feat(platform): Add Redis-based SSE reconnection for long-running CoPilot operations (#11877)

## Changes 🏗️

Adds Redis-based SSE reconnection support for long-running CoPilot
operations (like Agent Generator), enabling clients to reconnect and
resume receiving updates after disconnection.

### What this does:
- **Stream Registry** - Redis-backed task tracking with message
persistence via Redis Streams
- **SSE Reconnection** - Clients can reconnect to active tasks using
`task_id` and `last_message_id`
- **Duplicate Message Fix** - Filters out in-progress assistant messages
from session response when active stream exists
- **Completion Consumer** - Handles background task completion
notifications via Redis Streams

### Architecture:
```
1. User sends message → Backend creates task in Redis
2. SSE chunks written to Redis Stream for persistence
3. Client receives chunks via SSE subscription
4. If client disconnects → Task continues in background
5. Client reconnects → GET /sessions/{id} returns active_stream info
6. Client subscribes to /tasks/{task_id}/stream with last_message_id
7. Missed messages replayed from Redis Stream
```

### Key endpoints:
- `GET /sessions/{session_id}` - Returns `active_stream` info if task is
running
- `GET /tasks/{task_id}/stream?last_message_id=X` - SSE endpoint for
reconnection
- `GET /tasks/{task_id}` - Get task status
- `POST /operations/{op_id}/complete` - Webhook for external service
completion

### Duplicate message fix:
When `GET /sessions/{id}` detects an active stream:
1. Filters out the in-progress assistant message from response
2. Returns `last_message_id="0-0"` so client replays stream from
beginning
3. Client receives complete response only through SSE (single source of
truth)

### Frontend changes:
- Task persistence in localStorage for cross-tab reconnection
- Stream event dispatcher handles reconnection flow
- Deduplication logic prevents duplicate messages

### Testing:
- Manual testing of reconnection scenarios
- Verified duplicate message fix works correctly

## Related
- Resolves SSE timeout issues for Agent Generator
- Fixes duplicate message bug on reconnection

.gitignore

@@ -180,3 +180,4 @@ autogpt_platform/backend/settings.py
 .claude/settings.local.json
 CLAUDE.local.md
 /autogpt_platform/backend/logs
+.next

