Merge branch 'dev' into claude/fix-block-search-vector-error-QMbO4

Enhance debug logging for vector query failures to help identify which
database replicas have pgvector installed vs not:

- Add get_pod_info() to capture pod hostname, name, namespace, and IP
- Extend get_connection_debug_info() to check pgvector extension status,
  including which schema it's installed in and list all extensions
- Log connection details BEFORE every vector query (not just on failure)
  to correlate backend_pid with success/failure patterns
- Include server_addr to identify which DB replica is being hit

This helps diagnose the 30% failure rate where identical queries randomly
fail with "type vector does not exist" - likely due to some replicas
missing the pgvector extension.

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

autogpt_platform/CLAUDE.md

@@ -194,50 +194,6 @@ ex: do the inputs and outputs tie well together?
 
 If you get any pushback or hit complex block conditions check the new_blocks guide in the docs.
 
-**Handling files in blocks with `store_media_file()`:**
-
-When blocks need to work with files (images, videos, documents), use `store_media_file()` from `backend.util.file`. The `return_format` parameter determines what you get back:
-
-| Format | Use When | Returns |
-|--------|----------|---------|
-| `"for_local_processing"` | Processing with local tools (ffmpeg, MoviePy, PIL) | Local file path (e.g., `"image.png"`) |
-| `"for_external_api"` | Sending content to external APIs (Replicate, OpenAI) | Data URI (e.g., `"data:image/png;base64,..."`) |
-| `"for_block_output"` | Returning output from your block | Smart: `workspace://` in CoPilot, data URI in graphs |
-
-**Examples:**
-```python
-# INPUT: Need to process file locally with ffmpeg
-local_path = await store_media_file(
-    file=input_data.video,
-    execution_context=execution_context,
-    return_format="for_local_processing",
-)
-# local_path = "video.mp4" - use with Path/ffmpeg/etc
-
-# INPUT: Need to send to external API like Replicate
-image_b64 = await store_media_file(
-    file=input_data.image,
-    execution_context=execution_context,
-    return_format="for_external_api",
-)
-# image_b64 = "data:image/png;base64,iVBORw0..." - send to API
-
-# OUTPUT: Returning result from block
-result_url = await store_media_file(
-    file=generated_image_url,
-    execution_context=execution_context,
-    return_format="for_block_output",
-)
-yield "image_url", result_url
-# In CoPilot: result_url = "workspace://abc123"
-# In graphs:  result_url = "data:image/png;base64,..."
-```
-
-**Key points:**
-- `for_block_output` is the ONLY format that auto-adapts to execution context
-- Always use `for_block_output` for block outputs unless you have a specific reason not to
-- Never hardcode workspace checks - let `for_block_output` handle it
-
 **Modifying the API:**
 
 1. Update route in `/backend/backend/server/routers/`

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

@@ -33,15 +33,9 @@ class ChatConfig(BaseSettings):
 
     stream_timeout: int = Field(default=300, description="Stream timeout in seconds")
     max_retries: int = Field(default=3, description="Maximum number of retries")
-    max_agent_runs: int = Field(default=30, description="Maximum number of agent runs")
+    max_agent_runs: int = Field(default=3, description="Maximum number of agent runs")
     max_agent_schedules: int = Field(
-        default=30, description="Maximum number of agent schedules"
-    )
-
-    # Long-running operation configuration
-    long_running_operation_ttl: int = Field(
-        default=600,
-        description="TTL in seconds for long-running operation tracking in Redis (safety net if pod dies)",
+        default=3, description="Maximum number of agent schedules"
     )
 
     # Langfuse Prompt Management Configuration

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

@@ -247,45 +247,3 @@ async def get_chat_session_message_count(session_id: str) -> int:
     """Get the number of messages in a chat session."""
     count = await PrismaChatMessage.prisma().count(where={"sessionId": session_id})
     return count
-
-
-async def update_tool_message_content(
-    session_id: str,
-    tool_call_id: str,
-    new_content: str,
-) -> bool:
-    """Update the content of a tool message in chat history.
-
-    Used by background tasks to update pending operation messages with final results.
-
-    Args:
-        session_id: The chat session ID.
-        tool_call_id: The tool call ID to find the message.
-        new_content: The new content to set.
-
-    Returns:
-        True if a message was updated, False otherwise.
-    """
-    try:
-        result = await PrismaChatMessage.prisma().update_many(
-            where={
-                "sessionId": session_id,
-                "toolCallId": tool_call_id,
-            },
-            data={
-                "content": new_content,
-            },
-        )
-        if result == 0:
-            logger.warning(
-                f"No message found to update for session {session_id}, "
-                f"tool_call_id {tool_call_id}"
-            )
-            return False
-        return True
-    except Exception as e:
-        logger.error(
-            f"Failed to update tool message for session {session_id}, "
-            f"tool_call_id {tool_call_id}: {e}"
-        )
-        return False

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

@@ -295,21 +295,6 @@ async def cache_chat_session(session: ChatSession) -> None:
     await _cache_session(session)
 
 
-async def invalidate_session_cache(session_id: str) -> None:
-    """Invalidate a chat session from Redis cache.
-
-    Used by background tasks to ensure fresh data is loaded on next access.
-    This is best-effort - Redis failures are logged but don't fail the operation.
-    """
-    try:
-        redis_key = _get_session_cache_key(session_id)
-        async_redis = await get_redis_async()
-        await async_redis.delete(redis_key)
-    except Exception as e:
-        # Best-effort: log but don't fail - cache will expire naturally
-        logger.warning(f"Failed to invalidate session cache for {session_id}: {e}")
-
-
 async def _get_session_from_db(session_id: str) -> ChatSession | None:
     """Get a chat session from the database."""
     prisma_session = await chat_db.get_chat_session(session_id)

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

@@ -17,7 +17,6 @@ from openai import (
 )
 from openai.types.chat import ChatCompletionChunk, ChatCompletionToolParam
 
-from backend.data.redis_client import get_redis_async
 from backend.data.understanding import (
     format_understanding_for_prompt,
     get_business_understanding,
@@ -25,7 +24,6 @@ from backend.data.understanding import (
 from backend.util.exceptions import NotFoundError
 from backend.util.settings import Settings
 
-from . import db as chat_db
 from .config import ChatConfig
 from .model import (
     ChatMessage,
@@ -33,7 +31,6 @@ from .model import (
     Usage,
     cache_chat_session,
     get_chat_session,
-    invalidate_session_cache,
     update_session_title,
     upsert_chat_session,
 )
@@ -51,13 +48,8 @@ from .response_model import (
     StreamToolOutputAvailable,
     StreamUsage,
 )
-from .tools import execute_tool, get_tool, tools
-from .tools.models import (
-    ErrorResponse,
-    OperationInProgressResponse,
-    OperationPendingResponse,
-    OperationStartedResponse,
-)
+from .tools import execute_tool, tools
+from .tools.models import ErrorResponse
 from .tracking import track_user_message
 
 logger = logging.getLogger(__name__)
@@ -69,126 +61,11 @@ client = openai.AsyncOpenAI(api_key=config.api_key, base_url=config.base_url)
 
 langfuse = get_client()
 
-# Redis key prefix for tracking running long-running operations
-# Used for idempotency across Kubernetes pods - prevents duplicate executions on browser refresh
-RUNNING_OPERATION_PREFIX = "chat:running_operation:"
 
-# Default system prompt used when Langfuse is not configured
-# This is a snapshot of the "CoPilot Prompt" from Langfuse (version 11)
-DEFAULT_SYSTEM_PROMPT = """You are **Otto**, an AI Co-Pilot for AutoGPT and a Forward-Deployed Automation Engineer serving small business owners. Your mission is to help users automate business tasks with AI by delivering tangible value through working automations—not through documentation or lengthy explanations.
+class LangfuseNotConfiguredError(Exception):
+    """Raised when Langfuse is required but not configured."""
 
-Here is everything you know about the current user from previous interactions:
-
-<users_information>
-{users_information}
-</users_information>
-
-## YOUR CORE MANDATE
-
-You are action-oriented. Your success is measured by:
-- **Value Delivery**: Does the user think "wow, that was amazing" or "what was the point"?
-- **Demonstrable Proof**: Show working automations, not descriptions of what's possible
-- **Time Saved**: Focus on tangible efficiency gains
-- **Quality Output**: Deliver results that meet or exceed expectations
-
-## YOUR WORKFLOW
-
-Adapt flexibly to the conversation context. Not every interaction requires all stages:
-
-1. **Explore & Understand**: Learn about the user's business, tasks, and goals. Use `add_understanding` to capture important context that will improve future conversations.
-
-2. **Assess Automation Potential**: Help the user understand whether and how AI can automate their task.
-
-3. **Prepare for AI**: Provide brief, actionable guidance on prerequisites (data, access, etc.).
-
-4. **Discover or Create Agents**:
-   - **Always check the user's library first** with `find_library_agent` (these may be customized to their needs)
-   - Search the marketplace with `find_agent` for pre-built automations
-   - Find reusable components with `find_block`
-   - Create custom solutions with `create_agent` if nothing suitable exists
-   - Modify existing library agents with `edit_agent`
-
-5. **Execute**: Run automations immediately, schedule them, or set up webhooks using `run_agent`. Test specific components with `run_block`.
-
-6. **Show Results**: Display outputs using `agent_output`.
-
-## AVAILABLE TOOLS
-
-**Understanding & Discovery:**
-- `add_understanding`: Create a memory about the user's business or use cases for future sessions
-- `search_docs`: Search platform documentation for specific technical information
-- `get_doc_page`: Retrieve full text of a specific documentation page
-
-**Agent Discovery:**
-- `find_library_agent`: Search the user's existing agents (CHECK HERE FIRST—these may be customized)
-- `find_agent`: Search the marketplace for pre-built automations
-- `find_block`: Find pre-written code units that perform specific tasks (agents are built from blocks)
-
-**Agent Creation & Editing:**
-- `create_agent`: Create a new automation agent
-- `edit_agent`: Modify an agent in the user's library
-
-**Execution & Output:**
-- `run_agent`: Run an agent now, schedule it, or set up a webhook trigger
-- `run_block`: Test or run a specific block independently
-- `agent_output`: View results from previous agent runs
-
-## BEHAVIORAL GUIDELINES
-
-**Be Concise:**
-- Target 2-5 short lines maximum
-- Make every word count—no repetition or filler
-- Use lightweight structure for scannability (bullets, numbered lists, short prompts)
-- Avoid jargon (blocks, slugs, cron) unless the user asks
-
-**Be Proactive:**
-- Suggest next steps before being asked
-- Anticipate needs based on conversation context and user information
-- Look for opportunities to expand scope when relevant
-- Reveal capabilities through action, not explanation
-
-**Use Tools Effectively:**
-- Select the right tool for each task
-- **Always check `find_library_agent` before searching the marketplace**
-- Use `add_understanding` to capture valuable business context
-- When tool calls fail, try alternative approaches
-
-## CRITICAL REMINDER
-
-You are NOT a chatbot. You are NOT documentation. You are a partner who helps busy business owners get value quickly by showing proof through working automations. Bias toward action over explanation."""
-
-# Module-level set to hold strong references to background tasks.
-# This prevents asyncio from garbage collecting tasks before they complete.
-# Tasks are automatically removed on completion via done_callback.
-_background_tasks: set[asyncio.Task] = set()
-
-
-async def _mark_operation_started(tool_call_id: str) -> bool:
-    """Mark a long-running operation as started (Redis-based).
-
-    Returns True if successfully marked (operation was not already running),
-    False if operation was already running (lost race condition).
-    Raises exception if Redis is unavailable (fail-closed).
-    """
-    redis = await get_redis_async()
-    key = f"{RUNNING_OPERATION_PREFIX}{tool_call_id}"
-    # SETNX with TTL - atomic "set if not exists"
-    result = await redis.set(key, "1", ex=config.long_running_operation_ttl, nx=True)
-    return result is not None
-
-
-async def _mark_operation_completed(tool_call_id: str) -> None:
-    """Mark a long-running operation as completed (remove Redis key).
-
-    This is best-effort - if Redis fails, the TTL will eventually clean up.
-    """
-    try:
-        redis = await get_redis_async()
-        key = f"{RUNNING_OPERATION_PREFIX}{tool_call_id}"
-        await redis.delete(key)
-    except Exception as e:
-        # Non-critical: TTL will clean up eventually
-        logger.warning(f"Failed to delete running operation key {tool_call_id}: {e}")
+    pass
 
 
 def _is_langfuse_configured() -> bool:
@@ -198,30 +75,6 @@ def _is_langfuse_configured() -> bool:
     )
 
 
-async def _get_system_prompt_template(context: str) -> str:
-    """Get the system prompt, trying Langfuse first with fallback to default.
-
-    Args:
-        context: The user context/information to compile into the prompt.
-
-    Returns:
-        The compiled system prompt string.
-    """
-    if _is_langfuse_configured():
-        try:
-            # cache_ttl_seconds=0 disables SDK caching to always get the latest prompt
-            # Use asyncio.to_thread to avoid blocking the event loop
-            prompt = await asyncio.to_thread(
-                langfuse.get_prompt, config.langfuse_prompt_name, cache_ttl_seconds=0
-            )
-            return prompt.compile(users_information=context)
-        except Exception as e:
-            logger.warning(f"Failed to fetch prompt from Langfuse, using default: {e}")
-
-    # Fallback to default prompt
-    return DEFAULT_SYSTEM_PROMPT.format(users_information=context)
-
-
 async def _build_system_prompt(user_id: str | None) -> tuple[str, Any]:
     """Build the full system prompt including business understanding if available.
 
@@ -230,8 +83,12 @@ async def _build_system_prompt(user_id: str | None) -> tuple[str, Any]:
                      If "default" and this is the user's first session, will use "onboarding" instead.
 
     Returns:
-        Tuple of (compiled prompt string, business understanding object)
+        Tuple of (compiled prompt string, Langfuse prompt object for tracing)
     """
+
+    # cache_ttl_seconds=0 disables SDK caching to always get the latest prompt
+    prompt = langfuse.get_prompt(config.langfuse_prompt_name, cache_ttl_seconds=0)
+
     # If user is authenticated, try to fetch their business understanding
     understanding = None
     if user_id:
@@ -240,13 +97,12 @@ async def _build_system_prompt(user_id: str | None) -> tuple[str, Any]:
         except Exception as e:
             logger.warning(f"Failed to fetch business understanding: {e}")
             understanding = None
-
     if understanding:
         context = format_understanding_for_prompt(understanding)
     else:
         context = "This is the first time you are meeting the user. Greet them and introduce them to the platform"
 
-    compiled = await _get_system_prompt_template(context)
+    compiled = prompt.compile(users_information=context)
     return compiled, understanding
 
 
@@ -354,6 +210,16 @@ async def stream_chat_completion(
         f"Streaming chat completion for session {session_id} for message {message} and user id {user_id}. Message is user message: {is_user_message}"
     )
 
+    # Check if Langfuse is configured - required for chat functionality
+    if not _is_langfuse_configured():
+        logger.error("Chat request failed: Langfuse is not configured")
+        yield StreamError(
+            errorText="Chat service is not available. Langfuse must be configured "
+            "with LANGFUSE_PUBLIC_KEY and LANGFUSE_SECRET_KEY environment variables."
+        )
+        yield StreamFinish()
+        return
+
     # Only fetch from Redis if session not provided (initial call)
     if session is None:
         session = await get_chat_session(session_id, user_id)
@@ -449,7 +315,6 @@ async def stream_chat_completion(
     has_yielded_end = False
     has_yielded_error = False
     has_done_tool_call = False
-    has_long_running_tool_call = False  # Track if we had a long-running tool call
     has_received_text = False
     text_streaming_ended = False
     tool_response_messages: list[ChatMessage] = []
@@ -471,6 +336,7 @@ async def stream_chat_completion(
             system_prompt=system_prompt,
             text_block_id=text_block_id,
         ):
+
             if isinstance(chunk, StreamTextStart):
                 # Emit text-start before first text delta
                 if not has_received_text:
@@ -528,34 +394,13 @@ async def stream_chat_completion(
                     if isinstance(chunk.output, str)
                     else orjson.dumps(chunk.output).decode("utf-8")
                 )
-                # Skip saving long-running operation responses - messages already saved in _yield_tool_call
-                # Use JSON parsing instead of substring matching to avoid false positives
-                is_long_running_response = False
-                try:
-                    parsed = orjson.loads(result_content)
-                    if isinstance(parsed, dict) and parsed.get("type") in (
-                        "operation_started",
-                        "operation_in_progress",
-                    ):
-                        is_long_running_response = True
-                except (orjson.JSONDecodeError, TypeError):
-                    pass  # Not JSON or not a dict - treat as regular response
-                if is_long_running_response:
-                    # Remove from accumulated_tool_calls since assistant message was already saved
-                    accumulated_tool_calls[:] = [
-                        tc
-                        for tc in accumulated_tool_calls
-                        if tc["id"] != chunk.toolCallId
-                    ]
-                    has_long_running_tool_call = True
-                else:
-                    tool_response_messages.append(
-                        ChatMessage(
-                            role="tool",
-                            content=result_content,
-                            tool_call_id=chunk.toolCallId,
-                        )
+                tool_response_messages.append(
+                    ChatMessage(
+                        role="tool",
+                        content=result_content,
+                        tool_call_id=chunk.toolCallId,
                     )
+                )
                 has_done_tool_call = True
                 # Track if any tool execution failed
                 if not chunk.success:
@@ -731,14 +576,7 @@ async def stream_chat_completion(
             logger.info(
                 f"Extended session messages, new message_count={len(session.messages)}"
             )
-        # Save if there are regular (non-long-running) tool responses or streaming message.
-        # Long-running tools save their own state, but we still need to save regular tools
-        # that may be in the same response.
-        has_regular_tool_responses = len(tool_response_messages) > 0
-        if has_regular_tool_responses or (
-            not has_long_running_tool_call
-            and (messages_to_save or has_appended_streaming_message)
-        ):
+        if messages_to_save or has_appended_streaming_message:
             await upsert_chat_session(session)
     else:
         logger.info(
@@ -747,9 +585,7 @@ async def stream_chat_completion(
         )
 
     # If we did a tool call, stream the chat completion again to get the next response
-    # Skip only if ALL tools were long-running (they handle their own completion)
-    has_regular_tools = len(tool_response_messages) > 0
-    if has_done_tool_call and (has_regular_tools or not has_long_running_tool_call):
+    if has_done_tool_call:
         logger.info(
             "Tool call executed, streaming chat completion again to get assistant response"
         )
@@ -889,114 +725,6 @@ async def _summarize_messages(
     return summary or "No summary available."
 
 
-def _ensure_tool_pairs_intact(
-    recent_messages: list[dict],
-    all_messages: list[dict],
-    start_index: int,
-) -> list[dict]:
-    """
-    Ensure tool_call/tool_response pairs stay together after slicing.
-
-    When slicing messages for context compaction, a naive slice can separate
-    an assistant message containing tool_calls from its corresponding tool
-    response messages. This causes API validation errors (e.g., Anthropic's
-    "unexpected tool_use_id found in tool_result blocks").
-
-    This function checks for orphan tool responses in the slice and extends
-    backwards to include their corresponding assistant messages.
-
-    Args:
-        recent_messages: The sliced messages to validate
-        all_messages: The complete message list (for looking up missing assistants)
-        start_index: The index in all_messages where recent_messages begins
-
-    Returns:
-        A potentially extended list of messages with tool pairs intact
-    """
-    if not recent_messages:
-        return recent_messages
-
-    # Collect all tool_call_ids from assistant messages in the slice
-    available_tool_call_ids: set[str] = set()
-    for msg in recent_messages:
-        if msg.get("role") == "assistant" and msg.get("tool_calls"):
-            for tc in msg["tool_calls"]:
-                tc_id = tc.get("id")
-                if tc_id:
-                    available_tool_call_ids.add(tc_id)
-
-    # Find orphan tool responses (tool messages whose tool_call_id is missing)
-    orphan_tool_call_ids: set[str] = set()
-    for msg in recent_messages:
-        if msg.get("role") == "tool":
-            tc_id = msg.get("tool_call_id")
-            if tc_id and tc_id not in available_tool_call_ids:
-                orphan_tool_call_ids.add(tc_id)
-
-    if not orphan_tool_call_ids:
-        # No orphans, slice is valid
-        return recent_messages
-
-    # Find the assistant messages that contain the orphan tool_call_ids
-    # Search backwards from start_index in all_messages
-    messages_to_prepend: list[dict] = []
-    for i in range(start_index - 1, -1, -1):
-        msg = all_messages[i]
-        if msg.get("role") == "assistant" and msg.get("tool_calls"):
-            msg_tool_ids = {tc.get("id") for tc in msg["tool_calls"] if tc.get("id")}
-            if msg_tool_ids & orphan_tool_call_ids:
-                # This assistant message has tool_calls we need
-                # Also collect its contiguous tool responses that follow it
-                assistant_and_responses: list[dict] = [msg]
-
-                # Scan forward from this assistant to collect tool responses
-                for j in range(i + 1, start_index):
-                    following_msg = all_messages[j]
-                    if following_msg.get("role") == "tool":
-                        tool_id = following_msg.get("tool_call_id")
-                        if tool_id and tool_id in msg_tool_ids:
-                            assistant_and_responses.append(following_msg)
-                    else:
-                        # Stop at first non-tool message
-                        break
-
-                # Prepend the assistant and its tool responses (maintain order)
-                messages_to_prepend = assistant_and_responses + messages_to_prepend
-                # Mark these as found
-                orphan_tool_call_ids -= msg_tool_ids
-                # Also add this assistant's tool_call_ids to available set
-                available_tool_call_ids |= msg_tool_ids
-
-        if not orphan_tool_call_ids:
-            # Found all missing assistants
-            break
-
-    if orphan_tool_call_ids:
-        # Some tool_call_ids couldn't be resolved - remove those tool responses
-        # This shouldn't happen in normal operation but handles edge cases
-        logger.warning(
-            f"Could not find assistant messages for tool_call_ids: {orphan_tool_call_ids}. "
-            "Removing orphan tool responses."
-        )
-        recent_messages = [
-            msg
-            for msg in recent_messages
-            if not (
-                msg.get("role") == "tool"
-                and msg.get("tool_call_id") in orphan_tool_call_ids
-            )
-        ]
-
-    if messages_to_prepend:
-        logger.info(
-            f"Extended recent messages by {len(messages_to_prepend)} to preserve "
-            f"tool_call/tool_response pairs"
-        )
-        return messages_to_prepend + recent_messages
-
-    return recent_messages
-
-
 async def _stream_chat_chunks(
     session: ChatSession,
     tools: list[ChatCompletionToolParam],
@@ -1088,15 +816,7 @@ async def _stream_chat_chunks(
             # Always attempt mitigation when over limit, even with few messages
             if messages:
                 # Split messages based on whether system prompt exists
-                # Calculate start index for the slice
-                slice_start = max(0, len(messages_dict) - KEEP_RECENT)
-                recent_messages = messages_dict[-KEEP_RECENT:]
-
-                # Ensure tool_call/tool_response pairs stay together
-                # This prevents API errors from orphan tool responses
-                recent_messages = _ensure_tool_pairs_intact(
-                    recent_messages, messages_dict, slice_start
-                )
+                recent_messages = messages[-KEEP_RECENT:]
 
                 if has_system_prompt:
                     # Keep system prompt separate, summarize everything between system and recent
@@ -1183,13 +903,6 @@ async def _stream_chat_chunks(
                                     if len(recent_messages) >= keep_count
                                     else recent_messages
                                 )
-                                # Ensure tool pairs stay intact in the reduced slice
-                                reduced_slice_start = max(
-                                    0, len(recent_messages) - keep_count
-                                )
-                                reduced_recent = _ensure_tool_pairs_intact(
-                                    reduced_recent, recent_messages, reduced_slice_start
-                                )
                                 if has_system_prompt:
                                     messages = [
                                         system_msg,
@@ -1248,10 +961,7 @@ async def _stream_chat_chunks(
 
                     # Create a base list excluding system prompt to avoid duplication
                     # This is the pool of messages we'll slice from in the loop
-                    # Use messages_dict for type consistency with _ensure_tool_pairs_intact
-                    base_msgs = (
-                        messages_dict[1:] if has_system_prompt else messages_dict
-                    )
+                    base_msgs = messages[1:] if has_system_prompt else messages
 
                     # Try progressively smaller keep counts
                     new_token_count = token_count  # Initialize with current count
@@ -1274,12 +984,6 @@ async def _stream_chat_chunks(
                             # Slice from base_msgs to get recent messages (without system prompt)
                             recent_messages = base_msgs[-keep_count:]
 
-                            # Ensure tool pairs stay intact in the reduced slice
-                            reduced_slice_start = max(0, len(base_msgs) - keep_count)
-                            recent_messages = _ensure_tool_pairs_intact(
-                                recent_messages, base_msgs, reduced_slice_start
-                            )
-
                             if has_system_prompt:
                                 messages = [system_msg] + recent_messages
                             else:
@@ -1556,19 +1260,17 @@ async def _yield_tool_call(
     """
     Yield a tool call and its execution result.
 
-    For tools marked with `is_long_running=True` (like agent generation), spawns a
-    background task so the operation survives SSE disconnections. For other tools,
-    yields heartbeat events every 15 seconds to keep the SSE connection alive.
+    For long-running tools, yields heartbeat events every 15 seconds to keep
+    the SSE connection alive through proxies and load balancers.
 
     Raises:
         orjson.JSONDecodeError: If tool call arguments cannot be parsed as JSON
         KeyError: If expected tool call fields are missing
         TypeError: If tool call structure is invalid
     """
-    import uuid as uuid_module
-
     tool_name = tool_calls[yield_idx]["function"]["name"]
     tool_call_id = tool_calls[yield_idx]["id"]
+    logger.info(f"Yielding tool call: {tool_calls[yield_idx]}")
 
     # Parse tool call arguments - handle empty arguments gracefully
     raw_arguments = tool_calls[yield_idx]["function"]["arguments"]
@@ -1583,151 +1285,7 @@ async def _yield_tool_call(
         input=arguments,
     )
 
-    # Check if this tool is long-running (survives SSE disconnection)
-    tool = get_tool(tool_name)
-    if tool and tool.is_long_running:
-        # Atomic check-and-set: returns False if operation already running (lost race)
-        if not await _mark_operation_started(tool_call_id):
-            logger.info(
-                f"Tool call {tool_call_id} already in progress, returning status"
-            )
-            # Build dynamic message based on tool name
-            if tool_name == "create_agent":
-                in_progress_msg = "Agent creation already in progress. Please wait..."
-            elif tool_name == "edit_agent":
-                in_progress_msg = "Agent edit already in progress. Please wait..."
-            else:
-                in_progress_msg = f"{tool_name} already in progress. Please wait..."
-
-            yield StreamToolOutputAvailable(
-                toolCallId=tool_call_id,
-                toolName=tool_name,
-                output=OperationInProgressResponse(
-                    message=in_progress_msg,
-                    tool_call_id=tool_call_id,
-                ).model_dump_json(),
-                success=True,
-            )
-            return
-
-        # Generate operation ID
-        operation_id = str(uuid_module.uuid4())
-
-        # Build a user-friendly message based on tool and arguments
-        if tool_name == "create_agent":
-            agent_desc = arguments.get("description", "")
-            # Truncate long descriptions for the message
-            desc_preview = (
-                (agent_desc[:100] + "...") if len(agent_desc) > 100 else agent_desc
-            )
-            pending_msg = (
-                f"Creating your agent: {desc_preview}"
-                if desc_preview
-                else "Creating agent... This may take a few minutes."
-            )
-            started_msg = (
-                "Agent creation started. You can close this tab - "
-                "check your library in a few minutes."
-            )
-        elif tool_name == "edit_agent":
-            changes = arguments.get("changes", "")
-            changes_preview = (changes[:100] + "...") if len(changes) > 100 else changes
-            pending_msg = (
-                f"Editing agent: {changes_preview}"
-                if changes_preview
-                else "Editing agent... This may take a few minutes."
-            )
-            started_msg = (
-                "Agent edit started. You can close this tab - "
-                "check your library in a few minutes."
-            )
-        else:
-            pending_msg = f"Running {tool_name}... This may take a few minutes."
-            started_msg = (
-                f"{tool_name} started. You can close this tab - "
-                "check back in a few minutes."
-            )
-
-        # Track appended messages for rollback on failure
-        assistant_message: ChatMessage | None = None
-        pending_message: ChatMessage | None = None
-
-        # Wrap session save and task creation in try-except to release lock on failure
-        try:
-            # Save assistant message with tool_call FIRST (required by LLM)
-            assistant_message = ChatMessage(
-                role="assistant",
-                content="",
-                tool_calls=[tool_calls[yield_idx]],
-            )
-            session.messages.append(assistant_message)
-
-            # Then save pending tool result
-            pending_message = ChatMessage(
-                role="tool",
-                content=OperationPendingResponse(
-                    message=pending_msg,
-                    operation_id=operation_id,
-                    tool_name=tool_name,
-                ).model_dump_json(),
-                tool_call_id=tool_call_id,
-            )
-            session.messages.append(pending_message)
-            await upsert_chat_session(session)
-            logger.info(
-                f"Saved pending operation {operation_id} for tool {tool_name} "
-                f"in session {session.session_id}"
-            )
-
-            # Store task reference in module-level set to prevent GC before completion
-            task = asyncio.create_task(
-                _execute_long_running_tool(
-                    tool_name=tool_name,
-                    parameters=arguments,
-                    tool_call_id=tool_call_id,
-                    operation_id=operation_id,
-                    session_id=session.session_id,
-                    user_id=session.user_id,
-                )
-            )
-            _background_tasks.add(task)
-            task.add_done_callback(_background_tasks.discard)
-        except Exception as e:
-            # Roll back appended messages to prevent data corruption on subsequent saves
-            if (
-                pending_message
-                and session.messages
-                and session.messages[-1] == pending_message
-            ):
-                session.messages.pop()
-            if (
-                assistant_message
-                and session.messages
-                and session.messages[-1] == assistant_message
-            ):
-                session.messages.pop()
-
-            # Release the Redis lock since the background task won't be spawned
-            await _mark_operation_completed(tool_call_id)
-            logger.error(
-                f"Failed to setup long-running tool {tool_name}: {e}", exc_info=True
-            )
-            raise
-
-        # Return immediately - don't wait for completion
-        yield StreamToolOutputAvailable(
-            toolCallId=tool_call_id,
-            toolName=tool_name,
-            output=OperationStartedResponse(
-                message=started_msg,
-                operation_id=operation_id,
-                tool_name=tool_name,
-            ).model_dump_json(),
-            success=True,
-        )
-        return
-
-    # Normal flow: Run tool execution in background task with heartbeats
+    # Run tool execution in background task with heartbeats to keep connection alive
     tool_task = asyncio.create_task(
         execute_tool(
             tool_name=tool_name,
@@ -1777,190 +1335,3 @@ async def _yield_tool_call(
         )
 
     yield tool_execution_response
-
-
-async def _execute_long_running_tool(
-    tool_name: str,
-    parameters: dict[str, Any],
-    tool_call_id: str,
-    operation_id: str,
-    session_id: str,
-    user_id: str | None,
-) -> None:
-    """Execute a long-running tool in background and update chat history with result.
-
-    This function runs independently of the SSE connection, so the operation
-    survives if the user closes their browser tab.
-    """
-    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")
-            return
-
-        # Execute the actual tool
-        result = await execute_tool(
-            tool_name=tool_name,
-            parameters=parameters,
-            tool_call_id=tool_call_id,
-            user_id=user_id,
-            session=session,
-        )
-
-        # Update the pending message with result
-        await _update_pending_operation(
-            session_id=session_id,
-            tool_call_id=tool_call_id,
-            result=(
-                result.output
-                if isinstance(result.output, str)
-                else orjson.dumps(result.output).decode("utf-8")
-            ),
-        )
-
-        logger.info(f"Background tool {tool_name} completed for session {session_id}")
-
-        # Generate LLM continuation so user sees response when they poll/refresh
-        await _generate_llm_continuation(session_id=session_id, user_id=user_id)
-
-    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)}",
-        )
-        await _update_pending_operation(
-            session_id=session_id,
-            tool_call_id=tool_call_id,
-            result=error_response.model_dump_json(),
-        )
-    finally:
-        await _mark_operation_completed(tool_call_id)
-
-
-async def _update_pending_operation(
-    session_id: str,
-    tool_call_id: str,
-    result: str,
-) -> None:
-    """Update the pending tool message with final result.
-
-    This is called by background tasks when long-running operations complete.
-    """
-    # Update the message in database
-    updated = await chat_db.update_tool_message_content(
-        session_id=session_id,
-        tool_call_id=tool_call_id,
-        new_content=result,
-    )
-
-    if updated:
-        # Invalidate Redis cache so next load gets fresh data
-        # Wrap in try/except to prevent cache failures from triggering error handling
-        # that would overwrite our successful DB update
-        try:
-            await invalidate_session_cache(session_id)
-        except Exception as e:
-            # Non-critical: cache will eventually be refreshed on next load
-            logger.warning(f"Failed to invalidate cache for session {session_id}: {e}")
-        logger.info(
-            f"Updated pending operation for tool_call_id {tool_call_id} "
-            f"in session {session_id}"
-        )
-    else:
-        logger.warning(
-            f"Failed to update pending operation for tool_call_id {tool_call_id} "
-            f"in session {session_id}"
-        )
-
-
-async def _generate_llm_continuation(
-    session_id: str,
-    user_id: str | None,
-) -> None:
-    """Generate an LLM response after a long-running tool completes.
-
-    This is called by background tasks to continue the conversation
-    after a tool result is saved. The response is saved to the database
-    so users see it when they refresh or poll.
-    """
-    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 non-streaming LLM call (no tools - just text response)
-        from typing import cast
-
-        from openai.types.chat import ChatCompletionMessageParam
-
-        # No tools parameter = text-only response (no tool calls)
-        response = await client.chat.completions.create(
-            model=config.model,
-            messages=cast(list[ChatCompletionMessageParam], messages),
-            extra_body=extra_body,
-        )
-
-        if response.choices and response.choices[0].message.content:
-            assistant_content = response.choices[0].message.content
-
-            # Reload session from DB to avoid race condition with user messages
-            # that may have been sent while we were generating the LLM response
-            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 LLM continuation for session {session_id}, "
-                f"response length: {len(assistant_content)}"
-            )
-        else:
-            logger.warning(f"LLM continuation returned empty response for {session_id}")
-
-    except Exception as e:
-        logger.error(f"Failed to generate LLM continuation: {e}", exc_info=True)

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

@@ -18,12 +18,6 @@ from .get_doc_page import GetDocPageTool
 from .run_agent import RunAgentTool
 from .run_block import RunBlockTool
 from .search_docs import SearchDocsTool
-from .workspace_tools import (
-    DeleteWorkspaceFileTool,
-    ListWorkspaceFilesTool,
-    ReadWorkspaceFileTool,
-    WriteWorkspaceFileTool,
-)
 
 if TYPE_CHECKING:
     from backend.api.features.chat.response_model import StreamToolOutputAvailable
@@ -43,11 +37,6 @@ TOOL_REGISTRY: dict[str, BaseTool] = {
     "view_agent_output": AgentOutputTool(),
     "search_docs": SearchDocsTool(),
     "get_doc_page": GetDocPageTool(),
-    # Workspace tools for CoPilot file operations
-    "list_workspace_files": ListWorkspaceFilesTool(),
-    "read_workspace_file": ReadWorkspaceFileTool(),
-    "write_workspace_file": WriteWorkspaceFileTool(),
-    "delete_workspace_file": DeleteWorkspaceFileTool(),
 }
 
 # Export individual tool instances for backwards compatibility
@@ -60,11 +49,6 @@ tools: list[ChatCompletionToolParam] = [
 ]
 
 
-def get_tool(tool_name: str) -> BaseTool | None:
-    """Get a tool instance by name."""
-    return TOOL_REGISTRY.get(tool_name)
-
-
 async def execute_tool(
     tool_name: str,
     parameters: dict[str, Any],
@@ -73,7 +57,7 @@ async def execute_tool(
     tool_call_id: str,
 ) -> "StreamToolOutputAvailable":
     """Execute a tool by name."""
-    tool = get_tool(tool_name)
+    tool = TOOL_REGISTRY.get(tool_name)
     if not tool:
         raise ValueError(f"Tool {tool_name} not found")
 

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

@@ -36,16 +36,6 @@ class BaseTool:
         """Whether this tool requires authentication."""
         return False
 
-    @property
-    def is_long_running(self) -> bool:
-        """Whether this tool is long-running and should execute in background.
-
-        Long-running tools (like agent generation) are executed via background
-        tasks to survive SSE disconnections. The result is persisted to chat
-        history and visible when the user refreshes.
-        """
-        return False
-
     def as_openai_tool(self) -> ChatCompletionToolParam:
         """Convert to OpenAI tool format."""
         return ChatCompletionToolParam(

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

@@ -42,10 +42,6 @@ class CreateAgentTool(BaseTool):
     def requires_auth(self) -> bool:
         return True
 
-    @property
-    def is_long_running(self) -> bool:
-        return True
-
     @property
     def parameters(self) -> dict[str, Any]:
         return {

