("#", 1)
        mime_type = fragment or None
    return WorkspaceUri(
        file_ref=raw,
        mime_type=mime_type,
        is_path=raw.startswith("/"),
    )
 # Return format options for store_media_file
 # - "for_local_processing": Returns local file path - use with ffmpeg, MoviePy, PIL, etc.
 # - "for_external_api": Returns data URI (base64) - use when sending content to external APIs
@@ -214,20 +183,22 @@ async def store_media_file(
                "This file type is only available in CoPilot sessions."
            )
-        # Parse workspace reference (strips #mimeType fragment from file ID)
+        # Parse workspace reference
-        ws = parse_workspace_uri(file)
+        # workspace://abc123 - by file ID
        # workspace:///path/to/file.txt - by virtual path
        file_ref = file[12:]  # Remove "workspace://"
-        if ws.is_path:
+        if file_ref.startswith("/"):
-            # Path reference: workspace:///path/to/file.txt
+            # Path reference
-            workspace_content = await workspace_manager.read_file(ws.file_ref)
+            workspace_content = await workspace_manager.read_file(file_ref)
-            file_info = await workspace_manager.get_file_info_by_path(ws.file_ref)
+            file_info = await workspace_manager.get_file_info_by_path(file_ref)
            filename = sanitize_filename(
                file_info.name if file_info else f"{uuid.uuid4()}.bin"
            )
        else:
-            # ID reference: workspace://abc123 or workspace://abc123#video/mp4
+            # ID reference
-            workspace_content = await workspace_manager.read_file_by_id(ws.file_ref)
+            workspace_content = await workspace_manager.read_file_by_id(file_ref)
-            file_info = await workspace_manager.get_file_info(ws.file_ref)
+            file_info = await workspace_manager.get_file_info(file_ref)
            filename = sanitize_filename(
                file_info.name if file_info else f"{uuid.uuid4()}.bin"
            )
@@ -363,21 +334,7 @@ async def store_media_file(
        # Don't re-save if input was already from workspace
        if is_from_workspace:
-            # Return original workspace reference, ensuring MIME type fragment
+            # Return original workspace reference
            ws = parse_workspace_uri(file)
            if not ws.mime_type:
                # Add MIME type fragment if missing (older refs without it)
                try:
                    if ws.is_path:
                        info = await workspace_manager.get_file_info_by_path(
                            ws.file_ref
                        )
                    else:
                        info = await workspace_manager.get_file_info(ws.file_ref)
                    if info:
                        return MediaFileType(f"{file}#{info.mimeType}")
                except Exception:
                    pass
            return MediaFileType(file)
        # Save new content to workspace
@@ -389,7 +346,7 @@ async def store_media_file(
            filename=filename,
            overwrite=True,
        )
-        return MediaFileType(f"workspace://{file_record.id}#{file_record.mimeType}")
+        return MediaFileType(f"workspace://{file_record.id}")
    else:
        raise ValueError(f"Invalid return_format: {return_format}")
--- a/autogpt_platform/backend/backend/util/request.py
+++ b/autogpt_platform/backend/backend/util/request.py
@@ -157,7 +157,12 @@ async def validate_url(
        is_trusted: Boolean indicating if the hostname is in trusted_origins
        ip_addresses: List of IP addresses for the host; empty if the host is trusted
    """
-    parsed = parse_url(url)
+    # Canonicalize URL
    url = url.strip("/ ").replace("\\", "/")
    parsed = urlparse(url)
    if not parsed.scheme:
        url = f"http://{url}"
        parsed = urlparse(url)
    # Check scheme
    if parsed.scheme not in ALLOWED_SCHEMES:
@@ -215,17 +220,6 @@ async def validate_url(
    )
 def parse_url(url: str) -> URL:
    """Canonicalizes and parses a URL string."""
    url = url.strip("/ ").replace("\\", "/")
    # Ensure scheme is present for proper parsing
    if not re.match(r"[a-z0-9+.\-]+://", url):
        url = f"http://{url}"
    return urlparse(url)
 def pin_url(url: URL, ip_addresses: Optional[list[str]] = None) -> URL:
    """
    Pins a URL to a specific IP address to prevent DNS rebinding attacks.
--- a/autogpt_platform/backend/backend/util/settings.py
+++ b/autogpt_platform/backend/backend/util/settings.py
@@ -656,7 +656,6 @@ class Secrets(UpdateTrackingModel["Secrets"], BaseSettings):
    e2b_api_key: str = Field(default="", description="E2B API key")
    nvidia_api_key: str = Field(default="", description="Nvidia API key")
    mem0_api_key: str = Field(default="", description="Mem0 API key")
    elevenlabs_api_key: str = Field(default="", description="ElevenLabs API key")
    linear_client_id: str = Field(default="", description="Linear client ID")
    linear_client_secret: str = Field(default="", description="Linear client secret")
--- a/autogpt_platform/backend/poetry.lock
+++ b/autogpt_platform/backend/poetry.lock
@@ -1169,29 +1169,6 @@ attrs = ">=21.3.0"
 e2b = ">=1.5.4,<2.0.0"
 httpx = ">=0.20.0,<1.0.0"
 [[package]]
 name = "elevenlabs"
 version = "1.59.0"
 description = ""
 optional = false
 python-versions = "<4.0,>=3.8"
 groups = ["main"]
 files = [
    {file = "elevenlabs-1.59.0-py3-none-any.whl", hash = "sha256:468145db81a0bc867708b4a8619699f75583e9481b395ec1339d0b443da771ed"},
    {file = "elevenlabs-1.59.0.tar.gz", hash = "sha256:16e735bd594e86d415dd445d249c8cc28b09996cfd627fbc10102c0a84698859"},
 ]
 [package.dependencies]
 httpx = ">=0.21.2"
 pydantic = ">=1.9.2"
 pydantic-core = ">=2.18.2,<3.0.0"
 requests = ">=2.20"
 typing_extensions = ">=4.0.0"
 websockets = ">=11.0"
 [package.extras]
 pyaudio = ["pyaudio (>=0.2.14)"]
 [[package]]
 name = "email-validator"
 version = "2.2.0"
@@ -7384,28 +7361,6 @@ files = [
 defusedxml = ">=0.7.1,<0.8.0"
 requests = "*"
 [[package]]
 name = "yt-dlp"
 version = "2025.12.8"
 description = "A feature-rich command-line audio/video downloader"
 optional = false
 python-versions = ">=3.10"
 groups = ["main"]
 files = [
    {file = "yt_dlp-2025.12.8-py3-none-any.whl", hash = "sha256:36e2584342e409cfbfa0b5e61448a1c5189e345cf4564294456ee509e7d3e065"},
    {file = "yt_dlp-2025.12.8.tar.gz", hash = "sha256:b773c81bb6b71cb2c111cfb859f453c7a71cf2ef44eff234ff155877184c3e4f"},
 ]
 [package.extras]
 build = ["build", "hatchling (>=1.27.0)", "pip", "setuptools (>=71.0.2)", "wheel"]
 curl-cffi = ["curl-cffi (>=0.5.10,<0.6.dev0 || >=0.10.dev0,<0.14) ; implementation_name == \"cpython\""]
 default = ["brotli ; implementation_name == \"cpython\"", "brotlicffi ; implementation_name != \"cpython\"", "certifi", "mutagen", "pycryptodomex", "requests (>=2.32.2,<3)", "urllib3 (>=2.0.2,<3)", "websockets (>=13.0)", "yt-dlp-ejs (==0.3.2)"]
 dev = ["autopep8 (>=2.0,<3.0)", "pre-commit", "pytest (>=8.1,<9.0)", "pytest-rerunfailures (>=14.0,<15.0)", "ruff (>=0.14.0,<0.15.0)"]
 pyinstaller = ["pyinstaller (>=6.17.0)"]
 secretstorage = ["cffi", "secretstorage"]
 static-analysis = ["autopep8 (>=2.0,<3.0)", "ruff (>=0.14.0,<0.15.0)"]
 test = ["pytest (>=8.1,<9.0)", "pytest-rerunfailures (>=14.0,<15.0)"]
 [[package]]
 name = "zerobouncesdk"
 version = "1.1.2"
@@ -7557,4 +7512,4 @@ cffi = ["cffi (>=1.11)"]
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.10,<3.14"
-content-hash = "8239323f9ae6713224dffd1fe8ba8b449fe88b6c3c7a90940294a74f43a0387a"
+content-hash = "ee5742dc1a9df50dfc06d4b26a1682cbb2b25cab6b79ce5625ec272f93e4f4bf"
--- a/autogpt_platform/backend/pyproject.toml
+++ b/autogpt_platform/backend/pyproject.toml
@@ -20,7 +20,6 @@ click = "^8.2.0"
 cryptography = "^45.0"
 discord-py = "^2.5.2"
 e2b-code-interpreter = "^1.5.2"
 elevenlabs = "^1.50.0"
 fastapi = "^0.116.1"
 feedparser = "^6.0.11"
 flake8 = "^7.3.0"
@@ -72,7 +71,6 @@ tweepy = "^4.16.0"
 uvicorn = { extras = ["standard"], version = "^0.35.0" }
 websockets = "^15.0"
 youtube-transcript-api = "^1.2.1"
 yt-dlp = "2025.12.08"
 zerobouncesdk = "^1.1.2"
 # NOTE: please insert new dependencies in their alphabetical location
 pytest-snapshot = "^0.9.0"
--- a/autogpt_platform/backend/test/agent_generator/test_core_integration.py
+++ b/autogpt_platform/backend/test/agent_generator/test_core_integration.py
@@ -111,7 +111,9 @@ class TestGenerateAgent:
            instructions = {"type": "instructions", "steps": ["Step 1"]}
            result = await core.generate_agent(instructions)
-            mock_external.assert_called_once_with(instructions, None, None, None)
+            # library_agents defaults to None
            mock_external.assert_called_once_with(instructions, None)
            # Result should have id, version, is_active added if not present
            assert result is not None
            assert result["name"] == "Test Agent"
            assert "id" in result
@@ -175,9 +177,8 @@ class TestGenerateAgentPatch:
            current_agent = {"nodes": [], "links": []}
            result = await core.generate_agent_patch("Add a node", current_agent)
-            mock_external.assert_called_once_with(
+            # library_agents defaults to None
-                "Add a node", current_agent, None, None, None
+            mock_external.assert_called_once_with("Add a node", current_agent, None)
            )
            assert result == expected_result
    @pytest.mark.asyncio
--- a/autogpt_platform/frontend/src/app/(no-navbar)/onboarding/page.tsx
+++ b/autogpt_platform/frontend/src/app/(no-navbar)/onboarding/page.tsx
@@ -1,9 +1,10 @@
 "use client";
 import { getV1OnboardingState } from "@/app/api/__generated__/endpoints/onboarding/onboarding";
 import { getOnboardingStatus, resolveResponse } from "@/app/api/helpers";
 import { LoadingSpinner } from "@/components/atoms/LoadingSpinner/LoadingSpinner";
 import { useRouter } from "next/navigation";
 import { useEffect } from "react";
 import { resolveResponse, getOnboardingStatus } from "@/app/api/helpers";
 import { getV1OnboardingState } from "@/app/api/__generated__/endpoints/onboarding/onboarding";
 import { getHomepageRoute } from "@/lib/constants";
 export default function OnboardingPage() {
  const router = useRouter();
@@ -12,10 +13,12 @@ export default function OnboardingPage() {
    async function redirectToStep() {
      try {
        // Check if onboarding is enabled (also gets chat flag for redirect)
-        const { shouldShowOnboarding } = await getOnboardingStatus();
+        const { shouldShowOnboarding, isChatEnabled } =
          await getOnboardingStatus();
        const homepageRoute = getHomepageRoute(isChatEnabled);
        if (!shouldShowOnboarding) {
-          router.replace("/");
+          router.replace(homepageRoute);
          return;
        }
@@ -23,7 +26,7 @@ export default function OnboardingPage() {
        // Handle completed onboarding
        if (onboarding.completedSteps.includes("GET_RESULTS")) {
-          router.replace("/");
+          router.replace(homepageRoute);
          return;
        }
--- a/autogpt_platform/frontend/src/app/(platform)/auth/callback/route.ts
+++ b/autogpt_platform/frontend/src/app/(platform)/auth/callback/route.ts
@@ -1,8 +1,9 @@
 import { getOnboardingStatus } from "@/app/api/helpers";
 import BackendAPI from "@/lib/autogpt-server-api";
 import { getServerSupabase } from "@/lib/supabase/server/getServerSupabase";
-import { revalidatePath } from "next/cache";
+import { getHomepageRoute } from "@/lib/constants";
 import BackendAPI from "@/lib/autogpt-server-api";
 import { NextResponse } from "next/server";
 import { revalidatePath } from "next/cache";
 import { getOnboardingStatus } from "@/app/api/helpers";
 // Handle the callback to complete the user session login
 export async function GET(request: Request) {
@@ -26,12 +27,13 @@ export async function GET(request: Request) {
        await api.createUser();
        // Get onboarding status from backend (includes chat flag evaluated for this user)
-        const { shouldShowOnboarding } = await getOnboardingStatus();
+        const { shouldShowOnboarding, isChatEnabled } =
          await getOnboardingStatus();
        if (shouldShowOnboarding) {
          next = "/onboarding";
          revalidatePath("/onboarding", "layout");
        } else {
-          next = "/";
+          next = getHomepageRoute(isChatEnabled);
          revalidatePath(next, "layout");
        }
      } catch (createUserError) {
--- a/autogpt_platform/frontend/src/app/(platform)/auth/integrations/oauth_callback/route.ts
+++ b/autogpt_platform/frontend/src/app/(platform)/auth/integrations/oauth_callback/route.ts
@@ -1,17 +1,6 @@
 import { OAuthPopupResultMessage } from "./types";
 import { NextResponse } from "next/server";
 /**
 * Safely encode a value as JSON for embedding in a script tag.
 * Escapes characters that could break out of the script context to prevent XSS.
 */
 function safeJsonStringify(value: unknown): string {
  return JSON.stringify(value)
    .replace(/</g, "\\u003c")
    .replace(/>/g, "\\u003e")
    .replace(/&/g, "\\u0026");
 }
 // This route is intended to be used as the callback for integration OAuth flows,
 // controlled by the CredentialsInput component. The CredentialsInput opens the login
 // page in a pop-up window, which then redirects to this route to close the loop.