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

@@ -0,0 +1,368 @@
+"""Redis Streams consumer for operation completion messages.
+
+This module provides a consumer (ChatCompletionConsumer) that listens for
+completion notifications (OperationCompleteMessage) from external services
+(like Agent Generator) and triggers the appropriate stream registry and
+chat service updates via process_operation_success/process_operation_failure.
+
+Why Redis Streams instead of RabbitMQ?
+--------------------------------------
+While the project typically uses RabbitMQ for async task queues (e.g., execution
+queue), Redis Streams was chosen for chat completion notifications because:
+
+1. **Unified Infrastructure**: The SSE reconnection feature already uses Redis
+   Streams (via stream_registry) for message persistence and replay. Using Redis
+   Streams for completion notifications keeps all chat streaming infrastructure
+   in one system, simplifying operations and reducing cross-system coordination.
+
+2. **Message Replay**: Redis Streams support XREAD with arbitrary message IDs,
+   allowing consumers to replay missed messages after reconnection. This aligns
+   with the SSE reconnection pattern where clients can resume from last_message_id.
+
+3. **Consumer Groups with XAUTOCLAIM**: Redis consumer groups provide automatic
+   load balancing across pods with explicit message claiming (XAUTOCLAIM) for
+   recovering from dead consumers - ideal for the completion callback pattern.
+
+4. **Lower Latency**: For real-time SSE updates, Redis (already in-memory for
+   stream_registry) provides lower latency than an additional RabbitMQ hop.
+
+5. **Atomicity with Task State**: Completion processing often needs to update
+   task metadata stored in Redis. Keeping both in Redis enables simpler
+   transactional semantics without distributed coordination.
+
+The consumer uses Redis Streams with consumer groups for reliable message
+processing across multiple platform pods, with XAUTOCLAIM for reclaiming
+stale pending messages from dead consumers.
+"""
+
+import asyncio
+import logging
+import os
+import uuid
+from typing import Any
+
+import orjson
+from prisma import Prisma
+from pydantic import BaseModel
+from redis.exceptions import ResponseError
+
+from backend.data.redis_client import get_redis_async
+
+from . import stream_registry
+from .completion_handler import process_operation_failure, process_operation_success
+from .config import ChatConfig
+
+logger = logging.getLogger(__name__)
+config = ChatConfig()
+
+
+class OperationCompleteMessage(BaseModel):
+    """Message format for operation completion notifications."""
+
+    operation_id: str
+    task_id: str
+    success: bool
+    result: dict | str | None = None
+    error: str | None = None
+
+
+class ChatCompletionConsumer:
+    """Consumer for chat operation completion messages from Redis Streams.
+
+    This consumer initializes its own Prisma client in start() to ensure
+    database operations work correctly within this async context.
+
+    Uses Redis consumer groups to allow multiple platform pods to consume
+    messages reliably with automatic redelivery on failure.
+    """
+
+    def __init__(self):
+        self._consumer_task: asyncio.Task | None = None
+        self._running = False
+        self._prisma: Prisma | None = None
+        self._consumer_name = f"consumer-{uuid.uuid4().hex[:8]}"
+
+    async def start(self) -> None:
+        """Start the completion consumer."""
+        if self._running:
+            logger.warning("Completion consumer already running")
+            return
+
+        # Create consumer group if it doesn't exist
+        try:
+            redis = await get_redis_async()
+            await redis.xgroup_create(
+                config.stream_completion_name,
+                config.stream_consumer_group,
+                id="0",
+                mkstream=True,
+            )
+            logger.info(
+                f"Created consumer group '{config.stream_consumer_group}' "
+                f"on stream '{config.stream_completion_name}'"
+            )
+        except ResponseError as e:
+            if "BUSYGROUP" in str(e):
+                logger.debug(
+                    f"Consumer group '{config.stream_consumer_group}' already exists"
+                )
+            else:
+                raise
+
+        self._running = True
+        self._consumer_task = asyncio.create_task(self._consume_messages())
+        logger.info(
+            f"Chat completion consumer started (consumer: {self._consumer_name})"
+        )
+
+    async def _ensure_prisma(self) -> Prisma:
+        """Lazily initialize Prisma client on first use."""
+        if self._prisma is None:
+            database_url = os.getenv("DATABASE_URL", "postgresql://localhost:5432")
+            self._prisma = Prisma(datasource={"url": database_url})
+            await self._prisma.connect()
+            logger.info("[COMPLETION] Consumer Prisma client connected (lazy init)")
+        return self._prisma
+
+    async def stop(self) -> None:
+        """Stop the completion consumer."""
+        self._running = False
+
+        if self._consumer_task:
+            self._consumer_task.cancel()
+            try:
+                await self._consumer_task
+            except asyncio.CancelledError:
+                pass
+            self._consumer_task = None
+
+        if self._prisma:
+            await self._prisma.disconnect()
+            self._prisma = None
+            logger.info("[COMPLETION] Consumer Prisma client disconnected")
+
+        logger.info("Chat completion consumer stopped")
+
+    async def _consume_messages(self) -> None:
+        """Main message consumption loop with retry logic."""
+        max_retries = 10
+        retry_delay = 5  # seconds
+        retry_count = 0
+        block_timeout = 5000  # milliseconds
+
+        while self._running and retry_count < max_retries:
+            try:
+                redis = await get_redis_async()
+
+                # Reset retry count on successful connection
+                retry_count = 0
+
+                while self._running:
+                    # First, claim any stale pending messages from dead consumers
+                    # Redis does NOT auto-redeliver pending messages; we must explicitly
+                    # claim them using XAUTOCLAIM
+                    try:
+                        claimed_result = await redis.xautoclaim(
+                            name=config.stream_completion_name,
+                            groupname=config.stream_consumer_group,
+                            consumername=self._consumer_name,
+                            min_idle_time=config.stream_claim_min_idle_ms,
+                            start_id="0-0",
+                            count=10,
+                        )
+                        # xautoclaim returns: (next_start_id, [(id, data), ...], [deleted_ids])
+                        if claimed_result and len(claimed_result) >= 2:
+                            claimed_entries = claimed_result[1]