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

@@ -42,10 +42,6 @@ class EditAgentTool(BaseTool):
     def requires_auth(self) -> bool:
         return True
 
-    @property
-    def is_long_running(self) -> bool:
-        return True
-
     @property
     def parameters(self) -> dict[str, Any]:
         return {

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

@@ -28,16 +28,6 @@ class ResponseType(str, Enum):
     BLOCK_OUTPUT = "block_output"
     DOC_SEARCH_RESULTS = "doc_search_results"
     DOC_PAGE = "doc_page"
-    # Workspace response types
-    WORKSPACE_FILE_LIST = "workspace_file_list"
-    WORKSPACE_FILE_CONTENT = "workspace_file_content"
-    WORKSPACE_FILE_METADATA = "workspace_file_metadata"
-    WORKSPACE_FILE_WRITTEN = "workspace_file_written"
-    WORKSPACE_FILE_DELETED = "workspace_file_deleted"
-    # Long-running operation types
-    OPERATION_STARTED = "operation_started"
-    OPERATION_PENDING = "operation_pending"
-    OPERATION_IN_PROGRESS = "operation_in_progress"
 
 
 # Base response model
@@ -344,39 +334,3 @@ class BlockOutputResponse(ToolResponseBase):
     block_name: str
     outputs: dict[str, list[Any]]
     success: bool = True
-
-
-# Long-running operation models
-class OperationStartedResponse(ToolResponseBase):
-    """Response when a long-running operation has been started in the background.
-
-    This is returned immediately to the client while the operation continues
-    to execute. The user can close the tab and check back later.
-    """
-
-    type: ResponseType = ResponseType.OPERATION_STARTED
-    operation_id: str
-    tool_name: str
-
-
-class OperationPendingResponse(ToolResponseBase):
-    """Response stored in chat history while a long-running operation is executing.
-
-    This is persisted to the database so users see a pending state when they
-    refresh before the operation completes.
-    """
-
-    type: ResponseType = ResponseType.OPERATION_PENDING
-    operation_id: str
-    tool_name: str
-
-
-class OperationInProgressResponse(ToolResponseBase):
-    """Response when an operation is already in progress.
-
-    Returned for idempotency when the same tool_call_id is requested again
-    while the background task is still running.
-    """
-
-    type: ResponseType = ResponseType.OPERATION_IN_PROGRESS
-    tool_call_id: str

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

@@ -1,7 +1,6 @@
 """Tool for executing blocks directly."""
 
 import logging
-import uuid
 from collections import defaultdict
 from typing import Any
 
@@ -9,7 +8,6 @@ from backend.api.features.chat.model import ChatSession
 from backend.data.block import get_block
 from backend.data.execution import ExecutionContext
 from backend.data.model import CredentialsMetaInput
-from backend.data.workspace import get_or_create_workspace
 from backend.integrations.creds_manager import IntegrationCredentialsManager
 from backend.util.exceptions import BlockError
 
@@ -225,48 +223,11 @@ class RunBlockTool(BaseTool):
             )
 
         try:
-            # Get or create user's workspace for CoPilot file operations
-            workspace = await get_or_create_workspace(user_id)
-
-            # Generate synthetic IDs for CoPilot context
-            # Each chat session is treated as its own agent with one continuous run
-            # This means:
-            # - graph_id (agent) = session (memories scoped to session when limit_to_agent=True)
-            # - graph_exec_id (run) = session (memories scoped to session when limit_to_run=True)
-            # - node_exec_id = unique per block execution
-            synthetic_graph_id = f"copilot-session-{session.session_id}"
-            synthetic_graph_exec_id = f"copilot-session-{session.session_id}"
-            synthetic_node_id = f"copilot-node-{block_id}"
-            synthetic_node_exec_id = (
-                f"copilot-{session.session_id}-{uuid.uuid4().hex[:8]}"
-            )
-
-            # Create unified execution context with all required fields
-            execution_context = ExecutionContext(
-                # Execution identity
-                user_id=user_id,
-                graph_id=synthetic_graph_id,
-                graph_exec_id=synthetic_graph_exec_id,
-                graph_version=1,  # Versions are 1-indexed
-                node_id=synthetic_node_id,
-                node_exec_id=synthetic_node_exec_id,
-                # Workspace with session scoping
-                workspace_id=workspace.id,
-                session_id=session.session_id,
-            )
-
-            # Prepare kwargs for block execution
-            # Keep individual kwargs for backwards compatibility with existing blocks
+            # Fetch actual credentials and prepare kwargs for block execution
+            # Create execution context with defaults (blocks may require it)
             exec_kwargs: dict[str, Any] = {
                 "user_id": user_id,
-                "execution_context": execution_context,
-                # Legacy: individual kwargs for blocks not yet using execution_context
-                "workspace_id": workspace.id,
-                "graph_exec_id": synthetic_graph_exec_id,
-                "node_exec_id": synthetic_node_exec_id,
-                "node_id": synthetic_node_id,
-                "graph_version": 1,  # Versions are 1-indexed
-                "graph_id": synthetic_graph_id,
+                "execution_context": ExecutionContext(),
             }
 
             for field_name, cred_meta in matched_credentials.items():

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

@@ -1,625 +0,0 @@
-"""CoPilot tools for workspace file operations."""
-
-import base64
-import logging
-from typing import Any, Optional
-
-from prisma.enums import WorkspaceFileSource
-from pydantic import BaseModel
-
-from backend.api.features.chat.model import ChatSession
-from backend.data.workspace import get_or_create_workspace
-from backend.util.settings import Config
-from backend.util.virus_scanner import scan_content_safe
-from backend.util.workspace import WorkspaceManager
-
-from .base import BaseTool
-from .models import ErrorResponse, ResponseType, ToolResponseBase
-
-logger = logging.getLogger(__name__)
-
-
-class WorkspaceFileInfoData(BaseModel):
-    """Data model for workspace file information (not a response itself)."""
-
-    file_id: str
-    name: str
-    path: str
-    mime_type: str
-    size_bytes: int
-    source: str
-
-
-class WorkspaceFileListResponse(ToolResponseBase):
-    """Response containing list of workspace files."""
-
-    type: ResponseType = ResponseType.WORKSPACE_FILE_LIST
-    files: list[WorkspaceFileInfoData]
-    total_count: int
-
-
-class WorkspaceFileContentResponse(ToolResponseBase):
-    """Response containing workspace file content (legacy, for small text files)."""
-
-    type: ResponseType = ResponseType.WORKSPACE_FILE_CONTENT
-    file_id: str
-    name: str
-    path: str
-    mime_type: str
-    content_base64: str
-
-
-class WorkspaceFileMetadataResponse(ToolResponseBase):
-    """Response containing workspace file metadata and download URL (prevents context bloat)."""
-
-    type: ResponseType = ResponseType.WORKSPACE_FILE_METADATA
-    file_id: str
-    name: str
-    path: str
-    mime_type: str
-    size_bytes: int
-    download_url: str
-    preview: str | None = None  # First 500 chars for text files
-
-
-class WorkspaceWriteResponse(ToolResponseBase):
-    """Response after writing a file to workspace."""
-
-    type: ResponseType = ResponseType.WORKSPACE_FILE_WRITTEN
-    file_id: str
-    name: str
-    path: str
-    size_bytes: int
-
-
-class WorkspaceDeleteResponse(ToolResponseBase):
-    """Response after deleting a file from workspace."""
-
-    type: ResponseType = ResponseType.WORKSPACE_FILE_DELETED
-    file_id: str
-    success: bool
-
-
-class ListWorkspaceFilesTool(BaseTool):
-    """Tool for listing files in user's workspace."""
-
-    @property
-    def name(self) -> str:
-        return "list_workspace_files"
-
-    @property
-    def description(self) -> str:
-        return (
-            "List files in the user's workspace. "
-            "Returns file names, paths, sizes, and metadata. "
-            "Optionally filter by path prefix."
-        )
-
-    @property
-    def parameters(self) -> dict[str, Any]:
-        return {
-            "type": "object",
-            "properties": {
-                "path_prefix": {
-                    "type": "string",
-                    "description": (
-                        "Optional path prefix to filter files "
-                        "(e.g., '/documents/' to list only files in documents folder). "
-                        "By default, only files from the current session are listed."
-                    ),
-                },
-                "limit": {
-                    "type": "integer",
-                    "description": "Maximum number of files to return (default 50, max 100)",
-                    "minimum": 1,
-                    "maximum": 100,
-                },
-                "include_all_sessions": {
-                    "type": "boolean",
-                    "description": (
-                        "If true, list files from all sessions. "
-                        "Default is false (only current session's files)."
-                    ),
-                },
-            },
-            "required": [],
-        }
-
-    @property
-    def requires_auth(self) -> bool:
-        return True
-
-    async def _execute(
-        self,
-        user_id: str | None,
-        session: ChatSession,
-        **kwargs,
-    ) -> ToolResponseBase:
-        session_id = session.session_id
-
-        if not user_id:
-            return ErrorResponse(
-                message="Authentication required",
-                session_id=session_id,
-            )
-
-        path_prefix: Optional[str] = kwargs.get("path_prefix")
-        limit = min(kwargs.get("limit", 50), 100)
-        include_all_sessions: bool = kwargs.get("include_all_sessions", False)
-
-        try:
-            workspace = await get_or_create_workspace(user_id)
-            # Pass session_id for session-scoped file access
-            manager = WorkspaceManager(user_id, workspace.id, session_id)
-
-            files = await manager.list_files(
-                path=path_prefix,
-                limit=limit,
-                include_all_sessions=include_all_sessions,
-            )
-            total = await manager.get_file_count(
-                path=path_prefix,
-                include_all_sessions=include_all_sessions,
-            )
-
-            file_infos = [
-                WorkspaceFileInfoData(
-                    file_id=f.id,
-                    name=f.name,
-                    path=f.path,
-                    mime_type=f.mimeType,
-                    size_bytes=f.sizeBytes,
-                    source=f.source,
-                )
-                for f in files
-            ]
-
-            scope_msg = "all sessions" if include_all_sessions else "current session"
-            return WorkspaceFileListResponse(
-                files=file_infos,
-                total_count=total,
-                message=f"Found {len(files)} files in workspace ({scope_msg})",
-                session_id=session_id,
-            )
-
-        except Exception as e:
-            logger.error(f"Error listing workspace files: {e}", exc_info=True)
-            return ErrorResponse(
-                message=f"Failed to list workspace files: {str(e)}",
-                error=str(e),
-                session_id=session_id,
-            )
-
-
-class ReadWorkspaceFileTool(BaseTool):
-    """Tool for reading file content from workspace."""
-
-    # Size threshold for returning full content vs metadata+URL
-    # Files larger than this return metadata with download URL to prevent context bloat
-    MAX_INLINE_SIZE_BYTES = 32 * 1024  # 32KB
-    # Preview size for text files
-    PREVIEW_SIZE = 500
-
-    @property
-    def name(self) -> str:
-        return "read_workspace_file"
-
-    @property
-    def description(self) -> str:
-        return (
-            "Read a file from the user's workspace. "
-            "Specify either file_id or path to identify the file. "
-            "For small text files, returns content directly. "
-            "For large or binary files, returns metadata and a download URL. "
-            "Paths are scoped to the current session by default. "
-            "Use /sessions/<session_id>/... for cross-session access."
-        )
-
-    @property
-    def parameters(self) -> dict[str, Any]:
-        return {
-            "type": "object",
-            "properties": {
-                "file_id": {
-                    "type": "string",
-                    "description": "The file's unique ID (from list_workspace_files)",
-                },
-                "path": {
-                    "type": "string",
-                    "description": (
-                        "The virtual file path (e.g., '/documents/report.pdf'). "
-                        "Scoped to current session by default."
-                    ),
-                },
-                "force_download_url": {
-                    "type": "boolean",
-                    "description": (
-                        "If true, always return metadata+URL instead of inline content. "
-                        "Default is false (auto-selects based on file size/type)."
-                    ),
-                },
-            },
-            "required": [],  # At least one must be provided
-        }
-
-    @property
-    def requires_auth(self) -> bool:
-        return True
-
-    def _is_text_mime_type(self, mime_type: str) -> bool:
-        """Check if the MIME type is a text-based type."""
-        text_types = [
-            "text/",
-            "application/json",
-            "application/xml",
-            "application/javascript",
-            "application/x-python",
-            "application/x-sh",
-        ]
-        return any(mime_type.startswith(t) for t in text_types)
-
-    async def _execute(
-        self,
-        user_id: str | None,
-        session: ChatSession,
-        **kwargs,
-    ) -> ToolResponseBase:
-        session_id = session.session_id
-
-        if not user_id:
-            return ErrorResponse(
-                message="Authentication required",
-                session_id=session_id,
-            )
-
-        file_id: Optional[str] = kwargs.get("file_id")
-        path: Optional[str] = kwargs.get("path")
-        force_download_url: bool = kwargs.get("force_download_url", False)
-
-        if not file_id and not path:
-            return ErrorResponse(
-                message="Please provide either file_id or path",
-                session_id=session_id,
-            )
-
-        try:
-            workspace = await get_or_create_workspace(user_id)
-            # Pass session_id for session-scoped file access
-            manager = WorkspaceManager(user_id, workspace.id, session_id)
-
-            # Get file info
-            if file_id:
-                file_info = await manager.get_file_info(file_id)
-                if file_info is None:
-                    return ErrorResponse(
-                        message=f"File not found: {file_id}",
-                        session_id=session_id,
-                    )
-                target_file_id = file_id
-            else:
-                # path is guaranteed to be non-None here due to the check above
-                assert path is not None
-                file_info = await manager.get_file_info_by_path(path)
-                if file_info is None:
-                    return ErrorResponse(
-                        message=f"File not found at path: {path}",
-                        session_id=session_id,
-                    )
-                target_file_id = file_info.id
-
-            # Decide whether to return inline content or metadata+URL
-            is_small_file = file_info.sizeBytes <= self.MAX_INLINE_SIZE_BYTES
-            is_text_file = self._is_text_mime_type(file_info.mimeType)
-
-            # Return inline content for small text files (unless force_download_url)
-            if is_small_file and is_text_file and not force_download_url:
-                content = await manager.read_file_by_id(target_file_id)
-                content_b64 = base64.b64encode(content).decode("utf-8")
-
-                return WorkspaceFileContentResponse(
-                    file_id=file_info.id,
-                    name=file_info.name,
-                    path=file_info.path,
-                    mime_type=file_info.mimeType,
-                    content_base64=content_b64,
-                    message=f"Successfully read file: {file_info.name}",
-                    session_id=session_id,
-                )
-
-            # Return metadata + workspace:// reference for large or binary files
-            # This prevents context bloat (100KB file = ~133KB as base64)
-            # Use workspace:// format so frontend urlTransform can add proxy prefix
-            download_url = f"workspace://{target_file_id}"
-
-            # Generate preview for text files
-            preview: str | None = None
-            if is_text_file:
-                try:
-                    content = await manager.read_file_by_id(target_file_id)
-                    preview_text = content[: self.PREVIEW_SIZE].decode(
-                        "utf-8", errors="replace"
-                    )
-                    if len(content) > self.PREVIEW_SIZE:
-                        preview_text += "..."
-                    preview = preview_text
-                except Exception:
-                    pass  # Preview is optional
-
-            return WorkspaceFileMetadataResponse(
-                file_id=file_info.id,
-                name=file_info.name,
-                path=file_info.path,
-                mime_type=file_info.mimeType,
-                size_bytes=file_info.sizeBytes,
-                download_url=download_url,
-                preview=preview,
-                message=f"File: {file_info.name} ({file_info.sizeBytes} bytes). Use download_url to retrieve content.",
-                session_id=session_id,
-            )
-
-        except FileNotFoundError as e:
-            return ErrorResponse(
-                message=str(e),
-                session_id=session_id,
-            )
-        except Exception as e:
-            logger.error(f"Error reading workspace file: {e}", exc_info=True)
-            return ErrorResponse(
-                message=f"Failed to read workspace file: {str(e)}",
-                error=str(e),
-                session_id=session_id,
-            )
-
-
-class WriteWorkspaceFileTool(BaseTool):
-    """Tool for writing files to workspace."""
-
-    @property
-    def name(self) -> str:
-        return "write_workspace_file"
-
-    @property
-    def description(self) -> str:
-        return (
-            "Write or create a file in the user's workspace. "
-            "Provide the content as a base64-encoded string. "
-            f"Maximum file size is {Config().max_file_size_mb}MB. "
-            "Files are saved to the current session's folder by default. "
-            "Use /sessions/<session_id>/... for cross-session access."
-        )
-
-    @property
-    def parameters(self) -> dict[str, Any]:
-        return {
-            "type": "object",
-            "properties": {
-                "filename": {
-                    "type": "string",
-                    "description": "Name for the file (e.g., 'report.pdf')",
-                },
-                "content_base64": {
-                    "type": "string",
-                    "description": "Base64-encoded file content",
-                },
-                "path": {
-                    "type": "string",
-                    "description": (
-                        "Optional virtual path where to save the file "
-                        "(e.g., '/documents/report.pdf'). "
-                        "Defaults to '/{filename}'. Scoped to current session."
-                    ),
-                },
-                "mime_type": {
-                    "type": "string",
-                    "description": (
-                        "Optional MIME type of the file. "
-                        "Auto-detected from filename if not provided."
-                    ),
-                },
-                "overwrite": {
-                    "type": "boolean",
-                    "description": "Whether to overwrite if file exists at path (default: false)",
-                },
-            },
-            "required": ["filename", "content_base64"],
-        }
-
-    @property
-    def requires_auth(self) -> bool:
-        return True
-
-    async def _execute(
-        self,
-        user_id: str | None,
-        session: ChatSession,
-        **kwargs,
-    ) -> ToolResponseBase:
-        session_id = session.session_id
-
-        if not user_id:
-            return ErrorResponse(
-                message="Authentication required",
-                session_id=session_id,
-            )
-
-        filename: str = kwargs.get("filename", "")
-        content_b64: str = kwargs.get("content_base64", "")
-        path: Optional[str] = kwargs.get("path")
-        mime_type: Optional[str] = kwargs.get("mime_type")
-        overwrite: bool = kwargs.get("overwrite", False)
-
-        if not filename:
-            return ErrorResponse(
-                message="Please provide a filename",
-                session_id=session_id,
-            )
-
-        if not content_b64:
-            return ErrorResponse(
-                message="Please provide content_base64",
-                session_id=session_id,
-            )
-
-        # Decode content
-        try:
-            content = base64.b64decode(content_b64)
-        except Exception:
-            return ErrorResponse(
-                message="Invalid base64-encoded content",
-                session_id=session_id,
-            )
-
-        # Check size
-        max_file_size = Config().max_file_size_mb * 1024 * 1024
-        if len(content) > max_file_size:
-            return ErrorResponse(
-                message=f"File too large. Maximum size is {Config().max_file_size_mb}MB",
-                session_id=session_id,
-            )
-
-        try:
-            # Virus scan
-            await scan_content_safe(content, filename=filename)
-
-            workspace = await get_or_create_workspace(user_id)
-            # Pass session_id for session-scoped file access
-            manager = WorkspaceManager(user_id, workspace.id, session_id)
-
-            file_record = await manager.write_file(
-                content=content,
-                filename=filename,
-                path=path,
-                mime_type=mime_type,
-                source=WorkspaceFileSource.COPILOT,
-                source_session_id=session.session_id,
-                overwrite=overwrite,
-            )
-
-            return WorkspaceWriteResponse(
-                file_id=file_record.id,
-                name=file_record.name,
-                path=file_record.path,
-                size_bytes=file_record.sizeBytes,
-                message=f"Successfully wrote file: {file_record.name}",
-                session_id=session_id,
-            )
-
-        except ValueError as e:
-            return ErrorResponse(
-                message=str(e),
-                session_id=session_id,
-            )
-        except Exception as e:
-            logger.error(f"Error writing workspace file: {e}", exc_info=True)
-            return ErrorResponse(
-                message=f"Failed to write workspace file: {str(e)}",
-                error=str(e),
-                session_id=session_id,
-            )
-
-
-class DeleteWorkspaceFileTool(BaseTool):
-    """Tool for deleting files from workspace."""
-
-    @property
-    def name(self) -> str:
-        return "delete_workspace_file"
-
-    @property
-    def description(self) -> str:
-        return (
-            "Delete a file from the user's workspace. "
-            "Specify either file_id or path to identify the file. "
-            "Paths are scoped to the current session by default. "
-            "Use /sessions/<session_id>/... for cross-session access."
-        )
-
-    @property
-    def parameters(self) -> dict[str, Any]:
-        return {
-            "type": "object",
-            "properties": {
-                "file_id": {
-                    "type": "string",
-                    "description": "The file's unique ID (from list_workspace_files)",
-                },
-                "path": {
-                    "type": "string",
-                    "description": (
-                        "The virtual file path (e.g., '/documents/report.pdf'). "
-                        "Scoped to current session by default."
-                    ),
-                },
-            },
-            "required": [],  # At least one must be provided
-        }
-
-    @property
-    def requires_auth(self) -> bool:
-        return True
-
-    async def _execute(
-        self,
-        user_id: str | None,
-        session: ChatSession,
-        **kwargs,
-    ) -> ToolResponseBase:
-        session_id = session.session_id
-
-        if not user_id:
-            return ErrorResponse(
-                message="Authentication required",
-                session_id=session_id,
-            )
-
-        file_id: Optional[str] = kwargs.get("file_id")
-        path: Optional[str] = kwargs.get("path")
-
-        if not file_id and not path:
-            return ErrorResponse(
-                message="Please provide either file_id or path",
-                session_id=session_id,
-            )
-
-        try:
-            workspace = await get_or_create_workspace(user_id)
-            # Pass session_id for session-scoped file access
-            manager = WorkspaceManager(user_id, workspace.id, session_id)
-
-            # Determine the file_id to delete
-            target_file_id: str
-            if file_id:
-                target_file_id = file_id
-            else:
-                # path is guaranteed to be non-None here due to the check above
-                assert path is not None
-                file_info = await manager.get_file_info_by_path(path)
-                if file_info is None:
-                    return ErrorResponse(
-                        message=f"File not found at path: {path}",
-                        session_id=session_id,
-                    )
-                target_file_id = file_info.id
-
-            success = await manager.delete_file(target_file_id)
-
-            if not success:
-                return ErrorResponse(
-                    message=f"File not found: {target_file_id}",
-                    session_id=session_id,
-                )
-
-            return WorkspaceDeleteResponse(
-                file_id=target_file_id,
-                success=True,
-                message="File deleted successfully",
-                session_id=session_id,
-            )
-
-        except Exception as e:
-            logger.error(f"Error deleting workspace file: {e}", exc_info=True)
-            return ErrorResponse(
-                message=f"Failed to delete workspace file: {str(e)}",
-                error=str(e),
-                session_id=session_id,
-            )

autogpt_platform/backend/backend/api/features/v1.py

@@ -265,13 +265,9 @@ async def get_onboarding_agents(
     "/onboarding/enabled",
     summary="Is onboarding enabled",
     tags=["onboarding", "public"],
+    dependencies=[Security(requires_user)],
 )
-async def is_onboarding_enabled(
-    user_id: Annotated[str, Security(get_user_id)],
-) -> bool:
-    # If chat is enabled for user, skip legacy onboarding
-    if await is_feature_enabled(Flag.CHAT, user_id, False):
-        return False
+async def is_onboarding_enabled() -> bool:
     return await onboarding_enabled()
 
 

autogpt_platform/backend/backend/api/features/workspace/__init__.py

@@ -1 +0,0 @@
-# Workspace API feature module

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

@@ -1,122 +0,0 @@
-"""
-Workspace API routes for managing user file storage.
-"""
-
-import logging
-import re
-from typing import Annotated
-from urllib.parse import quote
-
-import fastapi
-from autogpt_libs.auth.dependencies import get_user_id, requires_user
-from fastapi.responses import Response
-
-from backend.data.workspace import get_workspace, get_workspace_file
-from backend.util.workspace_storage import get_workspace_storage
-
-
-def _sanitize_filename_for_header(filename: str) -> str:
-    """
-    Sanitize filename for Content-Disposition header to prevent header injection.
-
-    Removes/replaces characters that could break the header or inject new headers.
-    Uses RFC5987 encoding for non-ASCII characters.
-    """
-    # Remove CR, LF, and null bytes (header injection prevention)
-    sanitized = re.sub(r"[\r\n\x00]", "", filename)
-    # Escape quotes
-    sanitized = sanitized.replace('"', '\\"')
-    # For non-ASCII, use RFC5987 filename* parameter
-    # Check if filename has non-ASCII characters
-    try:
-        sanitized.encode("ascii")
-        return f'attachment; filename="{sanitized}"'
-    except UnicodeEncodeError:
-        # Use RFC5987 encoding for UTF-8 filenames
-        encoded = quote(sanitized, safe="")
-        return f"attachment; filename*=UTF-8''{encoded}"
-
-
-logger = logging.getLogger(__name__)
-
-router = fastapi.APIRouter(
-    dependencies=[fastapi.Security(requires_user)],
-)
-
-
-def _create_streaming_response(content: bytes, file) -> Response:
-    """Create a streaming response for file content."""
-    return Response(
-        content=content,
-        media_type=file.mimeType,
-        headers={
-            "Content-Disposition": _sanitize_filename_for_header(file.name),
-            "Content-Length": str(len(content)),
-        },
-    )
-
-
-async def _create_file_download_response(file) -> Response:
-    """
-    Create a download response for a workspace file.
-
-    Handles both local storage (direct streaming) and GCS (signed URL redirect
-    with fallback to streaming).
-    """
-    storage = await get_workspace_storage()
-
-    # For local storage, stream the file directly
-    if file.storagePath.startswith("local://"):
-        content = await storage.retrieve(file.storagePath)
-        return _create_streaming_response(content, file)
-
-    # For GCS, try to redirect to signed URL, fall back to streaming
-    try:
-        url = await storage.get_download_url(file.storagePath, expires_in=300)
-        # If we got back an API path (fallback), stream directly instead
-        if url.startswith("/api/"):
-            content = await storage.retrieve(file.storagePath)
-            return _create_streaming_response(content, file)
-        return fastapi.responses.RedirectResponse(url=url, status_code=302)
-    except Exception as e:
-        # Log the signed URL failure with context
-        logger.error(
-            f"Failed to get signed URL for file {file.id} "
-            f"(storagePath={file.storagePath}): {e}",
-            exc_info=True,
-        )
-        # Fall back to streaming directly from GCS
-        try:
-            content = await storage.retrieve(file.storagePath)
-            return _create_streaming_response(content, file)
-        except Exception as fallback_error:
-            logger.error(
-                f"Fallback streaming also failed for file {file.id} "
-                f"(storagePath={file.storagePath}): {fallback_error}",
-                exc_info=True,
-            )
-            raise
-
-
-@router.get(
-    "/files/{file_id}/download",
-    summary="Download file by ID",
-)
-async def download_file(
-    user_id: Annotated[str, fastapi.Security(get_user_id)],
-    file_id: str,
-) -> Response:
-    """
-    Download a file by its ID.
-
-    Returns the file content directly or redirects to a signed URL for GCS.
-    """
-    workspace = await get_workspace(user_id)
-    if workspace is None:
-        raise fastapi.HTTPException(status_code=404, detail="Workspace not found")
-
-    file = await get_workspace_file(file_id, workspace.id)
-    if file is None:
-        raise fastapi.HTTPException(status_code=404, detail="File not found")
-
-    return await _create_file_download_response(file)

autogpt_platform/backend/backend/api/rest_api.py

@@ -32,7 +32,6 @@ import backend.api.features.postmark.postmark
 import backend.api.features.store.model
 import backend.api.features.store.routes
 import backend.api.features.v1
-import backend.api.features.workspace.routes as workspace_routes
 import backend.data.block
 import backend.data.db
 import backend.data.graph
@@ -53,7 +52,6 @@ from backend.util.exceptions import (
 )
 from backend.util.feature_flag import initialize_launchdarkly, shutdown_launchdarkly
 from backend.util.service import UnhealthyServiceError
-from backend.util.workspace_storage import shutdown_workspace_storage
 
 from .external.fastapi_app import external_api
 from .features.analytics import router as analytics_router
@@ -126,11 +124,6 @@ async def lifespan_context(app: fastapi.FastAPI):
     except Exception as e:
         logger.warning(f"Error shutting down cloud storage handler: {e}")
 
-    try:
-        await shutdown_workspace_storage()
-    except Exception as e:
-        logger.warning(f"Error shutting down workspace storage: {e}")
-
     await backend.data.db.disconnect()
 
 
@@ -322,11 +315,6 @@ app.include_router(
     tags=["v2", "chat"],
     prefix="/api/chat",
 )
-app.include_router(
-    workspace_routes.router,
-    tags=["v2", "workspace"],
-    prefix="/api/workspace",
-)
 app.include_router(
     backend.api.features.oauth.router,
     tags=["oauth"],

autogpt_platform/backend/backend/blocks/ai_image_customizer.py

@@ -13,7 +13,6 @@ from backend.data.block import (
     BlockSchemaInput,
     BlockSchemaOutput,
 )
-from backend.data.execution import ExecutionContext
 from backend.data.model import (
     APIKeyCredentials,
     CredentialsField,
@@ -118,13 +117,11 @@ class AIImageCustomizerBlock(Block):
                 "credentials": TEST_CREDENTIALS_INPUT,
             },
             test_output=[
-                # Output will be a workspace ref or data URI depending on context
-                ("image_url", lambda x: x.startswith(("workspace://", "data:"))),
+                ("image_url", "https://replicate.delivery/generated-image.jpg"),
             ],
             test_mock={
-                # Use data URI to avoid HTTP requests during tests
                 "run_model": lambda *args, **kwargs: MediaFileType(
-                    "data:image/jpeg;base64,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"
+                    "https://replicate.delivery/generated-image.jpg"
                 ),
             },
             test_credentials=TEST_CREDENTIALS,
@@ -135,7 +132,8 @@ class AIImageCustomizerBlock(Block):
         input_data: Input,
         *,
         credentials: APIKeyCredentials,
-        execution_context: ExecutionContext,
+        graph_exec_id: str,
+        user_id: str,
         **kwargs,
     ) -> BlockOutput:
         try:
@@ -143,9 +141,10 @@ class AIImageCustomizerBlock(Block):
             processed_images = await asyncio.gather(
                 *(
                     store_media_file(
+                        graph_exec_id=graph_exec_id,
                         file=img,
-                        execution_context=execution_context,
-                        return_format="for_external_api",  # Get content for Replicate API
+                        user_id=user_id,
+                        return_content=True,
                     )
                     for img in input_data.images
                 )
@@ -159,14 +158,7 @@ class AIImageCustomizerBlock(Block):
                 aspect_ratio=input_data.aspect_ratio.value,
                 output_format=input_data.output_format.value,
             )
-
-            # Store the generated image to the user's workspace for persistence
-            stored_url = await store_media_file(
-                file=result,
-                execution_context=execution_context,
-                return_format="for_block_output",
-            )
-            yield "image_url", stored_url
+            yield "image_url", result
         except Exception as e:
             yield "error", str(e)
 

autogpt_platform/backend/backend/blocks/ai_image_generator_block.py

@@ -6,7 +6,6 @@ from replicate.client import Client as ReplicateClient
 from replicate.helpers import FileOutput
 
 from backend.data.block import Block, BlockCategory, BlockSchemaInput, BlockSchemaOutput
-from backend.data.execution import ExecutionContext
 from backend.data.model import (
     APIKeyCredentials,
     CredentialsField,
@@ -14,8 +13,6 @@ from backend.data.model import (
     SchemaField,
 )
 from backend.integrations.providers import ProviderName
-from backend.util.file import store_media_file
-from backend.util.type import MediaFileType
 
 
 class ImageSize(str, Enum):
@@ -168,13 +165,11 @@ class AIImageGeneratorBlock(Block):
             test_output=[
                 (
                     "image_url",
-                    # Test output is a data URI since we now store images
-                    lambda x: x.startswith("data:image/"),
+                    "https://replicate.delivery/generated-image.webp",
                 ),
             ],
             test_mock={
-                # Return a data URI directly so store_media_file doesn't need to download
-                "_run_client": lambda *args, **kwargs: "data:image/webp;base64,UklGRiQAAABXRUJQVlA4IBgAAAAwAQCdASoBAAEAAQAcJYgCdAEO"
+                "_run_client": lambda *args, **kwargs: "https://replicate.delivery/generated-image.webp"
             },
         )
 
@@ -323,24 +318,11 @@ class AIImageGeneratorBlock(Block):
         style_text = style_map.get(style, "")
         return f"{style_text} of" if style_text else ""
 
-    async def run(
-        self,
-        input_data: Input,
-        *,
-        credentials: APIKeyCredentials,
-        execution_context: ExecutionContext,
-        **kwargs,
-    ):
+    async def run(self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs):
         try:
             url = await self.generate_image(input_data, credentials)
             if url:
-                # Store the generated image to the user's workspace/execution folder
-                stored_url = await store_media_file(
-                    file=MediaFileType(url),
-                    execution_context=execution_context,
-                    return_format="for_block_output",
-                )
-                yield "image_url", stored_url
+                yield "image_url", url
             else:
                 yield "error", "Image generation returned an empty result."
         except Exception as e:

autogpt_platform/backend/backend/blocks/ai_shortform_video_block.py

@@ -13,7 +13,6 @@ from backend.data.block import (
     BlockSchemaInput,
     BlockSchemaOutput,
 )
-from backend.data.execution import ExecutionContext
 from backend.data.model import (
     APIKeyCredentials,
     CredentialsField,
@@ -22,9 +21,7 @@ from backend.data.model import (
 )
 from backend.integrations.providers import ProviderName
 from backend.util.exceptions import BlockExecutionError
-from backend.util.file import store_media_file
 from backend.util.request import Requests
-from backend.util.type import MediaFileType
 
 TEST_CREDENTIALS = APIKeyCredentials(
     id="01234567-89ab-cdef-0123-456789abcdef",
@@ -274,10 +271,7 @@ class AIShortformVideoCreatorBlock(Block):
                 "voice": Voice.LILY,
                 "video_style": VisualMediaType.STOCK_VIDEOS,
             },
-            test_output=(
-                "video_url",
-                lambda x: x.startswith(("workspace://", "data:")),
-            ),
+            test_output=("video_url", "https://example.com/video.mp4"),
             test_mock={
                 "create_webhook": lambda *args, **kwargs: (
                     "test_uuid",
@@ -286,21 +280,15 @@ class AIShortformVideoCreatorBlock(Block):
                 "create_video": lambda *args, **kwargs: {"pid": "test_pid"},
                 "check_video_status": lambda *args, **kwargs: {
                     "status": "ready",
-                    "videoUrl": "data:video/mp4;base64,AAAA",
+                    "videoUrl": "https://example.com/video.mp4",
                 },
-                # Use data URI to avoid HTTP requests during tests
-                "wait_for_video": lambda *args, **kwargs: "data:video/mp4;base64,AAAA",
+                "wait_for_video": lambda *args, **kwargs: "https://example.com/video.mp4",
             },
             test_credentials=TEST_CREDENTIALS,
         )
 
     async def run(
-        self,
-        input_data: Input,
-        *,
-        credentials: APIKeyCredentials,
-        execution_context: ExecutionContext,
-        **kwargs,
+        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
     ) -> BlockOutput:
         # Create a new Webhook.site URL
         webhook_token, webhook_url = await self.create_webhook()
@@ -352,13 +340,7 @@ class AIShortformVideoCreatorBlock(Block):
             )
             video_url = await self.wait_for_video(credentials.api_key, pid)
             logger.debug(f"Video ready: {video_url}")
-            # Store the generated video to the user's workspace for persistence
-            stored_url = await store_media_file(
-                file=MediaFileType(video_url),
-                execution_context=execution_context,
-                return_format="for_block_output",
-            )
-            yield "video_url", stored_url
+            yield "video_url", video_url
 
 
 class AIAdMakerVideoCreatorBlock(Block):
@@ -465,10 +447,7 @@ class AIAdMakerVideoCreatorBlock(Block):
                     "https://cdn.revid.ai/uploads/1747076315114-image.png",
                 ],
             },
-            test_output=(
-                "video_url",
-                lambda x: x.startswith(("workspace://", "data:")),
-            ),
+            test_output=("video_url", "https://example.com/ad.mp4"),
             test_mock={
                 "create_webhook": lambda *args, **kwargs: (
                     "test_uuid",
@@ -477,21 +456,14 @@ class AIAdMakerVideoCreatorBlock(Block):
                 "create_video": lambda *args, **kwargs: {"pid": "test_pid"},
                 "check_video_status": lambda *args, **kwargs: {
                     "status": "ready",
-                    "videoUrl": "data:video/mp4;base64,AAAA",
+                    "videoUrl": "https://example.com/ad.mp4",
                 },
-                "wait_for_video": lambda *args, **kwargs: "data:video/mp4;base64,AAAA",
+                "wait_for_video": lambda *args, **kwargs: "https://example.com/ad.mp4",
             },
             test_credentials=TEST_CREDENTIALS,
         )
 