@@ -34,13 +23,12 @@ export async function GET(request: Request) {
  console.debug("Sending message to opener:", message);
  // Return a response with the message as JSON and a script to close the window
  // Use safeJsonStringify to prevent XSS by escaping <, >, and & characters
  return new NextResponse(
    `
    <html>
      <body>
        <script>
-          window.opener.postMessage(${safeJsonStringify(message)});
+          window.opener.postMessage(${JSON.stringify(message)});
          window.close();
        </script>
      </body>
--- a/autogpt_platform/frontend/src/app/(platform)/build/components/legacy-builder/DataTable.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/build/components/legacy-builder/DataTable.tsx
@@ -1,6 +1,6 @@
 import { beautifyString } from "@/lib/utils";
 import { Clipboard, Maximize2 } from "lucide-react";
-import React, { useMemo, useState } from "react";
+import React, { useState } from "react";
 import { Button } from "../../../../../components/__legacy__/ui/button";
 import { ContentRenderer } from "../../../../../components/__legacy__/ui/render";
 import {
@@ -11,12 +11,6 @@ import {
  TableHeader,
  TableRow,
 } from "../../../../../components/__legacy__/ui/table";
 import type { OutputMetadata } from "@/components/contextual/OutputRenderers";
 import {
  globalRegistry,
  OutputItem,
 } from "@/components/contextual/OutputRenderers";
 import { Flag, useGetFlag } from "@/services/feature-flags/use-get-flag";
 import { useToast } from "../../../../../components/molecules/Toast/use-toast";
 import ExpandableOutputDialog from "./ExpandableOutputDialog";
@@ -32,9 +26,6 @@ export default function DataTable({
  data,
 }: DataTableProps) {
  const { toast } = useToast();
  const enableEnhancedOutputHandling = useGetFlag(
    Flag.ENABLE_ENHANCED_OUTPUT_HANDLING,
  );
  const [expandedDialog, setExpandedDialog] = useState<{
    isOpen: boolean;
    execId: string;
@@ -42,15 +33,6 @@ export default function DataTable({
    data: any[];
  } | null>(null);
  // Prepare renderers for each item when enhanced mode is enabled
  const getItemRenderer = useMemo(() => {
    if (!enableEnhancedOutputHandling) return null;
    return (item: unknown) => {
      const metadata: OutputMetadata = {};
      return globalRegistry.getRenderer(item, metadata);
    };
  }, [enableEnhancedOutputHandling]);
  const copyData = (pin: string, data: string) => {
    navigator.clipboard.writeText(data).then(() => {
      toast({
@@ -120,31 +102,15 @@ export default function DataTable({
                      <Clipboard size={18} />
                    </Button>
                  </div>
-                  {value.map((item, index) => {
+                  {value.map((item, index) => (
-                    const renderer = getItemRenderer?.(item);
+                    <React.Fragment key={index}>
-                    if (enableEnhancedOutputHandling && renderer) {
+                      <ContentRenderer
-                      const metadata: OutputMetadata = {};
+                        value={item}
-                      return (
+                        truncateLongData={truncateLongData}
-                        <React.Fragment key={index}>
+                      />
-                          <OutputItem
+                      {index < value.length - 1 && ", "}
-                            value={item}
+                    </React.Fragment>
-                            metadata={metadata}
+                  ))}
                            renderer={renderer}
                          />
                          {index < value.length - 1 && ", "}
                        </React.Fragment>
                      );
                    }
                    return (
                      <React.Fragment key={index}>
                        <ContentRenderer
                          value={item}
                          truncateLongData={truncateLongData}
                        />
                        {index < value.length - 1 && ", "}
                      </React.Fragment>
                    );
                  })}
                </div>
              </TableCell>
            </TableRow>
--- a/autogpt_platform/frontend/src/app/(platform)/build/components/legacy-builder/NodeOutputs.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/build/components/legacy-builder/NodeOutputs.tsx
@@ -1,14 +1,8 @@
-import React, { useContext, useMemo, useState } from "react";
+import React, { useContext, useState } from "react";
 import { Button } from "@/components/__legacy__/ui/button";
 import { Maximize2 } from "lucide-react";
 import * as Separator from "@radix-ui/react-separator";
 import { ContentRenderer } from "@/components/__legacy__/ui/render";
 import type { OutputMetadata } from "@/components/contextual/OutputRenderers";
 import {
  globalRegistry,
  OutputItem,
 } from "@/components/contextual/OutputRenderers";
 import { Flag, useGetFlag } from "@/services/feature-flags/use-get-flag";
 import { beautifyString } from "@/lib/utils";
@@ -27,9 +21,6 @@ export default function NodeOutputs({
  data,
 }: NodeOutputsProps) {
  const builderContext = useContext(BuilderContext);
  const enableEnhancedOutputHandling = useGetFlag(
    Flag.ENABLE_ENHANCED_OUTPUT_HANDLING,
  );
  const [expandedDialog, setExpandedDialog] = useState<{
    isOpen: boolean;
@@ -46,15 +37,6 @@ export default function NodeOutputs({
  const { getNodeTitle } = builderContext;
  // Prepare renderers for each item when enhanced mode is enabled
  const getItemRenderer = useMemo(() => {
    if (!enableEnhancedOutputHandling) return null;
    return (item: unknown) => {
      const metadata: OutputMetadata = {};
      return globalRegistry.getRenderer(item, metadata);
    };
  }, [enableEnhancedOutputHandling]);
  const getBeautifiedPinName = (pin: string) => {
    if (!pin.startsWith("tools_^_")) {
      return beautifyString(pin);
@@ -105,31 +87,15 @@ export default function NodeOutputs({
          <div className="mt-2">
            <strong className="mr-2">Data:</strong>
            <div className="mt-1">
-              {dataArray.slice(0, 10).map((item, index) => {
+              {dataArray.slice(0, 10).map((item, index) => (
-                const renderer = getItemRenderer?.(item);
+                <React.Fragment key={index}>
-                if (enableEnhancedOutputHandling && renderer) {
+                  <ContentRenderer
-                  const metadata: OutputMetadata = {};
+                    value={item}
-                  return (
+                    truncateLongData={truncateLongData}
-                    <React.Fragment key={index}>
+                  />
-                      <OutputItem
+                  {index < Math.min(dataArray.length, 10) - 1 && ", "}
-                        value={item}
+                </React.Fragment>
-                        metadata={metadata}
+              ))}
                        renderer={renderer}
                      />
                      {index < Math.min(dataArray.length, 10) - 1 && ", "}
                    </React.Fragment>
                  );
                }
                return (
                  <React.Fragment key={index}>
                    <ContentRenderer
                      value={item}
                      truncateLongData={truncateLongData}
                    />
                    {index < Math.min(dataArray.length, 10) - 1 && ", "}
                  </React.Fragment>
                );
              })}
              {dataArray.length > 10 && (
                <span style={{ color: "#888" }}>
                  <br />
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/useCopilotShell.ts
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/useCopilotShell.ts
@@ -11,6 +11,7 @@ import { useBreakpoint } from "@/lib/hooks/useBreakpoint";
 import { useSupabase } from "@/lib/supabase/hooks/useSupabase";
 import { useQueryClient } from "@tanstack/react-query";
 import { usePathname, useSearchParams } from "next/navigation";
 import { useRef } from "react";
 import { useCopilotStore } from "../../copilot-page-store";
 import { useCopilotSessionId } from "../../useCopilotSessionId";
 import { useMobileDrawer } from "./components/MobileDrawer/useMobileDrawer";
@@ -69,16 +70,41 @@ export function useCopilotShell() {
  });
  const stopStream = useChatStore((s) => s.stopStream);
  const onStreamComplete = useChatStore((s) => s.onStreamComplete);
  const isStreaming = useCopilotStore((s) => s.isStreaming);
  const isCreatingSession = useCopilotStore((s) => s.isCreatingSession);
  const setIsSwitchingSession = useCopilotStore((s) => s.setIsSwitchingSession);
  const openInterruptModal = useCopilotStore((s) => s.openInterruptModal);
-  function handleSessionClick(sessionId: string) {
+  const pendingActionRef = useRef<(() => void) | null>(null);
    if (sessionId === currentSessionId) return;
-    // Stop current stream - SSE reconnection allows resuming later
+  async function stopCurrentStream() {
-    if (currentSessionId) {
+    if (!currentSessionId) return;
    setIsSwitchingSession(true);
    await new Promise<void>((resolve) => {
      const unsubscribe = onStreamComplete((completedId) => {
        if (completedId === currentSessionId) {
          clearTimeout(timeout);
          unsubscribe();
          resolve();
        }
      });
      const timeout = setTimeout(() => {
        unsubscribe();
        resolve();
      }, 3000);
      stopStream(currentSessionId);
-    }
+    });
    queryClient.invalidateQueries({
      queryKey: getGetV2GetSessionQueryKey(currentSessionId),
    });
    setIsSwitchingSession(false);
  }
  function selectSession(sessionId: string) {
    if (sessionId === currentSessionId) return;
    if (recentlyCreatedSessionsRef.current.has(sessionId)) {
      queryClient.invalidateQueries({
        queryKey: getGetV2GetSessionQueryKey(sessionId),
@@ -88,12 +114,7 @@ export function useCopilotShell() {
    if (isMobile) handleCloseDrawer();
  }
-  function handleNewChatClick() {
+  function startNewChat() {
    // Stop current stream - SSE reconnection allows resuming later
    if (currentSessionId) {
      stopStream(currentSessionId);
    }
    resetPagination();
    queryClient.invalidateQueries({
      queryKey: getGetV2ListSessionsQueryKey(),
@@ -102,6 +123,32 @@ export function useCopilotShell() {
    if (isMobile) handleCloseDrawer();
  }
  function handleSessionClick(sessionId: string) {
    if (sessionId === currentSessionId) return;
    if (isStreaming) {
      pendingActionRef.current = async () => {
        await stopCurrentStream();
        selectSession(sessionId);
      };
      openInterruptModal(pendingActionRef.current);
    } else {
      selectSession(sessionId);
    }
  }
  function handleNewChatClick() {
    if (isStreaming) {
      pendingActionRef.current = async () => {
        await stopCurrentStream();
        startNewChat();
      };
      openInterruptModal(pendingActionRef.current);
    } else {
      startNewChat();
    }
  }
  return {
    isMobile,
    isDrawerOpen,
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/helpers.ts
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/helpers.ts
@@ -26,20 +26,8 @@ export function buildCopilotChatUrl(prompt: string): string {
 export function getQuickActions(): string[] {
  return [
-    "I don't know where to start, just ask me stuff",
+    "Show me what I can automate",
-    "I do the same thing every week and it's killing me",
+    "Design a custom workflow",
-    "Help me find where I'm wasting my time",
+    "Help me with content creation",
  ];
 }
 export function getInputPlaceholder(width?: number) {
  if (!width) return "What's your role and what eats up most of your day?";
  if (width < 500) {
    return "I'm a chef and I hate...";
  }
  if (width <= 1080) {
    return "What's your role and what eats up most of your day?";
  }
  return "What's your role and what eats up most of your day? e.g. 'I'm a recruiter and I hate...'";
 }
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/layout.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/layout.tsx
@@ -1,13 +1,6 @@
-"use client";
+import type { ReactNode } from "react";
 import { FeatureFlagPage } from "@/services/feature-flags/FeatureFlagPage";
 import { Flag } from "@/services/feature-flags/use-get-flag";
 import { type ReactNode } from "react";
 import { CopilotShell } from "./components/CopilotShell/CopilotShell";
 export default function CopilotLayout({ children }: { children: ReactNode }) {
-  return (
+  return <CopilotShell>{children}</CopilotShell>;
    <FeatureFlagPage flag={Flag.CHAT} whenDisabled="/library">
      <CopilotShell>{children}</CopilotShell>
    </FeatureFlagPage>
  );
 }
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/page.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/page.tsx
@@ -6,9 +6,7 @@ import { Text } from "@/components/atoms/Text/Text";
 import { Chat } from "@/components/contextual/Chat/Chat";
 import { ChatInput } from "@/components/contextual/Chat/components/ChatInput/ChatInput";
 import { Dialog } from "@/components/molecules/Dialog/Dialog";
 import { useEffect, useState } from "react";
 import { useCopilotStore } from "./copilot-page-store";
 import { getInputPlaceholder } from "./helpers";
 import { useCopilotPage } from "./useCopilotPage";
 export default function CopilotPage() {
@@ -16,25 +14,14 @@ export default function CopilotPage() {
  const isInterruptModalOpen = useCopilotStore((s) => s.isInterruptModalOpen);
  const confirmInterrupt = useCopilotStore((s) => s.confirmInterrupt);
  const cancelInterrupt = useCopilotStore((s) => s.cancelInterrupt);
-
+  const {
-  const [inputPlaceholder, setInputPlaceholder] = useState(
+    greetingName,
-    getInputPlaceholder(),
+    quickActions,
-  );
+    isLoading,
-
+    hasSession,
-  useEffect(() => {
+    initialPrompt,
-    const handleResize = () => {
+    isReady,
-      setInputPlaceholder(getInputPlaceholder(window.innerWidth));
+  } = state;
    };
    handleResize();
    window.addEventListener("resize", handleResize);
    return () => window.removeEventListener("resize", handleResize);
  }, []);
  const { greetingName, quickActions, isLoading, hasSession, initialPrompt } =
    state;
  const {
    handleQuickAction,
    startChatWithPrompt,
@@ -42,6 +29,8 @@ export default function CopilotPage() {
    handleStreamingChange,
  } = handlers;
  if (!isReady) return null;
  if (hasSession) {
    return (
      <div className="flex h-full flex-col">
@@ -92,7 +81,7 @@ export default function CopilotPage() {
  }
  return (
-    <div className="flex h-full flex-1 items-center justify-center overflow-y-auto bg-[#f8f8f9] px-3 py-5 md:px-6 md:py-10">
+    <div className="flex h-full flex-1 items-center justify-center overflow-y-auto bg-[#f8f8f9] px-6 py-10">
      <div className="w-full text-center">
        {isLoading ? (
          <div className="mx-auto max-w-2xl">
@@ -109,25 +98,25 @@ export default function CopilotPage() {
          </div>
        ) : (
          <>
-            <div className="mx-auto max-w-3xl">
+            <div className="mx-auto max-w-2xl">
              <Text
                variant="h3"
-                className="mb-1 !text-[1.375rem] text-zinc-700"
+                className="mb-3 !text-[1.375rem] text-zinc-700"
              >
                Hey, <span className="text-violet-600">{greetingName}</span>
              </Text>
              <Text variant="h3" className="mb-8 !font-normal">
-                Tell me about your work — I&apos;ll find what to automate.
+                What do you want to automate?