+                            if claimed_entries:
+                                logger.info(
+                                    f"Claimed {len(claimed_entries)} stale pending messages"
+                                )
+                                for entry_id, data in claimed_entries:
+                                    if not self._running:
+                                        return
+                                    await self._process_entry(redis, entry_id, data)
+                    except Exception as e:
+                        logger.warning(f"XAUTOCLAIM failed (non-fatal): {e}")
+
+                    # Read new messages from the stream
+                    messages = await redis.xreadgroup(
+                        groupname=config.stream_consumer_group,
+                        consumername=self._consumer_name,
+                        streams={config.stream_completion_name: ">"},
+                        block=block_timeout,
+                        count=10,
+                    )
+
+                    if not messages:
+                        continue
+
+                    for stream_name, entries in messages:
+                        for entry_id, data in entries:
+                            if not self._running:
+                                return
+                            await self._process_entry(redis, entry_id, data)
+
+            except asyncio.CancelledError:
+                logger.info("Consumer cancelled")
+                return
+            except Exception as e:
+                retry_count += 1
+                logger.error(
+                    f"Consumer error (retry {retry_count}/{max_retries}): {e}",
+                    exc_info=True,
+                )
+                if self._running and retry_count < max_retries:
+                    await asyncio.sleep(retry_delay)
+                else:
+                    logger.error("Max retries reached, stopping consumer")
+                    return
+
+    async def _process_entry(
+        self, redis: Any, entry_id: str, data: dict[str, Any]
+    ) -> None:
+        """Process a single stream entry and acknowledge it on success.
+
+        Args:
+            redis: Redis client connection
+            entry_id: The stream entry ID
+            data: The entry data dict
+        """
+        try:
+            # Handle the message
+            message_data = data.get("data")
+            if message_data:
+                await self._handle_message(
+                    message_data.encode()
+                    if isinstance(message_data, str)
+                    else message_data
+                )
+
+            # Acknowledge the message after successful processing
+            await redis.xack(
+                config.stream_completion_name,
+                config.stream_consumer_group,
+                entry_id,
+            )
+        except Exception as e:
+            logger.error(
+                f"Error processing completion message {entry_id}: {e}",
+                exc_info=True,
+            )
+            # Message remains in pending state and will be claimed by
+            # XAUTOCLAIM after min_idle_time expires
+
+    async def _handle_message(self, body: bytes) -> None:
+        """Handle a completion message using our own Prisma client."""
+        try:
+            data = orjson.loads(body)
+            message = OperationCompleteMessage(**data)
+        except Exception as e:
+            logger.error(f"Failed to parse completion message: {e}")
+            return
+
+        logger.info(
+            f"[COMPLETION] Received completion for operation {message.operation_id} "
+            f"(task_id={message.task_id}, success={message.success})"
+        )
+
+        # Find task in registry
+        task = await stream_registry.find_task_by_operation_id(message.operation_id)
+        if task is None:
+            task = await stream_registry.get_task(message.task_id)
+
+        if task is None:
+            logger.warning(
+                f"[COMPLETION] Task not found for operation {message.operation_id} "
+                f"(task_id={message.task_id})"
+            )
+            return
+
+        logger.info(
+            f"[COMPLETION] Found task: task_id={task.task_id}, "
+            f"session_id={task.session_id}, tool_call_id={task.tool_call_id}"
+        )
+
+        # Guard against empty task fields
+        if not task.task_id or not task.session_id or not task.tool_call_id:
+            logger.error(
+                f"[COMPLETION] Task has empty critical fields! "
+                f"task_id={task.task_id!r}, session_id={task.session_id!r}, "
+                f"tool_call_id={task.tool_call_id!r}"
+            )
+            return
+
+        if message.success:
+            await self._handle_success(task, message)
+        else:
+            await self._handle_failure(task, message)
+
+    async def _handle_success(
+        self,
+        task: stream_registry.ActiveTask,
+        message: OperationCompleteMessage,
+    ) -> None:
+        """Handle successful operation completion."""
+        prisma = await self._ensure_prisma()
+        await process_operation_success(task, message.result, prisma)
+
+    async def _handle_failure(
+        self,
+        task: stream_registry.ActiveTask,
+        message: OperationCompleteMessage,
+    ) -> None:
+        """Handle failed operation completion."""
+        prisma = await self._ensure_prisma()
+        await process_operation_failure(task, message.error, prisma)
+
+
+# Module-level consumer instance
+_consumer: ChatCompletionConsumer | None = None
+
+
+async def start_completion_consumer() -> None:
+    """Start the global completion consumer."""
+    global _consumer
+    if _consumer is None:
+        _consumer = ChatCompletionConsumer()
+    await _consumer.start()
+
+
+async def stop_completion_consumer() -> None:
+    """Stop the global completion consumer."""
+    global _consumer
+    if _consumer:
+        await _consumer.stop()
+        _consumer = None
+
+
+async def publish_operation_complete(
+    operation_id: str,
+    task_id: str,
+    success: bool,
+    result: dict | str | None = None,
+    error: str | None = None,
+) -> None:
+    """Publish an operation completion message to Redis Streams.
+
+    Args:
+        operation_id: The operation ID that completed.
+        task_id: The task ID associated with the operation.
+        success: Whether the operation succeeded.
+        result: The result data (for success).
+        error: The error message (for failure).
+    """
+    message = OperationCompleteMessage(
+        operation_id=operation_id,
+        task_id=task_id,
+        success=success,
+        result=result,
+        error=error,
+    )
+
+    redis = await get_redis_async()
+    await redis.xadd(
+        config.stream_completion_name,
+        {"data": message.model_dump_json()},
+        maxlen=config.stream_max_length,
+    )
+    logger.info(f"Published completion for operation {operation_id}")