-    async def run(
-        self,
-        input_data: Input,
-        *,
-        credentials: APIKeyCredentials,
-        execution_context: ExecutionContext,
-        **kwargs,
-    ):
+    async def run(self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs):
         webhook_token, webhook_url = await self.create_webhook()
 
         payload = {
@@ -559,13 +531,7 @@ class AIAdMakerVideoCreatorBlock(Block):
             raise RuntimeError("Failed to create video: No project ID returned")
 
         video_url = await self.wait_for_video(credentials.api_key, pid)
-        # Store the generated video to the user's workspace for persistence
-        stored_url = await store_media_file(
-            file=MediaFileType(video_url),
-            execution_context=execution_context,
-            return_format="for_block_output",
-        )
-        yield "video_url", stored_url
+        yield "video_url", video_url
 
 
 class AIScreenshotToVideoAdBlock(Block):
@@ -660,10 +626,7 @@ class AIScreenshotToVideoAdBlock(Block):
                 "script": "Amazing numbers!",
                 "screenshot_url": "https://cdn.revid.ai/uploads/1747080376028-image.png",
             },
-            test_output=(
-                "video_url",
-                lambda x: x.startswith(("workspace://", "data:")),
-            ),
+            test_output=("video_url", "https://example.com/screenshot.mp4"),
             test_mock={
                 "create_webhook": lambda *args, **kwargs: (
                     "test_uuid",
@@ -672,21 +635,14 @@ class AIScreenshotToVideoAdBlock(Block):
                 "create_video": lambda *args, **kwargs: {"pid": "test_pid"},
                 "check_video_status": lambda *args, **kwargs: {
                     "status": "ready",
-                    "videoUrl": "data:video/mp4;base64,AAAA",
+                    "videoUrl": "https://example.com/screenshot.mp4",
                 },
-                "wait_for_video": lambda *args, **kwargs: "data:video/mp4;base64,AAAA",
+                "wait_for_video": lambda *args, **kwargs: "https://example.com/screenshot.mp4",
             },
             test_credentials=TEST_CREDENTIALS,
         )
 
-    async def run(
-        self,
-        input_data: Input,
-        *,
-        credentials: APIKeyCredentials,
-        execution_context: ExecutionContext,
-        **kwargs,
-    ):
+    async def run(self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs):
         webhook_token, webhook_url = await self.create_webhook()
 
         payload = {
@@ -754,10 +710,4 @@ class AIScreenshotToVideoAdBlock(Block):
             raise RuntimeError("Failed to create video: No project ID returned")
 
         video_url = await self.wait_for_video(credentials.api_key, pid)
-        # Store the generated video to the user's workspace for persistence
-        stored_url = await store_media_file(
-            file=MediaFileType(video_url),
-            execution_context=execution_context,
-            return_format="for_block_output",
-        )
-        yield "video_url", stored_url
+        yield "video_url", video_url

autogpt_platform/backend/backend/blocks/bannerbear/text_overlay.py

@@ -6,7 +6,6 @@ if TYPE_CHECKING:
 
 from pydantic import SecretStr
 
-from backend.data.execution import ExecutionContext
 from backend.sdk import (
     APIKeyCredentials,
     Block,
@@ -18,8 +17,6 @@ from backend.sdk import (
     Requests,
     SchemaField,
 )
-from backend.util.file import store_media_file
-from backend.util.type import MediaFileType
 
 from ._config import bannerbear
 
@@ -138,17 +135,15 @@ class BannerbearTextOverlayBlock(Block):
             },
             test_output=[
                 ("success", True),
-                # Output will be a workspace ref or data URI depending on context
-                ("image_url", lambda x: x.startswith(("workspace://", "data:"))),
+                ("image_url", "https://cdn.bannerbear.com/test-image.jpg"),
                 ("uid", "test-uid-123"),
                 ("status", "completed"),
             ],
             test_mock={
-                # Use data URI to avoid HTTP requests during tests
                 "_make_api_request": lambda *args, **kwargs: {
                     "uid": "test-uid-123",
                     "status": "completed",
-                    "image_url": "data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0aHBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/wAALCAABAAEBAREA/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQAAAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWmp6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/9oACAEBAAA/APn+v//Z",
+                    "image_url": "https://cdn.bannerbear.com/test-image.jpg",
                 }
             },
             test_credentials=TEST_CREDENTIALS,
@@ -182,12 +177,7 @@ class BannerbearTextOverlayBlock(Block):
             raise Exception(error_msg)
 
     async def run(
-        self,
-        input_data: Input,
-        *,
-        credentials: APIKeyCredentials,
-        execution_context: ExecutionContext,
-        **kwargs,
+        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
     ) -> BlockOutput:
         # Build the modifications array
         modifications = []
@@ -244,18 +234,6 @@ class BannerbearTextOverlayBlock(Block):
 
         # Synchronous request - image should be ready
         yield "success", True
-
-        # Store the generated image to workspace for persistence
-        image_url = data.get("image_url", "")
-        if image_url:
-            stored_url = await store_media_file(
-                file=MediaFileType(image_url),
-                execution_context=execution_context,
-                return_format="for_block_output",
-            )
-            yield "image_url", stored_url
-        else:
-            yield "image_url", ""
-
+        yield "image_url", data.get("image_url", "")
         yield "uid", data.get("uid", "")
         yield "status", data.get("status", "completed")

autogpt_platform/backend/backend/blocks/basic.py

@@ -9,7 +9,6 @@ from backend.data.block import (
     BlockSchemaOutput,
     BlockType,
 )
-from backend.data.execution import ExecutionContext
 from backend.data.model import SchemaField
 from backend.util.file import store_media_file
 from backend.util.type import MediaFileType, convert
@@ -18,10 +17,10 @@ from backend.util.type import MediaFileType, convert
 class FileStoreBlock(Block):
     class Input(BlockSchemaInput):
         file_in: MediaFileType = SchemaField(
-            description="The file to download and store. Can be a URL (https://...), data URI, or local path."
+            description="The file to store in the temporary directory, it can be a URL, data URI, or local path."
         )
         base_64: bool = SchemaField(
-            description="Whether to produce output in base64 format (not recommended, you can pass the file reference across blocks).",
+            description="Whether produce an output in base64 format (not recommended, you can pass the string path just fine accross blocks).",
             default=False,
             advanced=True,
             title="Produce Base64 Output",
@@ -29,18 +28,13 @@ class FileStoreBlock(Block):
 
     class Output(BlockSchemaOutput):
         file_out: MediaFileType = SchemaField(
-            description="Reference to the stored file. In CoPilot: workspace:// URI (visible in list_workspace_files). In graphs: data URI for passing to other blocks."
+            description="The relative path to the stored file in the temporary directory."
         )
 
     def __init__(self):
         super().__init__(
             id="cbb50872-625b-42f0-8203-a2ae78242d8a",
-            description=(
-                "Downloads and stores a file from a URL, data URI, or local path. "
-                "Use this to fetch images, documents, or other files for processing. "
-                "In CoPilot: saves to workspace (use list_workspace_files to see it). "
-                "In graphs: outputs a data URI to pass to other blocks."
-            ),
+            description="Stores the input file in the temporary directory.",
             categories={BlockCategory.BASIC, BlockCategory.MULTIMEDIA},
             input_schema=FileStoreBlock.Input,
             output_schema=FileStoreBlock.Output,
@@ -51,18 +45,15 @@ class FileStoreBlock(Block):
         self,
         input_data: Input,
         *,
-        execution_context: ExecutionContext,
+        graph_exec_id: str,
+        user_id: str,
         **kwargs,
     ) -> BlockOutput:
-        # Determine return format based on user preference
-        # for_external_api: always returns data URI (base64) - honors "Produce Base64 Output"
-        # for_block_output: smart format - workspace:// in CoPilot, data URI in graphs
-        return_format = "for_external_api" if input_data.base_64 else "for_block_output"
-
         yield "file_out", await store_media_file(
+            graph_exec_id=graph_exec_id,
             file=input_data.file_in,
-            execution_context=execution_context,
-            return_format=return_format,
+            user_id=user_id,
+            return_content=input_data.base_64,
         )
 
 

autogpt_platform/backend/backend/blocks/discord/bot_blocks.py

@@ -15,7 +15,6 @@ from backend.data.block import (
     BlockSchemaInput,
     BlockSchemaOutput,
 )
-from backend.data.execution import ExecutionContext
 from backend.data.model import APIKeyCredentials, SchemaField
 from backend.util.file import store_media_file
 from backend.util.request import Requests
@@ -667,7 +666,8 @@ class SendDiscordFileBlock(Block):
         file: MediaFileType,
         filename: str,
         message_content: str,
-        execution_context: ExecutionContext,
+        graph_exec_id: str,
+        user_id: str,
     ) -> dict:
         intents = discord.Intents.default()
         intents.guilds = True
@@ -731,9 +731,10 @@ class SendDiscordFileBlock(Block):
                     # Local file path - read from stored media file
                     # This would be a path from a previous block's output
                     stored_file = await store_media_file(
+                        graph_exec_id=graph_exec_id,
                         file=file,
-                        execution_context=execution_context,
-                        return_format="for_external_api",  # Get content to send to Discord
+                        user_id=user_id,
+                        return_content=True,  # Get as data URI
                     )
                     # Now process as data URI
                     header, encoded = stored_file.split(",", 1)
@@ -780,7 +781,8 @@ class SendDiscordFileBlock(Block):
         input_data: Input,
         *,
         credentials: APIKeyCredentials,
-        execution_context: ExecutionContext,
+        graph_exec_id: str,
+        user_id: str,
         **kwargs,
     ) -> BlockOutput:
         try:
@@ -791,7 +793,8 @@ class SendDiscordFileBlock(Block):
                 file=input_data.file,
                 filename=input_data.filename,
                 message_content=input_data.message_content,
-                execution_context=execution_context,
+                graph_exec_id=graph_exec_id,
+                user_id=user_id,
             )
 
             yield "status", result.get("status", "Unknown error")

autogpt_platform/backend/backend/blocks/fal/ai_video_generator.py

@@ -17,11 +17,8 @@ from backend.data.block import (
     BlockSchemaInput,
     BlockSchemaOutput,
 )
-from backend.data.execution import ExecutionContext
 from backend.data.model import SchemaField
-from backend.util.file import store_media_file
 from backend.util.request import ClientResponseError, Requests
-from backend.util.type import MediaFileType
 
 logger = logging.getLogger(__name__)
 
@@ -67,13 +64,9 @@ class AIVideoGeneratorBlock(Block):
                 "credentials": TEST_CREDENTIALS_INPUT,
             },
             test_credentials=TEST_CREDENTIALS,
-            test_output=[
-                # Output will be a workspace ref or data URI depending on context
-                ("video_url", lambda x: x.startswith(("workspace://", "data:"))),
-            ],
+            test_output=[("video_url", "https://fal.media/files/example/video.mp4")],
             test_mock={
-                # Use data URI to avoid HTTP requests during tests
-                "generate_video": lambda *args, **kwargs: "data:video/mp4;base64,AAAA"
+                "generate_video": lambda *args, **kwargs: "https://fal.media/files/example/video.mp4"
             },
         )
 
@@ -215,22 +208,11 @@ class AIVideoGeneratorBlock(Block):
             raise RuntimeError(f"API request failed: {str(e)}")
 
     async def run(
-        self,
-        input_data: Input,
-        *,
-        credentials: FalCredentials,
-        execution_context: ExecutionContext,
-        **kwargs,
+        self, input_data: Input, *, credentials: FalCredentials, **kwargs
     ) -> BlockOutput:
         try:
             video_url = await self.generate_video(input_data, credentials)
-            # Store the generated video to the user's workspace for persistence
-            stored_url = await store_media_file(
-                file=MediaFileType(video_url),
-                execution_context=execution_context,
-                return_format="for_block_output",
-            )
-            yield "video_url", stored_url
+            yield "video_url", video_url
         except Exception as e:
             error_message = str(e)
             yield "error", error_message

autogpt_platform/backend/backend/blocks/flux_kontext.py

@@ -12,7 +12,6 @@ from backend.data.block import (
     BlockSchemaInput,
     BlockSchemaOutput,
 )
-from backend.data.execution import ExecutionContext
 from backend.data.model import (
     APIKeyCredentials,
     CredentialsField,
@@ -122,12 +121,10 @@ class AIImageEditorBlock(Block):
                 "credentials": TEST_CREDENTIALS_INPUT,
             },
             test_output=[
-                # Output will be a workspace ref or data URI depending on context
-                ("output_image", lambda x: x.startswith(("workspace://", "data:"))),
+                ("output_image", "https://replicate.com/output/edited-image.png"),
             ],
             test_mock={
-                # Use data URI to avoid HTTP requests during tests
-                "run_model": lambda *args, **kwargs: "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mNk+M9QDwADhgGAWjR9awAAAABJRU5ErkJggg==",
+                "run_model": lambda *args, **kwargs: "https://replicate.com/output/edited-image.png",
             },
             test_credentials=TEST_CREDENTIALS,
         )
@@ -137,7 +134,8 @@ class AIImageEditorBlock(Block):
         input_data: Input,
         *,
         credentials: APIKeyCredentials,
-        execution_context: ExecutionContext,
+        graph_exec_id: str,
+        user_id: str,
         **kwargs,
     ) -> BlockOutput:
         result = await self.run_model(
@@ -146,25 +144,20 @@ class AIImageEditorBlock(Block):
             prompt=input_data.prompt,
             input_image_b64=(
                 await store_media_file(
+                    graph_exec_id=graph_exec_id,
                     file=input_data.input_image,
-                    execution_context=execution_context,
-                    return_format="for_external_api",  # Get content for Replicate API
+                    user_id=user_id,
+                    return_content=True,
                 )
                 if input_data.input_image
                 else None
             ),
             aspect_ratio=input_data.aspect_ratio.value,
             seed=input_data.seed,
-            user_id=execution_context.user_id or "",
-            graph_exec_id=execution_context.graph_exec_id or "",
+            user_id=user_id,
+            graph_exec_id=graph_exec_id,
         )
-        # Store the generated image to the user's workspace for persistence
-        stored_url = await store_media_file(
-            file=result,
-            execution_context=execution_context,
-            return_format="for_block_output",
-        )
-        yield "output_image", stored_url
+        yield "output_image", result
 
     async def run_model(
         self,

autogpt_platform/backend/backend/blocks/google/gmail.py

@@ -21,7 +21,6 @@ from backend.data.block import (
     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
 from backend.util.settings import Settings
@@ -96,7 +95,8 @@ def _make_mime_text(
 
 async def create_mime_message(
     input_data,
-    execution_context: ExecutionContext,
+    graph_exec_id: str,
+    user_id: str,
 ) -> str:
     """Create a MIME message with attachments and return base64-encoded raw message."""
 
@@ -117,12 +117,12 @@ async def create_mime_message(
     if input_data.attachments:
         for attach in input_data.attachments:
             local_path = await store_media_file(
+                user_id=user_id,
+                graph_exec_id=graph_exec_id,
                 file=attach,
-                execution_context=execution_context,
-                return_format="for_local_processing",
+                return_content=False,
             )
-            assert execution_context.graph_exec_id  # Validated by store_media_file
-            abs_path = get_exec_file_path(execution_context.graph_exec_id, local_path)
+            abs_path = get_exec_file_path(graph_exec_id, local_path)
             part = MIMEBase("application", "octet-stream")
             with open(abs_path, "rb") as f:
                 part.set_payload(f.read())
@@ -582,25 +582,27 @@ class GmailSendBlock(GmailBase):
         input_data: Input,
         *,
         credentials: GoogleCredentials,
-        execution_context: ExecutionContext,
+        graph_exec_id: str,
+        user_id: str,
         **kwargs,
     ) -> BlockOutput:
         service = self._build_service(credentials, **kwargs)
         result = await self._send_email(
             service,
             input_data,
-            execution_context,
+            graph_exec_id,
+            user_id,
         )
         yield "result", result
 
     async def _send_email(
-        self, service, input_data: Input, execution_context: ExecutionContext
+        self, service, input_data: Input, graph_exec_id: str, user_id: str
     ) -> dict:
         if not input_data.to or not input_data.subject or not input_data.body:
             raise ValueError(
                 "At least one recipient, subject, and body are required for sending an email"
             )
-        raw_message = await create_mime_message(input_data, execution_context)
+        raw_message = await create_mime_message(input_data, graph_exec_id, user_id)
         sent_message = await asyncio.to_thread(
             lambda: service.users()
             .messages()
@@ -690,28 +692,30 @@ class GmailCreateDraftBlock(GmailBase):
         input_data: Input,
         *,
         credentials: GoogleCredentials,
-        execution_context: ExecutionContext,
+        graph_exec_id: str,
+        user_id: str,
         **kwargs,
     ) -> BlockOutput:
         service = self._build_service(credentials, **kwargs)
         result = await self._create_draft(
             service,
             input_data,
-            execution_context,
+            graph_exec_id,
+            user_id,
         )
         yield "result", GmailDraftResult(
             id=result["id"], message_id=result["message"]["id"], status="draft_created"
         )
 
     async def _create_draft(
-        self, service, input_data: Input, execution_context: ExecutionContext
+        self, service, input_data: Input, graph_exec_id: str, user_id: str
     ) -> dict:
         if not input_data.to or not input_data.subject:
             raise ValueError(
                 "At least one recipient and subject are required for creating a draft"
             )
 
-        raw_message = await create_mime_message(input_data, execution_context)
+        raw_message = await create_mime_message(input_data, graph_exec_id, user_id)
         draft = await asyncio.to_thread(
             lambda: service.users()
             .drafts()
@@ -1096,7 +1100,7 @@ class GmailGetThreadBlock(GmailBase):
 
 
 async def _build_reply_message(
-    service, input_data, execution_context: ExecutionContext
+    service, input_data, graph_exec_id: str, user_id: str
 ) -> tuple[str, str]:
     """
     Builds a reply MIME message for Gmail threads.
@@ -1186,12 +1190,12 @@ async def _build_reply_message(
     # Handle attachments
     for attach in input_data.attachments:
         local_path = await store_media_file(
+            user_id=user_id,
+            graph_exec_id=graph_exec_id,
             file=attach,
-            execution_context=execution_context,
-            return_format="for_local_processing",
+            return_content=False,
         )
-        assert execution_context.graph_exec_id  # Validated by store_media_file
-        abs_path = get_exec_file_path(execution_context.graph_exec_id, local_path)
+        abs_path = get_exec_file_path(graph_exec_id, local_path)
         part = MIMEBase("application", "octet-stream")
         with open(abs_path, "rb") as f:
             part.set_payload(f.read())
@@ -1307,14 +1311,16 @@ class GmailReplyBlock(GmailBase):
         input_data: Input,
         *,
         credentials: GoogleCredentials,
-        execution_context: ExecutionContext,
+        graph_exec_id: str,
+        user_id: str,
         **kwargs,
     ) -> BlockOutput:
         service = self._build_service(credentials, **kwargs)
         message = await self._reply(
             service,
             input_data,
-            execution_context,
+            graph_exec_id,
+            user_id,
         )
         yield "messageId", message["id"]
         yield "threadId", message.get("threadId", input_data.threadId)
@@ -1337,11 +1343,11 @@ class GmailReplyBlock(GmailBase):
         yield "email", email
 
     async def _reply(
-        self, service, input_data: Input, execution_context: ExecutionContext
+        self, service, input_data: Input, graph_exec_id: str, user_id: str
     ) -> dict:
         # Build the reply message using the shared helper
         raw, thread_id = await _build_reply_message(
-            service, input_data, execution_context
+            service, input_data, graph_exec_id, user_id
         )
 
         # Send the message
@@ -1435,14 +1441,16 @@ class GmailDraftReplyBlock(GmailBase):
         input_data: Input,
         *,
         credentials: GoogleCredentials,
-        execution_context: ExecutionContext,
+        graph_exec_id: str,
+        user_id: str,
         **kwargs,
     ) -> BlockOutput:
         service = self._build_service(credentials, **kwargs)
         draft = await self._create_draft_reply(
             service,
             input_data,
-            execution_context,
+            graph_exec_id,
+            user_id,
         )
         yield "draftId", draft["id"]
         yield "messageId", draft["message"]["id"]
@@ -1450,11 +1458,11 @@ class GmailDraftReplyBlock(GmailBase):
         yield "status", "draft_created"
 
     async def _create_draft_reply(
-        self, service, input_data: Input, execution_context: ExecutionContext
+        self, service, input_data: Input, graph_exec_id: str, user_id: str
     ) -> dict:
         # Build the reply message using the shared helper
         raw, thread_id = await _build_reply_message(
-            service, input_data, execution_context
+            service, input_data, graph_exec_id, user_id
         )
 
         # Create draft with proper thread association
@@ -1621,21 +1629,23 @@ class GmailForwardBlock(GmailBase):
         input_data: Input,
         *,
         credentials: GoogleCredentials,
-        execution_context: ExecutionContext,
+        graph_exec_id: str,
+        user_id: str,
         **kwargs,
     ) -> BlockOutput:
         service = self._build_service(credentials, **kwargs)
         result = await self._forward_message(
             service,
             input_data,
-            execution_context,
+            graph_exec_id,
+            user_id,
         )
         yield "messageId", result["id"]
         yield "threadId", result.get("threadId", "")
         yield "status", "forwarded"
 
     async def _forward_message(
-        self, service, input_data: Input, execution_context: ExecutionContext
+        self, service, input_data: Input, graph_exec_id: str, user_id: str
     ) -> dict:
         if not input_data.to:
             raise ValueError("At least one recipient is required for forwarding")
@@ -1717,12 +1727,12 @@ To: {original_to}
         # Add any additional attachments
         for attach in input_data.additionalAttachments:
             local_path = await store_media_file(
+                user_id=user_id,
+                graph_exec_id=graph_exec_id,
                 file=attach,
-                execution_context=execution_context,
-                return_format="for_local_processing",
+                return_content=False,
             )
-            assert execution_context.graph_exec_id  # Validated by store_media_file
-            abs_path = get_exec_file_path(execution_context.graph_exec_id, local_path)
+            abs_path = get_exec_file_path(graph_exec_id, local_path)
             part = MIMEBase("application", "octet-stream")
             with open(abs_path, "rb") as f:
                 part.set_payload(f.read())

autogpt_platform/backend/backend/blocks/http.py

@@ -15,7 +15,6 @@ from backend.data.block import (
     BlockSchemaInput,
     BlockSchemaOutput,
 )
-from backend.data.execution import ExecutionContext
 from backend.data.model import (
     CredentialsField,
     CredentialsMetaInput,
@@ -117,9 +116,10 @@ class SendWebRequestBlock(Block):
 
     @staticmethod
     async def _prepare_files(
-        execution_context: ExecutionContext,
+        graph_exec_id: str,
         files_name: str,
         files: list[MediaFileType],
+        user_id: str,
     ) -> list[tuple[str, tuple[str, BytesIO, str]]]:
         """
         Prepare files for the request by storing them and reading their content.
@@ -127,16 +127,11 @@ class SendWebRequestBlock(Block):
         (files_name, (filename, BytesIO, mime_type))
         """
         files_payload: list[tuple[str, tuple[str, BytesIO, str]]] = []
-        graph_exec_id = execution_context.graph_exec_id
-        if graph_exec_id is None:
-            raise ValueError("graph_exec_id is required for file operations")
 
         for media in files:
             # Normalise to a list so we can repeat the same key
             rel_path = await store_media_file(
-                file=media,
-                execution_context=execution_context,
-                return_format="for_local_processing",
+                graph_exec_id, media, user_id, return_content=False
             )
             abs_path = get_exec_file_path(graph_exec_id, rel_path)
             async with aiofiles.open(abs_path, "rb") as f:
@@ -148,7 +143,7 @@ class SendWebRequestBlock(Block):
         return files_payload
 
     async def run(
-        self, input_data: Input, *, execution_context: ExecutionContext, **kwargs
+        self, input_data: Input, *, graph_exec_id: str, user_id: str, **kwargs
     ) -> BlockOutput:
         # ─── Parse/normalise body ────────────────────────────────────
         body = input_data.body
@@ -179,7 +174,7 @@ class SendWebRequestBlock(Block):
         files_payload: list[tuple[str, tuple[str, BytesIO, str]]] = []
         if use_files:
             files_payload = await self._prepare_files(
-                execution_context, input_data.files_name, input_data.files
+                graph_exec_id, input_data.files_name, input_data.files, user_id
             )
 
         # Enforce body format rules
@@ -243,8 +238,9 @@ class SendAuthenticatedWebRequestBlock(SendWebRequestBlock):
         self,
         input_data: Input,
         *,
-        execution_context: ExecutionContext,
+        graph_exec_id: str,
         credentials: HostScopedCredentials,
+        user_id: str,
         **kwargs,
     ) -> BlockOutput:
         # Create SendWebRequestBlock.Input from our input (removing credentials field)
@@ -275,6 +271,6 @@ class SendAuthenticatedWebRequestBlock(SendWebRequestBlock):
 
         # Use parent class run method
         async for output_name, output_data in super().run(
-            base_input, execution_context=execution_context, **kwargs
+            base_input, graph_exec_id=graph_exec_id, user_id=user_id, **kwargs
         ):
             yield output_name, output_data

autogpt_platform/backend/backend/blocks/io.py

@@ -12,7 +12,6 @@ from backend.data.block import (
     BlockSchemaInput,
     BlockType,
 )
-from backend.data.execution import ExecutionContext
 from backend.data.model import SchemaField
 from backend.util.file import store_media_file
 from backend.util.mock import MockObject
@@ -463,23 +462,18 @@ class AgentFileInputBlock(AgentInputBlock):
         self,
         input_data: Input,
         *,
-        execution_context: ExecutionContext,
+        graph_exec_id: str,
+        user_id: str,
         **kwargs,
     ) -> BlockOutput:
         if not input_data.value:
             return
 
-        # Determine return format based on user preference
-        # for_external_api: always returns data URI (base64) - honors "Produce Base64 Output"
-        # for_local_processing: returns local file path
-        return_format = (
-            "for_external_api" if input_data.base_64 else "for_local_processing"
-        )
-
         yield "result", await store_media_file(
+            graph_exec_id=graph_exec_id,
             file=input_data.value,
-            execution_context=execution_context,
-            return_format=return_format,
+            user_id=user_id,
+            return_content=input_data.base_64,
         )
 
 

autogpt_platform/backend/backend/blocks/media.py

@@ -1,6 +1,6 @@
 import os
 import tempfile
-from typing import Optional
+from typing import Literal, Optional
 
 from moviepy.audio.io.AudioFileClip import AudioFileClip
 from moviepy.video.fx.Loop import Loop
@@ -13,7 +13,6 @@ from backend.data.block import (
     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
 
@@ -47,19 +46,18 @@ class MediaDurationBlock(Block):
         self,
         input_data: Input,
         *,
-        execution_context: ExecutionContext,
+        graph_exec_id: str,
+        user_id: str,
         **kwargs,
     ) -> BlockOutput:
         # 1) Store the input media locally
         local_media_path = await store_media_file(
+            graph_exec_id=graph_exec_id,
             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
+            user_id=user_id,
+            return_content=False,
         )
+        media_abspath = get_exec_file_path(graph_exec_id, local_media_path)
 
         # 2) Load the clip
         if input_data.is_video:
@@ -90,6 +88,10 @@ class LoopVideoBlock(Block):
             default=None,
             ge=1,
         )
+        output_return_type: Literal["file_path", "data_uri"] = SchemaField(
+            description="How to return the output video. Either a relative path or base64 data URI.",
+            default="file_path",
+        )
 
     class Output(BlockSchemaOutput):
         video_out: str = SchemaField(
@@ -109,19 +111,17 @@ class LoopVideoBlock(Block):
         self,
         input_data: Input,
         *,
-        execution_context: ExecutionContext,
+        node_exec_id: str,
+        graph_exec_id: str,
+        user_id: str,
         **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(
+            graph_exec_id=graph_exec_id,
             file=input_data.video_in,
-            execution_context=execution_context,
-            return_format="for_local_processing",
+            user_id=user_id,
+            return_content=False,
         )
         input_abspath = get_exec_file_path(graph_exec_id, local_video_path)
 
@@ -149,11 +149,12 @@ class LoopVideoBlock(Block):
         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
+        # Return as data URI
         video_out = await store_media_file(
+            graph_exec_id=graph_exec_id,
             file=output_filename,
-            execution_context=execution_context,
-            return_format="for_block_output",
+            user_id=user_id,
+            return_content=input_data.output_return_type == "data_uri",
         )
 
         yield "video_out", video_out
@@ -176,6 +177,10 @@ class AddAudioToVideoBlock(Block):
             description="Volume scale for the newly attached audio track (1.0 = original).",
             default=1.0,
         )
+        output_return_type: Literal["file_path", "data_uri"] = SchemaField(
+            description="Return the final output as a relative path or base64 data URI.",
+            default="file_path",
+        )
 
     class Output(BlockSchemaOutput):
         video_out: MediaFileType = SchemaField(
@@ -195,24 +200,23 @@ class AddAudioToVideoBlock(Block):
         self,
         input_data: Input,
         *,
-        execution_context: ExecutionContext,
+        node_exec_id: str,
+        graph_exec_id: str,
+        user_id: str,
         **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(
+            graph_exec_id=graph_exec_id,
             file=input_data.video_in,
-            execution_context=execution_context,
-            return_format="for_local_processing",
+            user_id=user_id,
+            return_content=False,
         )
         local_audio_path = await store_media_file(
+            graph_exec_id=graph_exec_id,
             file=input_data.audio_in,
-            execution_context=execution_context,
-            return_format="for_local_processing",
+            user_id=user_id,
+            return_content=False,
         )
 
         abs_temp_dir = os.path.join(tempfile.gettempdir(), "exec_file", graph_exec_id)
@@ -236,11 +240,12 @@ class AddAudioToVideoBlock(Block):
         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
+        # 5) Return either path or data URI
         video_out = await store_media_file(
+            graph_exec_id=graph_exec_id,
             file=output_filename,
-            execution_context=execution_context,
-            return_format="for_block_output",
+            user_id=user_id,
+            return_content=input_data.output_return_type == "data_uri",
         )
 
         yield "video_out", video_out

autogpt_platform/backend/backend/blocks/screenshotone.py

@@ -11,7 +11,6 @@ from backend.data.block import (
     BlockSchemaInput,
     BlockSchemaOutput,
 )
-from backend.data.execution import ExecutionContext
 from backend.data.model import (
     APIKeyCredentials,
     CredentialsField,
@@ -113,7 +112,8 @@ class ScreenshotWebPageBlock(Block):
     @staticmethod
     async def take_screenshot(
         credentials: APIKeyCredentials,
-        execution_context: ExecutionContext,
+        graph_exec_id: str,
+        user_id: str,
         url: str,
         viewport_width: int,
         viewport_height: int,
@@ -155,11 +155,12 @@ class ScreenshotWebPageBlock(Block):
 
         return {
             "image": await store_media_file(
+                graph_exec_id=graph_exec_id,
                 file=MediaFileType(
                     f"data:image/{format.value};base64,{b64encode(content).decode('utf-8')}"
                 ),
-                execution_context=execution_context,
-                return_format="for_block_output",
+                user_id=user_id,
+                return_content=True,
             )
         }
 
@@ -168,13 +169,15 @@ class ScreenshotWebPageBlock(Block):
         input_data: Input,
         *,
         credentials: APIKeyCredentials,
-        execution_context: ExecutionContext,
+        graph_exec_id: str,
+        user_id: str,
         **kwargs,
     ) -> BlockOutput:
         try:
             screenshot_data = await self.take_screenshot(
                 credentials=credentials,
-                execution_context=execution_context,
+                graph_exec_id=graph_exec_id,
+                user_id=user_id,
                 url=input_data.url,
                 viewport_width=input_data.viewport_width,
                 viewport_height=input_data.viewport_height,

autogpt_platform/backend/backend/blocks/spreadsheet.py

@@ -7,7 +7,6 @@ from backend.data.block import (
     BlockSchemaInput,
     BlockSchemaOutput,
 )
-from backend.data.execution import ExecutionContext
 from backend.data.model import ContributorDetails, SchemaField
 from backend.util.file import get_exec_file_path, store_media_file
 from backend.util.type import MediaFileType
@@ -99,7 +98,7 @@ class ReadSpreadsheetBlock(Block):
         )
 
     async def run(
-        self, input_data: Input, *, execution_context: ExecutionContext, **_kwargs
+        self, input_data: Input, *, graph_exec_id: str, user_id: str, **_kwargs
     ) -> BlockOutput:
         import csv
         from io import StringIO
@@ -107,16 +106,14 @@ class ReadSpreadsheetBlock(Block):
         # Determine data source - prefer file_input if provided, otherwise use contents
         if input_data.file_input:
             stored_file_path = await store_media_file(
+                user_id=user_id,
+                graph_exec_id=graph_exec_id,
                 file=input_data.file_input,
-                execution_context=execution_context,
-                return_format="for_local_processing",
+                return_content=False,
             )
 
             # Get full file path
-            assert execution_context.graph_exec_id  # Validated by store_media_file
-            file_path = get_exec_file_path(
-                execution_context.graph_exec_id, stored_file_path
-            )
+            file_path = get_exec_file_path(graph_exec_id, stored_file_path)
             if not Path(file_path).exists():
                 raise ValueError(f"File does not exist: {file_path}")
 

autogpt_platform/backend/backend/blocks/talking_head.py

@@ -10,7 +10,6 @@ from backend.data.block import (
     BlockSchemaInput,
     BlockSchemaOutput,
 )
-from backend.data.execution import ExecutionContext
 from backend.data.model import (
     APIKeyCredentials,
     CredentialsField,
@@ -18,9 +17,7 @@ from backend.data.model import (
     SchemaField,
 )
 from backend.integrations.providers import ProviderName
-from backend.util.file import store_media_file
 from backend.util.request import Requests
-from backend.util.type import MediaFileType
 
 TEST_CREDENTIALS = APIKeyCredentials(
     id="01234567-89ab-cdef-0123-456789abcdef",
@@ -105,7 +102,7 @@ class CreateTalkingAvatarVideoBlock(Block):
             test_output=[
                 (
                     "video_url",
-                    lambda x: x.startswith(("workspace://", "data:")),
+                    "https://d-id.com/api/clips/abcd1234-5678-efgh-ijkl-mnopqrstuvwx/video",
                 ),
             ],
             test_mock={
@@ -113,10 +110,9 @@ class CreateTalkingAvatarVideoBlock(Block):
                     "id": "abcd1234-5678-efgh-ijkl-mnopqrstuvwx",
                     "status": "created",
                 },
-                # Use data URI to avoid HTTP requests during tests
                 "get_clip_status": lambda *args, **kwargs: {
                     "status": "done",
-                    "result_url": "data:video/mp4;base64,AAAA",
+                    "result_url": "https://d-id.com/api/clips/abcd1234-5678-efgh-ijkl-mnopqrstuvwx/video",
                 },
             },
             test_credentials=TEST_CREDENTIALS,
@@ -142,12 +138,7 @@ class CreateTalkingAvatarVideoBlock(Block):
         return response.json()
 
     async def run(
-        self,
-        input_data: Input,
-        *,
-        credentials: APIKeyCredentials,
-        execution_context: ExecutionContext,
-        **kwargs,
+        self, input_data: Input, *, credentials: APIKeyCredentials, **kwargs
     ) -> BlockOutput:
         # Create the clip
         payload = {
@@ -174,14 +165,7 @@ class CreateTalkingAvatarVideoBlock(Block):
         for _ in range(input_data.max_polling_attempts):
             status_response = await self.get_clip_status(credentials.api_key, clip_id)
             if status_response["status"] == "done":
-                # Store the generated video to the user's workspace for persistence
-                video_url = status_response["result_url"]
-                stored_url = await store_media_file(
-                    file=MediaFileType(video_url),
-                    execution_context=execution_context,
-                    return_format="for_block_output",
-                )
-                yield "video_url", stored_url
+                yield "video_url", status_response["result_url"]
                 return
             elif status_response["status"] == "error":
                 raise RuntimeError(

autogpt_platform/backend/backend/blocks/test/test_blocks_dos_vulnerability.py

@@ -12,7 +12,6 @@ from backend.blocks.iteration import StepThroughItemsBlock
 from backend.blocks.llm import AITextSummarizerBlock
 from backend.blocks.text import ExtractTextInformationBlock
 from backend.blocks.xml_parser import XMLParserBlock
-from backend.data.execution import ExecutionContext
 from backend.util.file import store_media_file
 from backend.util.type import MediaFileType
 
@@ -234,12 +233,9 @@ class TestStoreMediaFileSecurity:
 
         with pytest.raises(ValueError, match="File too large"):
             await store_media_file(
+                graph_exec_id="test",
                 file=MediaFileType(large_data_uri),
-                execution_context=ExecutionContext(
-                    user_id="test_user",
-                    graph_exec_id="test",
-                ),
-                return_format="for_local_processing",
+                user_id="test_user",
             )
 
     @patch("backend.util.file.Path")
@@ -274,12 +270,9 @@ class TestStoreMediaFileSecurity:
         # Should raise an error when directory size exceeds limit
         with pytest.raises(ValueError, match="Disk usage limit exceeded"):
             await store_media_file(
+                graph_exec_id="test",
                 file=MediaFileType(
                     "data:text/plain;base64,dGVzdA=="
                 ),  # Small test file
-                execution_context=ExecutionContext(
-                    user_id="test_user",
-                    graph_exec_id="test",
-                ),
-                return_format="for_local_processing",
+                user_id="test_user",
             )

autogpt_platform/backend/backend/blocks/test/test_http.py

@@ -11,22 +11,10 @@ from backend.blocks.http import (
     HttpMethod,
     SendAuthenticatedWebRequestBlock,
 )
-from backend.data.execution import ExecutionContext
 from backend.data.model import HostScopedCredentials
 from backend.util.request import Response
 
 
-def make_test_context(
-    graph_exec_id: str = "test-exec-id",
-    user_id: str = "test-user-id",
-) -> ExecutionContext:
-    """Helper to create test ExecutionContext."""
-    return ExecutionContext(
-        user_id=user_id,
-        graph_exec_id=graph_exec_id,
-    )
-
-
 class TestHttpBlockWithHostScopedCredentials:
     """Test suite for HTTP block integration with HostScopedCredentials."""
 