              </Text>
              <div className="mb-6">
                <ChatInput
                  onSend={startChatWithPrompt}
-                  placeholder={inputPlaceholder}
+                  placeholder='You can search or just ask - e.g. "create a blog post outline"'
                />
              </div>
            </div>
-            <div className="flex flex-wrap items-center justify-center gap-3 overflow-x-auto [-ms-overflow-style:none] [scrollbar-width:none] [&::-webkit-scrollbar]:hidden">
+            <div className="flex flex-nowrap items-center justify-center gap-3 overflow-x-auto [-ms-overflow-style:none] [scrollbar-width:none] [&::-webkit-scrollbar]:hidden">
              {quickActions.map((action) => (
                <Button
                  key={action}
@@ -135,7 +124,7 @@ export default function CopilotPage() {
                  variant="outline"
                  size="small"
                  onClick={() => handleQuickAction(action)}
-                  className="h-auto shrink-0 border-zinc-300 px-3 py-2 text-[.9rem] text-zinc-600"
+                  className="h-auto shrink-0 border-zinc-600 !px-4 !py-2 text-[1rem] text-zinc-600"
                >
                  {action}
                </Button>
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/useCopilotPage.ts
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/useCopilotPage.ts
@@ -3,11 +3,18 @@ import {
  postV2CreateSession,
 } from "@/app/api/__generated__/endpoints/chat/chat";
 import { useToast } from "@/components/molecules/Toast/use-toast";
 import { getHomepageRoute } from "@/lib/constants";
 import { useSupabase } from "@/lib/supabase/hooks/useSupabase";
 import { useOnboarding } from "@/providers/onboarding/onboarding-provider";
 import {
  Flag,
  type FlagValues,
  useGetFlag,
 } from "@/services/feature-flags/use-get-flag";
 import { SessionKey, sessionStorage } from "@/services/storage/session-storage";
 import * as Sentry from "@sentry/nextjs";
 import { useQueryClient } from "@tanstack/react-query";
 import { useFlags } from "launchdarkly-react-client-sdk";
 import { useRouter } from "next/navigation";
 import { useEffect } from "react";
 import { useCopilotStore } from "./copilot-page-store";
@@ -26,6 +33,22 @@ export function useCopilotPage() {
  const isCreating = useCopilotStore((s) => s.isCreatingSession);
  const setIsCreating = useCopilotStore((s) => s.setIsCreatingSession);
  // Complete VISIT_COPILOT onboarding step to grant $5 welcome bonus
  useEffect(() => {
    if (isLoggedIn) {
      completeStep("VISIT_COPILOT");
    }
  }, [completeStep, isLoggedIn]);
  const isChatEnabled = useGetFlag(Flag.CHAT);
  const flags = useFlags<FlagValues>();
  const homepageRoute = getHomepageRoute(isChatEnabled);
  const envEnabled = process.env.NEXT_PUBLIC_LAUNCHDARKLY_ENABLED === "true";
  const clientId = process.env.NEXT_PUBLIC_LAUNCHDARKLY_CLIENT_ID;
  const isLaunchDarklyConfigured = envEnabled && Boolean(clientId);
  const isFlagReady =
    !isLaunchDarklyConfigured || flags[Flag.CHAT] !== undefined;
  const greetingName = getGreetingName(user);
  const quickActions = getQuickActions();
@@ -35,8 +58,11 @@ export function useCopilotPage() {
    : undefined;
  useEffect(() => {
-    if (isLoggedIn) completeStep("VISIT_COPILOT");
+    if (!isFlagReady) return;
-  }, [completeStep, isLoggedIn]);
+    if (isChatEnabled === false) {
      router.replace(homepageRoute);
    }
  }, [homepageRoute, isChatEnabled, isFlagReady, router]);
  async function startChatWithPrompt(prompt: string) {
    if (!prompt?.trim()) return;
@@ -90,6 +116,7 @@ export function useCopilotPage() {
      isLoading: isUserLoading,
      hasSession,
      initialPrompt,
      isReady: isFlagReady && isChatEnabled !== false && isLoggedIn,
    },
    handlers: {
      handleQuickAction,
--- a/autogpt_platform/frontend/src/app/(platform)/error/page.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/error/page.tsx
@@ -1,6 +1,8 @@
 "use client";
 import { ErrorCard } from "@/components/molecules/ErrorCard/ErrorCard";
 import { getHomepageRoute } from "@/lib/constants";
 import { Flag, useGetFlag } from "@/services/feature-flags/use-get-flag";
 import { useSearchParams } from "next/navigation";
 import { Suspense } from "react";
 import { getErrorDetails } from "./helpers";
@@ -9,6 +11,8 @@ function ErrorPageContent() {
  const searchParams = useSearchParams();
  const errorMessage = searchParams.get("message");
  const errorDetails = getErrorDetails(errorMessage);
  const isChatEnabled = useGetFlag(Flag.CHAT);
  const homepageRoute = getHomepageRoute(isChatEnabled);
  function handleRetry() {
    // Auth-related errors should redirect to login
@@ -26,7 +30,7 @@ function ErrorPageContent() {
      }, 2000);
    } else {
      // For server/network errors, go to home
-      window.location.href = "/";
+      window.location.href = homepageRoute;
    }
  }
--- a/autogpt_platform/frontend/src/app/(platform)/login/actions.ts
+++ b/autogpt_platform/frontend/src/app/(platform)/login/actions.ts
@@ -1,5 +1,6 @@
 "use server";
 import { getHomepageRoute } from "@/lib/constants";
 import BackendAPI from "@/lib/autogpt-server-api";
 import { getServerSupabase } from "@/lib/supabase/server/getServerSupabase";
 import { loginFormSchema } from "@/types/auth";
@@ -37,8 +38,10 @@ export async function login(email: string, password: string) {
    await api.createUser();
    // Get onboarding status from backend (includes chat flag evaluated for this user)
-    const { shouldShowOnboarding } = await getOnboardingStatus();
+    const { shouldShowOnboarding, isChatEnabled } = await getOnboardingStatus();
-    const next = shouldShowOnboarding ? "/onboarding" : "/";
+    const next = shouldShowOnboarding
      ? "/onboarding"
      : getHomepageRoute(isChatEnabled);
    return {
      success: true,
--- a/autogpt_platform/frontend/src/app/(platform)/login/useLoginPage.ts
+++ b/autogpt_platform/frontend/src/app/(platform)/login/useLoginPage.ts
@@ -1,6 +1,8 @@
 import { useToast } from "@/components/molecules/Toast/use-toast";
 import { getHomepageRoute } from "@/lib/constants";
 import { useSupabase } from "@/lib/supabase/hooks/useSupabase";
 import { environment } from "@/services/environment";
 import { Flag, useGetFlag } from "@/services/feature-flags/use-get-flag";
 import { loginFormSchema, LoginProvider } from "@/types/auth";
 import { zodResolver } from "@hookform/resolvers/zod";
 import { useRouter, useSearchParams } from "next/navigation";
@@ -20,15 +22,17 @@ export function useLoginPage() {
  const [isGoogleLoading, setIsGoogleLoading] = useState(false);
  const [showNotAllowedModal, setShowNotAllowedModal] = useState(false);
  const isCloudEnv = environment.isCloud();
  const isChatEnabled = useGetFlag(Flag.CHAT);
  const homepageRoute = getHomepageRoute(isChatEnabled);
  // Get redirect destination from 'next' query parameter
  const nextUrl = searchParams.get("next");
  useEffect(() => {
    if (isLoggedIn && !isLoggingIn) {
-      router.push(nextUrl || "/");
+      router.push(nextUrl || homepageRoute);
    }
-  }, [isLoggedIn, isLoggingIn, nextUrl, router]);
+  }, [homepageRoute, isLoggedIn, isLoggingIn, nextUrl, router]);
  const form = useForm<z.infer<typeof loginFormSchema>>({
    resolver: zodResolver(loginFormSchema),
@@ -94,7 +98,7 @@ export function useLoginPage() {
      }
      // Prefer URL's next parameter, then use backend-determined route
-      router.replace(nextUrl || result.next || "/");
+      router.replace(nextUrl || result.next || homepageRoute);
    } catch (error) {
      toast({
        title:
--- a/autogpt_platform/frontend/src/app/(platform)/signup/actions.ts
+++ b/autogpt_platform/frontend/src/app/(platform)/signup/actions.ts
@@ -1,5 +1,6 @@
 "use server";
 import { getHomepageRoute } from "@/lib/constants";
 import { getServerSupabase } from "@/lib/supabase/server/getServerSupabase";
 import { signupFormSchema } from "@/types/auth";
 import * as Sentry from "@sentry/nextjs";
@@ -58,8 +59,10 @@ export async function signup(
    }
    // Get onboarding status from backend (includes chat flag evaluated for this user)
-    const { shouldShowOnboarding } = await getOnboardingStatus();
+    const { shouldShowOnboarding, isChatEnabled } = await getOnboardingStatus();
-    const next = shouldShowOnboarding ? "/onboarding" : "/";
+    const next = shouldShowOnboarding
      ? "/onboarding"
      : getHomepageRoute(isChatEnabled);
    return { success: true, next };
  } catch (err) {
--- a/autogpt_platform/frontend/src/app/(platform)/signup/useSignupPage.ts
+++ b/autogpt_platform/frontend/src/app/(platform)/signup/useSignupPage.ts
@@ -1,6 +1,8 @@
 import { useToast } from "@/components/molecules/Toast/use-toast";
 import { getHomepageRoute } from "@/lib/constants";
 import { useSupabase } from "@/lib/supabase/hooks/useSupabase";
 import { environment } from "@/services/environment";
 import { Flag, useGetFlag } from "@/services/feature-flags/use-get-flag";
 import { LoginProvider, signupFormSchema } from "@/types/auth";
 import { zodResolver } from "@hookform/resolvers/zod";
 import { useRouter, useSearchParams } from "next/navigation";
@@ -20,15 +22,17 @@ export function useSignupPage() {
  const [isGoogleLoading, setIsGoogleLoading] = useState(false);
  const [showNotAllowedModal, setShowNotAllowedModal] = useState(false);
  const isCloudEnv = environment.isCloud();
  const isChatEnabled = useGetFlag(Flag.CHAT);
  const homepageRoute = getHomepageRoute(isChatEnabled);
  // Get redirect destination from 'next' query parameter
  const nextUrl = searchParams.get("next");
  useEffect(() => {
    if (isLoggedIn && !isSigningUp) {
-      router.push(nextUrl || "/");
+      router.push(nextUrl || homepageRoute);
    }
-  }, [isLoggedIn, isSigningUp, nextUrl, router]);
+  }, [homepageRoute, isLoggedIn, isSigningUp, nextUrl, router]);
  const form = useForm<z.infer<typeof signupFormSchema>>({
    resolver: zodResolver(signupFormSchema),
@@ -129,7 +133,7 @@ export function useSignupPage() {
      }
      // Prefer the URL's next parameter, then result.next (for onboarding), then default
-      const redirectTo = nextUrl || result.next || "/";
+      const redirectTo = nextUrl || result.next || homepageRoute;
      router.replace(redirectTo);
    } catch (error) {
      setIsLoading(false);
--- a/autogpt_platform/frontend/src/app/api/chat/tasks/[taskId]/stream/route.ts
+++ b/autogpt_platform/frontend/src/app/api/chat/tasks/[taskId]/stream/route.ts
@@ -1,81 +0,0 @@
 import { environment } from "@/services/environment";
 import { getServerAuthToken } from "@/lib/autogpt-server-api/helpers";
 import { NextRequest } from "next/server";
 /**
 * SSE Proxy for task stream reconnection.
 *
 * This endpoint allows clients to reconnect to an ongoing or recently completed
 * background task's stream. It replays missed messages from Redis Streams and
 * subscribes to live updates if the task is still running.
 *
 * Client contract:
 * 1. When receiving an operation_started event, store the task_id
 * 2. To reconnect: GET /api/chat/tasks/{taskId}/stream?last_message_id={idx}
 * 3. Messages are replayed from the last_message_id position
 * 4. Stream ends when "finish" event is received
 */
 export async function GET(
  request: NextRequest,
  { params }: { params: Promise<{ taskId: string }> },
 ) {
  const { taskId } = await params;
  const searchParams = request.nextUrl.searchParams;
  const lastMessageId = searchParams.get("last_message_id") || "0-0";
  try {
    // Get auth token from server-side session
    const token = await getServerAuthToken();
    // Build backend URL
    const backendUrl = environment.getAGPTServerBaseUrl();
    const streamUrl = new URL(`/api/chat/tasks/${taskId}/stream`, backendUrl);
    streamUrl.searchParams.set("last_message_id", lastMessageId);
    // Forward request to backend with auth header
    const headers: Record<string, string> = {
      Accept: "text/event-stream",
      "Cache-Control": "no-cache",
      Connection: "keep-alive",
    };
    if (token) {
      headers["Authorization"] = `Bearer ${token}`;
    }
    const response = await fetch(streamUrl.toString(), {
      method: "GET",
      headers,
    });
    if (!response.ok) {
      const error = await response.text();
      return new Response(error, {
        status: response.status,
        headers: { "Content-Type": "application/json" },
      });
    }
    // Return the SSE stream directly
    return new Response(response.body, {
      headers: {
        "Content-Type": "text/event-stream",
        "Cache-Control": "no-cache, no-transform",
        Connection: "keep-alive",
        "X-Accel-Buffering": "no",
      },
    });
  } catch (error) {
    console.error("Task stream proxy error:", error);
    return new Response(
      JSON.stringify({
        error: "Failed to connect to task stream",
        detail: error instanceof Error ? error.message : String(error),
      }),
      {
        status: 500,
        headers: { "Content-Type": "application/json" },
      },
    );
  }
 }
--- a/autogpt_platform/frontend/src/app/api/helpers.ts
+++ b/autogpt_platform/frontend/src/app/api/helpers.ts
@@ -181,5 +181,6 @@ export async function getOnboardingStatus() {
  const isCompleted = onboarding.completedSteps.includes("CONGRATS");
  return {
    shouldShowOnboarding: status.is_onboarding_enabled && !isCompleted,
    isChatEnabled: status.is_chat_enabled,
  };
 }
--- a/autogpt_platform/frontend/src/app/api/openapi.json
+++ b/autogpt_platform/frontend/src/app/api/openapi.json
@@ -917,28 +917,6 @@
        "security": [{ "HTTPBearerJWT": [] }]
      }
    },
    "/api/chat/config/ttl": {
      "get": {
        "tags": ["v2", "chat", "chat"],
        "summary": "Get Ttl Config",
        "description": "Get the stream TTL configuration.\n\nReturns the Time-To-Live settings for chat streams, which determines\nhow long clients can reconnect to an active stream.\n\nReturns:\n    dict: TTL configuration with seconds and milliseconds values.",
        "operationId": "getV2GetTtlConfig",
        "responses": {
          "200": {
            "description": "Successful Response",
            "content": {
              "application/json": {
                "schema": {
                  "additionalProperties": true,
                  "type": "object",
                  "title": "Response Getv2Getttlconfig"
                }
              }
            }
          }
        }
      }
    },
    "/api/chat/health": {
      "get": {
        "tags": ["v2", "chat", "chat"],
@@ -961,63 +939,6 @@
        }
      }
    },
    "/api/chat/operations/{operation_id}/complete": {
      "post": {
        "tags": ["v2", "chat", "chat"],
        "summary": "Complete Operation",
        "description": "External completion webhook for long-running operations.\n\nCalled by Agent Generator (or other services) when an operation completes.\nThis triggers the stream registry to publish completion and continue LLM generation.\n\nArgs:\n    operation_id: The operation ID to complete.\n    request: Completion payload with success status and result/error.\n    x_api_key: Internal API key for authentication.\n\nReturns:\n    dict: Status of the completion.\n\nRaises:\n    HTTPException: If API key is invalid or operation not found.",
        "operationId": "postV2CompleteOperation",
        "parameters": [
          {
            "name": "operation_id",
            "in": "path",
            "required": true,
            "schema": { "type": "string", "title": "Operation Id" }
          },
          {
            "name": "x-api-key",
            "in": "header",
            "required": false,
            "schema": {
              "anyOf": [{ "type": "string" }, { "type": "null" }],
              "title": "X-Api-Key"
            }
          }
        ],
        "requestBody": {
          "required": true,
          "content": {
            "application/json": {
              "schema": {
                "$ref": "#/components/schemas/OperationCompleteRequest"
              }
            }
          }
        },
        "responses": {
          "200": {
            "description": "Successful Response",
            "content": {
              "application/json": {
                "schema": {
                  "type": "object",
                  "additionalProperties": true,
                  "title": "Response Postv2Completeoperation"
                }
              }
            }
          },
          "422": {
            "description": "Validation Error",
            "content": {
              "application/json": {
                "schema": { "$ref": "#/components/schemas/HTTPValidationError" }
              }
            }
          }