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

@@ -0,0 +1,344 @@
+"""Shared completion handling for operation success and failure.
+
+This module provides common logic for handling operation completion from both:
+- The Redis Streams consumer (completion_consumer.py)
+- The HTTP webhook endpoint (routes.py)
+"""
+
+import logging
+from typing import Any
+
+import orjson
+from prisma import Prisma
+
+from . import service as chat_service
+from . import stream_registry
+from .response_model import StreamError, StreamToolOutputAvailable
+from .tools.models import ErrorResponse
+
+logger = logging.getLogger(__name__)
+
+# Tools that produce agent_json that needs to be saved to library
+AGENT_GENERATION_TOOLS = {"create_agent", "edit_agent"}
+
+# Keys that should be stripped from agent_json when returning in error responses
+SENSITIVE_KEYS = frozenset(
+    {
+        "api_key",
+        "apikey",
+        "api_secret",
+        "password",
+        "secret",
+        "credentials",
+        "credential",
+        "token",
+        "access_token",
+        "refresh_token",
+        "private_key",
+        "privatekey",
+        "auth",
+        "authorization",
+    }
+)
+
+
+def _sanitize_agent_json(obj: Any) -> Any:
+    """Recursively sanitize agent_json by removing sensitive keys.
+
+    Args:
+        obj: The object to sanitize (dict, list, or primitive)
+
+    Returns:
+        Sanitized copy with sensitive keys removed/redacted
+    """
+    if isinstance(obj, dict):
+        return {
+            k: "[REDACTED]" if k.lower() in SENSITIVE_KEYS else _sanitize_agent_json(v)
+            for k, v in obj.items()
+        }
+    elif isinstance(obj, list):
+        return [_sanitize_agent_json(item) for item in obj]
+    else:
+        return obj
+
+
+class ToolMessageUpdateError(Exception):
+    """Raised when updating a tool message in the database fails."""
+
+    pass
+
+
+async def _update_tool_message(
+    session_id: str,
+    tool_call_id: str,
+    content: str,
+    prisma_client: Prisma | None,
+) -> None:
+    """Update tool message in database.
+
+    Args:
+        session_id: The session ID
+        tool_call_id: The tool call ID to update
+        content: The new content for the message
+        prisma_client: Optional Prisma client. If None, uses chat_service.
+
+    Raises:
+        ToolMessageUpdateError: If the database update fails. The caller should
+            handle this to avoid marking the task as completed with inconsistent state.
+    """
+    try:
+        if prisma_client:
+            # Use provided Prisma client (for consumer with its own connection)
+            updated_count = await prisma_client.chatmessage.update_many(
+                where={
+                    "sessionId": session_id,
+                    "toolCallId": tool_call_id,
+                },
+                data={"content": content},
+            )
+            # Check if any rows were updated - 0 means message not found
+            if updated_count == 0:
+                raise ToolMessageUpdateError(
+                    f"No message found with tool_call_id={tool_call_id} in session {session_id}"
+                )
+        else:
+            # Use service function (for webhook endpoint)
+            await chat_service._update_pending_operation(
+                session_id=session_id,
+                tool_call_id=tool_call_id,
+                result=content,
+            )
+    except ToolMessageUpdateError:
+        raise
+    except Exception as e:
+        logger.error(f"[COMPLETION] Failed to update tool message: {e}", exc_info=True)
+        raise ToolMessageUpdateError(
+            f"Failed to update tool message for tool_call_id={tool_call_id}: {e}"
+        ) from e
+
+
+def serialize_result(result: dict | list | str | int | float | bool | None) -> str:
+    """Serialize result to JSON string with sensible defaults.
+
+    Args:
+        result: The result to serialize. Can be a dict, list, string,
+            number, boolean, or None.
+
+    Returns:
+        JSON string representation of the result. Returns '{"status": "completed"}'
+        only when result is explicitly None.
+    """
+    if isinstance(result, str):
+        return result
+    if result is None:
+        return '{"status": "completed"}'
+    return orjson.dumps(result).decode("utf-8")
+
+
+async def _save_agent_from_result(
+    result: dict[str, Any],
+    user_id: str | None,
+    tool_name: str,
+) -> dict[str, Any]:
+    """Save agent to library if result contains agent_json.
+
+    Args:
+        result: The result dict that may contain agent_json
+        user_id: The user ID to save the agent for
+        tool_name: The tool name (create_agent or edit_agent)
+
+    Returns:
+        Updated result dict with saved agent details, or original result if no agent_json
+    """
+    if not user_id:
+        logger.warning("[COMPLETION] Cannot save agent: no user_id in task")
+        return result
+
+    agent_json = result.get("agent_json")
+    if not agent_json:
+        logger.warning(
+            f"[COMPLETION] {tool_name} completed but no agent_json in result"
+        )
+        return result
+
+    try:
+        from .tools.agent_generator import save_agent_to_library
+
+        is_update = tool_name == "edit_agent"
+        created_graph, library_agent = await save_agent_to_library(
+            agent_json, user_id, is_update=is_update
+        )
+
+        logger.info(
+            f"[COMPLETION] Saved agent '{created_graph.name}' to library "
+            f"(graph_id={created_graph.id}, library_agent_id={library_agent.id})"
+        )
+
+        # Return a response similar to AgentSavedResponse
+        return {
+            "type": "agent_saved",
+            "message": f"Agent '{created_graph.name}' has been saved to your library!",
+            "agent_id": created_graph.id,
+            "agent_name": created_graph.name,
+            "library_agent_id": library_agent.id,