@@ -117,7 +105,8 @@ class TestHttpBlockWithHostScopedCredentials:
         async for output_name, output_data in http_block.run(
             input_data,
             credentials=exact_match_credentials,
-            execution_context=make_test_context(),
+            graph_exec_id="test-exec-id",
+            user_id="test-user-id",
         ):
             result.append((output_name, output_data))
 
@@ -172,7 +161,8 @@ class TestHttpBlockWithHostScopedCredentials:
         async for output_name, output_data in http_block.run(
             input_data,
             credentials=wildcard_credentials,
-            execution_context=make_test_context(),
+            graph_exec_id="test-exec-id",
+            user_id="test-user-id",
         ):
             result.append((output_name, output_data))
 
@@ -218,7 +208,8 @@ class TestHttpBlockWithHostScopedCredentials:
         async for output_name, output_data in http_block.run(
             input_data,
             credentials=non_matching_credentials,
-            execution_context=make_test_context(),
+            graph_exec_id="test-exec-id",
+            user_id="test-user-id",
         ):
             result.append((output_name, output_data))
 
@@ -267,7 +258,8 @@ class TestHttpBlockWithHostScopedCredentials:
         async for output_name, output_data in http_block.run(
             input_data,
             credentials=exact_match_credentials,
-            execution_context=make_test_context(),
+            graph_exec_id="test-exec-id",
+            user_id="test-user-id",
         ):
             result.append((output_name, output_data))
 
@@ -326,7 +318,8 @@ class TestHttpBlockWithHostScopedCredentials:
         async for output_name, output_data in http_block.run(
             input_data,
             credentials=auto_discovered_creds,  # Execution manager found these
-            execution_context=make_test_context(),
+            graph_exec_id="test-exec-id",
+            user_id="test-user-id",
         ):
             result.append((output_name, output_data))
 
@@ -389,7 +382,8 @@ class TestHttpBlockWithHostScopedCredentials:
         async for output_name, output_data in http_block.run(
             input_data,
             credentials=multi_header_creds,
-            execution_context=make_test_context(),
+            graph_exec_id="test-exec-id",
+            user_id="test-user-id",
         ):
             result.append((output_name, output_data))
 
@@ -477,7 +471,8 @@ class TestHttpBlockWithHostScopedCredentials:
             async for output_name, output_data in http_block.run(
                 input_data,
                 credentials=test_creds,
-                execution_context=make_test_context(),
+                graph_exec_id="test-exec-id",
+                user_id="test-user-id",
             ):
                 result.append((output_name, output_data))
 

autogpt_platform/backend/backend/blocks/text.py

@@ -11,7 +11,6 @@ from backend.data.block import (
     BlockSchemaInput,
     BlockSchemaOutput,
 )
-from backend.data.execution import ExecutionContext
 from backend.data.model import SchemaField
 from backend.util import json, text
 from backend.util.file import get_exec_file_path, store_media_file
@@ -445,21 +444,18 @@ class FileReadBlock(Block):
         )
 
     async def run(
-        self, input_data: Input, *, execution_context: ExecutionContext, **_kwargs
+        self, input_data: Input, *, graph_exec_id: str, user_id: str, **_kwargs
     ) -> BlockOutput:
         # Store the media file properly (handles URLs, data URIs, etc.)
         stored_file_path = await store_media_file(
+            user_id=user_id,
+            graph_exec_id=graph_exec_id,
             file=input_data.file_input,
-            execution_context=execution_context,
-            return_format="for_local_processing",
+            return_content=False,
         )
 
-        # Get full file path (graph_exec_id validated by store_media_file above)
-        if not execution_context.graph_exec_id:
-            raise ValueError("execution_context.graph_exec_id is required")
-        file_path = get_exec_file_path(
-            execution_context.graph_exec_id, stored_file_path
-        )
+        # Get full file path
+        file_path = get_exec_file_path(graph_exec_id, stored_file_path)
 
         if not Path(file_path).exists():
             raise ValueError(f"File does not exist: {file_path}")

autogpt_platform/backend/backend/data/db.py

@@ -26,6 +26,31 @@ def add_param(url: str, key: str, value: str) -> str:
 
 DATABASE_URL = os.getenv("DATABASE_URL", "postgresql://localhost:5432")
 
+# Extract the application schema from DATABASE_URL for use in queries
+_parsed = urlparse(DATABASE_URL)
+_query_params = dict(parse_qsl(_parsed.query))
+_app_schema = _query_params.get("schema", "public")
+
+# Build search_path that includes app schema and extension schemas where pgvector may live.
+# This is used both in connection options (may be ignored by PgBouncer) and in SET LOCAL
+# statements before raw queries (guaranteed to work).
+SEARCH_PATH = (
+    f"{_app_schema},extensions,public"
+    if _app_schema != "public"
+    else "public,extensions"
+)
+
+# Try to set search_path via PostgreSQL options parameter at connection time.
+# NOTE: This may be ignored by PgBouncer in transaction pooling mode.
+# As a fallback, we also SET LOCAL search_path before raw queries.
+if "options" in _query_params:
+    _query_params["options"] = (
+        _query_params["options"] + f" -c search_path={SEARCH_PATH}"
+    )
+else:
+    _query_params["options"] = f"-c search_path={SEARCH_PATH}"
+DATABASE_URL = urlunparse(_parsed._replace(query=urlencode(_query_params)))
+
 CONN_LIMIT = os.getenv("DB_CONNECTION_LIMIT")
 if CONN_LIMIT:
     DATABASE_URL = add_param(DATABASE_URL, "connection_limit", CONN_LIMIT)
@@ -108,6 +133,70 @@ def get_database_schema() -> str:
     return query_params.get("schema", "public")
 
 
+def get_pod_info() -> dict:
+    """Get information about the current pod/host.
+
+    Returns dict with: hostname, pod_name (from HOSTNAME env var in k8s),
+    pod_namespace, pod_ip if available.
+    """
+    import socket
+
+    return {
+        "hostname": socket.gethostname(),
+        "pod_name": os.getenv("HOSTNAME", "unknown"),
+        "pod_namespace": os.getenv("POD_NAMESPACE", "unknown"),
+        "pod_ip": os.getenv("POD_IP", "unknown"),
+    }
+
+
+async def get_connection_debug_info(tx=None) -> dict:
+    """Get diagnostic info about the current database connection and pod.
+
+    Useful for debugging "table does not exist" or "type does not exist" errors
+    that may indicate connections going to different database instances or pods.
+
+    Args:
+        tx: Optional transaction client to use for the query (ensures same connection)
+
+    Returns dict with: search_path, current_schema, server_version, pg_backend_pid,
+    pgvector_installed, pgvector_schema, plus pod info
+    """
+    import prisma as prisma_module
+
+    pod_info = get_pod_info()
+    db_client = tx if tx else prisma_module.get_client()
+
+    try:
+        # Get connection info and check for pgvector in a single query
+        result = await db_client.query_raw(
+            """
+            SELECT
+                current_setting('search_path') as search_path,
+                current_schema() as current_schema,
+                current_database() as current_database,
+                inet_server_addr() as server_addr,
+                inet_server_port() as server_port,
+                pg_backend_pid() as backend_pid,
+                version() as server_version,
+                (SELECT EXISTS(
+                    SELECT 1 FROM pg_extension WHERE extname = 'vector'
+                )) as pgvector_installed,
+                (SELECT nspname FROM pg_extension e
+                 JOIN pg_namespace n ON e.extnamespace = n.oid
+                 WHERE e.extname = 'vector'
+                 LIMIT 1) as pgvector_schema,
+                (SELECT string_agg(extname || ' in ' || nspname, ', ')
+                 FROM pg_extension e
+                 JOIN pg_namespace n ON e.extnamespace = n.oid
+                ) as all_extensions
+            """
+        )
+        db_info = result[0] if result else {}
+        return {**pod_info, **db_info}
+    except Exception as e:
+        return {**pod_info, "db_error": str(e)}
+
+
 async def _raw_with_schema(
     query_template: str,
     *args,
@@ -124,8 +213,9 @@ async def _raw_with_schema(
 
     Note on pgvector types:
         Use unqualified ::vector and <=> operator in queries. PostgreSQL resolves
-        these via search_path, which includes the schema where pgvector is installed
-        on all environments (local, CI, dev).
+        these via search_path. The connection's search_path is configured at module
+        load to include common extension schemas (public, extensions) where pgvector
+        may be installed across different environments (local, CI, Supabase).
 
     Args:
         query_template: SQL query with {schema_prefix} and/or {schema} placeholders
@@ -155,12 +245,60 @@ async def _raw_with_schema(
 
     db_client = client if client else prisma_module.get_client()
 
-    if execute:
-        result = await db_client.execute_raw(formatted_query, *args)  # type: ignore
-    else:
-        result = await db_client.query_raw(formatted_query, *args)  # type: ignore
+    # For queries that might use pgvector types (::vector or <=> operator),
+    # we need to ensure search_path includes the schema where pgvector is installed.
+    # PgBouncer in transaction mode may ignore connection-level options, so we
+    # use SET LOCAL within a transaction to guarantee correct search_path.
+    needs_vector_search_path = "::vector" in formatted_query or "<=>" in formatted_query
 
-    return result
+    try:
+        if needs_vector_search_path and client is None:
+            # Use transaction to set search_path for vector queries
+            async with db_client.tx() as tx:
+                # Log debug info BEFORE the query to capture which backend we're hitting
+                debug_info = await get_connection_debug_info(tx)
+                logger.info(
+                    f"Vector query starting. backend_pid={debug_info.get('backend_pid')}, "
+                    f"server_addr={debug_info.get('server_addr')}, "
+                    f"pgvector_installed={debug_info.get('pgvector_installed')}, "
+                    f"pgvector_schema={debug_info.get('pgvector_schema')}, "
+                    f"search_path={debug_info.get('search_path')}, "
+                    f"pod={debug_info.get('pod_name')}"
+                )
+
+                await tx.execute_raw(f"SET LOCAL search_path TO {SEARCH_PATH}")
+                if execute:
+                    result = await tx.execute_raw(formatted_query, *args)  # type: ignore
+                else:
+                    result = await tx.query_raw(formatted_query, *args)  # type: ignore
+
+                logger.info(
+                    f"Vector query SUCCESS. backend_pid={debug_info.get('backend_pid')}"
+                )
+        else:
+            # Regular query without vector types, or already in a transaction
+            if execute:
+                result = await db_client.execute_raw(formatted_query, *args)  # type: ignore
+            else:
+                result = await db_client.query_raw(formatted_query, *args)  # type: ignore
+        return result
+    except Exception as e:
+        error_msg = str(e)
+        # Log connection debug info for "does not exist" errors to help diagnose
+        # whether connections are going to different database instances
+        if "does not exist" in error_msg:
+            try:
+                debug_info = await get_connection_debug_info()
+                logger.error(
+                    f"Vector query FAILED. Connection debug info: {debug_info}. "
+                    f"Query template: {query_template[:200]}... Error: {error_msg}"
+                )
+            except Exception:
+                logger.error(
+                    f"Vector query FAILED (debug info unavailable). "
+                    f"Query template: {query_template[:200]}... Error: {error_msg}"
+                )
+        raise
 
 
 async def query_raw_with_schema(query_template: str, *args) -> list[dict]:

autogpt_platform/backend/backend/data/execution.py

@@ -83,29 +83,12 @@ class ExecutionContext(BaseModel):
 
     model_config = {"extra": "ignore"}
 
-    # Execution identity
-    user_id: Optional[str] = None
-    graph_id: Optional[str] = None
-    graph_exec_id: Optional[str] = None
-    graph_version: Optional[int] = None
-    node_id: Optional[str] = None
-    node_exec_id: Optional[str] = None
-
-    # Safety settings
     human_in_the_loop_safe_mode: bool = True
     sensitive_action_safe_mode: bool = False
-
-    # User settings
     user_timezone: str = "UTC"
-
-    # Execution hierarchy
     root_execution_id: Optional[str] = None
     parent_execution_id: Optional[str] = None
 
-    # Workspace
-    workspace_id: Optional[str] = None
-    session_id: Optional[str] = None
-
 
 # -------------------------- Models -------------------------- #
 

autogpt_platform/backend/backend/data/onboarding.py

@@ -41,7 +41,6 @@ FrontendOnboardingStep = Literal[
     OnboardingStep.AGENT_NEW_RUN,
     OnboardingStep.AGENT_INPUT,
     OnboardingStep.CONGRATS,
-    OnboardingStep.VISIT_COPILOT,
     OnboardingStep.MARKETPLACE_VISIT,
     OnboardingStep.BUILDER_OPEN,
 ]
@@ -123,9 +122,6 @@ async def update_user_onboarding(user_id: str, data: UserOnboardingUpdate):
 async def _reward_user(user_id: str, onboarding: UserOnboarding, step: OnboardingStep):
     reward = 0
     match step:
-        # Welcome bonus for visiting copilot ($5 = 500 credits)
-        case OnboardingStep.VISIT_COPILOT:
-            reward = 500
         # Reward user when they clicked New Run during onboarding
         # This is because they need credits before scheduling a run (next step)
         # This is seen as a reward for the GET_RESULTS step in the wallet

autogpt_platform/backend/backend/data/understanding.py

@@ -216,7 +216,27 @@ async def get_business_understanding(
 
     # Cache miss - load from database
     logger.debug(f"Business understanding cache miss for user {user_id}")
-    record = await CoPilotUnderstanding.prisma().find_unique(where={"userId": user_id})
+    try:
+        record = await CoPilotUnderstanding.prisma().find_unique(where={"userId": user_id})
+    except Exception as e:
+        error_msg = str(e)
+        if "does not exist" in error_msg:
+            # Log connection debug info to diagnose if connections go to different DBs
+            from backend.data.db import get_connection_debug_info
+
+            try:
+                debug_info = await get_connection_debug_info()
+                logger.error(
+                    f"CoPilotUnderstanding table not found. Connection debug: {debug_info}. "
+                    f"Error: {error_msg}"
+                )
+            except Exception:
+                logger.error(
+                    f"CoPilotUnderstanding table not found (debug unavailable). "
+                    f"Error: {error_msg}"
+                )
+        raise
+
     if record is None:
         return None
 

autogpt_platform/backend/backend/data/workspace.py

@@ -1,285 +0,0 @@
-"""
-Database CRUD operations for User Workspace.
-
-This module provides functions for managing user workspaces and workspace files.
-"""
-
-import logging
-from datetime import datetime, timezone
-from typing import Optional
-
-from prisma.enums import WorkspaceFileSource
-from prisma.models import UserWorkspace, UserWorkspaceFile
-
-from backend.util.json import SafeJson
-
-logger = logging.getLogger(__name__)
-
-
-async def get_or_create_workspace(user_id: str) -> UserWorkspace:
-    """
-    Get user's workspace, creating one if it doesn't exist.
-
-    Uses upsert to handle race conditions when multiple concurrent requests
-    attempt to create a workspace for the same user.
-
-    Args:
-        user_id: The user's ID
-
-    Returns:
-        UserWorkspace instance
-    """
-    workspace = await UserWorkspace.prisma().upsert(
-        where={"userId": user_id},
-        data={
-            "create": {"userId": user_id},
-            "update": {},  # No updates needed if exists
-        },
-    )
-
-    return workspace
-
-
-async def get_workspace(user_id: str) -> Optional[UserWorkspace]:
-    """
-    Get user's workspace if it exists.
-
-    Args:
-        user_id: The user's ID
-
-    Returns:
-        UserWorkspace instance or None
-    """
-    return await UserWorkspace.prisma().find_unique(where={"userId": user_id})
-
-
-async def create_workspace_file(
-    workspace_id: str,
-    file_id: str,
-    name: str,
-    path: str,
-    storage_path: str,
-    mime_type: str,
-    size_bytes: int,
-    checksum: Optional[str] = None,
-    source: WorkspaceFileSource = WorkspaceFileSource.UPLOAD,
-    source_exec_id: Optional[str] = None,
-    source_session_id: Optional[str] = None,
-    metadata: Optional[dict] = None,
-) -> UserWorkspaceFile:
-    """
-    Create a new workspace file record.
-
-    Args:
-        workspace_id: The workspace ID
-        file_id: The file ID (same as used in storage path for consistency)
-        name: User-visible filename
-        path: Virtual path (e.g., "/documents/report.pdf")
-        storage_path: Actual storage path (GCS or local)
-        mime_type: MIME type of the file
-        size_bytes: File size in bytes
-        checksum: Optional SHA256 checksum
-        source: How the file was created
-        source_exec_id: Graph execution ID if from execution
-        source_session_id: Chat session ID if from CoPilot
-        metadata: Optional additional metadata
-
-    Returns:
-        Created UserWorkspaceFile instance
-    """
-    # Normalize path to start with /
-    if not path.startswith("/"):
-        path = f"/{path}"
-
-    file = await UserWorkspaceFile.prisma().create(
-        data={
-            "id": file_id,
-            "workspaceId": workspace_id,
-            "name": name,
-            "path": path,
-            "storagePath": storage_path,
-            "mimeType": mime_type,
-            "sizeBytes": size_bytes,
-            "checksum": checksum,
-            "source": source,
-            "sourceExecId": source_exec_id,
-            "sourceSessionId": source_session_id,
-            "metadata": SafeJson(metadata or {}),
-        }
-    )
-
-    logger.info(
-        f"Created workspace file {file.id} at path {path} "
-        f"in workspace {workspace_id}"
-    )
-    return file
-
-
-async def get_workspace_file(
-    file_id: str,
-    workspace_id: Optional[str] = None,
-) -> Optional[UserWorkspaceFile]:
-    """
-    Get a workspace file by ID.
-
-    Args:
-        file_id: The file ID
-        workspace_id: Optional workspace ID for validation
-
-    Returns:
-        UserWorkspaceFile instance or None
-    """
-    where_clause: dict = {"id": file_id, "isDeleted": False}
-    if workspace_id:
-        where_clause["workspaceId"] = workspace_id
-
-    return await UserWorkspaceFile.prisma().find_first(where=where_clause)
-
-
-async def get_workspace_file_by_path(
-    workspace_id: str,
-    path: str,
-) -> Optional[UserWorkspaceFile]:
-    """
-    Get a workspace file by its virtual path.
-
-    Args:
-        workspace_id: The workspace ID
-        path: Virtual path
-
-    Returns:
-        UserWorkspaceFile instance or None
-    """
-    # Normalize path
-    if not path.startswith("/"):
-        path = f"/{path}"
-
-    return await UserWorkspaceFile.prisma().find_first(
-        where={
-            "workspaceId": workspace_id,
-            "path": path,
-            "isDeleted": False,
-        }
-    )
-
-
-async def list_workspace_files(
-    workspace_id: str,
-    path_prefix: Optional[str] = None,
-    include_deleted: bool = False,
-    limit: Optional[int] = None,
-    offset: int = 0,
-) -> list[UserWorkspaceFile]:
-    """
-    List files in a workspace.
-
-    Args:
-        workspace_id: The workspace ID
-        path_prefix: Optional path prefix to filter (e.g., "/documents/")
-        include_deleted: Whether to include soft-deleted files
-        limit: Maximum number of files to return
-        offset: Number of files to skip
-
-    Returns:
-        List of UserWorkspaceFile instances
-    """
-    where_clause: dict = {"workspaceId": workspace_id}
-
-    if not include_deleted:
-        where_clause["isDeleted"] = False
-
-    if path_prefix:
-        # Normalize prefix
-        if not path_prefix.startswith("/"):
-            path_prefix = f"/{path_prefix}"
-        where_clause["path"] = {"startswith": path_prefix}
-
-    return await UserWorkspaceFile.prisma().find_many(
-        where=where_clause,
-        order={"createdAt": "desc"},
-        take=limit,
-        skip=offset,
-    )
-
-
-async def count_workspace_files(
-    workspace_id: str,
-    path_prefix: Optional[str] = None,
-    include_deleted: bool = False,
-) -> int:
-    """
-    Count files in a workspace.
-
-    Args:
-        workspace_id: The workspace ID
-        path_prefix: Optional path prefix to filter (e.g., "/sessions/abc123/")
-        include_deleted: Whether to include soft-deleted files
-
-    Returns:
-        Number of files
-    """
-    where_clause: dict = {"workspaceId": workspace_id}
-    if not include_deleted:
-        where_clause["isDeleted"] = False
-
-    if path_prefix:
-        # Normalize prefix
-        if not path_prefix.startswith("/"):
-            path_prefix = f"/{path_prefix}"
-        where_clause["path"] = {"startswith": path_prefix}
-
-    return await UserWorkspaceFile.prisma().count(where=where_clause)
-
-
-async def soft_delete_workspace_file(
-    file_id: str,
-    workspace_id: Optional[str] = None,
-) -> Optional[UserWorkspaceFile]:
-    """
-    Soft-delete a workspace file.
-
-    The path is modified to include a deletion timestamp to free up the original
-    path for new files while preserving the record for potential recovery.
-
-    Args:
-        file_id: The file ID
-        workspace_id: Optional workspace ID for validation
-
-    Returns:
-        Updated UserWorkspaceFile instance or None if not found
-    """
-    # First verify the file exists and belongs to workspace
-    file = await get_workspace_file(file_id, workspace_id)
-    if file is None:
-        return None
-
-    deleted_at = datetime.now(timezone.utc)
-    # Modify path to free up the unique constraint for new files at original path
-    # Format: {original_path}__deleted__{timestamp}
-    deleted_path = f"{file.path}__deleted__{int(deleted_at.timestamp())}"
-
-    updated = await UserWorkspaceFile.prisma().update(
-        where={"id": file_id},
-        data={
-            "isDeleted": True,
-            "deletedAt": deleted_at,
-            "path": deleted_path,
-        },
-    )
-
-    logger.info(f"Soft-deleted workspace file {file_id}")
-    return updated
-
-
-async def get_workspace_total_size(workspace_id: str) -> int:
-    """
-    Get the total size of all files in a workspace.
-
-    Args:
-        workspace_id: The workspace ID
-
-    Returns:
-        Total size in bytes
-    """
-    files = await list_workspace_files(workspace_id)
-    return sum(file.sizeBytes for file in files)

autogpt_platform/backend/backend/executor/manager.py

@@ -236,14 +236,7 @@ async def execute_node(
     input_size = len(input_data_str)
     log_metadata.debug("Executed node with input", input=input_data_str)
 
-    # Create node-specific execution context to avoid race conditions
-    # (multiple nodes can execute concurrently and would otherwise mutate shared state)
-    execution_context = execution_context.model_copy(
-        update={"node_id": node_id, "node_exec_id": node_exec_id}
-    )
-
     # Inject extra execution arguments for the blocks via kwargs
-    # Keep individual kwargs for backwards compatibility with existing blocks
     extra_exec_kwargs: dict = {
         "graph_id": graph_id,
         "graph_version": graph_version,

autogpt_platform/backend/backend/executor/utils.py

@@ -892,19 +892,11 @@ async def add_graph_execution(
         settings = await gdb.get_graph_settings(user_id=user_id, graph_id=graph_id)
 
         execution_context = ExecutionContext(
-            # Execution identity
-            user_id=user_id,
-            graph_id=graph_id,
-            graph_exec_id=graph_exec.id,
-            graph_version=graph_exec.graph_version,
-            # Safety settings
             human_in_the_loop_safe_mode=settings.human_in_the_loop_safe_mode,
             sensitive_action_safe_mode=settings.sensitive_action_safe_mode,
-            # User settings
             user_timezone=(
                 user.timezone if user.timezone != USER_TIMEZONE_NOT_SET else "UTC"
             ),
-            # Execution hierarchy
             root_execution_id=graph_exec.id,
         )
 

autogpt_platform/backend/backend/executor/utils_test.py

@@ -348,7 +348,6 @@ async def test_add_graph_execution_is_repeatable(mocker: MockerFixture):
     mock_graph_exec.id = "execution-id-123"
     mock_graph_exec.node_executions = []  # Add this to avoid AttributeError
     mock_graph_exec.status = ExecutionStatus.QUEUED  # Required for race condition check
-    mock_graph_exec.graph_version = graph_version
     mock_graph_exec.to_graph_execution_entry.return_value = mocker.MagicMock()
 
     # Mock the queue and event bus
@@ -435,9 +434,6 @@ async def test_add_graph_execution_is_repeatable(mocker: MockerFixture):
     # Create a second mock execution for the sanity check
     mock_graph_exec_2 = mocker.MagicMock(spec=GraphExecutionWithNodes)
     mock_graph_exec_2.id = "execution-id-456"
-    mock_graph_exec_2.node_executions = []
-    mock_graph_exec_2.status = ExecutionStatus.QUEUED
-    mock_graph_exec_2.graph_version = graph_version
     mock_graph_exec_2.to_graph_execution_entry.return_value = mocker.MagicMock()
 
     # Reset mocks and set up for second call
@@ -618,7 +614,6 @@ async def test_add_graph_execution_with_nodes_to_skip(mocker: MockerFixture):
     mock_graph_exec.id = "execution-id-123"
     mock_graph_exec.node_executions = []
     mock_graph_exec.status = ExecutionStatus.QUEUED  # Required for race condition check
-    mock_graph_exec.graph_version = graph_version
 
     # Track what's passed to to_graph_execution_entry
     captured_kwargs = {}

autogpt_platform/backend/backend/util/cloud_storage.py

@@ -13,7 +13,6 @@ import aiohttp
 from gcloud.aio import storage as async_gcs_storage
 from google.cloud import storage as gcs_storage
 
-from backend.util.gcs_utils import download_with_fresh_session, generate_signed_url
 from backend.util.settings import Config
 
 logger = logging.getLogger(__name__)
@@ -252,7 +251,7 @@ class CloudStorageHandler:
             f"in_task: {current_task is not None}"
         )
 
-        # Parse bucket and blob name from path (path already has gcs:// prefix removed)
+        # Parse bucket and blob name from path
         parts = path.split("/", 1)
         if len(parts) != 2:
             raise ValueError(f"Invalid GCS path: {path}")
@@ -262,19 +261,50 @@ class CloudStorageHandler:
         # Authorization check
         self._validate_file_access(blob_name, user_id, graph_exec_id)
 
-        logger.info(
-            f"[CloudStorage] About to download from GCS - bucket: {bucket_name}, blob: {blob_name}"
+        # Use a fresh client for each download to avoid session issues
+        # This is less efficient but more reliable with the executor's event loop
+        logger.info("[CloudStorage] Creating fresh GCS client for download")
+
+        # Create a new session specifically for this download
+        session = aiohttp.ClientSession(
+            connector=aiohttp.TCPConnector(limit=10, force_close=True)
         )
 
+        async_client = None
         try:
-            content = await download_with_fresh_session(bucket_name, blob_name)
+            # Create a new GCS client with the fresh session
+            async_client = async_gcs_storage.Storage(session=session)
+
+            logger.info(
+                f"[CloudStorage] About to download from GCS - bucket: {bucket_name}, blob: {blob_name}"
+            )
+
+            # Download content using the fresh client
+            content = await async_client.download(bucket_name, blob_name)
             logger.info(
                 f"[CloudStorage] GCS download successful - size: {len(content)} bytes"
             )
+
+            # Clean up
+            await async_client.close()
+            await session.close()
+
             return content
-        except FileNotFoundError:
-            raise
+
         except Exception as e:
+            # Always try to clean up
+            if async_client is not None:
+                try:
+                    await async_client.close()
+                except Exception as cleanup_error:
+                    logger.warning(
+                        f"[CloudStorage] Error closing GCS client: {cleanup_error}"
+                    )
+            try:
+                await session.close()
+            except Exception as cleanup_error:
+                logger.warning(f"[CloudStorage] Error closing session: {cleanup_error}")
+
             # Log the specific error for debugging
             logger.error(
                 f"[CloudStorage] GCS download failed - error: {str(e)}, "
@@ -289,6 +319,10 @@ class CloudStorageHandler:
                     f"current_task: {current_task}, "
                     f"bucket: {bucket_name}, blob: redacted for privacy"
                 )
+
+            # Convert gcloud-aio exceptions to standard ones
+            if "404" in str(e) or "Not Found" in str(e):
+                raise FileNotFoundError(f"File not found: gcs://{path}")
             raise
 
     def _validate_file_access(
@@ -411,7 +445,8 @@ class CloudStorageHandler:
         graph_exec_id: str | None = None,
     ) -> str:
         """Generate signed URL for GCS with authorization."""
-        # Parse bucket and blob name from path (path already has gcs:// prefix removed)
+
+        # Parse bucket and blob name from path
         parts = path.split("/", 1)
         if len(parts) != 2:
             raise ValueError(f"Invalid GCS path: {path}")
@@ -421,11 +456,21 @@ class CloudStorageHandler:
         # Authorization check
         self._validate_file_access(blob_name, user_id, graph_exec_id)
 
+        # Use sync client for signed URLs since gcloud-aio doesn't support them
         sync_client = self._get_sync_gcs_client()
-        return await generate_signed_url(
-            sync_client, bucket_name, blob_name, expiration_hours * 3600
+        bucket = sync_client.bucket(bucket_name)
+        blob = bucket.blob(blob_name)
+
+        # Generate signed URL asynchronously using sync client
+        url = await asyncio.to_thread(
+            blob.generate_signed_url,
+            version="v4",
+            expiration=datetime.now(timezone.utc) + timedelta(hours=expiration_hours),
+            method="GET",
         )
 
+        return url
+
     async def delete_expired_files(self, provider: str = "gcs") -> int:
         """
         Delete files that have passed their expiration time.

autogpt_platform/backend/backend/util/file.py

@@ -5,28 +5,13 @@ import shutil
 import tempfile
 import uuid
 from pathlib import Path
-from typing import TYPE_CHECKING, Literal
 from urllib.parse import urlparse
 
-from prisma.enums import WorkspaceFileSource
-
 from backend.util.cloud_storage import get_cloud_storage_handler
 from backend.util.request import Requests
-from backend.util.settings import Config
 from backend.util.type import MediaFileType
 from backend.util.virus_scanner import scan_content_safe
 
-if TYPE_CHECKING:
-    from backend.data.execution import ExecutionContext
-
-# 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
-# - "for_block_output": Returns best format for output - workspace:// in CoPilot, data URI in graphs
-MediaReturnFormat = Literal[
-    "for_local_processing", "for_external_api", "for_block_output"
-]
-
 TEMP_DIR = Path(tempfile.gettempdir()).resolve()
 
 # Maximum filename length (conservative limit for most filesystems)
@@ -82,56 +67,42 @@ def clean_exec_files(graph_exec_id: str, file: str = "") -> None:
 
 
 async def store_media_file(
+    graph_exec_id: str,
     file: MediaFileType,
-    execution_context: "ExecutionContext",
-    *,
-    return_format: MediaReturnFormat,
+    user_id: str,
+    return_content: bool = False,
 ) -> MediaFileType:
     """
-    Safely handle 'file' (a data URI, a URL, a workspace:// reference, or a local path
-    relative to {temp}/exec_file/{exec_id}), placing or verifying it under:
+    Safely handle 'file' (a data URI, a URL, or a local path relative to {temp}/exec_file/{exec_id}),
+    placing or verifying it under:
         {tempdir}/exec_file/{exec_id}/...
 
-    For each MediaFileType input:
-    - Data URI: decode and store locally
-    - URL: download and store locally
-    - workspace:// reference: read from workspace, store locally
-    - Local path: verify it exists in exec_file directory
+    If 'return_content=True', return a data URI (data:<mime>;base64,<content>).
+    Otherwise, returns the file media path relative to the exec_id folder.
 
-    Return format options:
-    - "for_local_processing": Returns local file path - use with ffmpeg, MoviePy, PIL, etc.
-    - "for_external_api": Returns data URI (base64) - use when sending to external APIs
-    - "for_block_output": Returns best format for output - workspace:// in CoPilot, data URI in graphs
+    For each MediaFileType type:
+    - Data URI:
+      -> decode and store in a new random file in that folder
+    - URL:
+      -> download and store in that folder
+    - Local path:
+      -> interpret as relative to that folder; verify it exists
+         (no copying, as it's presumably already there).
+         We realpath-check so no symlink or '..' can escape the folder.
 