        }
      }
    },
    "/api/chat/sessions": {
      "get": {
        "tags": ["v2", "chat", "chat"],
@@ -1101,7 +1022,7 @@
      "get": {
        "tags": ["v2", "chat", "chat"],
        "summary": "Get Session",
-        "description": "Retrieve the details of a specific chat session.\n\nLooks up a chat session by ID for the given user (if authenticated) and returns all session data including messages.\nIf there's an active stream for this session, returns the task_id for reconnection.\n\nArgs:\n    session_id: The unique identifier for the desired chat session.\n    user_id: The optional authenticated user ID, or None for anonymous access.\n\nReturns:\n    SessionDetailResponse: Details for the requested session, including active_stream info if applicable.",
+        "description": "Retrieve the details of a specific chat session.\n\nLooks up a chat session by ID for the given user (if authenticated) and returns all session data including messages.\n\nArgs:\n    session_id: The unique identifier for the desired chat session.\n    user_id: The optional authenticated user ID, or None for anonymous access.\n\nReturns:\n    SessionDetailResponse: Details for the requested session, or None if not found.",
        "operationId": "getV2GetSession",
        "security": [{ "HTTPBearerJWT": [] }],
        "parameters": [
@@ -1236,7 +1157,7 @@
      "post": {
        "tags": ["v2", "chat", "chat"],
        "summary": "Stream Chat Post",
-        "description": "Stream chat responses for a session (POST with context support).\n\nStreams the AI/completion responses in real time over Server-Sent Events (SSE), including:\n  - Text fragments as they are generated\n  - Tool call UI elements (if invoked)\n  - Tool execution results\n\nThe AI generation runs in a background task that continues even if the client disconnects.\nAll chunks are written to Redis for reconnection support. If the client disconnects,\nthey can reconnect using GET /tasks/{task_id}/stream to resume from where they left off.\n\nArgs:\n    session_id: The chat session identifier to associate with the streamed messages.\n    request: Request body containing message, is_user_message, and optional context.\n    user_id: Optional authenticated user ID.\nReturns:\n    StreamingResponse: SSE-formatted response chunks. First chunk is a \"start\" event\n    containing the task_id for reconnection.",
+        "description": "Stream chat responses for a session (POST with context support).\n\nStreams the AI/completion responses in real time over Server-Sent Events (SSE), including:\n  - Text fragments as they are generated\n  - Tool call UI elements (if invoked)\n  - Tool execution results\n\nArgs:\n    session_id: The chat session identifier to associate with the streamed messages.\n    request: Request body containing message, is_user_message, and optional context.\n    user_id: Optional authenticated user ID.\nReturns:\n    StreamingResponse: SSE-formatted response chunks.",
        "operationId": "postV2StreamChatPost",
        "security": [{ "HTTPBearerJWT": [] }],
        "parameters": [
@@ -1274,94 +1195,6 @@
        }
      }
    },
    "/api/chat/tasks/{task_id}": {
      "get": {
        "tags": ["v2", "chat", "chat"],
        "summary": "Get Task Status",
        "description": "Get the status of a long-running task.\n\nArgs:\n    task_id: The task ID to check.\n    user_id: Authenticated user ID for ownership validation.\n\nReturns:\n    dict: Task status including task_id, status, tool_name, and operation_id.\n\nRaises:\n    NotFoundError: If task_id is not found or user doesn't have access.",
        "operationId": "getV2GetTaskStatus",
        "security": [{ "HTTPBearerJWT": [] }],
        "parameters": [
          {
            "name": "task_id",
            "in": "path",
            "required": true,
            "schema": { "type": "string", "title": "Task Id" }
          }
        ],
        "responses": {
          "200": {
            "description": "Successful Response",
            "content": {
              "application/json": {
                "schema": {
                  "type": "object",
                  "additionalProperties": true,
                  "title": "Response Getv2Gettaskstatus"
                }
              }
            }
          },
          "401": {
            "$ref": "#/components/responses/HTTP401NotAuthenticatedError"
          },
          "422": {
            "description": "Validation Error",
            "content": {
              "application/json": {
                "schema": { "$ref": "#/components/schemas/HTTPValidationError" }
              }
            }
          }
        }
      }
    },
    "/api/chat/tasks/{task_id}/stream": {
      "get": {
        "tags": ["v2", "chat", "chat"],
        "summary": "Stream Task",
        "description": "Reconnect to a long-running task's SSE stream.\n\nWhen a long-running operation (like agent generation) starts, the client\nreceives a task_id. If the connection drops, the client can reconnect\nusing this endpoint to resume receiving updates.\n\nArgs:\n    task_id: The task ID from the operation_started response.\n    user_id: Authenticated user ID for ownership validation.\n    last_message_id: Last Redis Stream message ID received (\"0-0\" for full replay).\n\nReturns:\n    StreamingResponse: SSE-formatted response chunks starting after last_message_id.\n\nRaises:\n    HTTPException: 404 if task not found, 410 if task expired, 403 if access denied.",
        "operationId": "getV2StreamTask",
        "security": [{ "HTTPBearerJWT": [] }],
        "parameters": [
          {
            "name": "task_id",
            "in": "path",
            "required": true,
            "schema": { "type": "string", "title": "Task Id" }
          },
          {
            "name": "last_message_id",
            "in": "query",
            "required": false,
            "schema": {
              "type": "string",
              "description": "Last Redis Stream message ID received (e.g., '1706540123456-0'). Use '0-0' for full replay.",
              "default": "0-0",
              "title": "Last Message Id"
            },
            "description": "Last Redis Stream message ID received (e.g., '1706540123456-0'). Use '0-0' for full replay."
          }
        ],
        "responses": {
          "200": {
            "description": "Successful Response",
            "content": { "application/json": { "schema": {} } }
          },
          "401": {
            "$ref": "#/components/responses/HTTP401NotAuthenticatedError"
          },
          "422": {
            "description": "Validation Error",
            "content": {
              "application/json": {
                "schema": { "$ref": "#/components/schemas/HTTPValidationError" }
              }
            }
          }
        }
      }
    },
    "/api/credits": {
      "get": {
        "tags": ["v1", "credits"],
@@ -6335,18 +6168,6 @@
        "title": "AccuracyTrendsResponse",
        "description": "Response model for accuracy trends and alerts."
      },
      "ActiveStreamInfo": {
        "properties": {
          "task_id": { "type": "string", "title": "Task Id" },
          "last_message_id": { "type": "string", "title": "Last Message Id" },
          "operation_id": { "type": "string", "title": "Operation Id" },
          "tool_name": { "type": "string", "title": "Tool Name" }
        },
        "type": "object",
        "required": ["task_id", "last_message_id", "operation_id", "tool_name"],
        "title": "ActiveStreamInfo",
        "description": "Information about an active stream for reconnection."
      },
      "AddUserCreditsResponse": {
        "properties": {
          "new_balance": { "type": "integer", "title": "New Balance" },
@@ -9002,27 +8823,6 @@
        ],
        "title": "OnboardingStep"
      },
      "OperationCompleteRequest": {
        "properties": {
          "success": { "type": "boolean", "title": "Success" },
          "result": {
            "anyOf": [
              { "additionalProperties": true, "type": "object" },
              { "type": "string" },
              { "type": "null" }
            ],
            "title": "Result"
          },
          "error": {
            "anyOf": [{ "type": "string" }, { "type": "null" }],
            "title": "Error"
          }
        },
        "type": "object",
        "required": ["success"],
        "title": "OperationCompleteRequest",
        "description": "Request model for external completion webhook."
      },
      "Pagination": {
        "properties": {
          "total_items": {
@@ -9878,12 +9678,6 @@
            "items": { "additionalProperties": true, "type": "object" },
            "type": "array",
            "title": "Messages"
          },
          "active_stream": {
            "anyOf": [
              { "$ref": "#/components/schemas/ActiveStreamInfo" },
              { "type": "null" }
            ]
          }
        },
        "type": "object",
--- a/autogpt_platform/frontend/src/app/page.tsx
+++ b/autogpt_platform/frontend/src/app/page.tsx
@@ -1,15 +1,27 @@
 "use client";
-import { LoadingSpinner } from "@/components/atoms/LoadingSpinner/LoadingSpinner";
+import { getHomepageRoute } from "@/lib/constants";
 import { Flag, useGetFlag } from "@/services/feature-flags/use-get-flag";
 import { useRouter } from "next/navigation";
 import { useEffect } from "react";
 export default function Page() {
  const isChatEnabled = useGetFlag(Flag.CHAT);
  const router = useRouter();
  const homepageRoute = getHomepageRoute(isChatEnabled);
  const envEnabled = process.env.NEXT_PUBLIC_LAUNCHDARKLY_ENABLED === "true";
  const clientId = process.env.NEXT_PUBLIC_LAUNCHDARKLY_CLIENT_ID;
  const isLaunchDarklyConfigured = envEnabled && Boolean(clientId);
  const isFlagReady =
    !isLaunchDarklyConfigured || typeof isChatEnabled === "boolean";
-  useEffect(() => {
+  useEffect(
-    router.replace("/copilot");
+    function redirectToHomepage() {
-  }, [router]);
+      if (!isFlagReady) return;
      router.replace(homepageRoute);
    },
    [homepageRoute, isFlagReady, router],
  );
-  return <LoadingSpinner size="large" cover />;
+  return null;
 }
--- a/autogpt_platform/frontend/src/components/legacy/ui/render.tsx
+++ b/autogpt_platform/frontend/src/components/legacy/ui/render.tsx
@@ -22,7 +22,7 @@ const isValidVideoUrl = (url: string): boolean => {
  if (url.startsWith("data:video")) {
    return true;
  }
-  const videoExtensions = /\.(mp4|webm|ogg|mov|avi|mkv|m4v)$/i;
+  const videoExtensions = /\.(mp4|webm|ogg)$/i;
  const youtubeRegex = /^(https?:\/\/)?(www\.)?(youtube\.com|youtu\.?be)\/.+$/;
  const cleanedUrl = url.split("?")[0];
  return (
@@ -44,29 +44,11 @@ const isValidAudioUrl = (url: string): boolean => {
  if (url.startsWith("data:audio")) {
    return true;
  }
-  const audioExtensions = /\.(mp3|wav|ogg|m4a|aac|flac)$/i;
+  const audioExtensions = /\.(mp3|wav)$/i;
  const cleanedUrl = url.split("?")[0];
  return isValidMediaUri(url) && audioExtensions.test(cleanedUrl);
 };
 const getVideoMimeType = (url: string): string => {
  if (url.startsWith("data:video/")) {
    const match = url.match(/^data:(video\/[^;]+)/);
    return match?.[1] || "video/mp4";
  }
  const extension = url.split("?")[0].split(".").pop()?.toLowerCase();
  const mimeMap: Record<string, string> = {
    mp4: "video/mp4",
    webm: "video/webm",
    ogg: "video/ogg",
    mov: "video/quicktime",
    avi: "video/x-msvideo",
    mkv: "video/x-matroska",
    m4v: "video/mp4",
  };
  return mimeMap[extension || ""] || "video/mp4";
 };
 const VideoRenderer: React.FC<{ videoUrl: string }> = ({ videoUrl }) => {
  const videoId = getYouTubeVideoId(videoUrl);
  return (
@@ -81,7 +63,7 @@ const VideoRenderer: React.FC<{ videoUrl: string }> = ({ videoUrl }) => {
        ></iframe>
      ) : (
        <video controls width="100%" height="315">
-          <source src={videoUrl} type={getVideoMimeType(videoUrl)} />
+          <source src={videoUrl} type="video/mp4" />
          Your browser does not support the video tag.
        </video>
      )}
--- a/autogpt_platform/frontend/src/components/atoms/FileInput/FileInput.tsx
+++ b/autogpt_platform/frontend/src/components/atoms/FileInput/FileInput.tsx
@@ -104,7 +104,31 @@ export function FileInput(props: Props) {
    return false;
  }
-  const getFileLabelFromValue = (val: string) => {
+  const getFileLabelFromValue = (val: unknown): string => {
    // Handle object format from external API: { name, type, size, data }
    if (val && typeof val === "object") {
      const obj = val as Record<string, unknown>;
      if (typeof obj.name === "string") {
        return getFileLabel(
          obj.name,
          typeof obj.type === "string" ? obj.type : "",
        );
      }
      if (typeof obj.type === "string") {
        const mimeParts = obj.type.split("/");
        if (mimeParts.length > 1) {
          return `${mimeParts[1].toUpperCase()} file`;
        }
        return `${obj.type} file`;
      }
      return "File";
    }
    // Handle string values (data URIs or file paths)
    if (typeof val !== "string") {
      return "File";
    }
    if (val.startsWith("data:")) {
      const matches = val.match(/^data:([^;]+);/);
      if (matches?.[1]) {
--- a/autogpt_platform/frontend/src/components/contextual/Chat/Chat.tsx
+++ b/autogpt_platform/frontend/src/components/contextual/Chat/Chat.tsx
@@ -1,6 +1,7 @@
 "use client";
 import { useCopilotSessionId } from "@/app/(platform)/copilot/useCopilotSessionId";
 import { useCopilotStore } from "@/app/(platform)/copilot/copilot-page-store";
 import { LoadingSpinner } from "@/components/atoms/LoadingSpinner/LoadingSpinner";
 import { Text } from "@/components/atoms/Text/Text";
 import { cn } from "@/lib/utils";
@@ -24,8 +25,8 @@ export function Chat({
 }: ChatProps) {
  const { urlSessionId } = useCopilotSessionId();
  const hasHandledNotFoundRef = useRef(false);
  const isSwitchingSession = useCopilotStore((s) => s.isSwitchingSession);
  const {
    session,
    messages,
    isLoading,
    isCreating,
@@ -37,18 +38,6 @@ export function Chat({
    startPollingForOperation,
  } = useChat({ urlSessionId });
  // Extract active stream info for reconnection
  const activeStream = (
    session as {
      active_stream?: {
        task_id: string;
        last_message_id: string;
        operation_id: string;
        tool_name: string;
      };
    }
  )?.active_stream;
  useEffect(() => {
    if (!onSessionNotFound) return;
    if (!urlSessionId) return;
@@ -64,7 +53,8 @@ export function Chat({
    isCreating,
  ]);
-  const shouldShowLoader = showLoader && (isLoading || isCreating);
+  const shouldShowLoader =
    (showLoader && (isLoading || isCreating)) || isSwitchingSession;
  return (
    <div className={cn("flex h-full flex-col", className)}>
@@ -76,19 +66,21 @@ export function Chat({
            <div className="flex flex-col items-center gap-3">
              <LoadingSpinner size="large" className="text-neutral-400" />
              <Text variant="body" className="text-zinc-500">
-                Loading your chat...
+                {isSwitchingSession
                  ? "Switching chat..."
                  : "Loading your chat..."}
              </Text>
            </div>
          </div>
        )}
        {/* Error State */}
-        {error && !isLoading && (
+        {error && !isLoading && !isSwitchingSession && (
          <ChatErrorState error={error} onRetry={createSession} />
        )}
        {/* Session Content */}
-        {sessionId && !isLoading && !error && (
+        {sessionId && !isLoading && !error && !isSwitchingSession && (
          <ChatContainer
            sessionId={sessionId}
            initialMessages={messages}
@@ -96,16 +88,6 @@ export function Chat({
            className="flex-1"
            onStreamingChange={onStreamingChange}
            onOperationStarted={startPollingForOperation}
            activeStream={
              activeStream
                ? {
                    taskId: activeStream.task_id,
                    lastMessageId: activeStream.last_message_id,
                    operationId: activeStream.operation_id,
                    toolName: activeStream.tool_name,
                  }
                : undefined
            }
          />
        )}
      </main>
--- a/autogpt_platform/frontend/src/components/contextual/Chat/SSE_RECONNECTION.md
+++ b/autogpt_platform/frontend/src/components/contextual/Chat/SSE_RECONNECTION.md
@@ -1,159 +0,0 @@
 # SSE Reconnection Contract for Long-Running Operations
 This document describes the client-side contract for handling SSE (Server-Sent Events) disconnections and reconnecting to long-running background tasks.