+            "library_agent_link": f"/library/agents/{library_agent.id}",
+            "agent_page_link": f"/build?flowID={created_graph.id}",
+        }
+    except Exception as e:
+        logger.error(
+            f"[COMPLETION] Failed to save agent to library: {e}",
+            exc_info=True,
+        )
+        # Return error but don't fail the whole operation
+        # Sanitize agent_json to remove sensitive keys before returning
+        return {
+            "type": "error",
+            "message": f"Agent was generated but failed to save: {str(e)}",
+            "error": str(e),
+            "agent_json": _sanitize_agent_json(agent_json),
+        }
+
+
+async def process_operation_success(
+    task: stream_registry.ActiveTask,
+    result: dict | str | None,
+    prisma_client: Prisma | None = None,
+) -> None:
+    """Handle successful operation completion.
+
+    Publishes the result to the stream registry, updates the database,
+    generates LLM continuation, and marks the task as completed.
+
+    Args:
+        task: The active task that completed
+        result: The result data from the operation
+        prisma_client: Optional Prisma client for database operations.
+            If None, uses chat_service._update_pending_operation instead.
+
+    Raises:
+        ToolMessageUpdateError: If the database update fails. The task will be
+            marked as failed instead of completed to avoid inconsistent state.
+    """
+    # For agent generation tools, save the agent to library
+    if task.tool_name in AGENT_GENERATION_TOOLS and isinstance(result, dict):
+        result = await _save_agent_from_result(result, task.user_id, task.tool_name)
+
+    # Serialize result for output (only substitute default when result is exactly None)
+    result_output = result if result is not None else {"status": "completed"}
+    output_str = (
+        result_output
+        if isinstance(result_output, str)
+        else orjson.dumps(result_output).decode("utf-8")
+    )
+
+    # Publish result to stream registry
+    await stream_registry.publish_chunk(
+        task.task_id,
+        StreamToolOutputAvailable(
+            toolCallId=task.tool_call_id,
+            toolName=task.tool_name,
+            output=output_str,
+            success=True,
+        ),
+    )
+
+    # Update pending operation in database
+    # If this fails, we must not continue to mark the task as completed
+    result_str = serialize_result(result)
+    try:
+        await _update_tool_message(
+            session_id=task.session_id,
+            tool_call_id=task.tool_call_id,
+            content=result_str,
+            prisma_client=prisma_client,
+        )
+    except ToolMessageUpdateError:
+        # DB update failed - mark task as failed to avoid inconsistent state
+        logger.error(
+            f"[COMPLETION] DB update failed for task {task.task_id}, "
+            "marking as failed instead of completed"
+        )
+        await stream_registry.publish_chunk(
+            task.task_id,
+            StreamError(errorText="Failed to save operation result to database"),
+        )
+        await stream_registry.mark_task_completed(task.task_id, status="failed")
+        raise
+
+    # Generate LLM continuation with streaming
+    try:
+        await chat_service._generate_llm_continuation_with_streaming(
+            session_id=task.session_id,
+            user_id=task.user_id,
+            task_id=task.task_id,
+        )
+    except Exception as e:
+        logger.error(
+            f"[COMPLETION] Failed to generate LLM continuation: {e}",
+            exc_info=True,
+        )
+
+    # Mark task as completed and release Redis lock
+    await stream_registry.mark_task_completed(task.task_id, status="completed")
+    try:
+        await chat_service._mark_operation_completed(task.tool_call_id)
+    except Exception as e:
+        logger.error(f"[COMPLETION] Failed to mark operation completed: {e}")
+
+    logger.info(
+        f"[COMPLETION] Successfully processed completion for task {task.task_id}"
+    )
+
+
+async def process_operation_failure(
+    task: stream_registry.ActiveTask,
+    error: str | None,
+    prisma_client: Prisma | None = None,
+) -> None:
+    """Handle failed operation completion.
+
+    Publishes the error to the stream registry, updates the database with
+    the error response, and marks the task as failed.
+
+    Args:
+        task: The active task that failed
+        error: The error message from the operation
+        prisma_client: Optional Prisma client for database operations.
+            If None, uses chat_service._update_pending_operation instead.
+    """
+    error_msg = error or "Operation failed"
+
+    # Publish error to stream registry
+    await stream_registry.publish_chunk(
+        task.task_id,
+        StreamError(errorText=error_msg),
+    )
+
+    # Update pending operation with error
+    # If this fails, we still continue to mark the task as failed
+    error_response = ErrorResponse(
+        message=error_msg,
+        error=error,
+    )
+    try:
+        await _update_tool_message(
+            session_id=task.session_id,
+            tool_call_id=task.tool_call_id,
+            content=error_response.model_dump_json(),
+            prisma_client=prisma_client,
+        )
+    except ToolMessageUpdateError:
+        # DB update failed - log but continue with cleanup
+        logger.error(
+            f"[COMPLETION] DB update failed while processing failure for task {task.task_id}, "
+            "continuing with cleanup"
+        )
+
+    # Mark task as failed and release Redis lock
+    await stream_registry.mark_task_completed(task.task_id, status="failed")
+    try:
+        await chat_service._mark_operation_completed(task.tool_call_id)
+    except Exception as e:
+        logger.error(f"[COMPLETION] Failed to mark operation completed: {e}")
+
+    logger.info(f"[COMPLETION] Processed failure for task {task.task_id}: {error_msg}")