-    :param file:               Data URI, URL, workspace://, or local (relative) path.
-    :param execution_context:  ExecutionContext with user_id, graph_exec_id, workspace_id.
-    :param return_format:      What to return: "for_local_processing", "for_external_api", or "for_block_output".
-    :return:                   The requested result based on return_format.
+
+    :param graph_exec_id:  The unique ID of the graph execution.
+    :param file:           Data URI, URL, or local (relative) path.
+    :param return_content: If True, return a data URI of the file content.
+                           If False, return the *relative* path inside the exec_id folder.
+    :return:               The requested result: data URI or relative path of the media.
     """
-    # Extract values from execution_context
-    graph_exec_id = execution_context.graph_exec_id
-    user_id = execution_context.user_id
-
-    if not graph_exec_id:
-        raise ValueError("execution_context.graph_exec_id is required")
-    if not user_id:
-        raise ValueError("execution_context.user_id is required")
-
-    # Create workspace_manager if we have workspace_id (with session scoping)
-    # Import here to avoid circular import (file.py → workspace.py → data → blocks → file.py)
-    from backend.util.workspace import WorkspaceManager
-
-    workspace_manager: WorkspaceManager | None = None
-    if execution_context.workspace_id:
-        workspace_manager = WorkspaceManager(
-            user_id, execution_context.workspace_id, execution_context.session_id
-        )
     # Build base path
     base_path = Path(get_exec_file_path(graph_exec_id, ""))
     base_path.mkdir(parents=True, exist_ok=True)
 
     # Security fix: Add disk space limits to prevent DoS
-    MAX_FILE_SIZE_BYTES = Config().max_file_size_mb * 1024 * 1024
+    MAX_FILE_SIZE = 100 * 1024 * 1024  # 100MB per file
     MAX_TOTAL_DISK_USAGE = 1024 * 1024 * 1024  # 1GB total per execution directory
 
     # Check total disk usage in base_path
@@ -171,57 +142,9 @@ async def store_media_file(
         """
         return str(absolute_path.relative_to(base))
 
-    # Get cloud storage handler for checking cloud paths
-    cloud_storage = await get_cloud_storage_handler()
-
-    # Track if the input came from workspace (don't re-save it)
-    is_from_workspace = file.startswith("workspace://")
-
-    # Check if this is a workspace file reference
-    if is_from_workspace:
-        if workspace_manager is None:
-            raise ValueError(
-                "Workspace file reference requires workspace context. "
-                "This file type is only available in CoPilot sessions."
-            )
-
-        # Parse workspace reference
-        # workspace://abc123 - by file ID
-        # workspace:///path/to/file.txt - by virtual path
-        file_ref = file[12:]  # Remove "workspace://"
-
-        if file_ref.startswith("/"):
-            # Path reference
-            workspace_content = await workspace_manager.read_file(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_content = await workspace_manager.read_file_by_id(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"
-            )
-
-        try:
-            target_path = _ensure_inside_base(base_path / filename, base_path)
-        except OSError as e:
-            raise ValueError(f"Invalid file path '{filename}': {e}") from e
-
-        # Check file size limit
-        if len(workspace_content) > MAX_FILE_SIZE_BYTES:
-            raise ValueError(
-                f"File too large: {len(workspace_content)} bytes > {MAX_FILE_SIZE_BYTES} bytes"
-            )
-
-        # Virus scan the workspace content before writing locally
-        await scan_content_safe(workspace_content, filename=filename)
-        target_path.write_bytes(workspace_content)
-
     # Check if this is a cloud storage path
-    elif cloud_storage.is_cloud_path(file):
+    cloud_storage = await get_cloud_storage_handler()
+    if cloud_storage.is_cloud_path(file):
         # Download from cloud storage and store locally
         cloud_content = await cloud_storage.retrieve_file(
             file, user_id=user_id, graph_exec_id=graph_exec_id
@@ -236,9 +159,9 @@ async def store_media_file(
             raise ValueError(f"Invalid file path '{filename}': {e}") from e
 
         # Check file size limit
-        if len(cloud_content) > MAX_FILE_SIZE_BYTES:
+        if len(cloud_content) > MAX_FILE_SIZE:
             raise ValueError(
-                f"File too large: {len(cloud_content)} bytes > {MAX_FILE_SIZE_BYTES} bytes"
+                f"File too large: {len(cloud_content)} bytes > {MAX_FILE_SIZE} bytes"
             )
 
         # Virus scan the cloud content before writing locally
@@ -266,9 +189,9 @@ async def store_media_file(
         content = base64.b64decode(b64_content)
 
         # Check file size limit
-        if len(content) > MAX_FILE_SIZE_BYTES:
+        if len(content) > MAX_FILE_SIZE:
             raise ValueError(
-                f"File too large: {len(content)} bytes > {MAX_FILE_SIZE_BYTES} bytes"
+                f"File too large: {len(content)} bytes > {MAX_FILE_SIZE} bytes"
             )
 
         # Virus scan the base64 content before writing
@@ -276,31 +199,23 @@ async def store_media_file(
         target_path.write_bytes(content)
 
     elif file.startswith(("http://", "https://")):
-        # URL - download first to get Content-Type header
-        resp = await Requests().get(file)
-
-        # Check file size limit
-        if len(resp.content) > MAX_FILE_SIZE_BYTES:
-            raise ValueError(
-                f"File too large: {len(resp.content)} bytes > {MAX_FILE_SIZE_BYTES} bytes"
-            )
-
-        # Extract filename from URL path
+        # URL
         parsed_url = urlparse(file)
         filename = sanitize_filename(Path(parsed_url.path).name or f"{uuid.uuid4()}")
-
-        # If filename lacks extension, add one from Content-Type header
-        if "." not in filename:
-            content_type = resp.headers.get("Content-Type", "").split(";")[0].strip()
-            if content_type:
-                ext = _extension_from_mime(content_type)
-                filename = f"{filename}{ext}"
-
         try:
             target_path = _ensure_inside_base(base_path / filename, base_path)
         except OSError as e:
             raise ValueError(f"Invalid file path '{filename}': {e}") from e
 
+        # Download and save
+        resp = await Requests().get(file)
+
+        # Check file size limit
+        if len(resp.content) > MAX_FILE_SIZE:
+            raise ValueError(
+                f"File too large: {len(resp.content)} bytes > {MAX_FILE_SIZE} bytes"
+            )
+
         # Virus scan the downloaded content before writing
         await scan_content_safe(resp.content, filename=filename)
         target_path.write_bytes(resp.content)
@@ -315,45 +230,11 @@ async def store_media_file(
         if not target_path.is_file():
             raise ValueError(f"Local file does not exist: {target_path}")
 
-    # Return based on requested format
-    if return_format == "for_local_processing":
-        # Use when processing files locally with tools like ffmpeg, MoviePy, PIL
-        # Returns: relative path in exec_file directory (e.g., "image.png")
-        return MediaFileType(_strip_base_prefix(target_path, base_path))
-
-    elif return_format == "for_external_api":
-        # Use when sending content to external APIs that need base64
-        # Returns: data URI (e.g., "data:image/png;base64,iVBORw0...")
+    # Return result
+    if return_content:
         return MediaFileType(_file_to_data_uri(target_path))
-
-    elif return_format == "for_block_output":
-        # Use when returning output from a block to user/next block
-        # Returns: workspace:// ref (CoPilot) or data URI (graph execution)
-        if workspace_manager is None:
-            # No workspace available (graph execution without CoPilot)
-            # Fallback to data URI so the content can still be used/displayed
-            return MediaFileType(_file_to_data_uri(target_path))
-
-        # Don't re-save if input was already from workspace
-        if is_from_workspace:
-            # Return original workspace reference
-            return MediaFileType(file)
-
-        # Save new content to workspace
-        content = target_path.read_bytes()
-        filename = target_path.name
-
-        file_record = await workspace_manager.write_file(
-            content=content,
-            filename=filename,
-            source=WorkspaceFileSource.COPILOT,
-            source_session_id=execution_context.session_id,
-            overwrite=True,
-        )
-        return MediaFileType(f"workspace://{file_record.id}")
-
     else:
-        raise ValueError(f"Invalid return_format: {return_format}")
+        return MediaFileType(_strip_base_prefix(target_path, base_path))
 
 
 def get_dir_size(path: Path) -> int:

autogpt_platform/backend/backend/util/file_test.py

@@ -7,22 +7,10 @@ from unittest.mock import AsyncMock, MagicMock, patch
 
 import pytest
 
-from backend.data.execution import ExecutionContext
 from backend.util.file import store_media_file
 from backend.util.type import MediaFileType
 
 
-def make_test_context(
-    graph_exec_id: str = "test-exec-123",
-    user_id: str = "test-user-123",
-) -> ExecutionContext:
-    """Helper to create test ExecutionContext."""
-    return ExecutionContext(
-        user_id=user_id,
-        graph_exec_id=graph_exec_id,
-    )
-
-
 class TestFileCloudIntegration:
     """Test cases for cloud storage integration in file utilities."""
 
@@ -82,9 +70,10 @@ class TestFileCloudIntegration:
             mock_path_class.side_effect = path_constructor
 
             result = await store_media_file(
-                file=MediaFileType(cloud_path),
-                execution_context=make_test_context(graph_exec_id=graph_exec_id),
-                return_format="for_local_processing",
+                graph_exec_id,
+                MediaFileType(cloud_path),
+                "test-user-123",
+                return_content=False,
             )
 
             # Verify cloud storage operations
@@ -155,9 +144,10 @@ class TestFileCloudIntegration:
             mock_path_obj.name = "image.png"
             with patch("backend.util.file.Path", return_value=mock_path_obj):
                 result = await store_media_file(
-                    file=MediaFileType(cloud_path),
-                    execution_context=make_test_context(graph_exec_id=graph_exec_id),
-                    return_format="for_external_api",
+                    graph_exec_id,
+                    MediaFileType(cloud_path),
+                    "test-user-123",
+                    return_content=True,
                 )
 
             # Verify result is a data URI
@@ -208,9 +198,10 @@ class TestFileCloudIntegration:
             mock_resolved_path.relative_to.return_value = Path("test-uuid-789.txt")
 
             await store_media_file(
-                file=MediaFileType(data_uri),
-                execution_context=make_test_context(graph_exec_id=graph_exec_id),
-                return_format="for_local_processing",
+                graph_exec_id,
+                MediaFileType(data_uri),
+                "test-user-123",
+                return_content=False,
             )
 
             # Verify cloud handler was checked but not used for retrieval
@@ -243,7 +234,5 @@ class TestFileCloudIntegration:
                 FileNotFoundError, match="File not found in cloud storage"
             ):
                 await store_media_file(
-                    file=MediaFileType(cloud_path),
-                    execution_context=make_test_context(graph_exec_id=graph_exec_id),
-                    return_format="for_local_processing",
+                    graph_exec_id, MediaFileType(cloud_path), "test-user-123"
                 )

autogpt_platform/backend/backend/util/gcs_utils.py

@@ -1,160 +0,0 @@
-"""
-Shared GCS utilities for workspace and cloud storage backends.
-
-This module provides common functionality for working with Google Cloud Storage,
-including path parsing, client management, and signed URL generation.
-"""
-
-import asyncio
-import logging
-from datetime import datetime, timedelta, timezone
-from typing import Optional
-
-import aiohttp
-from gcloud.aio import storage as async_gcs_storage
-from google.cloud import storage as gcs_storage
-
-logger = logging.getLogger(__name__)
-
-
-def parse_gcs_path(path: str) -> tuple[str, str]:
-    """
-    Parse a GCS path in the format 'gcs://bucket/blob' to (bucket, blob).
-
-    Args:
-        path: GCS path string (e.g., "gcs://my-bucket/path/to/file")
-
-    Returns:
-        Tuple of (bucket_name, blob_name)
-
-    Raises:
-        ValueError: If the path format is invalid
-    """
-    if not path.startswith("gcs://"):
-        raise ValueError(f"Invalid GCS path: {path}")
-
-    path_without_prefix = path[6:]  # Remove "gcs://"
-    parts = path_without_prefix.split("/", 1)
-    if len(parts) != 2:
-        raise ValueError(f"Invalid GCS path format: {path}")
-
-    return parts[0], parts[1]
-
-
-class GCSClientManager:
-    """
-    Manages async and sync GCS clients with lazy initialization.
-
-    This class provides a unified way to manage GCS client lifecycle,
-    supporting both async operations (uploads, downloads) and sync
-    operations that require service account credentials (signed URLs).
-    """
-
-    def __init__(self):
-        self._async_client: Optional[async_gcs_storage.Storage] = None
-        self._sync_client: Optional[gcs_storage.Client] = None
-        self._session: Optional[aiohttp.ClientSession] = None
-
-    async def get_async_client(self) -> async_gcs_storage.Storage:
-        """
-        Get or create async GCS client.
-
-        Returns:
-            Async GCS storage client
-        """
-        if self._async_client is None:
-            self._session = aiohttp.ClientSession(
-                connector=aiohttp.TCPConnector(limit=100, force_close=False)
-            )
-            self._async_client = async_gcs_storage.Storage(session=self._session)
-        return self._async_client
-
-    def get_sync_client(self) -> gcs_storage.Client:
-        """
-        Get or create sync GCS client (used for signed URLs).
-
-        Returns:
-            Sync GCS storage client
-        """
-        if self._sync_client is None:
-            self._sync_client = gcs_storage.Client()
-        return self._sync_client
-
-    async def close(self) -> None:
-        """Close all client connections."""
-        if self._async_client is not None:
-            try:
-                await self._async_client.close()
-            except Exception as e:
-                logger.warning(f"Error closing GCS client: {e}")
-            self._async_client = None
-
-        if self._session is not None:
-            try:
-                await self._session.close()
-            except Exception as e:
-                logger.warning(f"Error closing session: {e}")
-            self._session = None
-
-
-async def download_with_fresh_session(bucket: str, blob: str) -> bytes:
-    """
-    Download file content using a fresh session.
-
-    This approach avoids event loop issues that can occur when reusing
-    sessions across different async contexts (e.g., in executors).
-
-    Args:
-        bucket: GCS bucket name
-        blob: Blob path within the bucket
-
-    Returns:
-        File content as bytes
-
-    Raises:
-        FileNotFoundError: If the file doesn't exist
-    """
-    session = aiohttp.ClientSession(
-        connector=aiohttp.TCPConnector(limit=10, force_close=True)
-    )
-    try:
-        client = async_gcs_storage.Storage(session=session)
-        content = await client.download(bucket, blob)
-        await client.close()
-        return content
-    except Exception as e:
-        if "404" in str(e) or "Not Found" in str(e):
-            raise FileNotFoundError(f"File not found: gcs://{bucket}/{blob}")
-        raise
-    finally:
-        await session.close()
-
-
-async def generate_signed_url(
-    sync_client: gcs_storage.Client,
-    bucket_name: str,
-    blob_name: str,
-    expires_in: int,
-) -> str:
-    """
-    Generate a signed URL for temporary access to a GCS file.
-
-    Uses asyncio.to_thread() to run the sync operation without blocking.
-
-    Args:
-        sync_client: Sync GCS client with service account credentials
-        bucket_name: GCS bucket name
-        blob_name: Blob path within the bucket
-        expires_in: URL expiration time in seconds
-
-    Returns:
-        Signed URL string
-    """
-    bucket = sync_client.bucket(bucket_name)
-    blob = bucket.blob(blob_name)
-    return await asyncio.to_thread(
-        blob.generate_signed_url,
-        version="v4",
-        expiration=datetime.now(timezone.utc) + timedelta(seconds=expires_in),
-        method="GET",
-    )

autogpt_platform/backend/backend/util/settings.py

@@ -263,12 +263,6 @@ class Config(UpdateTrackingModel["Config"], BaseSettings):
         description="The name of the Google Cloud Storage bucket for media files",
     )
 
-    workspace_storage_dir: str = Field(
-        default="",
-        description="Local directory for workspace file storage when GCS is not configured. "
-        "If empty, defaults to {app_data}/workspaces. Used for self-hosted deployments.",
-    )
-
     reddit_user_agent: str = Field(
         default="web:AutoGPT:v0.6.0 (by /u/autogpt)",
         description="The user agent for the Reddit API",
@@ -365,8 +359,8 @@ class Config(UpdateTrackingModel["Config"], BaseSettings):
         description="The port for the Agent Generator service",
     )
     agentgenerator_timeout: int = Field(
-        default=600,
-        description="The timeout in seconds for Agent Generator service requests (includes retries for rate limits)",
+        default=120,
+        description="The timeout in seconds for Agent Generator service requests",
     )
 
     enable_example_blocks: bool = Field(
@@ -395,13 +389,6 @@ class Config(UpdateTrackingModel["Config"], BaseSettings):
         description="Maximum file size in MB for file uploads (1-1024 MB)",
     )
 
-    max_file_size_mb: int = Field(
-        default=100,
-        ge=1,
-        le=1024,
-        description="Maximum file size in MB for workspace files (1-1024 MB)",
-    )
-
     # AutoMod configuration
     automod_enabled: bool = Field(
         default=False,

autogpt_platform/backend/backend/util/test.py

@@ -140,29 +140,14 @@ async def execute_block_test(block: Block):
             setattr(block, mock_name, mock_obj)
 
     # Populate credentials argument(s)
-    # Generate IDs for execution context
-    graph_id = str(uuid.uuid4())
-    node_id = str(uuid.uuid4())
-    graph_exec_id = str(uuid.uuid4())
-    node_exec_id = str(uuid.uuid4())
-    user_id = str(uuid.uuid4())
-    graph_version = 1  # Default version for tests
-
     extra_exec_kwargs: dict = {
-        "graph_id": graph_id,
-        "node_id": node_id,
-        "graph_exec_id": graph_exec_id,
-        "node_exec_id": node_exec_id,
-        "user_id": user_id,
-        "graph_version": graph_version,
-        "execution_context": ExecutionContext(
-            user_id=user_id,
-            graph_id=graph_id,
-            graph_exec_id=graph_exec_id,
-            graph_version=graph_version,
-            node_id=node_id,
-            node_exec_id=node_exec_id,
-        ),
+        "graph_id": str(uuid.uuid4()),
+        "node_id": str(uuid.uuid4()),
+        "graph_exec_id": str(uuid.uuid4()),
+        "node_exec_id": str(uuid.uuid4()),
+        "user_id": str(uuid.uuid4()),
+        "graph_version": 1,  # Default version for tests
+        "execution_context": ExecutionContext(),
     }
     input_model = cast(type[BlockSchema], block.input_schema)
 

autogpt_platform/backend/backend/util/workspace.py

@@ -1,432 +0,0 @@
-"""
-WorkspaceManager for managing user workspace file operations.
-
-This module provides a high-level interface for workspace file operations,
-combining the storage backend and database layer.
-"""
-
-import logging
-import mimetypes
-import uuid
-from typing import Optional
-
-from prisma.enums import WorkspaceFileSource
-from prisma.errors import UniqueViolationError
-from prisma.models import UserWorkspaceFile
-
-from backend.data.workspace import (
-    count_workspace_files,
-    create_workspace_file,
-    get_workspace_file,
-    get_workspace_file_by_path,
-    list_workspace_files,
-    soft_delete_workspace_file,
-)
-from backend.util.settings import Config
-from backend.util.workspace_storage import compute_file_checksum, get_workspace_storage
-
-logger = logging.getLogger(__name__)
-
-
-class WorkspaceManager:
-    """
-    Manages workspace file operations.
-
-    Combines storage backend operations with database record management.
-    Supports session-scoped file segmentation where files are stored in
-    session-specific virtual paths: /sessions/{session_id}/{filename}
-    """
-
-    def __init__(
-        self, user_id: str, workspace_id: str, session_id: Optional[str] = None
-    ):
-        """
-        Initialize WorkspaceManager.
-
-        Args:
-            user_id: The user's ID
-            workspace_id: The workspace ID
-            session_id: Optional session ID for session-scoped file access
-        """
-        self.user_id = user_id
-        self.workspace_id = workspace_id
-        self.session_id = session_id
-        # Session path prefix for file isolation
-        self.session_path = f"/sessions/{session_id}" if session_id else ""
-
-    def _resolve_path(self, path: str) -> str:
-        """
-        Resolve a path, defaulting to session folder if session_id is set.
-
-        Cross-session access is allowed by explicitly using /sessions/other-session-id/...
-
-        Args:
-            path: Virtual path (e.g., "/file.txt" or "/sessions/abc123/file.txt")
-
-        Returns:
-            Resolved path with session prefix if applicable
-        """
-        # If path explicitly references a session folder, use it as-is
-        if path.startswith("/sessions/"):
-            return path
-
-        # If we have a session context, prepend session path
-        if self.session_path:
-            # Normalize the path
-            if not path.startswith("/"):
-                path = f"/{path}"
-            return f"{self.session_path}{path}"
-
-        # No session context, use path as-is
-        return path if path.startswith("/") else f"/{path}"
-
-    def _get_effective_path(
-        self, path: Optional[str], include_all_sessions: bool
-    ) -> Optional[str]:
-        """
-        Get effective path for list/count operations based on session context.
-
-        Args:
-            path: Optional path prefix to filter
-            include_all_sessions: If True, don't apply session scoping
-
-        Returns:
-            Effective path prefix for database query
-        """
-        if include_all_sessions:
-            # Normalize path to ensure leading slash (stored paths are normalized)
-            if path is not None and not path.startswith("/"):
-                return f"/{path}"
-            return path
-        elif path is not None:
-            # Resolve the provided path with session scoping
-            return self._resolve_path(path)
-        elif self.session_path:
-            # Default to session folder with trailing slash to prevent prefix collisions
-            # e.g., "/sessions/abc" should not match "/sessions/abc123"
-            return self.session_path.rstrip("/") + "/"
-        else:
-            # No session context, use path as-is
-            return path
-
-    async def read_file(self, path: str) -> bytes:
-        """
-        Read file from workspace by virtual path.
-
-        When session_id is set, paths are resolved relative to the session folder
-        unless they explicitly reference /sessions/...
-
-        Args:
-            path: Virtual path (e.g., "/documents/report.pdf")
-
-        Returns:
-            File content as bytes
-
-        Raises:
-            FileNotFoundError: If file doesn't exist
-        """
-        resolved_path = self._resolve_path(path)
-        file = await get_workspace_file_by_path(self.workspace_id, resolved_path)
-        if file is None:
-            raise FileNotFoundError(f"File not found at path: {resolved_path}")
-
-        storage = await get_workspace_storage()
-        return await storage.retrieve(file.storagePath)
-
-    async def read_file_by_id(self, file_id: str) -> bytes:
-        """
-        Read file from workspace by file ID.
-
-        Args:
-            file_id: The file's ID
-
-        Returns:
-            File content as bytes
-
-        Raises:
-            FileNotFoundError: If file doesn't exist
-        """
-        file = await get_workspace_file(file_id, self.workspace_id)
-        if file is None:
-            raise FileNotFoundError(f"File not found: {file_id}")
-
-        storage = await get_workspace_storage()
-        return await storage.retrieve(file.storagePath)
-
-    async def write_file(
-        self,
-        content: bytes,
-        filename: str,
-        path: Optional[str] = None,
-        mime_type: Optional[str] = None,
-        source: WorkspaceFileSource = WorkspaceFileSource.UPLOAD,
-        source_exec_id: Optional[str] = None,
-        source_session_id: Optional[str] = None,
-        overwrite: bool = False,
-    ) -> UserWorkspaceFile:
-        """
-        Write file to workspace.
-
-        When session_id is set, files are written to /sessions/{session_id}/...
-        by default. Use explicit /sessions/... paths for cross-session access.
-
-        Args:
-            content: File content as bytes
-            filename: Filename for the file
-            path: Virtual path (defaults to "/{filename}", session-scoped if session_id set)
-            mime_type: MIME type (auto-detected if not provided)
-            source: How the file was created
-            source_exec_id: Graph execution ID if from execution
-            source_session_id: Chat session ID if from CoPilot
-            overwrite: Whether to overwrite existing file at path
-
-        Returns:
-            Created UserWorkspaceFile instance
-
-        Raises:
-            ValueError: If file exceeds size limit or path already exists
-        """
-        # Enforce file size limit
-        max_file_size = Config().max_file_size_mb * 1024 * 1024
-        if len(content) > max_file_size:
-            raise ValueError(
-                f"File too large: {len(content)} bytes exceeds "
-                f"{Config().max_file_size_mb}MB limit"
-            )
-
-        # Determine path with session scoping
-        if path is None:
-            path = f"/{filename}"
-        elif not path.startswith("/"):
-            path = f"/{path}"
-
-        # Resolve path with session prefix
-        path = self._resolve_path(path)
-
-        # Check if file exists at path
-        existing = await get_workspace_file_by_path(self.workspace_id, path)
-        if existing is not None:
-            if overwrite:
-                # Delete existing file first
-                await self.delete_file(existing.id)
-            else:
-                raise ValueError(f"File already exists at path: {path}")
-
-        # Auto-detect MIME type if not provided
-        if mime_type is None:
-            mime_type, _ = mimetypes.guess_type(filename)
-            mime_type = mime_type or "application/octet-stream"
-
-        # Compute checksum
-        checksum = compute_file_checksum(content)
-
-        # Generate unique file ID for storage
-        file_id = str(uuid.uuid4())
-
-        # Store file in storage backend
-        storage = await get_workspace_storage()
-        storage_path = await storage.store(
-            workspace_id=self.workspace_id,
-            file_id=file_id,
-            filename=filename,
-            content=content,
-        )
-
-        # Create database record - handle race condition where another request
-        # created a file at the same path between our check and create
-        try:
-            file = await create_workspace_file(
-                workspace_id=self.workspace_id,
-                file_id=file_id,
-                name=filename,
-                path=path,
-                storage_path=storage_path,
-                mime_type=mime_type,
-                size_bytes=len(content),
-                checksum=checksum,
-                source=source,
-                source_exec_id=source_exec_id,
-                source_session_id=source_session_id,
-            )
-        except UniqueViolationError:
-            # Race condition: another request created a file at this path
-            if overwrite:
-                # Re-fetch and delete the conflicting file, then retry
-                existing = await get_workspace_file_by_path(self.workspace_id, path)
-                if existing:
-                    await self.delete_file(existing.id)
-                # Retry the create - if this also fails, clean up storage file
-                try:
-                    file = await create_workspace_file(
-                        workspace_id=self.workspace_id,
-                        file_id=file_id,
-                        name=filename,
-                        path=path,
-                        storage_path=storage_path,
-                        mime_type=mime_type,
-                        size_bytes=len(content),
-                        checksum=checksum,
-                        source=source,
-                        source_exec_id=source_exec_id,
-                        source_session_id=source_session_id,
-                    )
-                except Exception:
-                    # Clean up orphaned storage file on retry failure
-                    try:
-                        await storage.delete(storage_path)
-                    except Exception as e:
-                        logger.warning(f"Failed to clean up orphaned storage file: {e}")
-                    raise
-            else:
-                # Clean up the orphaned storage file before raising
-                try:
-                    await storage.delete(storage_path)
-                except Exception as e:
-                    logger.warning(f"Failed to clean up orphaned storage file: {e}")
-                raise ValueError(f"File already exists at path: {path}")
-        except Exception:
-            # Any other database error (connection, validation, etc.) - clean up storage
-            try:
-                await storage.delete(storage_path)
-            except Exception as e:
-                logger.warning(f"Failed to clean up orphaned storage file: {e}")
-            raise
-
-        logger.info(
-            f"Wrote file {file.id} ({filename}) to workspace {self.workspace_id} "
-            f"at path {path}, size={len(content)} bytes"
-        )
-
-        return file
-
-    async def list_files(
-        self,
-        path: Optional[str] = None,
-        limit: Optional[int] = None,
-        offset: int = 0,
-        include_all_sessions: bool = False,
-    ) -> list[UserWorkspaceFile]:
-        """
-        List files in workspace.
-
-        When session_id is set and include_all_sessions is False (default),
-        only files in the current session's folder are listed.
-
-        Args:
-            path: Optional path prefix to filter (e.g., "/documents/")
-            limit: Maximum number of files to return
-            offset: Number of files to skip
-            include_all_sessions: If True, list files from all sessions.
-                                  If False (default), only list current session's files.
-
-        Returns:
-            List of UserWorkspaceFile instances
-        """
-        effective_path = self._get_effective_path(path, include_all_sessions)
-
-        return await list_workspace_files(
-            workspace_id=self.workspace_id,
-            path_prefix=effective_path,
-            limit=limit,
-            offset=offset,
-        )
-
-    async def delete_file(self, file_id: str) -> bool:
-        """
-        Delete a file (soft-delete).
-
-        Args:
-            file_id: The file's ID
-
-        Returns:
-            True if deleted, False if not found
-        """
-        file = await get_workspace_file(file_id, self.workspace_id)
-        if file is None:
-            return False
-
-        # Delete from storage
-        storage = await get_workspace_storage()
-        try:
-            await storage.delete(file.storagePath)
-        except Exception as e:
-            logger.warning(f"Failed to delete file from storage: {e}")
-            # Continue with database soft-delete even if storage delete fails
-
-        # Soft-delete database record
-        result = await soft_delete_workspace_file(file_id, self.workspace_id)
-        return result is not None
-
-    async def get_download_url(self, file_id: str, expires_in: int = 3600) -> str:
-        """
-        Get download URL for a file.
-
-        Args:
-            file_id: The file's ID
-            expires_in: URL expiration in seconds (default 1 hour)
-
-        Returns:
-            Download URL (signed URL for GCS, API endpoint for local)
-
-        Raises:
-            FileNotFoundError: If file doesn't exist
-        """
-        file = await get_workspace_file(file_id, self.workspace_id)
-        if file is None:
-            raise FileNotFoundError(f"File not found: {file_id}")
-
-        storage = await get_workspace_storage()
-        return await storage.get_download_url(file.storagePath, expires_in)
-
-    async def get_file_info(self, file_id: str) -> Optional[UserWorkspaceFile]:
-        """
-        Get file metadata.
-
-        Args:
-            file_id: The file's ID
-
-        Returns:
-            UserWorkspaceFile instance or None
-        """
-        return await get_workspace_file(file_id, self.workspace_id)
-
-    async def get_file_info_by_path(self, path: str) -> Optional[UserWorkspaceFile]:
-        """
-        Get file metadata by path.
-
-        When session_id is set, paths are resolved relative to the session folder
-        unless they explicitly reference /sessions/...
-
-        Args:
-            path: Virtual path
-
-        Returns:
-            UserWorkspaceFile instance or None
-        """
-        resolved_path = self._resolve_path(path)
-        return await get_workspace_file_by_path(self.workspace_id, resolved_path)
-
-    async def get_file_count(
-        self,
-        path: Optional[str] = None,
-        include_all_sessions: bool = False,
-    ) -> int:
-        """
-        Get number of files in workspace.
-
-        When session_id is set and include_all_sessions is False (default),
-        only counts files in the current session's folder.
-
-        Args:
-            path: Optional path prefix to filter (e.g., "/documents/")
-            include_all_sessions: If True, count all files in workspace.
-                                  If False (default), only count current session's files.
-
-        Returns:
-            Number of files
-        """
-        effective_path = self._get_effective_path(path, include_all_sessions)
-
-        return await count_workspace_files(
-            self.workspace_id, path_prefix=effective_path
-        )

autogpt_platform/backend/backend/util/workspace_storage.py

@@ -1,398 +0,0 @@
-"""
-Workspace storage backend abstraction for supporting both cloud and local deployments.
-
-This module provides a unified interface for storing workspace files, with implementations
-for Google Cloud Storage (cloud deployments) and local filesystem (self-hosted deployments).
-"""
-
-import asyncio
-import hashlib
-import logging
-from abc import ABC, abstractmethod
-from datetime import datetime, timezone
-from pathlib import Path
-from typing import Optional
-
-import aiofiles
-import aiohttp
-from gcloud.aio import storage as async_gcs_storage
-from google.cloud import storage as gcs_storage
-
-from backend.util.data import get_data_path
-from backend.util.gcs_utils import (
-    download_with_fresh_session,
-    generate_signed_url,
-    parse_gcs_path,
-)
-from backend.util.settings import Config
-
-logger = logging.getLogger(__name__)
-
-
-class WorkspaceStorageBackend(ABC):
-    """Abstract interface for workspace file storage."""
-
-    @abstractmethod
-    async def store(
-        self,
-        workspace_id: str,
-        file_id: str,
-        filename: str,
-        content: bytes,
-    ) -> str:
-        """
-        Store file content, return storage path.
-
-        Args:
-            workspace_id: The workspace ID
-            file_id: Unique file ID for storage
-            filename: Original filename
-            content: File content as bytes
-
-        Returns:
-            Storage path string (cloud path or local path)
-        """
-        pass
-
-    @abstractmethod
-    async def retrieve(self, storage_path: str) -> bytes:
-        """
-        Retrieve file content from storage.
-
-        Args:
-            storage_path: The storage path returned from store()
-
-        Returns:
-            File content as bytes
-        """
-        pass
-
-    @abstractmethod
-    async def delete(self, storage_path: str) -> None:
-        """
-        Delete file from storage.
-
-        Args:
-            storage_path: The storage path to delete
-        """
-        pass
-
-    @abstractmethod
-    async def get_download_url(self, storage_path: str, expires_in: int = 3600) -> str:
-        """
-        Get URL for downloading the file.
-
-        Args:
-            storage_path: The storage path
-            expires_in: URL expiration time in seconds (default 1 hour)
-
-        Returns:
-            Download URL (signed URL for GCS, direct API path for local)
-        """
-        pass
-
-
-class GCSWorkspaceStorage(WorkspaceStorageBackend):
-    """Google Cloud Storage implementation for workspace storage."""
-
-    def __init__(self, bucket_name: str):
-        self.bucket_name = bucket_name
-        self._async_client: Optional[async_gcs_storage.Storage] = None
-        self._sync_client: Optional[gcs_storage.Client] = None
-        self._session: Optional[aiohttp.ClientSession] = None
-
-    async def _get_async_client(self) -> async_gcs_storage.Storage:
-        """Get or create async GCS client."""
-        if self._async_client is None:
-            self._session = aiohttp.ClientSession(
-                connector=aiohttp.TCPConnector(limit=100, force_close=False)
-            )
-            self._async_client = async_gcs_storage.Storage(session=self._session)
-        return self._async_client
-
-    def _get_sync_client(self) -> gcs_storage.Client:
-        """Get or create sync GCS client (for signed URLs)."""
-        if self._sync_client is None:
-            self._sync_client = gcs_storage.Client()
-        return self._sync_client
-
-    async def close(self) -> None:
-        """Close all client connections."""
-        if self._async_client is not None:
-            try:
-                await self._async_client.close()
-            except Exception as e:
-                logger.warning(f"Error closing GCS client: {e}")
-            self._async_client = None
-
-        if self._session is not None:
-            try:
-                await self._session.close()
-            except Exception as e:
-                logger.warning(f"Error closing session: {e}")
-            self._session = None
-
-    def _build_blob_name(self, workspace_id: str, file_id: str, filename: str) -> str:
-        """Build the blob path for workspace files."""
-        return f"workspaces/{workspace_id}/{file_id}/{filename}"
-
-    async def store(
-        self,
-        workspace_id: str,
-        file_id: str,
-        filename: str,
-        content: bytes,
-    ) -> str:
-        """Store file in GCS."""
-        client = await self._get_async_client()
-        blob_name = self._build_blob_name(workspace_id, file_id, filename)
-
-        # Upload with metadata
-        upload_time = datetime.now(timezone.utc)
-        await client.upload(
-            self.bucket_name,
-            blob_name,
-            content,
-            metadata={
-                "uploaded_at": upload_time.isoformat(),
-                "workspace_id": workspace_id,
-                "file_id": file_id,
-            },
-        )
-
-        return f"gcs://{self.bucket_name}/{blob_name}"
-
-    async def retrieve(self, storage_path: str) -> bytes:
-        """Retrieve file from GCS."""
-        bucket_name, blob_name = parse_gcs_path(storage_path)
-        return await download_with_fresh_session(bucket_name, blob_name)
-
-    async def delete(self, storage_path: str) -> None:
-        """Delete file from GCS."""
-        bucket_name, blob_name = parse_gcs_path(storage_path)
-        client = await self._get_async_client()
-
-        try:
-            await client.delete(bucket_name, blob_name)
-        except Exception as e:
-            if "404" not in str(e) and "Not Found" not in str(e):
-                raise
-            # File already deleted, that's fine
-
-    async def get_download_url(self, storage_path: str, expires_in: int = 3600) -> str:
-        """
-        Generate download URL for GCS file.
-
-        Attempts to generate a signed URL if running with service account credentials.
-        Falls back to an API proxy endpoint if signed URL generation fails
-        (e.g., when running locally with user OAuth credentials).
-        """
-        bucket_name, blob_name = parse_gcs_path(storage_path)
-
-        # Extract file_id from blob_name for fallback: workspaces/{workspace_id}/{file_id}/{filename}
-        blob_parts = blob_name.split("/")
-        file_id = blob_parts[2] if len(blob_parts) >= 3 else None
-
-        # Try to generate signed URL (requires service account credentials)
-        try:
-            sync_client = self._get_sync_client()
-            return await generate_signed_url(
-                sync_client, bucket_name, blob_name, expires_in
-            )
-        except AttributeError as e:
-            # Signed URL generation requires service account with private key.
-            # When running with user OAuth credentials, fall back to API proxy.
-            if "private key" in str(e) and file_id:
-                logger.debug(
-                    "Cannot generate signed URL (no service account credentials), "
-                    "falling back to API proxy endpoint"
-                )
-                return f"/api/workspace/files/{file_id}/download"
-            raise
-
-
-class LocalWorkspaceStorage(WorkspaceStorageBackend):
-    """Local filesystem implementation for workspace storage (self-hosted deployments)."""
-
-    def __init__(self, base_dir: Optional[str] = None):
-        """
-        Initialize local storage backend.
-
-        Args:
-            base_dir: Base directory for workspace storage.
-                     If None, defaults to {app_data}/workspaces
-        """
-        if base_dir:
-            self.base_dir = Path(base_dir)
-        else:
-            self.base_dir = Path(get_data_path()) / "workspaces"
-
-        # Ensure base directory exists
-        self.base_dir.mkdir(parents=True, exist_ok=True)
-
-    def _build_file_path(self, workspace_id: str, file_id: str, filename: str) -> Path:
-        """Build the local file path with path traversal protection."""
-        # Import here to avoid circular import
-        # (file.py imports workspace.py which imports workspace_storage.py)
-        from backend.util.file import sanitize_filename
-
-        # Sanitize filename to prevent path traversal (removes / and \ among others)
-        safe_filename = sanitize_filename(filename)
-        file_path = (self.base_dir / workspace_id / file_id / safe_filename).resolve()
-
-        # Verify the resolved path is still under base_dir
-        if not file_path.is_relative_to(self.base_dir.resolve()):
-            raise ValueError("Invalid filename: path traversal detected")
-
-        return file_path
-
-    def _parse_storage_path(self, storage_path: str) -> Path:
-        """Parse local storage path to filesystem path."""
-        if storage_path.startswith("local://"):
-            relative_path = storage_path[8:]  # Remove "local://"
-        else:
-            relative_path = storage_path
-
-        full_path = (self.base_dir / relative_path).resolve()
-
-        # Security check: ensure path is under base_dir
-        # Use is_relative_to() for robust path containment check
-        # (handles case-insensitive filesystems and edge cases)
-        if not full_path.is_relative_to(self.base_dir.resolve()):
-            raise ValueError("Invalid storage path: path traversal detected")
-
-        return full_path
-
-    async def store(
-        self,
-        workspace_id: str,
-        file_id: str,
-        filename: str,
-        content: bytes,
-    ) -> str:
-        """Store file locally."""
-        file_path = self._build_file_path(workspace_id, file_id, filename)
-
-        # Create parent directories
-        file_path.parent.mkdir(parents=True, exist_ok=True)
-
-        # Write file asynchronously
-        async with aiofiles.open(file_path, "wb") as f:
-            await f.write(content)
-
-        # Return relative path as storage path
-        relative_path = file_path.relative_to(self.base_dir)
-        return f"local://{relative_path}"
-
-    async def retrieve(self, storage_path: str) -> bytes:
-        """Retrieve file from local storage."""
-        file_path = self._parse_storage_path(storage_path)
-
-        if not file_path.exists():
-            raise FileNotFoundError(f"File not found: {storage_path}")
-
-        async with aiofiles.open(file_path, "rb") as f:
-            return await f.read()
-
-    async def delete(self, storage_path: str) -> None:
-        """Delete file from local storage."""
-        file_path = self._parse_storage_path(storage_path)
-
-        if file_path.exists():
-            # Remove file
-            file_path.unlink()
-
-            # Clean up empty parent directories
-            parent = file_path.parent
-            while parent != self.base_dir:
-                try:
-                    if parent.exists() and not any(parent.iterdir()):
-                        parent.rmdir()
-                    else:
-                        break
-                except OSError:
-                    break
-                parent = parent.parent
-
-    async def get_download_url(self, storage_path: str, expires_in: int = 3600) -> str:
-        """
-        Get download URL for local file.
-
-        For local storage, this returns an API endpoint path.
-        The actual serving is handled by the API layer.
-        """
-        # Parse the storage path to get the components
-        if storage_path.startswith("local://"):
-            relative_path = storage_path[8:]
-        else:
-            relative_path = storage_path
-
-        # Return the API endpoint for downloading
-        # The file_id is extracted from the path: {workspace_id}/{file_id}/{filename}
-        parts = relative_path.split("/")
-        if len(parts) >= 2:
-            file_id = parts[1]  # Second component is file_id
-            return f"/api/workspace/files/{file_id}/download"
-        else:
-            raise ValueError(f"Invalid storage path format: {storage_path}")
-
-
-# Global storage backend instance
-_workspace_storage: Optional[WorkspaceStorageBackend] = None
-_storage_lock = asyncio.Lock()
-
-
-async def get_workspace_storage() -> WorkspaceStorageBackend:
-    """
-    Get the workspace storage backend instance.
-
-    Uses GCS if media_gcs_bucket_name is configured, otherwise uses local storage.
-    """
-    global _workspace_storage
-
-    if _workspace_storage is None:
-        async with _storage_lock:
-            if _workspace_storage is None:
-                config = Config()
-
-                if config.media_gcs_bucket_name:
-                    logger.info(
-                        f"Using GCS workspace storage: {config.media_gcs_bucket_name}"
-                    )
-                    _workspace_storage = GCSWorkspaceStorage(
-                        config.media_gcs_bucket_name
-                    )
-                else:
-                    storage_dir = (
-                        config.workspace_storage_dir
-                        if config.workspace_storage_dir
-                        else None
-                    )
-                    logger.info(
-                        f"Using local workspace storage: {storage_dir or 'default'}"
-                    )
-                    _workspace_storage = LocalWorkspaceStorage(storage_dir)
-
-    return _workspace_storage
-
-
-async def shutdown_workspace_storage() -> None:
-    """
-    Properly shutdown the global workspace storage backend.
-
-    Closes aiohttp sessions and other resources for GCS backend.
-    Should be called during application shutdown.
-    """
-    global _workspace_storage
-
-    if _workspace_storage is not None:
-        async with _storage_lock:
-            if _workspace_storage is not None:
-                if isinstance(_workspace_storage, GCSWorkspaceStorage):
-                    await _workspace_storage.close()
-                _workspace_storage = None
-
-
-def compute_file_checksum(content: bytes) -> str:
-    """Compute SHA256 checksum of file content."""
-    return hashlib.sha256(content).hexdigest()

autogpt_platform/backend/migrations/20260127211502_add_visit_copilot_onboarding_step/migration.sql