 ## Overview
 When a user triggers a long-running operation (like agent generation), the backend:
 1. Spawns a background task that survives SSE disconnections
 2. Returns an `operation_started` response with a `task_id`
 3. Stores stream messages in Redis Streams for replay
 Clients can reconnect to the task stream at any time to receive missed messages.
 ## Client-Side Flow
 ### 1. Receiving Operation Started
 When you receive an `operation_started` tool response:
 ```typescript
 // The response includes a task_id for reconnection
 {
  type: "operation_started",
  tool_name: "generate_agent",
  operation_id: "uuid-...",
  task_id: "task-uuid-...",  // <-- Store this for reconnection
  message: "Operation started. You can close this tab."
 }
 ```
 ### 2. Storing Task Info
 Use the chat store to track the active task:
 ```typescript
 import { useChatStore } from "./chat-store";
 // When operation_started is received:
 useChatStore.getState().setActiveTask(sessionId, {
  taskId: response.task_id,
  operationId: response.operation_id,
  toolName: response.tool_name,
  lastMessageId: "0",
 });
 ```
 ### 3. Reconnecting to a Task
 To reconnect (e.g., after page refresh or tab reopen):
 ```typescript
 const { reconnectToTask, getActiveTask } = useChatStore.getState();
 // Check if there's an active task for this session
 const activeTask = getActiveTask(sessionId);
 if (activeTask) {
  // Reconnect to the task stream
  await reconnectToTask(
    sessionId,
    activeTask.taskId,
    activeTask.lastMessageId, // Resume from last position
    (chunk) => {
      // Handle incoming chunks
      console.log("Received chunk:", chunk);
    },
  );
 }
 ```
 ### 4. Tracking Message Position
 To enable precise replay, update the last message ID as chunks arrive:
 ```typescript
 const { updateTaskLastMessageId } = useChatStore.getState();
 function handleChunk(chunk: StreamChunk) {
  // If chunk has an index/id, track it
  if (chunk.idx !== undefined) {
    updateTaskLastMessageId(sessionId, String(chunk.idx));
  }
 }
 ```
 ## API Endpoints
 ### Task Stream Reconnection
 ```
 GET /api/chat/tasks/{taskId}/stream?last_message_id={idx}
 ```
 - `taskId`: The task ID from `operation_started`
 - `last_message_id`: Last received message index (default: "0" for full replay)
 Returns: SSE stream of missed messages + live updates
 ## Chunk Types
 The reconnected stream follows the same Vercel AI SDK protocol:
 | Type                    | Description             |
 | ----------------------- | ----------------------- |
 | `start`                 | Message lifecycle start |
 | `text-delta`            | Streaming text content  |
 | `text-end`              | Text block completed    |
 | `tool-output-available` | Tool result available   |
 | `finish`                | Stream completed        |
 | `error`                 | Error occurred          |
 ## Error Handling
 If reconnection fails:
 1. Check if task still exists (may have expired - default TTL: 1 hour)
 2. Fall back to polling the session for final state
 3. Show appropriate UI message to user
 ## Persistence Considerations
 For robust reconnection across browser restarts:
 ```typescript
 // Store in localStorage/sessionStorage
 const ACTIVE_TASKS_KEY = "chat_active_tasks";
 function persistActiveTask(sessionId: string, task: ActiveTaskInfo) {
  const tasks = JSON.parse(localStorage.getItem(ACTIVE_TASKS_KEY) || "{}");
  tasks[sessionId] = task;
  localStorage.setItem(ACTIVE_TASKS_KEY, JSON.stringify(tasks));
 }
 function loadPersistedTasks(): Record<string, ActiveTaskInfo> {
  return JSON.parse(localStorage.getItem(ACTIVE_TASKS_KEY) || "{}");
 }
 ```
 ## Backend Configuration
 The following backend settings affect reconnection behavior:
 | Setting             | Default | Description                        |
 | ------------------- | ------- | ---------------------------------- |
 | `stream_ttl`        | 3600s   | How long streams are kept in Redis |
 | `stream_max_length` | 1000    | Max messages per stream            |
 ## Testing
 To test reconnection locally:
 1. Start a long-running operation (e.g., agent generation)
 2. Note the `task_id` from the `operation_started` response
 3. Close the browser tab
 4. Reopen and call `reconnectToTask` with the saved `task_id`
 5. Verify that missed messages are replayed
 See the main README for full local development setup.
--- a/autogpt_platform/frontend/src/components/contextual/Chat/chat-constants.ts
+++ b/autogpt_platform/frontend/src/components/contextual/Chat/chat-constants.ts
@@ -1,16 +0,0 @@
 /**
 * Constants for the chat system.
 *
 * Centralizes magic strings and values used across chat components.
 */
 // LocalStorage keys
 export const STORAGE_KEY_ACTIVE_TASKS = "chat_active_tasks";
 // Redis Stream IDs
 export const INITIAL_MESSAGE_ID = "0";
 export const INITIAL_STREAM_ID = "0-0";
 // TTL values (in milliseconds)
 export const COMPLETED_STREAM_TTL_MS = 5 * 60 * 1000; // 5 minutes
 export const ACTIVE_TASK_TTL_MS = 60 * 60 * 1000; // 1 hour
--- a/autogpt_platform/frontend/src/components/contextual/Chat/chat-store.ts
+++ b/autogpt_platform/frontend/src/components/contextual/Chat/chat-store.ts
@@ -1,12 +1,6 @@
 "use client";
 import { create } from "zustand";
 import {
  ACTIVE_TASK_TTL_MS,
  COMPLETED_STREAM_TTL_MS,
  INITIAL_STREAM_ID,
  STORAGE_KEY_ACTIVE_TASKS,
 } from "./chat-constants";
 import type {
  ActiveStream,
  StreamChunk,
@@ -14,59 +8,15 @@ import type {
  StreamResult,
  StreamStatus,
 } from "./chat-types";
-import { executeStream, executeTaskReconnect } from "./stream-executor";
+import { executeStream } from "./stream-executor";
-export interface ActiveTaskInfo {
+const COMPLETED_STREAM_TTL = 5 * 60 * 1000; // 5 minutes
  taskId: string;
  sessionId: string;
  operationId: string;
  toolName: string;
  lastMessageId: string;
  startedAt: number;
 }
 /** Load active tasks from localStorage */
 function loadPersistedTasks(): Map<string, ActiveTaskInfo> {
  if (typeof window === "undefined") return new Map();
  try {
    const stored = localStorage.getItem(STORAGE_KEY_ACTIVE_TASKS);
    if (!stored) return new Map();
    const parsed = JSON.parse(stored) as Record<string, ActiveTaskInfo>;
    const now = Date.now();
    const tasks = new Map<string, ActiveTaskInfo>();
    // Filter out expired tasks
    for (const [sessionId, task] of Object.entries(parsed)) {
      if (now - task.startedAt < ACTIVE_TASK_TTL_MS) {
        tasks.set(sessionId, task);
      }
    }
    return tasks;
  } catch {
    return new Map();
  }
 }
 /** Save active tasks to localStorage */
 function persistTasks(tasks: Map<string, ActiveTaskInfo>): void {
  if (typeof window === "undefined") return;
  try {
    const obj: Record<string, ActiveTaskInfo> = {};
    for (const [sessionId, task] of tasks) {
      obj[sessionId] = task;
    }
    localStorage.setItem(STORAGE_KEY_ACTIVE_TASKS, JSON.stringify(obj));
  } catch {
    // Ignore storage errors
  }
 }
 interface ChatStoreState {
  activeStreams: Map<string, ActiveStream>;
  completedStreams: Map<string, StreamResult>;
  activeSessions: Set<string>;
  streamCompleteCallbacks: Set<StreamCompleteCallback>;
  /** Active tasks for SSE reconnection - keyed by sessionId */
  activeTasks: Map<string, ActiveTaskInfo>;
 }
 interface ChatStoreActions {
@@ -91,24 +41,6 @@ interface ChatStoreActions {
  unregisterActiveSession: (sessionId: string) => void;
  isSessionActive: (sessionId: string) => boolean;
  onStreamComplete: (callback: StreamCompleteCallback) => () => void;
  /** Track active task for SSE reconnection */
  setActiveTask: (
    sessionId: string,
    taskInfo: Omit<ActiveTaskInfo, "sessionId" | "startedAt">,
  ) => void;
  /** Get active task for a session */
  getActiveTask: (sessionId: string) => ActiveTaskInfo | undefined;
  /** Clear active task when operation completes */
  clearActiveTask: (sessionId: string) => void;
  /** Reconnect to an existing task stream */
  reconnectToTask: (
    sessionId: string,
    taskId: string,
    lastMessageId?: string,
    onChunk?: (chunk: StreamChunk) => void,
  ) => Promise<void>;
  /** Update last message ID for a task (for tracking replay position) */
  updateTaskLastMessageId: (sessionId: string, lastMessageId: string) => void;
 }
 type ChatStore = ChatStoreState & ChatStoreActions;
@@ -132,126 +64,18 @@ function cleanupExpiredStreams(
  const now = Date.now();
  const cleaned = new Map(completedStreams);
  for (const [sessionId, result] of cleaned) {
-    if (now - result.completedAt > COMPLETED_STREAM_TTL_MS) {
+    if (now - result.completedAt > COMPLETED_STREAM_TTL) {
      cleaned.delete(sessionId);
    }
  }
  return cleaned;
 }
 /**
 * Finalize a stream by moving it from activeStreams to completedStreams.
 * Also handles cleanup and notifications.
 */
 function finalizeStream(
  sessionId: string,
  stream: ActiveStream,
  onChunk: ((chunk: StreamChunk) => void) | undefined,
  get: () => ChatStoreState & ChatStoreActions,
  set: (state: Partial<ChatStoreState>) => void,
 ): void {
  if (onChunk) stream.onChunkCallbacks.delete(onChunk);
  if (stream.status !== "streaming") {
    const currentState = get();
    const finalActiveStreams = new Map(currentState.activeStreams);
    let finalCompletedStreams = new Map(currentState.completedStreams);
    const storedStream = finalActiveStreams.get(sessionId);
    if (storedStream === stream) {
      const result: StreamResult = {
        sessionId,
        status: stream.status,
        chunks: stream.chunks,
        completedAt: Date.now(),
        error: stream.error,
      };
      finalCompletedStreams.set(sessionId, result);
      finalActiveStreams.delete(sessionId);
      finalCompletedStreams = cleanupExpiredStreams(finalCompletedStreams);
      set({
        activeStreams: finalActiveStreams,
        completedStreams: finalCompletedStreams,
      });
      if (stream.status === "completed" || stream.status === "error") {
        notifyStreamComplete(currentState.streamCompleteCallbacks, sessionId);
      }
    }
  }
 }
 /**
 * Clean up an existing stream for a session and move it to completed streams.
 * Returns updated maps for both active and completed streams.
 */
 function cleanupExistingStream(
  sessionId: string,
  activeStreams: Map<string, ActiveStream>,
  completedStreams: Map<string, StreamResult>,
  callbacks: Set<StreamCompleteCallback>,
 ): {
  activeStreams: Map<string, ActiveStream>;
  completedStreams: Map<string, StreamResult>;
 } {
  const newActiveStreams = new Map(activeStreams);
  let newCompletedStreams = new Map(completedStreams);
  const existingStream = newActiveStreams.get(sessionId);
  if (existingStream) {
    existingStream.abortController.abort();
    const normalizedStatus =
      existingStream.status === "streaming"
        ? "completed"
        : existingStream.status;
    const result: StreamResult = {
      sessionId,
      status: normalizedStatus,
      chunks: existingStream.chunks,
      completedAt: Date.now(),
      error: existingStream.error,
    };
    newCompletedStreams.set(sessionId, result);
    newActiveStreams.delete(sessionId);
    newCompletedStreams = cleanupExpiredStreams(newCompletedStreams);
    if (normalizedStatus === "completed" || normalizedStatus === "error") {
      notifyStreamComplete(callbacks, sessionId);
    }
  }
  return {
    activeStreams: newActiveStreams,
    completedStreams: newCompletedStreams,
  };
 }
 /**
 * Create a new active stream with initial state.