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

@@ -44,6 +44,48 @@ class ChatConfig(BaseSettings):
         description="TTL in seconds for long-running operation tracking in Redis (safety net if pod dies)",
     )
 
+    # Stream registry configuration for SSE reconnection
+    stream_ttl: int = Field(
+        default=3600,
+        description="TTL in seconds for stream data in Redis (1 hour)",
+    )
+    stream_max_length: int = Field(
+        default=10000,
+        description="Maximum number of messages to store per stream",
+    )
+
+    # Redis Streams configuration for completion consumer
+    stream_completion_name: str = Field(
+        default="chat:completions",
+        description="Redis Stream name for operation completions",
+    )
+    stream_consumer_group: str = Field(
+        default="chat_consumers",
+        description="Consumer group name for completion stream",
+    )
+    stream_claim_min_idle_ms: int = Field(
+        default=60000,
+        description="Minimum idle time in milliseconds before claiming pending messages from dead consumers",
+    )
+
+    # Redis key prefixes for stream registry
+    task_meta_prefix: str = Field(
+        default="chat:task:meta:",
+        description="Prefix for task metadata hash keys",
+    )
+    task_stream_prefix: str = Field(
+        default="chat:stream:",
+        description="Prefix for task message stream keys",
+    )
+    task_op_prefix: str = Field(
+        default="chat:task:op:",
+        description="Prefix for operation ID to task ID mapping keys",
+    )
+    internal_api_key: str | None = Field(
+        default=None,
+        description="API key for internal webhook callbacks (env: CHAT_INTERNAL_API_KEY)",
+    )
+
     # Langfuse Prompt Management Configuration
     # Note: Langfuse credentials are in Settings().secrets (settings.py)
     langfuse_prompt_name: str = Field(
@@ -82,6 +124,14 @@ class ChatConfig(BaseSettings):
                 v = "https://openrouter.ai/api/v1"
         return v
 
+    @field_validator("internal_api_key", mode="before")
+    @classmethod
+    def get_internal_api_key(cls, v):
+        """Get internal API key from environment if not provided."""
+        if v is None:
+            v = os.getenv("CHAT_INTERNAL_API_KEY")
+        return v
+
     # Prompt paths for different contexts
     PROMPT_PATHS: dict[str, str] = {
         "default": "prompts/chat_system.md",

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

@@ -52,6 +52,10 @@ class StreamStart(StreamBaseResponse):
 
     type: ResponseType = ResponseType.START
     messageId: str = Field(..., description="Unique message ID")
+    taskId: str | None = Field(
+        default=None,
+        description="Task ID for SSE reconnection. Clients can reconnect using GET /tasks/{taskId}/stream",
+    )
 
 
 class StreamFinish(StreamBaseResponse):

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

@@ -1,19 +1,23 @@
 """Chat API routes for chat session management and streaming via SSE."""
 
 import logging
+import uuid as uuid_module
 from collections.abc import AsyncGenerator
 from typing import Annotated
 
 from autogpt_libs import auth
-from fastapi import APIRouter, Depends, Query, Security
+from fastapi import APIRouter, Depends, Header, HTTPException, Query, Security
 from fastapi.responses import StreamingResponse
 from pydantic import BaseModel
 
 from backend.util.exceptions import NotFoundError
 
 from . import service as chat_service
+from . import stream_registry
+from .completion_handler import process_operation_failure, process_operation_success
 from .config import ChatConfig
 from .model import ChatSession, create_chat_session, get_chat_session, get_user_sessions
+from .response_model import StreamFinish, StreamHeartbeat, StreamStart
 
 config = ChatConfig()
 
@@ -55,6 +59,15 @@ class CreateSessionResponse(BaseModel):
     user_id: str | None
 
 
+class ActiveStreamInfo(BaseModel):
+    """Information about an active stream for reconnection."""
+
+    task_id: str
+    last_message_id: str  # Redis Stream message ID for resumption
+    operation_id: str  # Operation ID for completion tracking
+    tool_name: str  # Name of the tool being executed
+
+
 class SessionDetailResponse(BaseModel):
     """Response model providing complete details for a chat session, including messages."""
 
@@ -63,6 +76,7 @@ class SessionDetailResponse(BaseModel):
     updated_at: str
     user_id: str | None
     messages: list[dict]
+    active_stream: ActiveStreamInfo | None = None  # Present if stream is still active
 
 
 class SessionSummaryResponse(BaseModel):
@@ -81,6 +95,14 @@ class ListSessionsResponse(BaseModel):
     total: int
 
 
+class OperationCompleteRequest(BaseModel):
+    """Request model for external completion webhook."""
+
+    success: bool
+    result: dict | str | None = None
+    error: str | None = None
+
+
 # ========== Routes ==========
 
 
@@ -166,13 +188,14 @@ async def get_session(
     Retrieve the details of a specific chat session.
 
     Looks up a chat session by ID for the given user (if authenticated) and returns all session data including messages.
+    If there's an active stream for this session, returns the task_id for reconnection.
 
     Args:
         session_id: The unique identifier for the desired chat session.
         user_id: The optional authenticated user ID, or None for anonymous access.
 