@@ -1,2 +0,0 @@
--- AlterEnum
-ALTER TYPE "OnboardingStep" ADD VALUE 'VISIT_COPILOT';

autogpt_platform/backend/migrations/20260127230419_add_user_workspace/migration.sql

@@ -1,52 +0,0 @@
--- CreateEnum
-CREATE TYPE "WorkspaceFileSource" AS ENUM ('UPLOAD', 'EXECUTION', 'COPILOT', 'IMPORT');
-
--- CreateTable
-CREATE TABLE "UserWorkspace" (
-    "id" TEXT NOT NULL,
-    "createdAt" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP,
-    "updatedAt" TIMESTAMP(3) NOT NULL,
-    "userId" TEXT NOT NULL,
-
-    CONSTRAINT "UserWorkspace_pkey" PRIMARY KEY ("id")
-);
-
--- CreateTable
-CREATE TABLE "UserWorkspaceFile" (
-    "id" TEXT NOT NULL,
-    "createdAt" TIMESTAMP(3) NOT NULL DEFAULT CURRENT_TIMESTAMP,
-    "updatedAt" TIMESTAMP(3) NOT NULL,
-    "workspaceId" TEXT NOT NULL,
-    "name" TEXT NOT NULL,
-    "path" TEXT NOT NULL,
-    "storagePath" TEXT NOT NULL,
-    "mimeType" TEXT NOT NULL,
-    "sizeBytes" BIGINT NOT NULL,
-    "checksum" TEXT,
-    "isDeleted" BOOLEAN NOT NULL DEFAULT false,
-    "deletedAt" TIMESTAMP(3),
-    "source" "WorkspaceFileSource" NOT NULL DEFAULT 'UPLOAD',
-    "sourceExecId" TEXT,
-    "sourceSessionId" TEXT,
-    "metadata" JSONB NOT NULL DEFAULT '{}',
-
-    CONSTRAINT "UserWorkspaceFile_pkey" PRIMARY KEY ("id")
-);
-
--- CreateIndex
-CREATE UNIQUE INDEX "UserWorkspace_userId_key" ON "UserWorkspace"("userId");
-
--- CreateIndex
-CREATE INDEX "UserWorkspace_userId_idx" ON "UserWorkspace"("userId");
-
--- CreateIndex
-CREATE INDEX "UserWorkspaceFile_workspaceId_isDeleted_idx" ON "UserWorkspaceFile"("workspaceId", "isDeleted");
-
--- CreateIndex
-CREATE UNIQUE INDEX "UserWorkspaceFile_workspaceId_path_key" ON "UserWorkspaceFile"("workspaceId", "path");
-
--- AddForeignKey
-ALTER TABLE "UserWorkspace" ADD CONSTRAINT "UserWorkspace_userId_fkey" FOREIGN KEY ("userId") REFERENCES "User"("id") ON DELETE CASCADE ON UPDATE CASCADE;
-
--- AddForeignKey
-ALTER TABLE "UserWorkspaceFile" ADD CONSTRAINT "UserWorkspaceFile_workspaceId_fkey" FOREIGN KEY ("workspaceId") REFERENCES "UserWorkspace"("id") ON DELETE CASCADE ON UPDATE CASCADE;

autogpt_platform/backend/schema.prisma

@@ -63,7 +63,6 @@ model User {
   IntegrationWebhooks   IntegrationWebhook[]
   NotificationBatches   UserNotificationBatch[]
   PendingHumanReviews   PendingHumanReview[]
-  Workspace             UserWorkspace?
 
   // OAuth Provider relations
   OAuthApplications       OAuthApplication[]
@@ -82,7 +81,6 @@ enum OnboardingStep {
   AGENT_INPUT
   CONGRATS
   // First Wins
-  VISIT_COPILOT
   GET_RESULTS
   MARKETPLACE_VISIT
   MARKETPLACE_ADD_AGENT
@@ -138,66 +136,6 @@ model CoPilotUnderstanding {
   @@index([userId])
 }
 
-////////////////////////////////////////////////////////////
-////////////////////////////////////////////////////////////
-////////////////   USER WORKSPACE TABLES   /////////////////
-////////////////////////////////////////////////////////////
-////////////////////////////////////////////////////////////
-
-// User's persistent file storage workspace
-model UserWorkspace {
-  id        String   @id @default(uuid())
-  createdAt DateTime @default(now())
-  updatedAt DateTime @updatedAt
-
-  userId String @unique
-  User   User   @relation(fields: [userId], references: [id], onDelete: Cascade)
-
-  Files UserWorkspaceFile[]
-
-  @@index([userId])
-}
-
-// Source of workspace file creation
-enum WorkspaceFileSource {
-  UPLOAD // Direct user upload
-  EXECUTION // Created by graph execution
-  COPILOT // Created by CoPilot session
-  IMPORT // Imported from external source
-}
-
-// Individual files in a user's workspace
-model UserWorkspaceFile {
-  id        String   @id @default(uuid())
-  createdAt DateTime @default(now())
-  updatedAt DateTime @updatedAt
-
-  workspaceId String
-  Workspace   UserWorkspace @relation(fields: [workspaceId], references: [id], onDelete: Cascade)
-
-  // File metadata
-  name        String // User-visible filename
-  path        String // Virtual path (e.g., "/documents/report.pdf")
-  storagePath String // Actual GCS or local storage path
-  mimeType    String
-  sizeBytes   BigInt
-  checksum    String? // SHA256 for integrity
-
-  // File state
-  isDeleted Boolean   @default(false)
-  deletedAt DateTime?
-
-  // Source tracking
-  source          WorkspaceFileSource @default(UPLOAD)
-  sourceExecId    String? // graph_exec_id if from execution
-  sourceSessionId String? // chat_session_id if from CoPilot
-
-  metadata Json @default("{}")
-
-  @@unique([workspaceId, path])
-  @@index([workspaceId, isDeleted])
-}
-
 model BuilderSearchHistory {
   id        String   @id @default(uuid())
   createdAt DateTime @default(now())

autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/CopilotShell.tsx

@@ -1,10 +1,12 @@
 "use client";
 
 import { ChatLoader } from "@/components/contextual/Chat/components/ChatLoader/ChatLoader";
-import { Text } from "@/components/atoms/Text/Text";
 import { NAVBAR_HEIGHT_PX } from "@/lib/constants";
 import type { ReactNode } from "react";
+import { useEffect } from "react";
+import { useCopilotStore } from "../../copilot-page-store";
 import { DesktopSidebar } from "./components/DesktopSidebar/DesktopSidebar";
+import { LoadingState } from "./components/LoadingState/LoadingState";
 import { MobileDrawer } from "./components/MobileDrawer/MobileDrawer";
 import { MobileHeader } from "./components/MobileHeader/MobileHeader";
 import { useCopilotShell } from "./useCopilotShell";
@@ -18,21 +20,38 @@ export function CopilotShell({ children }: Props) {
     isMobile,
     isDrawerOpen,
     isLoading,
-    isCreatingSession,
     isLoggedIn,
     hasActiveSession,
     sessions,
     currentSessionId,
+    handleSelectSession,
     handleOpenDrawer,
     handleCloseDrawer,
     handleDrawerOpenChange,
-    handleNewChatClick,
-    handleSessionClick,
+    handleNewChat,
     hasNextPage,
     isFetchingNextPage,
     fetchNextPage,
+    isReadyToShowContent,
   } = useCopilotShell();
 
+  const setNewChatHandler = useCopilotStore((s) => s.setNewChatHandler);
+  const requestNewChat = useCopilotStore((s) => s.requestNewChat);
+
+  useEffect(
+    function registerNewChatHandler() {
+      setNewChatHandler(handleNewChat);
+      return function cleanup() {
+        setNewChatHandler(null);
+      };
+    },
+    [handleNewChat],
+  );
+
+  function handleNewChatClick() {
+    requestNewChat();
+  }
+
   if (!isLoggedIn) {
     return (
       <div className="flex h-full items-center justify-center">
@@ -53,7 +72,7 @@ export function CopilotShell({ children }: Props) {
           isLoading={isLoading}
           hasNextPage={hasNextPage}
           isFetchingNextPage={isFetchingNextPage}
-          onSelectSession={handleSessionClick}
+          onSelectSession={handleSelectSession}
           onFetchNextPage={fetchNextPage}
           onNewChat={handleNewChatClick}
           hasActiveSession={Boolean(hasActiveSession)}
@@ -63,18 +82,7 @@ export function CopilotShell({ children }: Props) {
       <div className="relative flex min-h-0 flex-1 flex-col">
         {isMobile && <MobileHeader onOpenDrawer={handleOpenDrawer} />}
         <div className="flex min-h-0 flex-1 flex-col">
-          {isCreatingSession ? (
-            <div className="flex h-full flex-1 flex-col items-center justify-center bg-[#f8f8f9]">
-              <div className="flex flex-col items-center gap-4">
-                <ChatLoader />
-                <Text variant="body" className="text-zinc-500">
-                  Creating your chat...
-                </Text>
-              </div>
-            </div>
-          ) : (
-            children
-          )}
+          {isReadyToShowContent ? children : <LoadingState />}
         </div>
       </div>
 
@@ -86,7 +94,7 @@ export function CopilotShell({ children }: Props) {
           isLoading={isLoading}
           hasNextPage={hasNextPage}
           isFetchingNextPage={isFetchingNextPage}
-          onSelectSession={handleSessionClick}
+          onSelectSession={handleSelectSession}
           onFetchNextPage={fetchNextPage}
           onNewChat={handleNewChatClick}
           onClose={handleCloseDrawer}

autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/components/LoadingState/LoadingState.tsx

@@ -0,0 +1,15 @@
+import { Text } from "@/components/atoms/Text/Text";
+import { ChatLoader } from "@/components/contextual/Chat/components/ChatLoader/ChatLoader";
+
+export function LoadingState() {
+  return (
+    <div className="flex flex-1 items-center justify-center">
+      <div className="flex flex-col items-center gap-4">
+        <ChatLoader />
+        <Text variant="body" className="text-zinc-500">
+          Loading your chats...
+        </Text>
+      </div>
+    </div>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/components/MobileDrawer/useMobileDrawer.ts

@@ -3,17 +3,17 @@ import { useState } from "react";
 export function useMobileDrawer() {
   const [isDrawerOpen, setIsDrawerOpen] = useState(false);
 
-  const handleOpenDrawer = () => {
+  function handleOpenDrawer() {
     setIsDrawerOpen(true);
-  };
+  }
 
-  const handleCloseDrawer = () => {
+  function handleCloseDrawer() {
     setIsDrawerOpen(false);
-  };
+  }
 
-  const handleDrawerOpenChange = (open: boolean) => {
+  function handleDrawerOpenChange(open: boolean) {
     setIsDrawerOpen(open);
-  };
+  }
 
   return {
     isDrawerOpen,

autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/components/SessionsList/useSessionsPagination.ts

@@ -1,6 +1,11 @@
-import { useGetV2ListSessions } from "@/app/api/__generated__/endpoints/chat/chat";
+import {
+  getGetV2ListSessionsQueryKey,
+  useGetV2ListSessions,
+} from "@/app/api/__generated__/endpoints/chat/chat";
 import type { SessionSummaryResponse } from "@/app/api/__generated__/models/sessionSummaryResponse";
 import { okData } from "@/app/api/helpers";
+import { useChatStore } from "@/components/contextual/Chat/chat-store";
+import { useQueryClient } from "@tanstack/react-query";
 import { useEffect, useState } from "react";
 
 const PAGE_SIZE = 50;
@@ -11,12 +16,12 @@ export interface UseSessionsPaginationArgs {
 
 export function useSessionsPagination({ enabled }: UseSessionsPaginationArgs) {
   const [offset, setOffset] = useState(0);
-
   const [accumulatedSessions, setAccumulatedSessions] = useState<
     SessionSummaryResponse[]
   >([]);
-
   const [totalCount, setTotalCount] = useState<number | null>(null);
+  const queryClient = useQueryClient();
+  const onStreamComplete = useChatStore((state) => state.onStreamComplete);
 
   const { data, isLoading, isFetching, isError } = useGetV2ListSessions(
     { limit: PAGE_SIZE, offset },
@@ -27,23 +32,38 @@ export function useSessionsPagination({ enabled }: UseSessionsPaginationArgs) {
     },
   );
 
-  useEffect(() => {
-    const responseData = okData(data);
-    if (responseData) {
-      const newSessions = responseData.sessions;
-      const total = responseData.total;
-      setTotalCount(total);
-
-      if (offset === 0) {
-        setAccumulatedSessions(newSessions);
-      } else {
-        setAccumulatedSessions((prev) => [...prev, ...newSessions]);
-      }
-    } else if (!enabled) {
+  useEffect(function refreshOnStreamComplete() {
+    const unsubscribe = onStreamComplete(function handleStreamComplete() {
+      setOffset(0);
       setAccumulatedSessions([]);
       setTotalCount(null);
-    }
-  }, [data, offset, enabled]);
+      queryClient.invalidateQueries({
+        queryKey: getGetV2ListSessionsQueryKey(),
+      });
+    });
+    return unsubscribe;
+  }, []);
+
+  useEffect(
+    function updateSessionsFromResponse() {
+      const responseData = okData(data);
+      if (responseData) {
+        const newSessions = responseData.sessions;
+        const total = responseData.total;
+        setTotalCount(total);
+
+        if (offset === 0) {
+          setAccumulatedSessions(newSessions);
+        } else {
+          setAccumulatedSessions((prev) => [...prev, ...newSessions]);
+        }
+      } else if (!enabled) {
+        setAccumulatedSessions([]);
+        setTotalCount(null);
+      }
+    },
+    [data, offset, enabled],
+  );
 
   const hasNextPage =
     totalCount !== null && accumulatedSessions.length < totalCount;
@@ -66,17 +86,17 @@ export function useSessionsPagination({ enabled }: UseSessionsPaginationArgs) {
     }
   }, [hasNextPage, isFetching, isLoading, isError, totalCount]);
 
-  const fetchNextPage = () => {
+  function fetchNextPage() {
     if (hasNextPage && !isFetching) {
       setOffset((prev) => prev + PAGE_SIZE);
     }
-  };
+  }
 
-  const reset = () => {
+  function reset() {
     setOffset(0);
     setAccumulatedSessions([]);
     setTotalCount(null);
-  };
+  }
 
   return {
     sessions: accumulatedSessions,

autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/helpers.ts

@@ -104,3 +104,76 @@ export function mergeCurrentSessionIntoList(
 export function getCurrentSessionId(searchParams: URLSearchParams) {
   return searchParams.get("sessionId");
 }
+
+export function shouldAutoSelectSession(
+  areAllSessionsLoaded: boolean,
+  hasAutoSelectedSession: boolean,
+  paramSessionId: string | null,
+  visibleSessions: SessionSummaryResponse[],
+  accumulatedSessions: SessionSummaryResponse[],
+  isLoading: boolean,
+  totalCount: number | null,
+) {
+  if (!areAllSessionsLoaded || hasAutoSelectedSession) {
+    return {
+      shouldSelect: false,
+      sessionIdToSelect: null,
+      shouldCreate: false,
+    };
+  }
+
+  if (paramSessionId) {
+    return {
+      shouldSelect: false,
+      sessionIdToSelect: null,
+      shouldCreate: false,
+    };
+  }
+
+  if (visibleSessions.length > 0) {
+    return {
+      shouldSelect: true,
+      sessionIdToSelect: visibleSessions[0].id,
+      shouldCreate: false,
+    };
+  }
+
+  if (accumulatedSessions.length === 0 && !isLoading && totalCount === 0) {
+    return { shouldSelect: false, sessionIdToSelect: null, shouldCreate: true };
+  }
+
+  if (totalCount === 0) {
+    return {
+      shouldSelect: false,
+      sessionIdToSelect: null,
+      shouldCreate: false,
+    };
+  }
+
+  return { shouldSelect: false, sessionIdToSelect: null, shouldCreate: false };
+}
+
+export function checkReadyToShowContent(
+  areAllSessionsLoaded: boolean,
+  paramSessionId: string | null,
+  accumulatedSessions: SessionSummaryResponse[],
+  isCurrentSessionLoading: boolean,
+  currentSessionData: SessionDetailResponse | null | undefined,
+  hasAutoSelectedSession: boolean,
+) {
+  if (!areAllSessionsLoaded) return false;
+
+  if (paramSessionId) {
+    const sessionFound = accumulatedSessions.some(
+      (s) => s.id === paramSessionId,
+    );
+    return (
+      sessionFound ||
+      (!isCurrentSessionLoading &&
+        currentSessionData !== undefined &&
+        currentSessionData !== null)
+    );
+  }
+
+  return hasAutoSelectedSession;
+}

autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/useCopilotShell.ts

@@ -1,22 +1,26 @@
 "use client";
 
 import {
-  getGetV2GetSessionQueryKey,
   getGetV2ListSessionsQueryKey,
   useGetV2GetSession,
 } from "@/app/api/__generated__/endpoints/chat/chat";
+import type { SessionSummaryResponse } from "@/app/api/__generated__/models/sessionSummaryResponse";
 import { okData } from "@/app/api/helpers";
-import { useChatStore } from "@/components/contextual/Chat/chat-store";
 import { useBreakpoint } from "@/lib/hooks/useBreakpoint";
 import { useSupabase } from "@/lib/supabase/hooks/useSupabase";
 import { useQueryClient } from "@tanstack/react-query";
+import { parseAsString, useQueryState } from "nuqs";
 import { usePathname, useSearchParams } from "next/navigation";
-import { useRef } from "react";
-import { useCopilotStore } from "../../copilot-page-store";
-import { useCopilotSessionId } from "../../useCopilotSessionId";
+import { useEffect, useRef, useState } from "react";
 import { useMobileDrawer } from "./components/MobileDrawer/useMobileDrawer";
-import { getCurrentSessionId } from "./helpers";
-import { useShellSessionList } from "./useShellSessionList";
+import { useSessionsPagination } from "./components/SessionsList/useSessionsPagination";
+import {
+  checkReadyToShowContent,
+  convertSessionDetailToSummary,
+  filterVisibleSessions,
+  getCurrentSessionId,
+  mergeCurrentSessionIntoList,
+} from "./helpers";
 
 export function useCopilotShell() {
   const pathname = usePathname();
@@ -27,7 +31,7 @@ export function useCopilotShell() {
   const isMobile =
     breakpoint === "base" || breakpoint === "sm" || breakpoint === "md";
 
-  const { urlSessionId, setUrlSessionId } = useCopilotSessionId();
+  const [, setUrlSessionId] = useQueryState("sessionId", parseAsString);
 
   const isOnHomepage = pathname === "/copilot";
   const paramSessionId = searchParams.get("sessionId");
@@ -41,80 +45,123 @@ export function useCopilotShell() {
 
   const paginationEnabled = !isMobile || isDrawerOpen || !!paramSessionId;
 
+  const {
+    sessions: accumulatedSessions,
+    isLoading: isSessionsLoading,
+    isFetching: isSessionsFetching,
+    hasNextPage,
+    areAllSessionsLoaded,
+    fetchNextPage,
+    reset: resetPagination,
+  } = useSessionsPagination({
+    enabled: paginationEnabled,
+  });
+
   const currentSessionId = getCurrentSessionId(searchParams);
 
-  const { data: currentSessionData } = useGetV2GetSession(
-    currentSessionId || "",
-    {
+  const { data: currentSessionData, isLoading: isCurrentSessionLoading } =
+    useGetV2GetSession(currentSessionId || "", {
       query: {
         enabled: !!currentSessionId,
         select: okData,
       },
-    },
-  );
-
-  const {
-    sessions,
-    isLoading,
-    isSessionsFetching,
-    hasNextPage,
-    fetchNextPage,
-    resetPagination,
-    recentlyCreatedSessionsRef,
-  } = useShellSessionList({
-    paginationEnabled,
-    currentSessionId,
-    currentSessionData,
-    isOnHomepage,
-    paramSessionId,
-  });
-
-  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);
-
-  const pendingActionRef = useRef<(() => void) | null>(null);
-
-  async function stopCurrentStream() {
-    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);
-  }
+  const [hasAutoSelectedSession, setHasAutoSelectedSession] = useState(false);
+  const hasAutoSelectedRef = useRef(false);
+  const recentlyCreatedSessionsRef = useRef<
+    Map<string, SessionSummaryResponse>
+  >(new Map());
 
-  function selectSession(sessionId: string) {
-    if (sessionId === currentSessionId) return;
-    if (recentlyCreatedSessionsRef.current.has(sessionId)) {
+  // Mark as auto-selected when sessionId is in URL
+  useEffect(() => {
+    if (paramSessionId && !hasAutoSelectedRef.current) {
+      hasAutoSelectedRef.current = true;
+      setHasAutoSelectedSession(true);
+    }
+  }, [paramSessionId]);
+
+  // On homepage without sessionId, mark as ready immediately
+  useEffect(() => {
+    if (isOnHomepage && !paramSessionId && !hasAutoSelectedRef.current) {
+      hasAutoSelectedRef.current = true;
+      setHasAutoSelectedSession(true);
+    }
+  }, [isOnHomepage, paramSessionId]);
+
+  // Invalidate sessions list when navigating to homepage (to show newly created sessions)
+  useEffect(() => {
+    if (isOnHomepage && !paramSessionId) {
       queryClient.invalidateQueries({
-        queryKey: getGetV2GetSessionQueryKey(sessionId),
+        queryKey: getGetV2ListSessionsQueryKey(),
       });
     }
+  }, [isOnHomepage, paramSessionId, queryClient]);
+
+  // Track newly created sessions to ensure they stay visible even when switching away
+  useEffect(() => {
+    if (currentSessionId && currentSessionData) {
+      const isNewSession =
+        currentSessionData.updated_at === currentSessionData.created_at;
+      const isNotInAccumulated = !accumulatedSessions.some(
+        (s) => s.id === currentSessionId,
+      );
+      if (isNewSession || isNotInAccumulated) {
+        const summary = convertSessionDetailToSummary(currentSessionData);
+        recentlyCreatedSessionsRef.current.set(currentSessionId, summary);
+      }
+    }
+  }, [currentSessionId, currentSessionData, accumulatedSessions]);
+
+  // Clean up recently created sessions that are now in the accumulated list
+  useEffect(() => {
+    for (const sessionId of recentlyCreatedSessionsRef.current.keys()) {
+      if (accumulatedSessions.some((s) => s.id === sessionId)) {
+        recentlyCreatedSessionsRef.current.delete(sessionId);
+      }
+    }
+  }, [accumulatedSessions]);
+
+  // Reset pagination when query becomes disabled
+  const prevPaginationEnabledRef = useRef(paginationEnabled);
+  useEffect(() => {
+    if (prevPaginationEnabledRef.current && !paginationEnabled) {
+      resetPagination();
+      resetAutoSelect();
+    }
+    prevPaginationEnabledRef.current = paginationEnabled;
+  }, [paginationEnabled, resetPagination]);
+
+  const sessions = mergeCurrentSessionIntoList(
+    accumulatedSessions,
+    currentSessionId,
+    currentSessionData,
+    recentlyCreatedSessionsRef.current,
+  );
+
+  const visibleSessions = filterVisibleSessions(sessions);
+
+  const sidebarSelectedSessionId =
+    isOnHomepage && !paramSessionId ? null : currentSessionId;
+
+  const isReadyToShowContent = isOnHomepage
+    ? true
+    : checkReadyToShowContent(
+        areAllSessionsLoaded,
+        paramSessionId,
+        accumulatedSessions,
+        isCurrentSessionLoading,
+        currentSessionData,
+        hasAutoSelectedSession,
+      );
+
+  function handleSelectSession(sessionId: string) {
     setUrlSessionId(sessionId, { shallow: false });
     if (isMobile) handleCloseDrawer();
   }
 
-  function startNewChat() {
+  function handleNewChat() {
+    resetAutoSelect();
     resetPagination();
     queryClient.invalidateQueries({
       queryKey: getGetV2ListSessionsQueryKey(),
@@ -123,31 +170,12 @@ 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 resetAutoSelect() {
+    hasAutoSelectedRef.current = false;
+    setHasAutoSelectedSession(false);
   }
 
-  function handleNewChatClick() {
-    if (isStreaming) {
-      pendingActionRef.current = async () => {
-        await stopCurrentStream();
-        startNewChat();
-      };
-      openInterruptModal(pendingActionRef.current);
-    } else {
-      startNewChat();
-    }
-  }
+  const isLoading = isSessionsLoading && accumulatedSessions.length === 0;
 
   return {
     isMobile,
@@ -155,17 +183,17 @@ export function useCopilotShell() {
     isLoggedIn,
     hasActiveSession:
       Boolean(currentSessionId) && (!isOnHomepage || Boolean(paramSessionId)),
-    isLoading: isLoading || isCreatingSession,
-    isCreatingSession,
-    sessions,
-    currentSessionId: urlSessionId,
+    isLoading,
+    sessions: visibleSessions,
+    currentSessionId: sidebarSelectedSessionId,
+    handleSelectSession,
     handleOpenDrawer,
     handleCloseDrawer,
     handleDrawerOpenChange,
-    handleNewChatClick,
-    handleSessionClick,
+    handleNewChat,
     hasNextPage,
     isFetchingNextPage: isSessionsFetching,
     fetchNextPage,
+    isReadyToShowContent,
   };
 }

autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/useShellSessionList.ts

@@ -1,113 +0,0 @@
-import { getGetV2ListSessionsQueryKey } from "@/app/api/__generated__/endpoints/chat/chat";
-import type { SessionDetailResponse } from "@/app/api/__generated__/models/sessionDetailResponse";
-import type { SessionSummaryResponse } from "@/app/api/__generated__/models/sessionSummaryResponse";
-import { useChatStore } from "@/components/contextual/Chat/chat-store";
-import { useQueryClient } from "@tanstack/react-query";
-import { useEffect, useMemo, useRef } from "react";
-import { useSessionsPagination } from "./components/SessionsList/useSessionsPagination";
-import {
-  convertSessionDetailToSummary,
-  filterVisibleSessions,
-  mergeCurrentSessionIntoList,
-} from "./helpers";
-
-interface UseShellSessionListArgs {
-  paginationEnabled: boolean;
-  currentSessionId: string | null;
-  currentSessionData: SessionDetailResponse | null | undefined;
-  isOnHomepage: boolean;
-  paramSessionId: string | null;
-}
-
-export function useShellSessionList({
-  paginationEnabled,
-  currentSessionId,
-  currentSessionData,
-  isOnHomepage,
-  paramSessionId,
-}: UseShellSessionListArgs) {
-  const queryClient = useQueryClient();
-  const onStreamComplete = useChatStore((s) => s.onStreamComplete);
-
-  const {
-    sessions: accumulatedSessions,
-    isLoading: isSessionsLoading,
-    isFetching: isSessionsFetching,
-    hasNextPage,
-    fetchNextPage,
-    reset: resetPagination,
-  } = useSessionsPagination({
-    enabled: paginationEnabled,
-  });
-
-  const recentlyCreatedSessionsRef = useRef<
-    Map<string, SessionSummaryResponse>
-  >(new Map());
-
-  useEffect(() => {
-    if (isOnHomepage && !paramSessionId) {
-      queryClient.invalidateQueries({
-        queryKey: getGetV2ListSessionsQueryKey(),
-      });
-    }
-  }, [isOnHomepage, paramSessionId, queryClient]);
-
-  useEffect(() => {
-    if (currentSessionId && currentSessionData) {
-      const isNewSession =
-        currentSessionData.updated_at === currentSessionData.created_at;
-      const isNotInAccumulated = !accumulatedSessions.some(
-        (s) => s.id === currentSessionId,
-      );
-      if (isNewSession || isNotInAccumulated) {
-        const summary = convertSessionDetailToSummary(currentSessionData);
-        recentlyCreatedSessionsRef.current.set(currentSessionId, summary);
-      }
-    }
-  }, [currentSessionId, currentSessionData, accumulatedSessions]);
-
-  useEffect(() => {
-    for (const sessionId of recentlyCreatedSessionsRef.current.keys()) {
-      if (accumulatedSessions.some((s) => s.id === sessionId)) {
-        recentlyCreatedSessionsRef.current.delete(sessionId);
-      }
-    }
-  }, [accumulatedSessions]);
-
-  useEffect(() => {
-    const unsubscribe = onStreamComplete(() => {
-      queryClient.invalidateQueries({
-        queryKey: getGetV2ListSessionsQueryKey(),
-      });
-    });
-    return unsubscribe;
-  }, [onStreamComplete, queryClient]);
-
-  const sessions = useMemo(
-    () =>
-      mergeCurrentSessionIntoList(
-        accumulatedSessions,
-        currentSessionId,
-        currentSessionData,
-        recentlyCreatedSessionsRef.current,
-      ),
-    [accumulatedSessions, currentSessionId, currentSessionData],
-  );
-
-  const visibleSessions = useMemo(
-    () => filterVisibleSessions(sessions),
-    [sessions],
-  );
-
-  const isLoading = isSessionsLoading && accumulatedSessions.length === 0;
-
-  return {
-    sessions: visibleSessions,
-    isLoading,
-    isSessionsFetching,
-    hasNextPage,
-    fetchNextPage,
-    resetPagination,
-    recentlyCreatedSessionsRef,
-  };
-}

autogpt_platform/frontend/src/app/(platform)/copilot/copilot-page-store.ts

@@ -4,53 +4,51 @@ import { create } from "zustand";
 
 interface CopilotStoreState {
   isStreaming: boolean;
-  isSwitchingSession: boolean;
-  isCreatingSession: boolean;
-  isInterruptModalOpen: boolean;
-  pendingAction: (() => void) | null;
+  isNewChatModalOpen: boolean;
+  newChatHandler: (() => void) | null;
 }
 
 interface CopilotStoreActions {
   setIsStreaming: (isStreaming: boolean) => void;
-  setIsSwitchingSession: (isSwitchingSession: boolean) => void;
-  setIsCreatingSession: (isCreating: boolean) => void;
-  openInterruptModal: (onConfirm: () => void) => void;
-  confirmInterrupt: () => void;
-  cancelInterrupt: () => void;
+  setNewChatHandler: (handler: (() => void) | null) => void;
+  requestNewChat: () => void;
+  confirmNewChat: () => void;
+  cancelNewChat: () => void;
 }
 
 type CopilotStore = CopilotStoreState & CopilotStoreActions;
 
 export const useCopilotStore = create<CopilotStore>((set, get) => ({
   isStreaming: false,
-  isSwitchingSession: false,
-  isCreatingSession: false,
-  isInterruptModalOpen: false,
-  pendingAction: null,
+  isNewChatModalOpen: false,
+  newChatHandler: null,
 
   setIsStreaming(isStreaming) {
     set({ isStreaming });
   },
 
-  setIsSwitchingSession(isSwitchingSession) {
-    set({ isSwitchingSession });
+  setNewChatHandler(handler) {
+    set({ newChatHandler: handler });
   },
 
-  setIsCreatingSession(isCreatingSession) {
-    set({ isCreatingSession });
+  requestNewChat() {
+    const { isStreaming, newChatHandler } = get();
+    if (isStreaming) {
+      set({ isNewChatModalOpen: true });
+    } else if (newChatHandler) {
+      newChatHandler();
+    }
   },
 
-  openInterruptModal(onConfirm) {
-    set({ isInterruptModalOpen: true, pendingAction: onConfirm });
+  confirmNewChat() {
+    const { newChatHandler } = get();
+    set({ isNewChatModalOpen: false });
+    if (newChatHandler) {
+      newChatHandler();
+    }
   },
 
-  confirmInterrupt() {
-    const { pendingAction } = get();
-    set({ isInterruptModalOpen: false, pendingAction: null });
-    if (pendingAction) pendingAction();
-  },
-
-  cancelInterrupt() {
-    set({ isInterruptModalOpen: false, pendingAction: null });
+  cancelNewChat() {
+    set({ isNewChatModalOpen: false });
   },
 }));

autogpt_platform/frontend/src/app/(platform)/copilot/helpers.ts

@@ -1,5 +1,28 @@
 import type { User } from "@supabase/supabase-js";
 
+export type PageState =
+  | { type: "welcome" }
+  | { type: "newChat" }
+  | { type: "creating"; prompt: string }
+  | { type: "chat"; sessionId: string; initialPrompt?: string };
+
+export function getInitialPromptFromState(
+  pageState: PageState,
+  storedInitialPrompt: string | undefined,
+) {
+  if (storedInitialPrompt) return storedInitialPrompt;
+  if (pageState.type === "creating") return pageState.prompt;
+  if (pageState.type === "chat") return pageState.initialPrompt;
+}
+
+export function shouldResetToWelcome(pageState: PageState) {
+  return (
+    pageState.type !== "newChat" &&
+    pageState.type !== "creating" &&
+    pageState.type !== "welcome"
+  );
+}
+
 export function getGreetingName(user?: User | null): string {
   if (!user) return "there";
   const metadata = user.user_metadata as Record<string, unknown> | undefined;

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

@@ -1,25 +1,25 @@
 "use client";
 
 import { Button } from "@/components/atoms/Button/Button";
+
 import { Skeleton } from "@/components/atoms/Skeleton/Skeleton";
 import { Text } from "@/components/atoms/Text/Text";
 import { Chat } from "@/components/contextual/Chat/Chat";
 import { ChatInput } from "@/components/contextual/Chat/components/ChatInput/ChatInput";
+import { ChatLoader } from "@/components/contextual/Chat/components/ChatLoader/ChatLoader";
 import { Dialog } from "@/components/molecules/Dialog/Dialog";
 import { useCopilotStore } from "./copilot-page-store";
 import { useCopilotPage } from "./useCopilotPage";
 
 export default function CopilotPage() {
   const { state, handlers } = useCopilotPage();
-  const isInterruptModalOpen = useCopilotStore((s) => s.isInterruptModalOpen);
-  const confirmInterrupt = useCopilotStore((s) => s.confirmInterrupt);
-  const cancelInterrupt = useCopilotStore((s) => s.cancelInterrupt);
+  const confirmNewChat = useCopilotStore((s) => s.confirmNewChat);
   const {
     greetingName,
     quickActions,
     isLoading,
-    hasSession,
-    initialPrompt,
+    pageState,
+    isNewChatModalOpen,
     isReady,
   } = state;
   const {
@@ -27,16 +27,20 @@ export default function CopilotPage() {
     startChatWithPrompt,
     handleSessionNotFound,
     handleStreamingChange,
+    handleCancelNewChat,
+    handleNewChatModalOpen,
   } = handlers;
 
   if (!isReady) return null;
 