 */
 function createActiveStream(
  sessionId: string,
  onChunk?: (chunk: StreamChunk) => void,
 ): ActiveStream {
  const abortController = new AbortController();
  const initialCallbacks = new Set<(chunk: StreamChunk) => void>();
  if (onChunk) initialCallbacks.add(onChunk);
  return {
    sessionId,
    abortController,
    status: "streaming",
    startedAt: Date.now(),
    chunks: [],
    onChunkCallbacks: initialCallbacks,
  };
 }
 export const useChatStore = create<ChatStore>((set, get) => ({
  activeStreams: new Map(),
  completedStreams: new Map(),
  activeSessions: new Set(),
  streamCompleteCallbacks: new Set(),
  activeTasks: loadPersistedTasks(),
  startStream: async function startStream(
    sessionId,
@@ -261,21 +85,45 @@ export const useChatStore = create<ChatStore>((set, get) => ({
    onChunk,
  ) {
    const state = get();
    const newActiveStreams = new Map(state.activeStreams);
    let newCompletedStreams = new Map(state.completedStreams);
    const callbacks = state.streamCompleteCallbacks;
-    // Clean up any existing stream for this session
+    const existingStream = newActiveStreams.get(sessionId);
-    const {
+    if (existingStream) {
-      activeStreams: newActiveStreams,
+      existingStream.abortController.abort();
-      completedStreams: newCompletedStreams,
+      const normalizedStatus =
-    } = cleanupExistingStream(
+        existingStream.status === "streaming"
-      sessionId,
+          ? "completed"
-      state.activeStreams,
+          : existingStream.status;
-      state.completedStreams,
+      const result: StreamResult = {
-      callbacks,
+        sessionId,
-    );
+        status: normalizedStatus,
        chunks: existingStream.chunks,
        completedAt: Date.now(),
        error: existingStream.error,
      };
      newCompletedStreams.set(sessionId, result);
      newActiveStreams.delete(sessionId);
      newCompletedStreams = cleanupExpiredStreams(newCompletedStreams);
      if (normalizedStatus === "completed" || normalizedStatus === "error") {
        notifyStreamComplete(callbacks, sessionId);
      }
    }
    const abortController = new AbortController();
    const initialCallbacks = new Set<(chunk: StreamChunk) => void>();
    if (onChunk) initialCallbacks.add(onChunk);
    const stream: ActiveStream = {
      sessionId,
      abortController,
      status: "streaming",
      startedAt: Date.now(),
      chunks: [],
      onChunkCallbacks: initialCallbacks,
    };
    // Create new stream
    const stream = createActiveStream(sessionId, onChunk);
    newActiveStreams.set(sessionId, stream);
    set({
      activeStreams: newActiveStreams,
@@ -285,7 +133,36 @@ export const useChatStore = create<ChatStore>((set, get) => ({
    try {
      await executeStream(stream, message, isUserMessage, context);
    } finally {
-      finalizeStream(sessionId, stream, onChunk, get, set);
+      if (onChunk) stream.onChunkCallbacks.delete(onChunk);
      if (stream.status !== "streaming") {
        const currentState = get();
        const finalActiveStreams = new Map(currentState.activeStreams);
        let finalCompletedStreams = new Map(currentState.completedStreams);
        const storedStream = finalActiveStreams.get(sessionId);
        if (storedStream === stream) {
          const result: StreamResult = {
            sessionId,
            status: stream.status,
            chunks: stream.chunks,
            completedAt: Date.now(),
            error: stream.error,
          };
          finalCompletedStreams.set(sessionId, result);
          finalActiveStreams.delete(sessionId);
          finalCompletedStreams = cleanupExpiredStreams(finalCompletedStreams);
          set({
            activeStreams: finalActiveStreams,
            completedStreams: finalCompletedStreams,
          });
          if (stream.status === "completed" || stream.status === "error") {
            notifyStreamComplete(
              currentState.streamCompleteCallbacks,
              sessionId,
            );
          }
        }
      }
    }
  },
@@ -409,93 +286,4 @@ export const useChatStore = create<ChatStore>((set, get) => ({
      set({ streamCompleteCallbacks: cleanedCallbacks });
    };
  },
  setActiveTask: function setActiveTask(sessionId, taskInfo) {
    const state = get();
    const newActiveTasks = new Map(state.activeTasks);
    newActiveTasks.set(sessionId, {
      ...taskInfo,
      sessionId,
      startedAt: Date.now(),
    });
    set({ activeTasks: newActiveTasks });
    persistTasks(newActiveTasks);
  },
  getActiveTask: function getActiveTask(sessionId) {
    return get().activeTasks.get(sessionId);
  },
  clearActiveTask: function clearActiveTask(sessionId) {
    const state = get();
    if (!state.activeTasks.has(sessionId)) return;
    const newActiveTasks = new Map(state.activeTasks);
    newActiveTasks.delete(sessionId);
    set({ activeTasks: newActiveTasks });
    persistTasks(newActiveTasks);
  },
  reconnectToTask: async function reconnectToTask(
    sessionId,
    taskId,
    lastMessageId = INITIAL_STREAM_ID,
    onChunk,
  ) {
    const state = get();
    const callbacks = state.streamCompleteCallbacks;
    // Clean up any existing stream for this session
    const {
      activeStreams: newActiveStreams,
      completedStreams: newCompletedStreams,
    } = cleanupExistingStream(
      sessionId,
      state.activeStreams,
      state.completedStreams,
      callbacks,
    );
    // Create new stream for reconnection
    const stream = createActiveStream(sessionId, onChunk);
    newActiveStreams.set(sessionId, stream);
    set({
      activeStreams: newActiveStreams,
      completedStreams: newCompletedStreams,
    });
    try {
      await executeTaskReconnect(stream, taskId, lastMessageId);
    } finally {
      finalizeStream(sessionId, stream, onChunk, get, set);
      // Clear active task on completion
      if (stream.status === "completed" || stream.status === "error") {
        const taskState = get();
        if (taskState.activeTasks.has(sessionId)) {
          const newActiveTasks = new Map(taskState.activeTasks);
          newActiveTasks.delete(sessionId);
          set({ activeTasks: newActiveTasks });
          persistTasks(newActiveTasks);
        }
      }
    }
  },
  updateTaskLastMessageId: function updateTaskLastMessageId(
    sessionId,
    lastMessageId,
  ) {
    const state = get();
    const task = state.activeTasks.get(sessionId);
    if (!task) return;
    const newActiveTasks = new Map(state.activeTasks);
    newActiveTasks.set(sessionId, {
      ...task,
      lastMessageId,
    });
    set({ activeTasks: newActiveTasks });
    persistTasks(newActiveTasks);
  },
 }));
--- a/autogpt_platform/frontend/src/components/contextual/Chat/chat-types.ts
+++ b/autogpt_platform/frontend/src/components/contextual/Chat/chat-types.ts
@@ -4,7 +4,6 @@ export type StreamStatus = "idle" | "streaming" | "completed" | "error";
 export interface StreamChunk {
  type:
    | "stream_start"
    | "text_chunk"
    | "text_ended"
    | "tool_call"
@@ -16,7 +15,6 @@ export interface StreamChunk {
    | "error"
    | "usage"
    | "stream_end";
  taskId?: string;
  timestamp?: string;
  content?: string;
  message?: string;
@@ -43,7 +41,7 @@ export interface StreamChunk {
 }
 export type VercelStreamChunk =
-  | { type: "start"; messageId: string; taskId?: string }
+  | { type: "start"; messageId: string }
  | { type: "finish" }
  | { type: "text-start"; id: string }
  | { type: "text-delta"; id: string; delta: string }
@@ -94,70 +92,3 @@ export interface StreamResult {
 }
 export type StreamCompleteCallback = (sessionId: string) => void;
 // Type guards for message types
 /**
 * Check if a message has a toolId property.
 */
 export function hasToolId<T extends { type: string }>(
  msg: T,
 ): msg is T & { toolId: string } {
  return (
    "toolId" in msg &&
    typeof (msg as Record<string, unknown>).toolId === "string"
  );
 }
 /**
 * Check if a message has an operationId property.
 */
 export function hasOperationId<T extends { type: string }>(
  msg: T,
 ): msg is T & { operationId: string } {
  return (
    "operationId" in msg &&
    typeof (msg as Record<string, unknown>).operationId === "string"
  );
 }
 /**
 * Check if a message has a toolCallId property.
 */
 export function hasToolCallId<T extends { type: string }>(
  msg: T,
 ): msg is T & { toolCallId: string } {
  return (
    "toolCallId" in msg &&
    typeof (msg as Record<string, unknown>).toolCallId === "string"
  );
 }
 /**
 * Check if a message is an operation message type.
 */
 export function isOperationMessage<T extends { type: string }>(
  msg: T,
 ): msg is T & {
  type: "operation_started" | "operation_pending" | "operation_in_progress";
 } {
  return (
    msg.type === "operation_started" ||
    msg.type === "operation_pending" ||
    msg.type === "operation_in_progress"
  );
 }
 /**
 * Get the tool ID from a message if available.
 * Checks toolId, operationId, and toolCallId properties.
 */
 export function getToolIdFromMessage<T extends { type: string }>(
  msg: T,
 ): string | undefined {
  const record = msg as Record<string, unknown>;
  if (typeof record.toolId === "string") return record.toolId;
  if (typeof record.operationId === "string") return record.operationId;
  if (typeof record.toolCallId === "string") return record.toolCallId;
  return undefined;
 }
--- a/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatContainer/ChatContainer.tsx
+++ b/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatContainer/ChatContainer.tsx
@@ -2,6 +2,7 @@ import type { SessionDetailResponse } from "@/app/api/__generated__/models/sessi
 import { Button } from "@/components/atoms/Button/Button";
 import { Text } from "@/components/atoms/Text/Text";
 import { Dialog } from "@/components/molecules/Dialog/Dialog";
 import { useBreakpoint } from "@/lib/hooks/useBreakpoint";
 import { cn } from "@/lib/utils";
 import { GlobeHemisphereEastIcon } from "@phosphor-icons/react";
 import { useEffect } from "react";
@@ -16,13 +17,6 @@ export interface ChatContainerProps {
  className?: string;
  onStreamingChange?: (isStreaming: boolean) => void;
  onOperationStarted?: () => void;
  /** Active stream info from the server for reconnection */
  activeStream?: {
    taskId: string;
    lastMessageId: string;
    operationId: string;
    toolName: string;
  };
 }
 export function ChatContainer({
@@ -32,7 +26,6 @@ export function ChatContainer({
  className,
  onStreamingChange,
  onOperationStarted,
  activeStream,
 }: ChatContainerProps) {
  const {
    messages,
@@ -48,13 +41,16 @@ export function ChatContainer({
    initialMessages,
    initialPrompt,
    onOperationStarted,
    activeStream,
  });
  useEffect(() => {
    onStreamingChange?.(isStreaming);
  }, [isStreaming, onStreamingChange]);
  const breakpoint = useBreakpoint();
  const isMobile =
    breakpoint === "base" || breakpoint === "sm" || breakpoint === "md";
  return (
    <div
      className={cn(
@@ -122,7 +118,11 @@ export function ChatContainer({
          disabled={isStreaming || !sessionId}
          isStreaming={isStreaming}
          onStop={stopStreaming}
-          placeholder="What else can I help with?"
+          placeholder={
            isMobile
              ? "You can search or just ask"
              : 'You can search or just ask — e.g. "create a blog post outline"'
          }
        />
      </div>
    </div>
--- a/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatContainer/createStreamEventDispatcher.ts
+++ b/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatContainer/createStreamEventDispatcher.ts
@@ -2,7 +2,6 @@ import { toast } from "sonner";
 import type { StreamChunk } from "../../chat-types";
 import type { HandlerDependencies } from "./handlers";
 import {
  getErrorDisplayMessage,
  handleError,
  handleLoginNeeded,
  handleStreamEnd,
@@ -25,22 +24,16 @@ export function createStreamEventDispatcher(
      chunk.type === "need_login" ||
      chunk.type === "error"
    ) {
      if (!deps.hasResponseRef.current) {
        console.info("[ChatStream] First response chunk:", {
          type: chunk.type,
          sessionId: deps.sessionId,
        });
      }
      deps.hasResponseRef.current = true;
    }
    switch (chunk.type) {
      case "stream_start":
        // Store task ID for SSE reconnection
        if (chunk.taskId && deps.onActiveTaskStarted) {
          deps.onActiveTaskStarted({
            taskId: chunk.taskId,
            operationId: chunk.taskId,
            toolName: "chat",
            toolCallId: "chat_stream",
          });
        }
        break;
      case "text_chunk":
        handleTextChunk(chunk, deps);
        break;
@@ -63,7 +56,11 @@ export function createStreamEventDispatcher(
        break;
      case "stream_end":
-        // Note: "finish" type from backend gets normalized to "stream_end" by normalizeStreamChunk
+        console.info("[ChatStream] Stream ended:", {
          sessionId: deps.sessionId,
          hasResponse: deps.hasResponseRef.current,
          chunkCount: deps.streamingChunksRef.current.length,
        });
        handleStreamEnd(chunk, deps);
        break;
@@ -73,7 +70,7 @@ export function createStreamEventDispatcher(
        // Show toast at dispatcher level to avoid circular dependencies
        if (!isRegionBlocked) {
          toast.error("Chat Error", {
-            description: getErrorDisplayMessage(chunk),
+            description: chunk.message || chunk.content || "An error occurred",
          });
        }
        break;
--- a/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatContainer/handlers.ts
+++ b/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatContainer/handlers.ts
@@ -18,19 +18,11 @@ export interface HandlerDependencies {
  setStreamingChunks: Dispatch<SetStateAction<string[]>>;
  streamingChunksRef: MutableRefObject<string[]>;
  hasResponseRef: MutableRefObject<boolean>;
  textFinalizedRef: MutableRefObject<boolean>;
  streamEndedRef: MutableRefObject<boolean>;
  setMessages: Dispatch<SetStateAction<ChatMessageData[]>>;
  setIsStreamingInitiated: Dispatch<SetStateAction<boolean>>;
  setIsRegionBlockedModalOpen: Dispatch<SetStateAction<boolean>>;
  sessionId: string;
  onOperationStarted?: () => void;
  onActiveTaskStarted?: (taskInfo: {
    taskId: string;
    operationId: string;
    toolName: string;
    toolCallId: string;
  }) => void;
 }
 export function isRegionBlockedError(chunk: StreamChunk): boolean {
@@ -40,25 +32,6 @@ export function isRegionBlockedError(chunk: StreamChunk): boolean {
  return message.toLowerCase().includes("not available in your region");
 }
 export function getUserFriendlyErrorMessage(
  code: string | undefined,
 ): string | undefined {
  switch (code) {
    case "TASK_EXPIRED":
      return "This operation has expired. Please try again.";
    case "TASK_NOT_FOUND":
      return "Could not find the requested operation.";
    case "ACCESS_DENIED":
      return "You do not have access to this operation.";
    case "QUEUE_OVERFLOW":
      return "Connection was interrupted. Please refresh to continue.";
    case "MODEL_NOT_AVAILABLE_REGION":
      return "This model is not available in your region.";
    default:
      return undefined;
  }
 }
 export function handleTextChunk(chunk: StreamChunk, deps: HandlerDependencies) {
  if (!chunk.content) return;
  deps.setHasTextChunks(true);
@@ -73,15 +46,10 @@ export function handleTextEnded(
  _chunk: StreamChunk,
  deps: HandlerDependencies,
 ) {
  if (deps.textFinalizedRef.current) {
    return;
  }
  const completedText = deps.streamingChunksRef.current.join("");
  if (completedText.trim()) {
    deps.textFinalizedRef.current = true;
    deps.setMessages((prev) => {
      // Check if this exact message already exists to prevent duplicates
      const exists = prev.some(
        (msg) =>
          msg.type === "message" &&
@@ -108,14 +76,9 @@ export function handleToolCallStart(
  chunk: StreamChunk,
  deps: HandlerDependencies,
 ) {
  // Use deterministic fallback instead of Date.now() to ensure same ID on replay
  const toolId =
    chunk.tool_id ||
    `tool-${deps.sessionId}-${chunk.idx ?? "unknown"}-${chunk.tool_name || "unknown"}`;
  const toolCallMessage: Extract<ChatMessageData, { type: "tool_call" }> = {
    type: "tool_call",
-    toolId,
+    toolId: chunk.tool_id || `tool-${Date.now()}-${chunk.idx || 0}`,
    toolName: chunk.tool_name || "Executing",
    arguments: chunk.arguments || {},
    timestamp: new Date(),
@@ -148,29 +111,6 @@ export function handleToolCallStart(
  deps.setMessages(updateToolCallMessages);
 }
 const TOOL_RESPONSE_TYPES = new Set([
  "tool_response",
  "operation_started",
  "operation_pending",
  "operation_in_progress",
  "execution_started",
  "agent_carousel",
  "clarification_needed",
 ]);
 function hasResponseForTool(
  messages: ChatMessageData[],
  toolId: string,
 ): boolean {
  return messages.some((msg) => {
    if (!TOOL_RESPONSE_TYPES.has(msg.type)) return false;
    const msgToolId =
      (msg as { toolId?: string }).toolId ||
      (msg as { toolCallId?: string }).toolCallId;
    return msgToolId === toolId;
  });
 }
 export function handleToolResponse(
  chunk: StreamChunk,
  deps: HandlerDependencies,
@@ -212,49 +152,31 @@ export function handleToolResponse(
    ) {
      const inputsMessage = extractInputsNeeded(parsedResult, chunk.tool_name);
      if (inputsMessage) {
-        deps.setMessages((prev) => {
+        deps.setMessages((prev) => [...prev, inputsMessage]);
          // Check for duplicate inputs_needed message
          const exists = prev.some((msg) => msg.type === "inputs_needed");
          if (exists) return prev;
          return [...prev, inputsMessage];
        });
      }
      const credentialsMessage = extractCredentialsNeeded(
        parsedResult,
        chunk.tool_name,
      );
      if (credentialsMessage) {