     Returns:
-        SessionDetailResponse: Details for the requested session, or None if not found.
+        SessionDetailResponse: Details for the requested session, including active_stream info if applicable.
 
     """
     session = await get_chat_session(session_id, user_id)
@@ -180,11 +203,28 @@ async def get_session(
         raise NotFoundError(f"Session {session_id} not found.")
 
     messages = [message.model_dump() for message in session.messages]
-    logger.info(
-        f"Returning session {session_id}: "
-        f"message_count={len(messages)}, "
-        f"roles={[m.get('role') for m in messages]}"
+
+    # Check if there's an active stream for this session
+    active_stream_info = None
+    active_task, last_message_id = await stream_registry.get_active_task_for_session(
+        session_id, user_id
     )
+    if active_task:
+        # Filter out the in-progress assistant message from the session response.
+        # The client will receive the complete assistant response through the SSE
+        # stream replay instead, preventing duplicate content.
+        if messages and messages[-1].get("role") == "assistant":
+            messages = messages[:-1]
+
+        # Use "0-0" as last_message_id to replay the stream from the beginning.
+        # Since we filtered out the cached assistant message, the client needs
+        # the full stream to reconstruct the response.
+        active_stream_info = ActiveStreamInfo(
+            task_id=active_task.task_id,
+            last_message_id="0-0",
+            operation_id=active_task.operation_id,
+            tool_name=active_task.tool_name,
+        )
 
     return SessionDetailResponse(
         id=session.session_id,
@@ -192,6 +232,7 @@ async def get_session(
         updated_at=session.updated_at.isoformat(),
         user_id=session.user_id or None,
         messages=messages,
+        active_stream=active_stream_info,
     )
 
 
@@ -211,49 +252,112 @@ async def stream_chat_post(
       - Tool call UI elements (if invoked)
       - Tool execution results
 
+    The AI generation runs in a background task that continues even if the client disconnects.
+    All chunks are written to Redis for reconnection support. If the client disconnects,
+    they can reconnect using GET /tasks/{task_id}/stream to resume from where they left off.
+
     Args:
         session_id: The chat session identifier to associate with the streamed messages.
         request: Request body containing message, is_user_message, and optional context.
         user_id: Optional authenticated user ID.
     Returns:
-        StreamingResponse: SSE-formatted response chunks.
+        StreamingResponse: SSE-formatted response chunks. First chunk is a "start" event
+        containing the task_id for reconnection.
 
     """
+    import asyncio
+
     session = await _validate_and_get_session(session_id, user_id)
 
+    # Create a task in the stream registry for reconnection support
+    task_id = str(uuid_module.uuid4())
+    operation_id = str(uuid_module.uuid4())
+    await stream_registry.create_task(
+        task_id=task_id,
+        session_id=session_id,
+        user_id=user_id,
+        tool_call_id="chat_stream",  # Not a tool call, but needed for the model
+        tool_name="chat",
+        operation_id=operation_id,
+    )
+
+    # Background task that runs the AI generation independently of SSE connection
+    async def run_ai_generation():
+        try:
+            # Emit a start event with task_id for reconnection
+            start_chunk = StreamStart(messageId=task_id, taskId=task_id)
+            await stream_registry.publish_chunk(task_id, start_chunk)
+
+            async for chunk in chat_service.stream_chat_completion(
+                session_id,
+                request.message,
+                is_user_message=request.is_user_message,
+                user_id=user_id,
+                session=session,  # Pass pre-fetched session to avoid double-fetch
+                context=request.context,
+            ):
+                # Write to Redis (subscribers will receive via XREAD)
+                await stream_registry.publish_chunk(task_id, chunk)
+
+            # Mark task as completed
+            await stream_registry.mark_task_completed(task_id, "completed")
+        except Exception as e:
+            logger.error(
+                f"Error in background AI generation for session {session_id}: {e}"
+            )
+            await stream_registry.mark_task_completed(task_id, "failed")
+
+    # Start the AI generation in a background task
+    bg_task = asyncio.create_task(run_ai_generation())
+    await stream_registry.set_task_asyncio_task(task_id, bg_task)
+
+    # SSE endpoint that subscribes to the task's stream
     async def event_generator() -> AsyncGenerator[str, None]:
-        chunk_count = 0
-        first_chunk_type: str | None = None
-        async for chunk in chat_service.stream_chat_completion(
-            session_id,
-            request.message,
-            is_user_message=request.is_user_message,
-            user_id=user_id,
-            session=session,  # Pass pre-fetched session to avoid double-fetch
-            context=request.context,
-        ):
-            if chunk_count < 3:
-                logger.info(
-                    "Chat stream chunk",
-                    extra={
-                        "session_id": session_id,
-                        "chunk_type": str(chunk.type),
-                    },
-                )
-            if not first_chunk_type:
-                first_chunk_type = str(chunk.type)
-            chunk_count += 1
-            yield chunk.to_sse()
-        logger.info(
-            "Chat stream completed",
-            extra={
-                "session_id": session_id,
-                "chunk_count": chunk_count,
-                "first_chunk_type": first_chunk_type,
-            },
-        )
-        # AI SDK protocol termination
-        yield "data: [DONE]\n\n"
+        subscriber_queue = None
+        try:
+            # Subscribe to the task stream (this replays existing messages + live updates)
+            subscriber_queue = await stream_registry.subscribe_to_task(
+                task_id=task_id,
+                user_id=user_id,
+                last_message_id="0-0",  # Get all messages from the beginning
+            )
+
+            if subscriber_queue is None:
+                yield StreamFinish().to_sse()
+                yield "data: [DONE]\n\n"
+                return
+
+            # Read from the subscriber queue and yield to SSE
+            while True:
+                try:
+                    chunk = await asyncio.wait_for(subscriber_queue.get(), timeout=30.0)
+                    yield chunk.to_sse()
+
+                    # Check for finish signal
+                    if isinstance(chunk, StreamFinish):
+                        break
+                except asyncio.TimeoutError:
+                    # Send heartbeat to keep connection alive
+                    yield StreamHeartbeat().to_sse()
+
+        except GeneratorExit:
+            pass  # Client disconnected - background task continues
+        except Exception as e:
+            logger.error(f"Error in SSE stream for task {task_id}: {e}")
+        finally:
+            # Unsubscribe when client disconnects or stream ends to prevent resource leak
+            if subscriber_queue is not None:
+                try:
+                    await stream_registry.unsubscribe_from_task(
+                        task_id, subscriber_queue
+                    )
+                except Exception as unsub_err:
+                    logger.error(
+                        f"Error unsubscribing from task {task_id}: {unsub_err}",
+                        exc_info=True,
+                    )
+            # AI SDK protocol termination - always yield even if unsubscribe fails
+            yield "data: [DONE]\n\n"
 