-  if (hasSession) {
+  if (pageState.type === "chat") {
     return (
       <div className="flex h-full flex-col">
         <Chat
+          key={pageState.sessionId ?? "welcome"}
           className="flex-1"
-          initialPrompt={initialPrompt}
+          urlSessionId={pageState.sessionId}
+          initialPrompt={pageState.initialPrompt}
           onSessionNotFound={handleSessionNotFound}
           onStreamingChange={handleStreamingChange}
         />
@@ -44,33 +48,31 @@ export default function CopilotPage() {
           title="Interrupt current chat?"
           styling={{ maxWidth: 300, width: "100%" }}
           controlled={{
-            isOpen: isInterruptModalOpen,
-            set: (open) => {
-              if (!open) cancelInterrupt();
-            },
+            isOpen: isNewChatModalOpen,
+            set: handleNewChatModalOpen,
           }}
-          onClose={cancelInterrupt}
+          onClose={handleCancelNewChat}
         >
           <Dialog.Content>
             <div className="flex flex-col gap-4">
               <Text variant="body">
                 The current chat response will be interrupted. Are you sure you
-                want to continue?
+                want to start a new chat?
               </Text>
               <Dialog.Footer>
                 <Button
                   type="button"
                   variant="outline"
-                  onClick={cancelInterrupt}
+                  onClick={handleCancelNewChat}
                 >
                   Cancel
                 </Button>
                 <Button
                   type="button"
                   variant="primary"
-                  onClick={confirmInterrupt}
+                  onClick={confirmNewChat}
                 >
-                  Continue
+                  Start new chat
                 </Button>
               </Dialog.Footer>
             </div>
@@ -80,6 +82,19 @@ export default function CopilotPage() {
     );
   }
 
+  if (pageState.type === "newChat" || pageState.type === "creating") {
+    return (
+      <div className="flex h-full flex-1 flex-col items-center justify-center bg-[#f8f8f9]">
+        <div className="flex flex-col items-center gap-4">
+          <ChatLoader />
+          <Text variant="body" className="text-zinc-500">
+            Loading your chats...
+          </Text>
+        </div>
+      </div>
+    );
+  }
+
   return (
     <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">

autogpt_platform/frontend/src/app/(platform)/copilot/useCopilotPage.ts

@@ -5,40 +5,79 @@ import {
 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 { useEffect, useReducer } from "react";
 import { useCopilotStore } from "./copilot-page-store";
-import { getGreetingName, getQuickActions } from "./helpers";
-import { useCopilotSessionId } from "./useCopilotSessionId";
+import { getGreetingName, getQuickActions, type PageState } from "./helpers";
+import { useCopilotURLState } from "./useCopilotURLState";
+
+type CopilotState = {
+  pageState: PageState;
+  initialPrompts: Record<string, string>;
+  previousSessionId: string | null;
+};
+
+type CopilotAction =
+  | { type: "setPageState"; pageState: PageState }
+  | { type: "setInitialPrompt"; sessionId: string; prompt: string }
+  | { type: "setPreviousSessionId"; sessionId: string | null };
+
+function isSamePageState(next: PageState, current: PageState) {
+  if (next.type !== current.type) return false;
+  if (next.type === "creating" && current.type === "creating") {
+    return next.prompt === current.prompt;
+  }
+  if (next.type === "chat" && current.type === "chat") {
+    return (
+      next.sessionId === current.sessionId &&
+      next.initialPrompt === current.initialPrompt
+    );
+  }
+  return true;
+}
+
+function copilotReducer(
+  state: CopilotState,
+  action: CopilotAction,
+): CopilotState {
+  if (action.type === "setPageState") {
+    if (isSamePageState(action.pageState, state.pageState)) return state;
+    return { ...state, pageState: action.pageState };
+  }
+  if (action.type === "setInitialPrompt") {
+    if (state.initialPrompts[action.sessionId] === action.prompt) return state;
+    return {
+      ...state,
+      initialPrompts: {
+        ...state.initialPrompts,
+        [action.sessionId]: action.prompt,
+      },
+    };
+  }
+  if (action.type === "setPreviousSessionId") {
+    if (state.previousSessionId === action.sessionId) return state;
+    return { ...state, previousSessionId: action.sessionId };
+  }
+  return state;
+}
 
 export function useCopilotPage() {
   const router = useRouter();
   const queryClient = useQueryClient();
   const { user, isLoggedIn, isUserLoading } = useSupabase();
   const { toast } = useToast();
-  const { completeStep } = useOnboarding();
 
-  const { urlSessionId, setUrlSessionId } = useCopilotSessionId();
+  const isNewChatModalOpen = useCopilotStore((s) => s.isNewChatModalOpen);
   const setIsStreaming = useCopilotStore((s) => s.setIsStreaming);
-  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 cancelNewChat = useCopilotStore((s) => s.cancelNewChat);
 
   const isChatEnabled = useGetFlag(Flag.CHAT);
   const flags = useFlags<FlagValues>();
@@ -49,27 +88,72 @@ export function useCopilotPage() {
   const isFlagReady =
     !isLaunchDarklyConfigured || flags[Flag.CHAT] !== undefined;
 
+  const [state, dispatch] = useReducer(copilotReducer, {
+    pageState: { type: "welcome" },
+    initialPrompts: {},
+    previousSessionId: null,
+  });
+
   const greetingName = getGreetingName(user);
   const quickActions = getQuickActions();
 
-  const hasSession = Boolean(urlSessionId);
-  const initialPrompt = urlSessionId
-    ? getInitialPrompt(urlSessionId)
-    : undefined;
+  function setPageState(pageState: PageState) {
+    dispatch({ type: "setPageState", pageState });
+  }
 
-  useEffect(() => {
-    if (!isFlagReady) return;
-    if (isChatEnabled === false) {
-      router.replace(homepageRoute);
-    }
-  }, [homepageRoute, isChatEnabled, isFlagReady, router]);
+  function setInitialPrompt(sessionId: string, prompt: string) {
+    dispatch({ type: "setInitialPrompt", sessionId, prompt });
+  }
+
+  function setPreviousSessionId(sessionId: string | null) {
+    dispatch({ type: "setPreviousSessionId", sessionId });
+  }
+
+  const { setUrlSessionId } = useCopilotURLState({
+    pageState: state.pageState,
+    initialPrompts: state.initialPrompts,
+    previousSessionId: state.previousSessionId,
+    setPageState,
+    setInitialPrompt,
+    setPreviousSessionId,
+  });
+
+  useEffect(
+    function transitionNewChatToWelcome() {
+      if (state.pageState.type === "newChat") {
+        function setWelcomeState() {
+          dispatch({ type: "setPageState", pageState: { type: "welcome" } });
+        }
+
+        const timer = setTimeout(setWelcomeState, 300);
+
+        return function cleanup() {
+          clearTimeout(timer);
+        };
+      }
+    },
+    [state.pageState.type],
+  );
+
+  useEffect(
+    function ensureAccess() {
+      if (!isFlagReady) return;
+      if (isChatEnabled === false) {
+        router.replace(homepageRoute);
+      }
+    },
+    [homepageRoute, isChatEnabled, isFlagReady, router],
+  );
 
   async function startChatWithPrompt(prompt: string) {
     if (!prompt?.trim()) return;
-    if (isCreating) return;
+    if (state.pageState.type === "creating") return;
 
     const trimmedPrompt = prompt.trim();
-    setIsCreating(true);
+    dispatch({
+      type: "setPageState",
+      pageState: { type: "creating", prompt: trimmedPrompt },
+    });
 
     try {
       const sessionResponse = await postV2CreateSession({
@@ -81,19 +165,27 @@ export function useCopilotPage() {
       }
 
       const sessionId = sessionResponse.data.id;
-      setInitialPrompt(sessionId, trimmedPrompt);
+
+      dispatch({
+        type: "setInitialPrompt",
+        sessionId,
+        prompt: trimmedPrompt,
+      });
 
       await queryClient.invalidateQueries({
         queryKey: getGetV2ListSessionsQueryKey(),
       });
 
-      await setUrlSessionId(sessionId, { shallow: true });
+      await setUrlSessionId(sessionId, { shallow: false });
+      dispatch({
+        type: "setPageState",
+        pageState: { type: "chat", sessionId, initialPrompt: trimmedPrompt },
+      });
     } catch (error) {
       console.error("[CopilotPage] Failed to start chat:", error);
       toast({ title: "Failed to start chat", variant: "destructive" });
       Sentry.captureException(error);
-    } finally {
-      setIsCreating(false);
+      dispatch({ type: "setPageState", pageState: { type: "welcome" } });
     }
   }
 
@@ -109,13 +201,21 @@ export function useCopilotPage() {
     setIsStreaming(isStreamingValue);
   }
 
+  function handleCancelNewChat() {
+    cancelNewChat();
+  }
+
+  function handleNewChatModalOpen(isOpen: boolean) {
+    if (!isOpen) cancelNewChat();
+  }
+
   return {
     state: {
       greetingName,
       quickActions,
       isLoading: isUserLoading,
-      hasSession,
-      initialPrompt,
+      pageState: state.pageState,
+      isNewChatModalOpen,
       isReady: isFlagReady && isChatEnabled !== false && isLoggedIn,
     },
     handlers: {
@@ -123,32 +223,8 @@ export function useCopilotPage() {
       startChatWithPrompt,
       handleSessionNotFound,
       handleStreamingChange,
+      handleCancelNewChat,
+      handleNewChatModalOpen,
     },
   };
 }
-
-function getInitialPrompt(sessionId: string): string | undefined {
-  try {
-    const prompts = JSON.parse(
-      sessionStorage.get(SessionKey.CHAT_INITIAL_PROMPTS) || "{}",
-    );
-    return prompts[sessionId];
-  } catch {
-    return undefined;
-  }
-}
-
-function setInitialPrompt(sessionId: string, prompt: string): void {
-  try {
-    const prompts = JSON.parse(
-      sessionStorage.get(SessionKey.CHAT_INITIAL_PROMPTS) || "{}",
-    );
-    prompts[sessionId] = prompt;
-    sessionStorage.set(
-      SessionKey.CHAT_INITIAL_PROMPTS,
-      JSON.stringify(prompts),
-    );
-  } catch {
-    // Ignore storage errors
-  }
-}

autogpt_platform/frontend/src/app/(platform)/copilot/useCopilotSessionId.ts

@@ -1,10 +0,0 @@
-import { parseAsString, useQueryState } from "nuqs";
-
-export function useCopilotSessionId() {
-  const [urlSessionId, setUrlSessionId] = useQueryState(
-    "sessionId",
-    parseAsString,
-  );
-
-  return { urlSessionId, setUrlSessionId };
-}

autogpt_platform/frontend/src/app/(platform)/copilot/useCopilotURLState.ts

@@ -0,0 +1,80 @@
+import { parseAsString, useQueryState } from "nuqs";
+import { useLayoutEffect } from "react";
+import {
+  getInitialPromptFromState,
+  type PageState,
+  shouldResetToWelcome,
+} from "./helpers";
+
+interface UseCopilotUrlStateArgs {
+  pageState: PageState;
+  initialPrompts: Record<string, string>;
+  previousSessionId: string | null;
+  setPageState: (pageState: PageState) => void;
+  setInitialPrompt: (sessionId: string, prompt: string) => void;
+  setPreviousSessionId: (sessionId: string | null) => void;
+}
+
+export function useCopilotURLState({
+  pageState,
+  initialPrompts,
+  previousSessionId,
+  setPageState,
+  setInitialPrompt,
+  setPreviousSessionId,
+}: UseCopilotUrlStateArgs) {
+  const [urlSessionId, setUrlSessionId] = useQueryState(
+    "sessionId",
+    parseAsString,
+  );
+
+  function syncSessionFromUrl() {
+    if (urlSessionId) {
+      if (pageState.type === "chat" && pageState.sessionId === urlSessionId) {
+        setPreviousSessionId(urlSessionId);
+        return;
+      }
+
+      const storedInitialPrompt = initialPrompts[urlSessionId];
+      const currentInitialPrompt = getInitialPromptFromState(
+        pageState,
+        storedInitialPrompt,
+      );
+
+      if (currentInitialPrompt) {
+        setInitialPrompt(urlSessionId, currentInitialPrompt);
+      }
+
+      setPageState({
+        type: "chat",
+        sessionId: urlSessionId,
+        initialPrompt: currentInitialPrompt,
+      });
+      setPreviousSessionId(urlSessionId);
+      return;
+    }
+
+    const wasInChat = previousSessionId !== null && pageState.type === "chat";
+    setPreviousSessionId(null);
+    if (wasInChat) {
+      setPageState({ type: "newChat" });
+      return;
+    }
+
+    if (shouldResetToWelcome(pageState)) {
+      setPageState({ type: "welcome" });
+    }
+  }
+
+  useLayoutEffect(syncSessionFromUrl, [
+    urlSessionId,
+    pageState.type,
+    previousSessionId,
+    initialPrompts,
+  ]);
+
+  return {
+    urlSessionId,
+    setUrlSessionId,
+  };
+}

autogpt_platform/frontend/src/app/api/openapi.json

@@ -4594,7 +4594,6 @@
                 "AGENT_NEW_RUN",
                 "AGENT_INPUT",
                 "CONGRATS",
-                "VISIT_COPILOT",
                 "MARKETPLACE_VISIT",
                 "BUILDER_OPEN"
               ],
@@ -5928,40 +5927,6 @@
         }
       }
     },
-    "/api/workspace/files/{file_id}/download": {
-      "get": {
-        "tags": ["v2", "workspace"],
-        "summary": "Download file by ID",
-        "description": "Download a file by its ID.\n\nReturns the file content directly or redirects to a signed URL for GCS.",
-        "operationId": "getV2Download file by id",
-        "security": [{ "HTTPBearerJWT": [] }],
-        "parameters": [
-          {
-            "name": "file_id",
-            "in": "path",
-            "required": true,
-            "schema": { "type": "string", "title": "File Id" }
-          }
-        ],
-        "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" }
-              }
-            }
-          }
-        }
-      }
-    },
     "/health": {
       "get": {
         "tags": ["health"],
@@ -8789,7 +8754,6 @@
           "AGENT_NEW_RUN",
           "AGENT_INPUT",
           "CONGRATS",
-          "VISIT_COPILOT",
           "GET_RESULTS",
           "MARKETPLACE_VISIT",
           "MARKETPLACE_ADD_AGENT",

autogpt_platform/frontend/src/app/api/proxy/[...path]/route.ts

@@ -1,6 +1,5 @@
 import {
   ApiError,
-  getServerAuthToken,
   makeAuthenticatedFileUpload,
   makeAuthenticatedRequest,
 } from "@/lib/autogpt-server-api/helpers";
@@ -16,69 +15,6 @@ function buildBackendUrl(path: string[], queryString: string): string {
   return `${environment.getAGPTServerBaseUrl()}/${backendPath}${queryString}`;
 }
 
-/**
- * Check if this is a workspace file download request that needs binary response handling.
- */
-function isWorkspaceDownloadRequest(path: string[]): boolean {
-  // Match pattern: api/workspace/files/{id}/download (5 segments)
-  return (
-    path.length >= 5 &&
-    path[0] === "api" &&
-    path[1] === "workspace" &&
-    path[2] === "files" &&
-    path[path.length - 1] === "download"
-  );
-}
-
-/**
- * Handle workspace file download requests with proper binary response streaming.
- */
-async function handleWorkspaceDownload(
-  req: NextRequest,
-  backendUrl: string,
-): Promise<NextResponse> {
-  const token = await getServerAuthToken();
-
-  const headers: Record<string, string> = {};
-  if (token && token !== "no-token-found") {
-    headers["Authorization"] = `Bearer ${token}`;
-  }
-
-  const response = await fetch(backendUrl, {
-    method: "GET",
-    headers,
-    redirect: "follow", // Follow redirects to signed URLs
-  });
-
-  if (!response.ok) {
-    return NextResponse.json(
-      { error: `Failed to download file: ${response.statusText}` },
-      { status: response.status },
-    );
-  }
-
-  // Get the content type from the backend response
-  const contentType =
-    response.headers.get("Content-Type") || "application/octet-stream";
-  const contentDisposition = response.headers.get("Content-Disposition");
-
-  // Stream the response body
-  const responseHeaders: Record<string, string> = {
-    "Content-Type": contentType,
-  };
-
-  if (contentDisposition) {
-    responseHeaders["Content-Disposition"] = contentDisposition;
-  }
-
-  // Return the binary content
-  const arrayBuffer = await response.arrayBuffer();
-  return new NextResponse(arrayBuffer, {
-    status: 200,
-    headers: responseHeaders,
-  });
-}
-
 async function handleJsonRequest(
   req: NextRequest,
   method: string,
@@ -244,11 +180,6 @@ async function handler(
   };
 
   try {
-    // Handle workspace file downloads separately (binary response)
-    if (method === "GET" && isWorkspaceDownloadRequest(path)) {
-      return await handleWorkspaceDownload(req, backendUrl);
-    }
-
     if (method === "GET" || method === "DELETE") {
       responseBody = await handleGetDeleteRequest(method, backendUrl, req);
     } else if (contentType?.includes("application/json")) {

autogpt_platform/frontend/src/components/contextual/Chat/Chat.tsx

@@ -1,17 +1,16 @@
 "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";
 import { useEffect, useRef } from "react";
 import { ChatContainer } from "./components/ChatContainer/ChatContainer";
 import { ChatErrorState } from "./components/ChatErrorState/ChatErrorState";
+import { ChatLoader } from "./components/ChatLoader/ChatLoader";
 import { useChat } from "./useChat";
 
 export interface ChatProps {
   className?: string;
+  urlSessionId?: string | null;
   initialPrompt?: string;
   onSessionNotFound?: () => void;
   onStreamingChange?: (isStreaming: boolean) => void;
@@ -19,13 +18,12 @@ export interface ChatProps {
 
 export function Chat({
   className,
+  urlSessionId,
   initialPrompt,
   onSessionNotFound,
   onStreamingChange,
 }: ChatProps) {
-  const { urlSessionId } = useCopilotSessionId();
   const hasHandledNotFoundRef = useRef(false);
-  const isSwitchingSession = useCopilotStore((s) => s.isSwitchingSession);
   const {
     messages,
     isLoading,
@@ -35,59 +33,49 @@ export function Chat({
     sessionId,
     createSession,
     showLoader,
-    startPollingForOperation,
   } = useChat({ urlSessionId });
 
-  useEffect(() => {
-    if (!onSessionNotFound) return;
-    if (!urlSessionId) return;
-    if (!isSessionNotFound || isLoading || isCreating) return;
-    if (hasHandledNotFoundRef.current) return;
-    hasHandledNotFoundRef.current = true;
-    onSessionNotFound();
-  }, [
-    onSessionNotFound,
-    urlSessionId,
-    isSessionNotFound,
-    isLoading,
-    isCreating,
-  ]);
-
-  const shouldShowLoader =
-    (showLoader && (isLoading || isCreating)) || isSwitchingSession;
+  useEffect(
+    function handleMissingSession() {
+      if (!onSessionNotFound) return;
+      if (!urlSessionId) return;
+      if (!isSessionNotFound || isLoading || isCreating) return;
+      if (hasHandledNotFoundRef.current) return;
+      hasHandledNotFoundRef.current = true;
+      onSessionNotFound();
+    },
+    [onSessionNotFound, urlSessionId, isSessionNotFound, isLoading, isCreating],
+  );
 
   return (
     <div className={cn("flex h-full flex-col", className)}>
       {/* Main Content */}
       <main className="flex min-h-0 w-full flex-1 flex-col overflow-hidden bg-[#f8f8f9]">
         {/* Loading State */}
-        {shouldShowLoader && (
+        {showLoader && (isLoading || isCreating) && (
           <div className="flex flex-1 items-center justify-center">
-            <div className="flex flex-col items-center gap-3">
-              <LoadingSpinner size="large" className="text-neutral-400" />
+            <div className="flex flex-col items-center gap-4">
+              <ChatLoader />
               <Text variant="body" className="text-zinc-500">
-                {isSwitchingSession
-                  ? "Switching chat..."
-                  : "Loading your chat..."}
+                Loading your chats...
               </Text>
             </div>
           </div>
         )}
 
         {/* Error State */}
-        {error && !isLoading && !isSwitchingSession && (
+        {error && !isLoading && (
           <ChatErrorState error={error} onRetry={createSession} />
         )}
 
         {/* Session Content */}
-        {sessionId && !isLoading && !error && !isSwitchingSession && (
+        {sessionId && !isLoading && !error && (
           <ChatContainer
             sessionId={sessionId}
             initialMessages={messages}
             initialPrompt={initialPrompt}
             className="flex-1"
             onStreamingChange={onStreamingChange}
-            onOperationStarted={startPollingForOperation}
           />
         )}
       </main>

autogpt_platform/frontend/src/components/contextual/Chat/chat-store.ts

@@ -58,17 +58,39 @@ function notifyStreamComplete(
   }
 }
 
-function cleanupExpiredStreams(
-  completedStreams: Map<string, StreamResult>,
-): Map<string, StreamResult> {
+function cleanupCompletedStreams(completedStreams: Map<string, StreamResult>) {
   const now = Date.now();
-  const cleaned = new Map(completedStreams);
-  for (const [sessionId, result] of cleaned) {
+  for (const [sessionId, result] of completedStreams) {
     if (now - result.completedAt > COMPLETED_STREAM_TTL) {
-      cleaned.delete(sessionId);
+      completedStreams.delete(sessionId);
     }
   }
-  return cleaned;
+}
+
+function moveToCompleted(
+  activeStreams: Map<string, ActiveStream>,
+  completedStreams: Map<string, StreamResult>,
+  streamCompleteCallbacks: Set<StreamCompleteCallback>,
+  sessionId: string,
+) {
+  const stream = activeStreams.get(sessionId);
+  if (!stream) return;
+
+  const result: StreamResult = {
+    sessionId,
+    status: stream.status,
+    chunks: stream.chunks,
+    completedAt: Date.now(),
+    error: stream.error,
+  };
+
+  completedStreams.set(sessionId, result);
+  activeStreams.delete(sessionId);
+  cleanupCompletedStreams(completedStreams);
+
+  if (stream.status === "completed" || stream.status === "error") {
+    notifyStreamComplete(streamCompleteCallbacks, sessionId);
+  }
 }
 
 export const useChatStore = create<ChatStore>((set, get) => ({
@@ -84,31 +106,17 @@ export const useChatStore = create<ChatStore>((set, get) => ({
     context,
     onChunk,
   ) {
-    const state = get();
-    const newActiveStreams = new Map(state.activeStreams);
-    let newCompletedStreams = new Map(state.completedStreams);
-    const callbacks = state.streamCompleteCallbacks;
+    const { activeStreams, completedStreams, streamCompleteCallbacks } = get();
 
-    const existingStream = newActiveStreams.get(sessionId);
+    const existingStream = activeStreams.get(sessionId);
     if (existingStream) {
       existingStream.abortController.abort();
-      const normalizedStatus =
-        existingStream.status === "streaming"
-          ? "completed"
-          : existingStream.status;
-      const result: StreamResult = {
+      moveToCompleted(
+        activeStreams,
+        completedStreams,
+        streamCompleteCallbacks,
         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();
@@ -124,76 +132,36 @@ export const useChatStore = create<ChatStore>((set, get) => ({
       onChunkCallbacks: initialCallbacks,
     };
 
-    newActiveStreams.set(sessionId, stream);
-    set({
-      activeStreams: newActiveStreams,
-      completedStreams: newCompletedStreams,
-    });
+    activeStreams.set(sessionId, stream);
 
     try {
       await executeStream(stream, message, isUserMessage, context);
     } finally {
       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,
-            );
-          }
-        }
+        moveToCompleted(
+          activeStreams,
+          completedStreams,
+          streamCompleteCallbacks,
+          sessionId,
+        );
       }
     }
   },
 
   stopStream: function stopStream(sessionId) {
-    const state = get();
-    const stream = state.activeStreams.get(sessionId);
-    if (!stream) return;
-
-    stream.abortController.abort();
-    stream.status = "completed";
-
-    const newActiveStreams = new Map(state.activeStreams);
-    let newCompletedStreams = new Map(state.completedStreams);
-
-    const result: StreamResult = {
-      sessionId,
-      status: stream.status,
-      chunks: stream.chunks,
-      completedAt: Date.now(),
-      error: stream.error,
-    };
-    newCompletedStreams.set(sessionId, result);
-    newActiveStreams.delete(sessionId);
-    newCompletedStreams = cleanupExpiredStreams(newCompletedStreams);
-
-    set({
-      activeStreams: newActiveStreams,
-      completedStreams: newCompletedStreams,
-    });
-
-    notifyStreamComplete(state.streamCompleteCallbacks, sessionId);
+    const { activeStreams, completedStreams, streamCompleteCallbacks } = get();
+    const stream = activeStreams.get(sessionId);
+    if (stream) {
+      stream.abortController.abort();
+      stream.status = "completed";
+      moveToCompleted(
+        activeStreams,
+        completedStreams,
+        streamCompleteCallbacks,
+        sessionId,
+      );
+    }
   },
 
   subscribeToStream: function subscribeToStream(
@@ -201,18 +169,16 @@ export const useChatStore = create<ChatStore>((set, get) => ({
     onChunk,
     skipReplay = false,
   ) {
-    const state = get();
-    const stream = state.activeStreams.get(sessionId);
+    const { activeStreams } = get();
 
+    const stream = activeStreams.get(sessionId);
     if (stream) {
       if (!skipReplay) {
         for (const chunk of stream.chunks) {
           onChunk(chunk);
         }
       }
-
       stream.onChunkCallbacks.add(onChunk);
-
       return function unsubscribe() {
         stream.onChunkCallbacks.delete(onChunk);
       };
@@ -238,12 +204,7 @@ export const useChatStore = create<ChatStore>((set, get) => ({
   },
 
   clearCompletedStream: function clearCompletedStream(sessionId) {
-    const state = get();
-    if (!state.completedStreams.has(sessionId)) return;
-
-    const newCompletedStreams = new Map(state.completedStreams);
-    newCompletedStreams.delete(sessionId);
-    set({ completedStreams: newCompletedStreams });
+    get().completedStreams.delete(sessionId);
   },
 
   isStreaming: function isStreaming(sessionId) {
@@ -252,21 +213,11 @@ export const useChatStore = create<ChatStore>((set, get) => ({
   },
 
   registerActiveSession: function registerActiveSession(sessionId) {
-    const state = get();
-    if (state.activeSessions.has(sessionId)) return;
-
-    const newActiveSessions = new Set(state.activeSessions);
-    newActiveSessions.add(sessionId);
-    set({ activeSessions: newActiveSessions });
+    get().activeSessions.add(sessionId);
   },
 
   unregisterActiveSession: function unregisterActiveSession(sessionId) {
-    const state = get();
-    if (!state.activeSessions.has(sessionId)) return;
-
-    const newActiveSessions = new Set(state.activeSessions);
-    newActiveSessions.delete(sessionId);
-    set({ activeSessions: newActiveSessions });
+    get().activeSessions.delete(sessionId);
   },
 
   isSessionActive: function isSessionActive(sessionId) {
@@ -274,16 +225,10 @@ export const useChatStore = create<ChatStore>((set, get) => ({
   },
 
   onStreamComplete: function onStreamComplete(callback) {
-    const state = get();
-    const newCallbacks = new Set(state.streamCompleteCallbacks);
-    newCallbacks.add(callback);
-    set({ streamCompleteCallbacks: newCallbacks });
-
+    const { streamCompleteCallbacks } = get();
+    streamCompleteCallbacks.add(callback);
     return function unsubscribe() {
-      const currentState = get();
-      const cleanedCallbacks = new Set(currentState.streamCompleteCallbacks);
-      cleanedCallbacks.delete(callback);
-      set({ streamCompleteCallbacks: cleanedCallbacks });
+      streamCompleteCallbacks.delete(callback);
     };
   },
 }));

autogpt_platform/frontend/src/components/contextual/Chat/components/ChatContainer/ChatContainer.tsx

@@ -16,7 +16,6 @@ export interface ChatContainerProps {
   initialPrompt?: string;
   className?: string;
   onStreamingChange?: (isStreaming: boolean) => void;
-  onOperationStarted?: () => void;
 }
 
 export function ChatContainer({
@@ -25,7 +24,6 @@ export function ChatContainer({
   initialPrompt,
   className,
   onStreamingChange,
-  onOperationStarted,
 }: ChatContainerProps) {
   const {
     messages,
@@ -40,7 +38,6 @@ export function ChatContainer({
     sessionId,
     initialMessages,
     initialPrompt,
-    onOperationStarted,
   });
 
   useEffect(() => {

autogpt_platform/frontend/src/components/contextual/Chat/components/ChatContainer/handlers.ts

@@ -22,7 +22,6 @@ export interface HandlerDependencies {
   setIsStreamingInitiated: Dispatch<SetStateAction<boolean>>;
   setIsRegionBlockedModalOpen: Dispatch<SetStateAction<boolean>>;
   sessionId: string;
-  onOperationStarted?: () => void;
 }
 
 export function isRegionBlockedError(chunk: StreamChunk): boolean {
@@ -49,15 +48,6 @@ export function handleTextEnded(
   const completedText = deps.streamingChunksRef.current.join("");
   if (completedText.trim()) {
     deps.setMessages((prev) => {
-      // Check if this exact message already exists to prevent duplicates
-      const exists = prev.some(
-        (msg) =>
-          msg.type === "message" &&
-          msg.role === "assistant" &&
-          msg.content === completedText,
-      );
-      if (exists) return prev;
-
       const assistantMessage: ChatMessageData = {
         type: "message",
         role: "assistant",
@@ -164,11 +154,6 @@ export function handleToolResponse(
     }
     return;
   }
-  // Trigger polling when operation_started is received
-  if (responseMessage.type === "operation_started") {
-    deps.onOperationStarted?.();
-  }
-
   deps.setMessages((prev) => {
     const toolCallIndex = prev.findIndex(
       (msg) => msg.type === "tool_call" && msg.toolId === chunk.tool_id,
@@ -218,24 +203,13 @@ export function handleStreamEnd(
     ]);
   }
   if (completedContent.trim()) {
-    deps.setMessages((prev) => {
-      // Check if this exact message already exists to prevent duplicates
-      const exists = prev.some(
-        (msg) =>
-          msg.type === "message" &&
-          msg.role === "assistant" &&
-          msg.content === completedContent,
-      );
-      if (exists) return prev;
-
-      const assistantMessage: ChatMessageData = {
-        type: "message",
-        role: "assistant",
-        content: completedContent,
-        timestamp: new Date(),
-      };
-      return [...prev, assistantMessage];
-    });
+    const assistantMessage: ChatMessageData = {
+      type: "message",
+      role: "assistant",
+      content: completedContent,
+      timestamp: new Date(),
+    };
+    deps.setMessages((prev) => [...prev, assistantMessage]);
   }
   deps.setStreamingChunks([]);
   deps.streamingChunksRef.current = [];

autogpt_platform/frontend/src/components/contextual/Chat/components/ChatContainer/helpers.ts

@@ -304,7 +304,6 @@ export function parseToolResponse(
       if (isAgentArray(agentsData)) {
         return {
           type: "agent_carousel",
-          toolId,
           toolName: "agent_carousel",
           agents: agentsData,
           totalCount: parsedResult.total_count as number | undefined,
@@ -317,7 +316,6 @@ export function parseToolResponse(
     if (responseType === "execution_started") {
       return {
         type: "execution_started",
-        toolId,
         toolName: "execution_started",
         executionId: (parsedResult.execution_id as string) || "",
         agentName: (parsedResult.graph_name as string) || undefined,
@@ -343,41 +341,6 @@ export function parseToolResponse(
         timestamp: timestamp || new Date(),
       };
     }
-    if (responseType === "operation_started") {
-      return {
-        type: "operation_started",
-        toolName: (parsedResult.tool_name as string) || toolName,
-        toolId,
-        operationId: (parsedResult.operation_id as string) || "",
-        message:
-          (parsedResult.message as string) ||
-          "Operation started. You can close this tab.",
-        timestamp: timestamp || new Date(),
-      };
-    }
-    if (responseType === "operation_pending") {
-      return {
-        type: "operation_pending",
-        toolName: (parsedResult.tool_name as string) || toolName,
-        toolId,
-        operationId: (parsedResult.operation_id as string) || "",
-        message:
-          (parsedResult.message as string) ||
-          "Operation in progress. Please wait...",
-        timestamp: timestamp || new Date(),
-      };
-    }
-    if (responseType === "operation_in_progress") {
-      return {
-        type: "operation_in_progress",
-        toolName: (parsedResult.tool_name as string) || toolName,
-        toolCallId: (parsedResult.tool_call_id as string) || toolId,
-        message:
-          (parsedResult.message as string) ||
-          "Operation already in progress. Please wait...",
-        timestamp: timestamp || new Date(),
-      };
-    }
     if (responseType === "need_login") {
       return {
         type: "login_needed",

autogpt_platform/frontend/src/components/contextual/Chat/components/ChatContainer/useChatContainer.ts

@@ -14,40 +14,16 @@ import {
   processInitialMessages,
 } from "./helpers";
 
-// Helper to generate deduplication key for a message
-function getMessageKey(msg: ChatMessageData): string {
-  if (msg.type === "message") {
-    // Don't include timestamp - dedupe by role + content only
-    // This handles the case where local and server timestamps differ
-    // Server messages are authoritative, so duplicates from local state are filtered
-    return `msg:${msg.role}:${msg.content}`;
-  } else if (msg.type === "tool_call") {
-    return `toolcall:${msg.toolId}`;
-  } else if (msg.type === "tool_response") {
-    return `toolresponse:${(msg as any).toolId}`;
-  } else if (
-    msg.type === "operation_started" ||
-    msg.type === "operation_pending" ||
-    msg.type === "operation_in_progress"
-  ) {
-    return `op:${(msg as any).toolId || (msg as any).operationId || (msg as any).toolCallId || ""}:${msg.toolName}`;
-  } else {
-    return `${msg.type}:${JSON.stringify(msg).slice(0, 100)}`;
-  }
-}
-
 interface Args {
   sessionId: string | null;
   initialMessages: SessionDetailResponse["messages"];
   initialPrompt?: string;
-  onOperationStarted?: () => void;
 }
 
 export function useChatContainer({
   sessionId,
   initialMessages,
   initialPrompt,
-  onOperationStarted,
 }: Args) {
   const [messages, setMessages] = useState<ChatMessageData[]>([]);
   const [streamingChunks, setStreamingChunks] = useState<string[]>([]);
@@ -97,102 +73,20 @@ export function useChatContainer({
         setIsRegionBlockedModalOpen,
         sessionId,
         setIsStreamingInitiated,
-        onOperationStarted,
       });
 
       setIsStreamingInitiated(true);
       const skipReplay = initialMessages.length > 0;
       return subscribeToStream(sessionId, dispatcher, skipReplay);
     },
-    [
-      sessionId,
-      stopStreaming,
-      activeStreams,
-      subscribeToStream,
-      onOperationStarted,
-    ],
+    [sessionId, stopStreaming, activeStreams, subscribeToStream],
   );
 
-  // Collect toolIds from completed tool results in initialMessages
-  // Used to filter out operation messages when their results arrive
-  const completedToolIds = useMemo(() => {
-    const processedInitial = processInitialMessages(initialMessages);
-    const ids = new Set<string>();
-    for (const msg of processedInitial) {
-      if (
-        msg.type === "tool_response" ||
-        msg.type === "agent_carousel" ||
-        msg.type === "execution_started"
-      ) {
-        const toolId = (msg as any).toolId;
-        if (toolId) {
-          ids.add(toolId);
-        }
-      }
-    }
-    return ids;
-  }, [initialMessages]);
-
-  // Clean up local operation messages when their completed results arrive from polling
-  // This effect runs when completedToolIds changes (i.e., when polling brings new results)
-  useEffect(
-    function cleanupCompletedOperations() {
-      if (completedToolIds.size === 0) return;
-
-      setMessages((prev) => {
-        const filtered = prev.filter((msg) => {
-          if (
-            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
-            }
-          }
-          return true;
-        });
-        // Only update state if something was actually filtered
-        return filtered.length === prev.length ? prev : filtered;
-      });
-    },
-    [completedToolIds],
+  const allMessages = useMemo(
+    () => [...processInitialMessages(initialMessages), ...messages],
+    [initialMessages, messages],
   );
 
-  // Combine initial messages from backend with local streaming messages,
-  // Server messages maintain correct order; only append truly new local messages
-  const allMessages = useMemo(() => {
-    const processedInitial = processInitialMessages(initialMessages);
-
-    // Build a set of keys from server messages for deduplication
-    const serverKeys = new Set<string>();
-    for (const msg of processedInitial) {
-      serverKeys.add(getMessageKey(msg));
-    }
-
-    // Filter local messages: remove duplicates and completed operation messages
-    const newLocalMessages = messages.filter((msg) => {
-      // Remove operation messages for completed tools
-      if (
-        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 messages that already exist in server data
-      const key = getMessageKey(msg);
-      return !serverKeys.has(key);
-    });
-
-    // Server messages first (correct order), then new local messages
-    return [...processedInitial, ...newLocalMessages];
-  }, [initialMessages, messages, completedToolIds]);
-
   async function sendMessage(
     content: string,
     isUserMessage: boolean = true,
@@ -224,7 +118,6 @@ export function useChatContainer({
       setIsRegionBlockedModalOpen,
       sessionId,
       setIsStreamingInitiated,
-      onOperationStarted,
     });
 
     try {

autogpt_platform/frontend/src/components/contextual/Chat/components/ChatMessage/ChatMessage.tsx