-        deps.setMessages((prev) => {
+        deps.setMessages((prev) => [...prev, credentialsMessage]);
          // Check for duplicate credentials_needed message
          const exists = prev.some((msg) => msg.type === "credentials_needed");
          if (exists) return prev;
          return [...prev, credentialsMessage];
        });
      }
    }
    return;
  }
  // Trigger polling when operation_started is received
  if (responseMessage.type === "operation_started") {
    deps.onOperationStarted?.();
    const taskId = (responseMessage as { taskId?: string }).taskId;
    if (taskId && deps.onActiveTaskStarted) {
      deps.onActiveTaskStarted({
        taskId,
        operationId:
          (responseMessage as { operationId?: string }).operationId || "",
        toolName: (responseMessage as { toolName?: string }).toolName || "",
        toolCallId: (responseMessage as { toolId?: string }).toolId || "",
      });
    }
  }
  deps.setMessages((prev) => {
    const toolCallIndex = prev.findIndex(
      (msg) => msg.type === "tool_call" && msg.toolId === chunk.tool_id,
    );
-    if (hasResponseForTool(prev, chunk.tool_id!)) {
+    const hasResponse = prev.some(
-      return prev;
+      (msg) => msg.type === "tool_response" && msg.toolId === chunk.tool_id,
-    }
+    );
    if (hasResponse) return prev;
    if (toolCallIndex !== -1) {
      const newMessages = [...prev];
      newMessages.splice(toolCallIndex + 1, 0, responseMessage);
@@ -276,48 +198,28 @@ export function handleLoginNeeded(
    agentInfo: chunk.agent_info,
    timestamp: new Date(),
  };
-  deps.setMessages((prev) => {
+  deps.setMessages((prev) => [...prev, loginNeededMessage]);
    // Check for duplicate login_needed message
    const exists = prev.some((msg) => msg.type === "login_needed");
    if (exists) return prev;
    return [...prev, loginNeededMessage];
  });
 }
 export function handleStreamEnd(
  _chunk: StreamChunk,
  deps: HandlerDependencies,
 ) {
  if (deps.streamEndedRef.current) {
    return;
  }
  deps.streamEndedRef.current = true;
  const completedContent = deps.streamingChunksRef.current.join("");
  if (!completedContent.trim() && !deps.hasResponseRef.current) {
-    deps.setMessages((prev) => {
+    deps.setMessages((prev) => [
-      const exists = prev.some(
+      ...prev,
-        (msg) =>
+      {
-          msg.type === "message" &&
+        type: "message",
-          msg.role === "assistant" &&
+        role: "assistant",
-          msg.content === "No response received. Please try again.",
+        content: "No response received. Please try again.",
-      );
+        timestamp: new Date(),
-      if (exists) return prev;
+      },
-      return [
+    ]);
        ...prev,
        {
          type: "message",
          role: "assistant",
          content: "No response received. Please try again.",
          timestamp: new Date(),
        },
      ];
    });
  }
-  if (completedContent.trim() && !deps.textFinalizedRef.current) {
+  if (completedContent.trim()) {
    deps.textFinalizedRef.current = true;
    deps.setMessages((prev) => {
      // Check if this exact message already exists to prevent duplicates
      const exists = prev.some(
        (msg) =>
          msg.type === "message" &&
@@ -342,6 +244,8 @@ export function handleStreamEnd(
 }
 export function handleError(chunk: StreamChunk, deps: HandlerDependencies) {
  const errorMessage = chunk.message || chunk.content || "An error occurred";
  console.error("Stream error:", errorMessage);
  if (isRegionBlockedError(chunk)) {
    deps.setIsRegionBlockedModalOpen(true);
  }
@@ -349,14 +253,4 @@ export function handleError(chunk: StreamChunk, deps: HandlerDependencies) {
  deps.setHasTextChunks(false);
  deps.setStreamingChunks([]);
  deps.streamingChunksRef.current = [];
  deps.textFinalizedRef.current = false;
  deps.streamEndedRef.current = true;
 }
 export function getErrorDisplayMessage(chunk: StreamChunk): string {
  const friendlyMessage = getUserFriendlyErrorMessage(chunk.code);
  if (friendlyMessage) {
    return friendlyMessage;
  }
  return chunk.message || chunk.content || "An error occurred";
 }
--- a/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatContainer/helpers.ts
+++ b/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatContainer/helpers.ts
@@ -349,7 +349,6 @@ export function parseToolResponse(
        toolName: (parsedResult.tool_name as string) || toolName,
        toolId,
        operationId: (parsedResult.operation_id as string) || "",
        taskId: (parsedResult.task_id as string) || undefined, // For SSE reconnection
        message:
          (parsedResult.message as string) ||
          "Operation started. You can close this tab.",
--- a/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatContainer/useChatContainer.ts
+++ b/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatContainer/useChatContainer.ts
@@ -1,17 +1,10 @@
 import type { SessionDetailResponse } from "@/app/api/__generated__/models/sessionDetailResponse";
 import { useEffect, useMemo, useRef, useState } from "react";
 import { INITIAL_STREAM_ID } from "../../chat-constants";
 import { useChatStore } from "../../chat-store";
 import { toast } from "sonner";
 import { useChatStream } from "../../useChatStream";
 import { usePageContext } from "../../usePageContext";
 import type { ChatMessageData } from "../ChatMessage/useChatMessage";
 import {
  getToolIdFromMessage,
  hasToolId,
  isOperationMessage,
  type StreamChunk,
 } from "../../chat-types";
 import { createStreamEventDispatcher } from "./createStreamEventDispatcher";
 import {
  createUserMessage,
@@ -21,13 +14,6 @@ import {
  processInitialMessages,
 } from "./helpers";
 const TOOL_RESULT_TYPES = new Set([
  "tool_response",
  "agent_carousel",
  "execution_started",
  "clarification_needed",
 ]);
 // Helper to generate deduplication key for a message
 function getMessageKey(msg: ChatMessageData): string {
  if (msg.type === "message") {
@@ -37,18 +23,14 @@ function getMessageKey(msg: ChatMessageData): string {
    return `msg:${msg.role}:${msg.content}`;
  } else if (msg.type === "tool_call") {
    return `toolcall:${msg.toolId}`;
-  } else if (TOOL_RESULT_TYPES.has(msg.type)) {
+  } else if (msg.type === "tool_response") {
-    // Unified key for all tool result types - same toolId with different types
+    return `toolresponse:${(msg as any).toolId}`;
-    // (tool_response vs agent_carousel) should deduplicate to the same key
+  } else if (
-    const toolId = getToolIdFromMessage(msg);
+    msg.type === "operation_started" ||
-    // If no toolId, fall back to content-based key to avoid empty key collisions
+    msg.type === "operation_pending" ||
-    if (!toolId) {
+    msg.type === "operation_in_progress"
-      return `toolresult:content:${JSON.stringify(msg).slice(0, 200)}`;
+  ) {
-    }
+    return `op:${(msg as any).toolId || (msg as any).operationId || (msg as any).toolCallId || ""}:${msg.toolName}`;
    return `toolresult:${toolId}`;
  } else if (isOperationMessage(msg)) {
    const toolId = getToolIdFromMessage(msg) || "";
    return `op:${toolId}:${msg.toolName}`;
  } else {
    return `${msg.type}:${JSON.stringify(msg).slice(0, 100)}`;
  }
@@ -59,13 +41,6 @@ interface Args {
  initialMessages: SessionDetailResponse["messages"];
  initialPrompt?: string;
  onOperationStarted?: () => void;
  /** Active stream info from the server for reconnection */
  activeStream?: {
    taskId: string;
    lastMessageId: string;
    operationId: string;
    toolName: string;
  };
 }
 export function useChatContainer({
@@ -73,7 +48,6 @@ export function useChatContainer({
  initialMessages,
  initialPrompt,
  onOperationStarted,
  activeStream,
 }: Args) {
  const [messages, setMessages] = useState<ChatMessageData[]>([]);
  const [streamingChunks, setStreamingChunks] = useState<string[]>([]);
@@ -83,8 +57,6 @@ export function useChatContainer({
    useState(false);
  const hasResponseRef = useRef(false);
  const streamingChunksRef = useRef<string[]>([]);
  const textFinalizedRef = useRef(false);
  const streamEndedRef = useRef(false);
  const previousSessionIdRef = useRef<string | null>(null);
  const {
    error,
@@ -93,182 +65,44 @@ export function useChatContainer({
  } = useChatStream();
  const activeStreams = useChatStore((s) => s.activeStreams);
  const subscribeToStream = useChatStore((s) => s.subscribeToStream);
  const setActiveTask = useChatStore((s) => s.setActiveTask);
  const getActiveTask = useChatStore((s) => s.getActiveTask);
  const reconnectToTask = useChatStore((s) => s.reconnectToTask);
  const isStreaming = isStreamingInitiated || hasTextChunks;
  // Track whether we've already connected to this activeStream to avoid duplicate connections
  const connectedActiveStreamRef = useRef<string | null>(null);
  // Track if component is mounted to prevent state updates after unmount
  const isMountedRef = useRef(true);
  // Track current dispatcher to prevent multiple dispatchers from adding messages
  const currentDispatcherIdRef = useRef(0);
  // Set mounted flag - reset on every mount, cleanup on unmount
  useEffect(function trackMountedState() {
    isMountedRef.current = true;
    return function cleanup() {
      isMountedRef.current = false;
    };
  }, []);
  // Callback to store active task info for SSE reconnection
  function handleActiveTaskStarted(taskInfo: {
    taskId: string;
    operationId: string;
    toolName: string;
    toolCallId: string;
  }) {
    if (!sessionId) return;
    setActiveTask(sessionId, {
      taskId: taskInfo.taskId,
      operationId: taskInfo.operationId,
      toolName: taskInfo.toolName,
      lastMessageId: INITIAL_STREAM_ID,
    });
  }
  // Create dispatcher for stream events - stable reference for current sessionId
  // Each dispatcher gets a unique ID to prevent stale dispatchers from updating state
  function createDispatcher() {
    if (!sessionId) return () => {};
    // Increment dispatcher ID - only the most recent dispatcher should update state
    const dispatcherId = ++currentDispatcherIdRef.current;
    const baseDispatcher = createStreamEventDispatcher({
      setHasTextChunks,
      setStreamingChunks,
      streamingChunksRef,
      hasResponseRef,
      textFinalizedRef,
      streamEndedRef,
      setMessages,
      setIsRegionBlockedModalOpen,
      sessionId,
      setIsStreamingInitiated,
      onOperationStarted,
      onActiveTaskStarted: handleActiveTaskStarted,
    });
    // Wrap dispatcher to check if it's still the current one
    return function guardedDispatcher(chunk: StreamChunk) {
      // Skip if component unmounted or this is a stale dispatcher
      if (!isMountedRef.current) {
        return;
      }
      if (dispatcherId !== currentDispatcherIdRef.current) {
        return;
      }
      baseDispatcher(chunk);
    };
  }
  useEffect(
    function handleSessionChange() {
-      const isSessionChange = sessionId !== previousSessionIdRef.current;
+      if (sessionId === previousSessionIdRef.current) return;
-      // Handle session change - reset state
+      const prevSession = previousSessionIdRef.current;
-      if (isSessionChange) {
+      if (prevSession) {
-        const prevSession = previousSessionIdRef.current;
+        stopStreaming(prevSession);
        if (prevSession) {
          stopStreaming(prevSession);
        }
        previousSessionIdRef.current = sessionId;
        connectedActiveStreamRef.current = null;
        setMessages([]);
        setStreamingChunks([]);
        streamingChunksRef.current = [];
        setHasTextChunks(false);
        setIsStreamingInitiated(false);
        hasResponseRef.current = false;
        textFinalizedRef.current = false;
        streamEndedRef.current = false;
      }
      previousSessionIdRef.current = sessionId;
      setMessages([]);
      setStreamingChunks([]);
      streamingChunksRef.current = [];
      setHasTextChunks(false);
      setIsStreamingInitiated(false);
      hasResponseRef.current = false;
      if (!sessionId) return;
-      // Priority 1: Check if server told us there's an active stream (most authoritative)
+      const activeStream = activeStreams.get(sessionId);
-      if (activeStream) {
+      if (!activeStream || activeStream.status !== "streaming") return;
        const streamKey = `${sessionId}:${activeStream.taskId}`;
-        if (connectedActiveStreamRef.current === streamKey) {
+      const dispatcher = createStreamEventDispatcher({
-          return;
+        setHasTextChunks,
-        }
+        setStreamingChunks,
-
+        streamingChunksRef,
-        // Skip if there's already an active stream for this session in the store
+        hasResponseRef,
-        const existingStream = activeStreams.get(sessionId);
+        setMessages,
-        if (existingStream && existingStream.status === "streaming") {
+        setIsRegionBlockedModalOpen,
-          connectedActiveStreamRef.current = streamKey;
+        sessionId,
-          return;
+        setIsStreamingInitiated,
-        }
+        onOperationStarted,
-
+      });
        connectedActiveStreamRef.current = streamKey;
        // Clear all state before reconnection to prevent duplicates
        // Server's initialMessages is authoritative; local state will be rebuilt from SSE replay
        setMessages([]);
        setStreamingChunks([]);
        streamingChunksRef.current = [];
        setHasTextChunks(false);
        textFinalizedRef.current = false;
        streamEndedRef.current = false;
        hasResponseRef.current = false;
        setIsStreamingInitiated(true);
        setActiveTask(sessionId, {
          taskId: activeStream.taskId,
          operationId: activeStream.operationId,
          toolName: activeStream.toolName,
          lastMessageId: activeStream.lastMessageId,
        });
        reconnectToTask(
          sessionId,
          activeStream.taskId,
          activeStream.lastMessageId,
          createDispatcher(),
        );
        // Don't return cleanup here - the guarded dispatcher handles stale events
        // and the stream will complete naturally. Cleanup would prematurely stop
        // the stream when effect re-runs due to activeStreams changing.
        return;
      }
      // Only check localStorage/in-memory on session change
      if (!isSessionChange) return;
      // Priority 2: Check localStorage for active task
      const activeTask = getActiveTask(sessionId);
      if (activeTask) {
        // Clear all state before reconnection to prevent duplicates
        // Server's initialMessages is authoritative; local state will be rebuilt from SSE replay
        setMessages([]);
        setStreamingChunks([]);
        streamingChunksRef.current = [];
        setHasTextChunks(false);
        textFinalizedRef.current = false;
        streamEndedRef.current = false;
        hasResponseRef.current = false;
        setIsStreamingInitiated(true);
        reconnectToTask(
          sessionId,
          activeTask.taskId,
          activeTask.lastMessageId,
          createDispatcher(),
        );
        // Don't return cleanup here - the guarded dispatcher handles stale events
        return;
      }
      // Priority 3: Check for an in-memory active stream (same-tab scenario)
      const inMemoryStream = activeStreams.get(sessionId);
      if (!inMemoryStream || inMemoryStream.status !== "streaming") {
        return;
      }
      setIsStreamingInitiated(true);
      const skipReplay = initialMessages.length > 0;
-      return subscribeToStream(sessionId, createDispatcher(), skipReplay);
+      return subscribeToStream(sessionId, dispatcher, skipReplay);
    },
    [
      sessionId,
@@ -276,10 +110,6 @@ export function useChatContainer({
      activeStreams,
      subscribeToStream,
      onOperationStarted,
      getActiveTask,
      reconnectToTask,
      activeStream,
      setActiveTask,
    ],
  );
@@ -294,7 +124,7 @@ export function useChatContainer({
        msg.type === "agent_carousel" ||
        msg.type === "execution_started"
      ) {
-        const toolId = hasToolId(msg) ? msg.toolId : undefined;
+        const toolId = (msg as any).toolId;
        if (toolId) {
          ids.add(toolId);
        }
@@ -311,8 +141,12 @@ export function useChatContainer({
      setMessages((prev) => {
        const filtered = prev.filter((msg) => {
-          if (isOperationMessage(msg)) {
+          if (
-            const toolId = getToolIdFromMessage(msg);
+            msg.type === "operation_started" ||
            msg.type === "operation_pending" ||
            msg.type === "operation_in_progress"
          ) {
            const toolId = (msg as any).toolId || (msg as any).toolCallId;
            if (toolId && completedToolIds.has(toolId)) {
              return false; // Remove - operation completed
            }
@@ -340,8 +174,12 @@ export function useChatContainer({
    // Filter local messages: remove duplicates and completed operation messages
    const newLocalMessages = messages.filter((msg) => {
      // Remove operation messages for completed tools
-      if (isOperationMessage(msg)) {
+      if (
-        const toolId = getToolIdFromMessage(msg);
+        msg.type === "operation_started" ||
        msg.type === "operation_pending" ||
        msg.type === "operation_in_progress"
      ) {
        const toolId = (msg as any).toolId || (msg as any).toolCallId;
        if (toolId && completedToolIds.has(toolId)) {
          return false;
        }
@@ -352,70 +190,7 @@ export function useChatContainer({
    });
    // Server messages first (correct order), then new local messages
-    const combined = [...processedInitial, ...newLocalMessages];
+    return [...processedInitial, ...newLocalMessages];
    // Post-processing: Remove duplicate assistant messages that can occur during
    // race conditions (e.g., rapid screen switching during SSE reconnection).