@@ -16,7 +16,6 @@ import { AuthPromptWidget } from "../AuthPromptWidget/AuthPromptWidget";
 import { ChatCredentialsSetup } from "../ChatCredentialsSetup/ChatCredentialsSetup";
 import { ClarificationQuestionsWidget } from "../ClarificationQuestionsWidget/ClarificationQuestionsWidget";
 import { ExecutionStartedMessage } from "../ExecutionStartedMessage/ExecutionStartedMessage";
-import { PendingOperationWidget } from "../PendingOperationWidget/PendingOperationWidget";
 import { MarkdownContent } from "../MarkdownContent/MarkdownContent";
 import { NoResultsMessage } from "../NoResultsMessage/NoResultsMessage";
 import { ToolCallMessage } from "../ToolCallMessage/ToolCallMessage";
@@ -72,9 +71,6 @@ export function ChatMessage({
     isLoginNeeded,
     isCredentialsNeeded,
     isClarificationNeeded,
-    isOperationStarted,
-    isOperationPending,
-    isOperationInProgress,
   } = useChatMessage(message);
   const displayContent = getDisplayContent(message, isUser);
 
@@ -130,6 +126,10 @@ export function ChatMessage({
     [displayContent, message],
   );
 
+  function isLongResponse(content: string): boolean {
+    return content.split("\n").length > 5;
+  }
+
   const handleTryAgain = useCallback(() => {
     if (message.type !== "message" || !onSendMessage) return;
     onSendMessage(message.content, message.role === "user");
@@ -294,42 +294,6 @@ export function ChatMessage({
     );
   }
 
-  // Render operation_started messages (long-running background operations)
-  if (isOperationStarted && message.type === "operation_started") {
-    return (
-      <PendingOperationWidget
-        status="started"
-        message={message.message}
-        toolName={message.toolName}
-        className={className}
-      />
-    );
-  }
-
-  // Render operation_pending messages (operations in progress when refreshing)
-  if (isOperationPending && message.type === "operation_pending") {
-    return (
-      <PendingOperationWidget
-        status="pending"
-        message={message.message}
-        toolName={message.toolName}
-        className={className}
-      />
-    );
-  }
-
-  // Render operation_in_progress messages (duplicate request while operation running)
-  if (isOperationInProgress && message.type === "operation_in_progress") {
-    return (
-      <PendingOperationWidget
-        status="in_progress"
-        message={message.message}
-        toolName={message.toolName}
-        className={className}
-      />
-    );
-  }
-
   // Render tool response messages (but skip agent_output if it's being rendered inside assistant message)
   if (isToolResponse && message.type === "tool_response") {
     return (
@@ -394,7 +358,7 @@ export function ChatMessage({
                   <ArrowsClockwiseIcon className="size-4 text-zinc-600" />
                 </Button>
               )}
-              {!isUser && isFinalMessage && !isStreaming && (
+              {!isUser && isFinalMessage && isLongResponse(displayContent) && (
                 <Button
                   variant="ghost"
                   size="icon"

autogpt_platform/frontend/src/components/contextual/Chat/components/ChatMessage/useChatMessage.ts

@@ -61,7 +61,6 @@ export type ChatMessageData =
     }
   | {
       type: "agent_carousel";
-      toolId: string;
       toolName: string;
       agents: Array<{
         id: string;
@@ -75,7 +74,6 @@ export type ChatMessageData =
     }
   | {
       type: "execution_started";
-      toolId: string;
       toolName: string;
       executionId: string;
       agentName?: string;
@@ -105,29 +103,6 @@ export type ChatMessageData =
       message: string;
       sessionId: string;
       timestamp?: string | Date;
-    }
-  | {
-      type: "operation_started";
-      toolName: string;
-      toolId: string;
-      operationId: string;
-      message: string;
-      timestamp?: string | Date;
-    }
-  | {
-      type: "operation_pending";
-      toolName: string;
-      toolId: string;
-      operationId: string;
-      message: string;
-      timestamp?: string | Date;
-    }
-  | {
-      type: "operation_in_progress";
-      toolName: string;
-      toolCallId: string;
-      message: string;
-      timestamp?: string | Date;
     };
 
 export function useChatMessage(message: ChatMessageData) {
@@ -149,8 +124,5 @@ export function useChatMessage(message: ChatMessageData) {
     isExecutionStarted: message.type === "execution_started",
     isInputsNeeded: message.type === "inputs_needed",
     isClarificationNeeded: message.type === "clarification_needed",
-    isOperationStarted: message.type === "operation_started",
-    isOperationPending: message.type === "operation_pending",
-    isOperationInProgress: message.type === "operation_in_progress",
   };
 }

autogpt_platform/frontend/src/components/contextual/Chat/components/ClarificationQuestionsWidget/ClarificationQuestionsWidget.tsx

@@ -30,7 +30,6 @@ export function ClarificationQuestionsWidget({
   className,
 }: Props) {
   const [answers, setAnswers] = useState<Record<string, string>>({});
-  const [isSubmitted, setIsSubmitted] = useState(false);
 
   function handleAnswerChange(keyword: string, value: string) {
     setAnswers((prev) => ({ ...prev, [keyword]: value }));
@@ -42,42 +41,11 @@ export function ClarificationQuestionsWidget({
     if (!allAnswered) {
       return;
     }
-    setIsSubmitted(true);
     onSubmitAnswers(answers);
   }
 
   const allAnswered = questions.every((q) => answers[q.keyword]?.trim());
 
-  // Show submitted state after answers are submitted
-  if (isSubmitted) {
-    return (
-      <div
-        className={cn(
-          "group relative flex w-full justify-start gap-3 px-4 py-3",
-          className,
-        )}
-      >
-        <div className="flex w-full max-w-3xl gap-3">
-          <div className="flex-shrink-0">
-            <div className="flex h-7 w-7 items-center justify-center rounded-lg bg-green-500">
-              <CheckCircleIcon className="h-4 w-4 text-white" weight="bold" />
-            </div>
-          </div>
-          <div className="flex min-w-0 flex-1 flex-col">
-            <Card className="p-4">
-              <Text variant="h4" className="mb-1 text-slate-900">
-                Answers submitted
-              </Text>
-              <Text variant="small" className="text-slate-600">
-                Processing your responses...
-              </Text>
-            </Card>
-          </div>
-        </div>
-      </div>
-    );
-  }
-
   return (
     <div
       className={cn(

autogpt_platform/frontend/src/components/contextual/Chat/components/MarkdownContent/MarkdownContent.tsx

@@ -1,8 +1,6 @@
 "use client";
 
-import { getGetV2DownloadFileByIdUrl } from "@/app/api/__generated__/endpoints/workspace/workspace";
 import { cn } from "@/lib/utils";
-import { EyeSlash } from "@phosphor-icons/react";
 import React from "react";
 import ReactMarkdown from "react-markdown";
 import remarkGfm from "remark-gfm";
@@ -31,88 +29,12 @@ interface InputProps extends React.InputHTMLAttributes<HTMLInputElement> {
   type?: string;
 }
 
-/**
- * Converts a workspace:// URL to a proxy URL that routes through Next.js to the backend.
- * workspace://abc123 -> /api/proxy/api/workspace/files/abc123/download
- *
- * Uses the generated API URL helper and routes through the Next.js proxy
- * which handles authentication and proper backend routing.
- */
-/**
- * URL transformer for ReactMarkdown.
- * Converts workspace:// URLs to proxy URLs that route through Next.js to the backend.
- * workspace://abc123 -> /api/proxy/api/workspace/files/abc123/download
- *
- * This is needed because ReactMarkdown sanitizes URLs and only allows
- * http, https, mailto, and tel protocols by default.
- */
-function resolveWorkspaceUrl(src: string): string {
-  if (src.startsWith("workspace://")) {
-    const fileId = src.replace("workspace://", "");
-    // Use the generated API URL helper to get the correct path
-    const apiPath = getGetV2DownloadFileByIdUrl(fileId);
-    // Route through the Next.js proxy (same pattern as customMutator for client-side)
-    return `/api/proxy${apiPath}`;
-  }
-  return src;
-}
-
-/**
- * Check if the image URL is a workspace file (AI cannot see these yet).
- * After URL transformation, workspace files have URLs like /api/proxy/api/workspace/files/...
- */
-function isWorkspaceImage(src: string | undefined): boolean {
-  return src?.includes("/workspace/files/") ?? false;
-}
-
-/**
- * Custom image component that shows an indicator when the AI cannot see the image.
- * 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 aiCannotSee = isWorkspaceImage(src);
-
-  // If no src, show a placeholder
-  if (!src) {
-    return (
-      <span className="my-2 inline-block rounded border border-amber-200 bg-amber-50 px-2 py-1 text-sm text-amber-700">
-        [Image: {alt || "missing src"}]
-      </span>
-    );
-  }
-
-  return (
-    <span className="relative my-2 inline-block">
-      {/* eslint-disable-next-line @next/next/no-img-element */}
-      <img
-        src={src}
-        alt={alt || "Image"}
-        className="h-auto max-w-full rounded-md border border-zinc-200"
-        loading="lazy"
-      />
-      {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 image"
-        >
-          <EyeSlash size={14} />
-          <span>AI cannot see this image</span>
-        </span>
-      )}
-    </span>
-  );
-}
-
 export function MarkdownContent({ content, className }: MarkdownContentProps) {
   return (
     <div className={cn("markdown-content", className)}>
       <ReactMarkdown
         skipHtml={true}
         remarkPlugins={[remarkGfm]}
-        urlTransform={resolveWorkspaceUrl}
         components={{
           code: ({ children, className, ...props }: CodeProps) => {
             const isInline = !className?.includes("language-");
@@ -284,9 +206,6 @@ export function MarkdownContent({ content, className }: MarkdownContentProps) {
               {children}
             </td>
           ),
-          img: ({ src, alt, ...props }) => (
-            <MarkdownImage src={src} alt={alt} {...props} />
-          ),
         }}
       >
         {content}

autogpt_platform/frontend/src/components/contextual/Chat/components/PendingOperationWidget/PendingOperationWidget.tsx

@@ -1,109 +0,0 @@
-"use client";
-
-import { Card } from "@/components/atoms/Card/Card";
-import { Text } from "@/components/atoms/Text/Text";
-import { cn } from "@/lib/utils";
-import { CircleNotch, CheckCircle, XCircle } from "@phosphor-icons/react";
-
-type OperationStatus =
-  | "pending"
-  | "started"
-  | "in_progress"
-  | "completed"
-  | "error";
-
-interface Props {
-  status: OperationStatus;
-  message: string;
-  toolName?: string;
-  className?: string;
-}
-
-function getOperationTitle(toolName?: string): string {
-  if (!toolName) return "Operation";
-  // Convert tool name to human-readable format
-  // e.g., "create_agent" -> "Creating Agent", "edit_agent" -> "Editing Agent"
-  if (toolName === "create_agent") return "Creating Agent";
-  if (toolName === "edit_agent") return "Editing Agent";
-  // Default: capitalize and format tool name
-  return toolName
-    .split("_")
-    .map((word) => word.charAt(0).toUpperCase() + word.slice(1))
-    .join(" ");
-}
-
-export function PendingOperationWidget({
-  status,
-  message,
-  toolName,
-  className,
-}: Props) {
-  const isPending =
-    status === "pending" || status === "started" || status === "in_progress";
-  const isCompleted = status === "completed";
-  const isError = status === "error";
-
-  const operationTitle = getOperationTitle(toolName);
-
-  return (
-    <div
-      className={cn(
-        "group relative flex w-full justify-start gap-3 px-4 py-3",
-        className,
-      )}
-    >
-      <div className="flex w-full max-w-3xl gap-3">
-        <div className="flex-shrink-0">
-          <div
-            className={cn(
-              "flex h-7 w-7 items-center justify-center rounded-lg",
-              isPending && "bg-blue-500",
-              isCompleted && "bg-green-500",
-              isError && "bg-red-500",
-            )}
-          >
-            {isPending && (
-              <CircleNotch
-                className="h-4 w-4 animate-spin text-white"
-                weight="bold"
-              />
-            )}
-            {isCompleted && (
-              <CheckCircle className="h-4 w-4 text-white" weight="bold" />
-            )}
-            {isError && (
-              <XCircle className="h-4 w-4 text-white" weight="bold" />
-            )}
-          </div>
-        </div>
-
-        <div className="flex min-w-0 flex-1 flex-col">
-          <Card className="space-y-2 p-4">
-            <div>
-              <Text variant="h4" className="mb-1 text-slate-900">
-                {isPending && operationTitle}
-                {isCompleted && `${operationTitle} Complete`}
-                {isError && `${operationTitle} Failed`}
-              </Text>
-              <Text variant="small" className="text-slate-600">
-                {message}
-              </Text>
-            </div>
-
-            {isPending && (
-              <Text variant="small" className="italic text-slate-500">
-                Check your library in a few minutes.
-              </Text>
-            )}
-
-            {toolName && (
-              <Text variant="small" className="text-slate-400">
-                Tool: {toolName}
-              </Text>
-            )}
-          </Card>
-        </div>
-      </div>
-    </div>
-  );
-}

autogpt_platform/frontend/src/components/contextual/Chat/components/ToolResponseMessage/helpers.ts

@@ -37,87 +37,6 @@ export function getErrorMessage(result: unknown): string {
   return "An error occurred";
 }
 
-/**
- * Check if a value is a workspace file reference.
- */
-function isWorkspaceRef(value: unknown): value is string {
-  return typeof value === "string" && value.startsWith("workspace://");
-}
-
-/**
- * Check if a workspace reference appears to be an image based on common patterns.
- * Since workspace refs don't have extensions, we check the context or assume image
- * for certain block types.
- *
- * TODO: Replace keyword matching with MIME type encoded in workspace ref.
- * e.g., workspace://abc123#image/png or workspace://abc123#video/mp4
- * This would let frontend render correctly without fragile keyword matching.
- */
-function isLikelyImageRef(value: string, outputKey?: string): boolean {
-  if (!isWorkspaceRef(value)) return false;
-
-  // Check output key name for video-related hints (these are NOT images)
-  const videoKeywords = ["video", "mp4", "mov", "avi", "webm", "movie", "clip"];
-  if (outputKey) {
-    const lowerKey = outputKey.toLowerCase();
-    if (videoKeywords.some((kw) => lowerKey.includes(kw))) {
-      return false;
-    }
-  }
-
-  // Check output key name for image-related hints
-  const imageKeywords = [
-    "image",
-    "img",
-    "photo",
-    "picture",
-    "thumbnail",
-    "avatar",
-    "icon",
-    "screenshot",
-  ];
-  if (outputKey) {
-    const lowerKey = outputKey.toLowerCase();
-    if (imageKeywords.some((kw) => lowerKey.includes(kw))) {
-      return true;
-    }
-  }
-
-  // Default to treating workspace refs as potential images
-  // since that's the most common case for generated content
-  return true;
-}
-
-/**
- * Format a single output value, converting workspace refs to markdown images.
- */
-function formatOutputValue(value: unknown, outputKey?: string): string {
-  if (isWorkspaceRef(value) && isLikelyImageRef(value, outputKey)) {
-    // Format as markdown image
-    return `![${outputKey || "Generated image"}](${value})`;
-  }
-
-  if (typeof value === "string") {
-    // Check for data URIs (images)
-    if (value.startsWith("data:image/")) {
-      return `![${outputKey || "Generated image"}](${value})`;
-    }
-    return value;
-  }
-
-  if (Array.isArray(value)) {
-    return value
-      .map((item, idx) => formatOutputValue(item, `${outputKey}_${idx}`))
-      .join("\n\n");
-  }
-
-  if (typeof value === "object" && value !== null) {
-    return JSON.stringify(value, null, 2);
-  }
-
-  return String(value);
-}
-
 function getToolCompletionPhrase(toolName: string): string {
   const toolCompletionPhrases: Record<string, string> = {
     add_understanding: "Updated your business information",
@@ -208,26 +127,10 @@ export function formatToolResponse(result: unknown, toolName: string): string {
 
     case "block_output":
       const blockName = (response.block_name as string) || "Block";
-      const outputs = response.outputs as Record<string, unknown[]> | undefined;
+      const outputs = response.outputs as Record<string, unknown> | undefined;
       if (outputs && Object.keys(outputs).length > 0) {
-        const formattedOutputs: string[] = [];
-
-        for (const [key, values] of Object.entries(outputs)) {
-          if (!Array.isArray(values) || values.length === 0) continue;
-
-          // Format each value in the output array
-          for (const value of values) {
-            const formatted = formatOutputValue(value, key);
-            if (formatted) {
-              formattedOutputs.push(formatted);
-            }
-          }
-        }
-
-        if (formattedOutputs.length > 0) {
-          return `${blockName} executed successfully.\n\n${formattedOutputs.join("\n\n")}`;
-        }
-        return `${blockName} executed successfully.`;
+        const outputKeys = Object.keys(outputs);
+        return `${blockName} executed successfully. Outputs: ${outputKeys.join(", ")}`;
       }
       return `${blockName} executed successfully.`;
 

autogpt_platform/frontend/src/components/contextual/Chat/components/UserChatBubble/UserChatBubble.tsx

@@ -10,7 +10,7 @@ export function UserChatBubble({ children, className }: UserChatBubbleProps) {
   return (
     <div
       className={cn(
-        "group relative min-w-20 overflow-hidden rounded-xl bg-purple-100 px-3 text-left text-[1rem] leading-relaxed transition-all duration-500 ease-in-out",
+        "group relative min-w-20 overflow-hidden rounded-xl bg-purple-100 px-3 text-right text-[1rem] leading-relaxed transition-all duration-500 ease-in-out",
         className,
       )}
       style={{

autogpt_platform/frontend/src/components/contextual/Chat/useChat.ts

@@ -26,7 +26,6 @@ export function useChat({ urlSessionId }: UseChatArgs = {}) {
     claimSession,
     clearSession: clearSessionBase,
     loadSession,
-    startPollingForOperation,
   } = useChatSession({
     urlSessionId,
     autoCreate: false,
@@ -95,6 +94,5 @@ export function useChat({ urlSessionId }: UseChatArgs = {}) {
     loadSession,
     sessionId: sessionIdFromHook,
     showLoader,
-    startPollingForOperation,
   };
 }

autogpt_platform/frontend/src/components/contextual/Chat/useChatSession.ts

@@ -59,7 +59,6 @@ export function useChatSession({
     query: {
       enabled: !!sessionId,
       select: okData,
-      staleTime: 0,
       retry: shouldRetrySessionLoad,
       retryDelay: getSessionRetryDelay,
     },
@@ -103,123 +102,15 @@ export function useChatSession({
     }
   }, [createError, loadError]);
 
-  // Track if we should be polling (set by external callers when they receive operation_started via SSE)
-  const [forcePolling, setForcePolling] = useState(false);
-  // Track if we've seen server acknowledge the pending operation (to avoid clearing forcePolling prematurely)
-  const hasSeenServerPendingRef = useRef(false);
-
-  // Check if there are any pending operations in the messages
-  // Must check all operation types: operation_pending, operation_started, operation_in_progress
-  const hasPendingOperationsFromServer = useMemo(() => {
-    if (!messages || messages.length === 0) return false;
-    const pendingTypes = new Set([
-      "operation_pending",
-      "operation_in_progress",
-      "operation_started",
-    ]);
-    return messages.some((msg) => {
-      if (msg.role !== "tool" || !msg.content) return false;
-      try {
-        const content =
-          typeof msg.content === "string"
-            ? JSON.parse(msg.content)
-            : msg.content;
-        return pendingTypes.has(content?.type);
-      } catch {
-        return false;
-      }
-    });
-  }, [messages]);
-
-  // Track when server has acknowledged the pending operation
-  useEffect(() => {
-    if (hasPendingOperationsFromServer) {
-      hasSeenServerPendingRef.current = true;
-    }
-  }, [hasPendingOperationsFromServer]);
-
-  // Combined: poll if server has pending ops OR if we received operation_started via SSE
-  const hasPendingOperations = hasPendingOperationsFromServer || forcePolling;
-
-  // Clear forcePolling only after server has acknowledged AND completed the operation
-  useEffect(() => {
-    if (
-      forcePolling &&
-      !hasPendingOperationsFromServer &&
-      hasSeenServerPendingRef.current
-    ) {
-      // Server acknowledged the operation and it's now complete
-      setForcePolling(false);
-      hasSeenServerPendingRef.current = false;
-    }
-  }, [forcePolling, hasPendingOperationsFromServer]);
-
-  // Function to trigger polling (called when operation_started is received via SSE)
-  function startPollingForOperation() {
-    setForcePolling(true);
-    hasSeenServerPendingRef.current = false; // Reset for new operation
-  }
-
-  // Refresh sessions list when a pending operation completes
-  // (hasPendingOperations transitions from true to false)
-  const prevHasPendingOperationsRef = useRef(hasPendingOperations);
   useEffect(
-    function refreshSessionsListOnOperationComplete() {
-      const wasHasPending = prevHasPendingOperationsRef.current;
-      prevHasPendingOperationsRef.current = hasPendingOperations;
-
-      // Only invalidate when transitioning from pending to not pending
-      if (wasHasPending && !hasPendingOperations && sessionId) {
+    function refreshSessionsListOnLoad() {
+      if (sessionId && sessionData && !isLoadingSession) {
         queryClient.invalidateQueries({
           queryKey: getGetV2ListSessionsQueryKey(),
         });
       }
     },
-    [hasPendingOperations, sessionId, queryClient],
-  );
-
-  // Poll for updates when there are pending operations
-  // Backoff: 2s, 4s, 6s, 8s, 10s, ... up to 30s max
-  const pollAttemptRef = useRef(0);
-  const hasPendingOperationsRef = useRef(hasPendingOperations);
-  hasPendingOperationsRef.current = hasPendingOperations;
-
-  useEffect(
-    function pollForPendingOperations() {
-      if (!sessionId || !hasPendingOperations) {
-        pollAttemptRef.current = 0;
-        return;
-      }
-
-      let cancelled = false;
-      let timeoutId: ReturnType<typeof setTimeout> | null = null;
-
-      function schedule() {
-        // 2s, 4s, 6s, 8s, 10s, ... 30s (max)
-        const delay = Math.min((pollAttemptRef.current + 1) * 2000, 30000);
-        timeoutId = setTimeout(async () => {
-          if (cancelled) return;
-          pollAttemptRef.current += 1;
-          try {
-            await refetch();
-          } catch (err) {
-            console.error("[useChatSession] Poll failed:", err);
-          } finally {
-            if (!cancelled && hasPendingOperationsRef.current) {
-              schedule();
-            }
-          }
-        }, delay);
-      }
-
-      schedule();
-
-      return () => {
-        cancelled = true;
-        if (timeoutId) clearTimeout(timeoutId);
-      };
-    },
-    [sessionId, hasPendingOperations, refetch],
+    [sessionId, sessionData, isLoadingSession, queryClient],
   );
 
   async function createSession() {
@@ -348,13 +239,11 @@ export function useChatSession({
     isCreating,
     error,
     isSessionNotFound: isNotFoundError(loadError),
-    hasPendingOperations,
     createSession,
     loadSession,
     refreshSession,
     claimSession,
     clearSession,
-    startPollingForOperation,
   };
 }
 

autogpt_platform/frontend/src/components/layout/Navbar/components/Wallet/Wallet.tsx

@@ -255,18 +255,13 @@ export function Wallet() {
     (notification: WebSocketNotification) => {
       if (
         notification.type !== "onboarding" ||
-        notification.event !== "step_completed"
+        notification.event !== "step_completed" ||
+        !walletRef.current
       ) {
         return;
       }
 
-      // Always refresh credits when any onboarding step completes
-      fetchCredits();
-
-      // Only trigger confetti for tasks that are in displayed groups
-      if (!walletRef.current) {
-        return;
-      }
+      // Only trigger confetti for tasks that are in groups
       const taskIds = groups
         .flatMap((group) => group.tasks)
         .map((task) => task.id);
@@ -279,6 +274,7 @@ export function Wallet() {
         return;
       }
 
+      fetchCredits();
       party.confetti(walletRef.current, {
         count: 30,
         spread: 120,
@@ -288,7 +284,7 @@ export function Wallet() {
         modules: [fadeOut],
       });
     },
-    [fetchCredits, fadeOut, groups],
+    [fetchCredits, fadeOut],
   );
 
   // WebSocket setup for onboarding notifications

autogpt_platform/frontend/src/lib/autogpt-server-api/types.ts

@@ -1003,7 +1003,6 @@ export type OnboardingStep =
   | "AGENT_INPUT"
   | "CONGRATS"
   // First Wins
-  | "VISIT_COPILOT"
   | "GET_RESULTS"
   | "MARKETPLACE_VISIT"
   | "MARKETPLACE_ADD_AGENT"

autogpt_platform/frontend/src/services/storage/session-storage.ts

@@ -3,7 +3,6 @@ import { environment } from "../environment";
 
 export enum SessionKey {
   CHAT_SENT_INITIAL_PROMPTS = "chat_sent_initial_prompts",
-  CHAT_INITIAL_PROMPTS = "chat_initial_prompts",
 }
 
 function get(key: SessionKey) {

autogpt_platform/frontend/src/tests/pages/login.page.ts

@@ -37,13 +37,9 @@ export class LoginPage {
     this.page.on("load", (page) => console.log(`ℹ️ Now at URL: ${page.url()}`));
 
     // Start waiting for navigation before clicking
-    // Wait for redirect to marketplace, onboarding, library, or copilot (new landing pages)
     const leaveLoginPage = this.page
       .waitForURL(
-        (url: URL) =>
-          /^\/(marketplace|onboarding(\/.*)?|library|copilot)?$/.test(
-            url.pathname,
-          ),
+        (url) => /^\/(marketplace|onboarding(\/.*)?)?$/.test(url.pathname),
         { timeout: 10_000 },
       )
       .catch((reason) => {

autogpt_platform/frontend/src/tests/utils/signup.ts

@@ -36,16 +36,14 @@ export async function signupTestUser(
     const signupButton = getButton("Sign up");
     await signupButton.click();
 
-    // Wait for successful signup - could redirect to various pages depending on onboarding state
+    // Wait for successful signup - could redirect to onboarding or marketplace
 
     try {
-      // Wait for redirect to onboarding, marketplace, copilot, or library
-      // Use a single waitForURL with a callback to avoid Promise.race race conditions
-      await page.waitForURL(
-        (url: URL) =>
-          /\/(onboarding|marketplace|copilot|library)/.test(url.pathname),
-        { timeout: 15000 },
-      );
+      // Wait for either onboarding or marketplace redirect
+      await Promise.race([
+        page.waitForURL(/\/onboarding/, { timeout: 15000 }),
+        page.waitForURL(/\/marketplace/, { timeout: 15000 }),
+      ]);
     } catch (error) {
       console.error(
         "❌ Timeout waiting for redirect, current URL:",
@@ -56,19 +54,14 @@ export async function signupTestUser(
 
     const currentUrl = page.url();
 
-    // Handle onboarding redirect if needed
+    // Handle onboarding or marketplace redirect
     if (currentUrl.includes("/onboarding") && ignoreOnboarding) {
       await page.goto("http://localhost:3000/marketplace");
       await page.waitForLoadState("domcontentloaded", { timeout: 10000 });
     }
 
-    // Verify we're on an expected final page and user is authenticated
-    if (currentUrl.includes("/copilot") || currentUrl.includes("/library")) {
-      // For copilot/library landing pages, just verify user is authenticated
-      await page
-        .getByTestId("profile-popout-menu-trigger")
-        .waitFor({ state: "visible", timeout: 10000 });
-    } else if (ignoreOnboarding || currentUrl.includes("/marketplace")) {
+    // Verify we're on the expected final page
+    if (ignoreOnboarding || currentUrl.includes("/marketplace")) {
       // Verify we're on marketplace
       await page
         .getByText(

backend/blocks/video/__init__.py

@@ -0,0 +1 @@
+# Video editing blocks

docs/integrations/README.md

@@ -53,7 +53,7 @@ Below is a comprehensive list of all available blocks, categorized by their prim
 | [Block Installation](block-integrations/basic.md#block-installation) | Given a code string, this block allows the verification and installation of a block code into the system |
 | [Concatenate Lists](block-integrations/basic.md#concatenate-lists) | Concatenates multiple lists into a single list |
 | [Dictionary Is Empty](block-integrations/basic.md#dictionary-is-empty) | Checks if a dictionary is empty |
-| [File Store](block-integrations/basic.md#file-store) | Downloads and stores a file from a URL, data URI, or local path |
+| [File Store](block-integrations/basic.md#file-store) | Stores the input file in the temporary directory |
 | [Find In Dictionary](block-integrations/basic.md#find-in-dictionary) | A block that looks up a value in a dictionary, list, or object by key or index and returns the corresponding value |
 | [Find In List](block-integrations/basic.md#find-in-list) | Finds the index of the value in the list |
 | [Get All Memories](block-integrations/basic.md#get-all-memories) | Retrieve all memories from Mem0 with optional conversation filtering |

docs/integrations/block-integrations/basic.md

@@ -709,7 +709,7 @@ This is useful for conditional logic where you need to verify if data was return
 ## File Store
 
 ### What it is
-Downloads and stores a file from a URL, data URI, or local path. Use this to fetch images, documents, or other files for processing. In CoPilot: saves to workspace (use list_workspace_files to see it). In graphs: outputs a data URI to pass to other blocks.
+Stores the input file in the temporary directory.
 
 ### How it works
 <!-- MANUAL: how_it_works -->
@@ -722,15 +722,15 @@ The block outputs a file path that other blocks can use to access the stored fil
 
 | Input | Description | Type | Required |
 |-------|-------------|------|----------|
-| file_in | The file to download and store. Can be a URL (https://...), data URI, or local path. | str (file) | Yes |
-| base_64 | Whether to produce output in base64 format (not recommended, you can pass the file reference across blocks). | bool | No |
+| file_in | The file to store in the temporary directory, it can be a URL, data URI, or local path. | str (file) | Yes |
+| base_64 | Whether produce an output in base64 format (not recommended, you can pass the string path just fine accross blocks). | bool | No |
 
 ### Outputs
 
 | Output | Description | Type |
 |--------|-------------|------|
 | error | Error message if the operation failed | str |
-| file_out | Reference to the stored file. In CoPilot: workspace:// URI (visible in list_workspace_files). In graphs: data URI for passing to other blocks. | str (file) |
+| file_out | The relative path to the stored file in the temporary directory. | str (file) |
 
 ### Possible use case
 <!-- MANUAL: use_case -->

docs/integrations/block-integrations/multimedia.md

@@ -12,7 +12,7 @@ Block to attach an audio file to a video file using moviepy.
 <!-- MANUAL: how_it_works -->
 This block combines a video file with an audio file using the moviepy library. The audio track is attached to the video, optionally with volume adjustment via the volume parameter (1.0 = original volume).
 
-Input files can be URLs, data URIs, or local paths. The output format is automatically determined: `workspace://` URLs in CoPilot, data URIs in graph executions.
+Input files can be URLs, data URIs, or local paths. The output can be returned as either a file path or base64 data URI.
 <!-- END MANUAL -->
 
 ### Inputs
@@ -22,6 +22,7 @@ Input files can be URLs, data URIs, or local paths. The output format is automat
 | video_in | Video input (URL, data URI, or local path). | str (file) | Yes |
 | audio_in | Audio input (URL, data URI, or local path). | str (file) | Yes |
 | volume | Volume scale for the newly attached audio track (1.0 = original). | float | No |
+| output_return_type | Return the final output as a relative path or base64 data URI. | "file_path" \| "data_uri" | No |
 
 ### Outputs
 
@@ -50,7 +51,7 @@ Block to loop a video to a given duration or number of repeats.
 <!-- MANUAL: how_it_works -->
 This block extends a video by repeating it to reach a target duration or number of loops. Set duration to specify the total length in seconds, or use n_loops to repeat the video a specific number of times.
 
-The looped video is seamlessly concatenated. The output format is automatically determined: `workspace://` URLs in CoPilot, data URIs in graph executions.
+The looped video is seamlessly concatenated and can be output as a file path or base64 data URI.
 <!-- END MANUAL -->
 
 ### Inputs
@@ -60,6 +61,7 @@ The looped video is seamlessly concatenated. The output format is automatically
 | video_in | The input video (can be a URL, data URI, or local path). | str (file) | Yes |
 | duration | Target duration (in seconds) to loop the video to. If omitted, defaults to no looping. | float | No |
 | n_loops | Number of times to repeat the video. If omitted, defaults to 1 (no repeat). | int | No |
+| output_return_type | How to return the output video. Either a relative path or base64 data URI. | "file_path" \| "data_uri" | No |
 
 ### Outputs
 

docs/platform/block-sdk-guide.md

@@ -277,50 +277,6 @@ async def run(
         token = credentials.api_key.get_secret_value()
 ```
 
-### Handling Files
-
-When your block works with files (images, videos, documents), use `store_media_file()`:
-
-```python
-from backend.data.execution import ExecutionContext
-from backend.util.file import store_media_file
-from backend.util.type import MediaFileType
-
-async def run(
-    self,
-    input_data: Input,
-    *,
-    execution_context: ExecutionContext,
-    **kwargs,
-):
-    # PROCESSING: Need local file path for tools like ffmpeg, MoviePy, PIL
-    local_path = await store_media_file(
-        file=input_data.video,
-        execution_context=execution_context,
-        return_format="for_local_processing",
-    )
-
-    # EXTERNAL API: Need base64 content for APIs like Replicate, OpenAI
-    image_b64 = await store_media_file(
-        file=input_data.image,
-        execution_context=execution_context,
-        return_format="for_external_api",
-    )
-
-    # OUTPUT: Return to user/next block (auto-adapts to context)
-    result = await store_media_file(
-        file=generated_url,
-        execution_context=execution_context,
-        return_format="for_block_output",  # workspace:// in CoPilot, data URI in graphs
-    )
-    yield "image_url", result
-```
-
-**Return format options:**
-- `"for_local_processing"` - Local file path for processing tools
-- `"for_external_api"` - Data URI for external APIs needing base64
-- `"for_block_output"` - **Always use for outputs** - automatically picks best format
-
 ## Testing Your Block
 
 ```bash

docs/platform/new_blocks.md

@@ -111,71 +111,6 @@ Follow these steps to create and test a new block:
      - `graph_exec_id`: The ID of the execution of the agent. This changes every time the agent has a new "run"
      - `node_exec_id`: The ID of the execution of the node. This changes every time the node is executed
      - `node_id`: The ID of the node that is being executed. It changes every version of the graph, but not every time the node is executed.
-     - `execution_context`: An `ExecutionContext` object containing user_id, graph_exec_id, workspace_id, and session_id. Required for file handling.
-
-### Handling Files in Blocks
-
-When your block needs to work with files (images, videos, documents), use `store_media_file()` from `backend.util.file`. This function handles downloading, validation, virus scanning, and storage.
-
-**Import:**
-```python
-from backend.data.execution import ExecutionContext
-from backend.util.file import store_media_file
-from backend.util.type import MediaFileType
-```
-
-**The `return_format` parameter determines what you get back:**
-
-| Format | Use When | Returns |
-|--------|----------|---------|
-| `"for_local_processing"` | Processing with local tools (ffmpeg, MoviePy, PIL) | Local file path (e.g., `"image.png"`) |
-| `"for_external_api"` | Sending content to external APIs (Replicate, OpenAI) | Data URI (e.g., `"data:image/png;base64,..."`) |
-| `"for_block_output"` | Returning output from your block | Smart: `workspace://` in CoPilot, data URI in graphs |
-
-**Examples:**
-
-```python
-async def run(
-    self,
-    input_data: Input,
-    *,
-    execution_context: ExecutionContext,
-    **kwargs,
-) -> BlockOutput:
-    # PROCESSING: Need to work with file locally (ffmpeg, MoviePy, PIL)
-    local_path = await store_media_file(
-        file=input_data.video,
-        execution_context=execution_context,
-        return_format="for_local_processing",
-    )
-    # local_path = "video.mp4" - use with Path, ffmpeg, subprocess, etc.
-    full_path = get_exec_file_path(execution_context.graph_exec_id, local_path)
-
-    # EXTERNAL API: Need to send content to an API like Replicate
-    image_b64 = await store_media_file(
-        file=input_data.image,
-        execution_context=execution_context,
-        return_format="for_external_api",
-    )
-    # image_b64 = "data:image/png;base64,iVBORw0..." - send to external API
-
-    # OUTPUT: Returning result from block to user/next block
-    result_url = await store_media_file(
-        file=generated_image_url,
-        execution_context=execution_context,
-        return_format="for_block_output",
-    )
-    yield "image_url", result_url
-    # In CoPilot: result_url = "workspace://abc123" (persistent, context-efficient)
-    # In graphs:  result_url = "data:image/png;base64,..." (for next block/display)
-```
-
-**Key points:**
-
-- `for_block_output` is the **only** format that auto-adapts to execution context
-- Always use `for_block_output` for block outputs unless you have a specific reason not to
-- Never manually check for `workspace_id` - let `for_block_output` handle the logic
-- The function handles URLs, data URIs, `workspace://` references, and local paths as input
 
 ### Field Types