    // Two assistant messages are considered duplicates if:
    // - They are both text messages with role "assistant"
    // - One message's content starts with the other's content (partial vs complete)
    // - Or they have very similar content (>80% overlap at the start)
    const deduplicated: ChatMessageData[] = [];
    for (let i = 0; i < combined.length; i++) {
      const current = combined[i];
      // Check if this is an assistant text message
      if (current.type !== "message" || current.role !== "assistant") {
        deduplicated.push(current);
        continue;
      }
      // Look for duplicate assistant messages in the rest of the array
      let dominated = false;
      for (let j = 0; j < combined.length; j++) {
        if (i === j) continue;
        const other = combined[j];
        if (other.type !== "message" || other.role !== "assistant") continue;
        const currentContent = current.content || "";
        const otherContent = other.content || "";
        // Skip empty messages
        if (!currentContent.trim() || !otherContent.trim()) continue;
        // Check if current is a prefix of other (current is incomplete version)
        if (
          otherContent.length > currentContent.length &&
          otherContent.startsWith(currentContent.slice(0, 100))
        ) {
          // Current is a shorter/incomplete version of other - skip it
          dominated = true;
          break;
        }
        // Check if messages are nearly identical (within a small difference)
        // This catches cases where content differs only slightly
        const minLen = Math.min(currentContent.length, otherContent.length);
        const compareLen = Math.min(minLen, 200); // Compare first 200 chars
        if (
          compareLen > 50 &&
          currentContent.slice(0, compareLen) ===
            otherContent.slice(0, compareLen)
        ) {
          // Same prefix - keep the longer one
          if (otherContent.length > currentContent.length) {
            dominated = true;
            break;
          }
        }
      }
      if (!dominated) {
        deduplicated.push(current);
      }
    }
    return deduplicated;
  }, [initialMessages, messages, completedToolIds]);
  async function sendMessage(
@@ -423,8 +198,10 @@ export function useChatContainer({
    isUserMessage: boolean = true,
    context?: { url: string; content: string },
  ) {
-    if (!sessionId) return;
+    if (!sessionId) {
-
+      console.error("[useChatContainer] Cannot send message: no session ID");
      return;
    }
    setIsRegionBlockedModalOpen(false);
    if (isUserMessage) {
      const userMessage = createUserMessage(content);
@@ -437,19 +214,31 @@ export function useChatContainer({
    setHasTextChunks(false);
    setIsStreamingInitiated(true);
    hasResponseRef.current = false;
-    textFinalizedRef.current = false;
+
-    streamEndedRef.current = false;
+    const dispatcher = createStreamEventDispatcher({
      setHasTextChunks,
      setStreamingChunks,
      streamingChunksRef,
      hasResponseRef,
      setMessages,
      setIsRegionBlockedModalOpen,
      sessionId,
      setIsStreamingInitiated,
      onOperationStarted,
    });
    try {
      await sendStreamMessage(
        sessionId,
        content,
-        createDispatcher(),
+        dispatcher,
        isUserMessage,
        context,
      );
    } catch (err) {
      console.error("[useChatContainer] Failed to send message:", err);
      setIsStreamingInitiated(false);
      if (err instanceof Error && err.name === "AbortError") return;
      const errorMessage =
--- a/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatInput/ChatInput.tsx
+++ b/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatInput/ChatInput.tsx
@@ -74,20 +74,19 @@ export function ChatInput({
            hasMultipleLines ? "rounded-xlarge" : "rounded-full",
          )}
        >
          {!value && !isRecording && (
            <div
              className="pointer-events-none absolute inset-0 top-0.5 flex items-center justify-start pl-14 text-[1rem] text-zinc-400"
              aria-hidden="true"
            >
              {isTranscribing ? "Transcribing..." : placeholder}
            </div>
          )}
          <textarea
            id={inputId}
            aria-label="Chat message input"
            value={value}
            onChange={handleChange}
            onKeyDown={handleKeyDown}
            placeholder={
              isTranscribing
                ? "Transcribing..."
                : isRecording
                  ? ""
                  : placeholder
            }
            disabled={isInputDisabled}
            rows={1}
            className={cn(
@@ -123,14 +122,13 @@ export function ChatInput({
              size="icon"
              aria-label={isRecording ? "Stop recording" : "Start recording"}
              onClick={toggleRecording}
-              disabled={disabled || isTranscribing || isStreaming}
+              disabled={disabled || isTranscribing}
              className={cn(
                isRecording
                  ? "animate-pulse border-red-500 bg-red-500 text-white hover:border-red-600 hover:bg-red-600"
                  : isTranscribing
                    ? "border-zinc-300 bg-zinc-100 text-zinc-400"
                    : "border-zinc-300 bg-white text-zinc-500 hover:border-zinc-400 hover:bg-zinc-50 hover:text-zinc-700",
                isStreaming && "opacity-40",
              )}
            >
              {isTranscribing ? (
--- a/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatInput/components/AudioWaveform.tsx
+++ b/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatInput/components/AudioWaveform.tsx
@@ -38,8 +38,8 @@ export function AudioWaveform({
    // Create audio context and analyser
    const audioContext = new AudioContext();
    const analyser = audioContext.createAnalyser();
-    analyser.fftSize = 256;
+    analyser.fftSize = 512;
-    analyser.smoothingTimeConstant = 0.3;
+    analyser.smoothingTimeConstant = 0.8;
    // Connect the stream to the analyser
    const source = audioContext.createMediaStreamSource(stream);
@@ -73,11 +73,10 @@ export function AudioWaveform({
          maxAmplitude = Math.max(maxAmplitude, amplitude);
        }
-        // Normalize amplitude (0-128 range) to 0-1
+        // Map amplitude (0-128) to bar height
-        const normalized = maxAmplitude / 128;
+        const normalized = (maxAmplitude / 128) * 255;
-        // Apply sensitivity boost (multiply by 4) and use sqrt curve to amplify quiet sounds
+        const height =
-        const boosted = Math.min(1, Math.sqrt(normalized) * 4);
+          minBarHeight + (normalized / 255) * (maxBarHeight - minBarHeight);
        const height = minBarHeight + boosted * (maxBarHeight - minBarHeight);
        newBars.push(height);
      }
--- a/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatInput/useVoiceRecording.ts
+++ b/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatInput/useVoiceRecording.ts
@@ -224,7 +224,7 @@ export function useVoiceRecording({
    [value, isTranscribing, toggleRecording, baseHandleKeyDown],
  );
-  const showMicButton = isSupported;
+  const showMicButton = isSupported && !isStreaming;
  const isInputDisabled = disabled || isStreaming || isTranscribing;
  // Cleanup on unmount
--- a/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatMessage/ChatMessage.tsx
+++ b/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatMessage/ChatMessage.tsx
@@ -346,7 +346,6 @@ export function ChatMessage({
          toolId={message.toolId}
          toolName={message.toolName}
          result={message.result}
          onSendMessage={onSendMessage}
        />
      </div>
    );
--- a/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatMessage/useChatMessage.ts
+++ b/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatMessage/useChatMessage.ts
@@ -111,7 +111,6 @@ export type ChatMessageData =
      toolName: string;
      toolId: string;
      operationId: string;
      taskId?: string; // For SSE reconnection
      message: string;
      timestamp?: string | Date;
    }
--- a/autogpt_platform/frontend/src/components/contextual/Chat/components/MarkdownContent/MarkdownContent.tsx
+++ b/autogpt_platform/frontend/src/components/contextual/Chat/components/MarkdownContent/MarkdownContent.tsx
@@ -3,7 +3,7 @@
 import { getGetWorkspaceDownloadFileByIdUrl } from "@/app/api/__generated__/endpoints/workspace/workspace";
 import { cn } from "@/lib/utils";
 import { EyeSlash } from "@phosphor-icons/react";
-import React, { useState } from "react";
+import React from "react";
 import ReactMarkdown from "react-markdown";
 import remarkGfm from "remark-gfm";
@@ -48,9 +48,7 @@ interface InputProps extends React.InputHTMLAttributes<HTMLInputElement> {
 */
 function resolveWorkspaceUrl(src: string): string {
  if (src.startsWith("workspace://")) {
-    // Strip MIME type fragment if present (e.g., workspace://abc123#video/mp4 → abc123)
+    const fileId = src.replace("workspace://", "");
    const withoutPrefix = src.replace("workspace://", "");
    const fileId = withoutPrefix.split("#")[0];
    // Use the generated API URL helper to get the correct path
    const apiPath = getGetWorkspaceDownloadFileByIdUrl(fileId);
    // Route through the Next.js proxy (same pattern as customMutator for client-side)
@@ -67,49 +65,13 @@ function isWorkspaceImage(src: string | undefined): boolean {
  return src?.includes("/workspace/files/") ?? false;
 }
 /**
 * Renders a workspace video with controls and an optional "AI cannot see" badge.
 */
 function WorkspaceVideo({
  src,
  aiCannotSee,
 }: {
  src: string;
  aiCannotSee: boolean;
 }) {
  return (
    <span className="relative my-2 inline-block">
      <video
        controls
        className="h-auto max-w-full rounded-md border border-zinc-200"
        preload="metadata"
      >
        <source src={src} />
        Your browser does not support the video tag.
      </video>
      {aiCannotSee && (
        <span
          className="absolute bottom-2 right-2 flex items-center gap-1 rounded bg-black/70 px-2 py-1 text-xs text-white"
          title="The AI cannot see this video"
        >
          <EyeSlash size={14} />
          <span>AI cannot see this video</span>
        </span>
      )}
    </span>
  );
 }
 /**
 * Custom image component that shows an indicator when the AI cannot see the image.
 * Also handles the "video:" alt-text prefix convention to render <video> elements.
 * For workspace files with unknown types, falls back to <video> if <img> fails.
 * Note: src is already transformed by urlTransform, so workspace:// is now /api/workspace/...
 */
 function MarkdownImage(props: Record<string, unknown>) {
  const src = props.src as string | undefined;
  const alt = props.alt as string | undefined;
  const [imgFailed, setImgFailed] = useState(false);
  const aiCannotSee = isWorkspaceImage(src);
@@ -122,18 +84,6 @@ function MarkdownImage(props: Record<string, unknown>) {
    );
  }
  // Detect video: prefix in alt text (set by formatOutputValue in helpers.ts)
  if (alt?.startsWith("video:")) {
    return <WorkspaceVideo src={src} aiCannotSee={aiCannotSee} />;
  }
  // If the <img> failed to load and this is a workspace file, try as video.
  // This handles generic output keys like "file_out" where the MIME type
  // isn't known from the key name alone.
  if (imgFailed && aiCannotSee) {
    return <WorkspaceVideo src={src} aiCannotSee={aiCannotSee} />;
  }
  return (
    <span className="relative my-2 inline-block">
      {/* eslint-disable-next-line @next/next/no-img-element */}
@@ -142,9 +92,6 @@ function MarkdownImage(props: Record<string, unknown>) {
        alt={alt || "Image"}
        className="h-auto max-w-full rounded-md border border-zinc-200"
        loading="lazy"
        onError={() => {
          if (aiCannotSee) setImgFailed(true);
        }}
      />
      {aiCannotSee && (
        <span
--- a/autogpt_platform/frontend/src/components/contextual/Chat/components/MessageList/MessageList.tsx
+++ b/autogpt_platform/frontend/src/components/contextual/Chat/components/MessageList/MessageList.tsx
@@ -31,6 +31,11 @@ export function MessageList({
    isStreaming,
  });
  /**
   * Keeps this for debugging purposes 💆🏽
   */
  console.log(messages);
  return (
    <div className="relative flex min-h-0 flex-1 flex-col">
      {/* Top fade shadow */}
@@ -73,7 +78,6 @@ export function MessageList({
                    key={index}
                    message={message}
                    prevMessage={messages[index - 1]}
                    onSendMessage={onSendMessage}
                  />
                );
              }
--- a/autogpt_platform/frontend/src/components/contextual/Chat/components/MessageList/components/LastToolResponse/LastToolResponse.tsx
+++ b/autogpt_platform/frontend/src/components/contextual/Chat/components/MessageList/components/LastToolResponse/LastToolResponse.tsx
@@ -5,13 +5,11 @@ import { shouldSkipAgentOutput } from "../../helpers";
 export interface LastToolResponseProps {
  message: ChatMessageData;
  prevMessage: ChatMessageData | undefined;
  onSendMessage?: (content: string) => void;
 }
 export function LastToolResponse({
  message,
  prevMessage,
  onSendMessage,
 }: LastToolResponseProps) {
  if (message.type !== "tool_response") return null;
@@ -23,7 +21,6 @@ export function LastToolResponse({
        toolId={message.toolId}
        toolName={message.toolName}
        result={message.result}
        onSendMessage={onSendMessage}
      />
    </div>
  );
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Bentlybro	ac826df61b	revert: remove screenshots from repo	2026-02-03 12:25:26 +00:00
Bentlybro	a9d12594c2	docs: add PR testing screenshots	2026-02-03 12:22:11 +00:00
Bentlybro	a967b0dc63	style: format	2026-02-03 11:57:07 +00:00
Bentlybro	e5420f8bd9	fix: add proper type guard for obj.type	2026-02-03 11:50:06 +00:00
Bentlybro	638e150a12	fix(frontend): Handle object values in FileInput component Fixes #11800 The FileInput component crashed with 'TypeError: e.startsWith is not a function' when the value was an object (from external API) instead of a string. Changes: - Updated getFileLabelFromValue() to handle object format: { name, type, size, data } - Added type guards for string vs object values - Graceful fallback for edge cases (null, undefined, empty object) Test cases verified: - Object with name: returns filename - Object with type only: extracts and formats MIME type - String data URI: parses correctly - String file path: extracts extension - Edge cases: returns 'File' fallback	2026-02-03 11:44:21 +00:00