     return StreamingResponse(
         event_generator(),
@@ -366,6 +470,251 @@ async def session_assign_user(
     return {"status": "ok"}
 
 
+# ========== Task Streaming (SSE Reconnection) ==========
+
+
+@router.get(
+    "/tasks/{task_id}/stream",
+)
+async def stream_task(
+    task_id: str,
+    user_id: str | None = Depends(auth.get_user_id),
+    last_message_id: str = Query(
+        default="0-0",
+        description="Last Redis Stream message ID received (e.g., '1706540123456-0'). Use '0-0' for full replay.",
+    ),
+):
+    """
+    Reconnect to a long-running task's SSE stream.
+
+    When a long-running operation (like agent generation) starts, the client
+    receives a task_id. If the connection drops, the client can reconnect
+    using this endpoint to resume receiving updates.
+
+    Args:
+        task_id: The task ID from the operation_started response.
+        user_id: Authenticated user ID for ownership validation.
+        last_message_id: Last Redis Stream message ID received ("0-0" for full replay).
+
+    Returns:
+        StreamingResponse: SSE-formatted response chunks starting after last_message_id.
+
+    Raises:
+        HTTPException: 404 if task not found, 410 if task expired, 403 if access denied.
+    """
+    # Check task existence and expiry before subscribing
+    task, error_code = await stream_registry.get_task_with_expiry_info(task_id)
+
+    if error_code == "TASK_EXPIRED":
+        raise HTTPException(
+            status_code=410,
+            detail={
+                "code": "TASK_EXPIRED",
+                "message": "This operation has expired. Please try again.",
+            },
+        )
+
+    if error_code == "TASK_NOT_FOUND":
+        raise HTTPException(
+            status_code=404,
+            detail={
+                "code": "TASK_NOT_FOUND",
+                "message": f"Task {task_id} not found.",
+            },
+        )
+
+    # Validate ownership if task has an owner
+    if task and task.user_id and user_id != task.user_id:
+        raise HTTPException(
+            status_code=403,
+            detail={
+                "code": "ACCESS_DENIED",
+                "message": "You do not have access to this task.",
+            },
+        )
+
+    # Get subscriber queue from stream registry
+    subscriber_queue = await stream_registry.subscribe_to_task(
+        task_id=task_id,
+        user_id=user_id,
+        last_message_id=last_message_id,
+    )
+
+    if subscriber_queue is None:
+        raise HTTPException(
+            status_code=404,
+            detail={
+                "code": "TASK_NOT_FOUND",
+                "message": f"Task {task_id} not found or access denied.",
+            },
+        )
+
+    async def event_generator() -> AsyncGenerator[str, None]:
+        import asyncio
+
+        heartbeat_interval = 15.0  # Send heartbeat every 15 seconds
+        try:
+            while True:
+                try:
+                    # Wait for next chunk with timeout for heartbeats
+                    chunk = await asyncio.wait_for(
+                        subscriber_queue.get(), timeout=heartbeat_interval
+                    )
+                    yield chunk.to_sse()
+
+                    # Check for finish signal
+                    if isinstance(chunk, StreamFinish):
+                        break
+                except asyncio.TimeoutError:
+                    # Send heartbeat to keep connection alive
+                    yield StreamHeartbeat().to_sse()
+        except Exception as e:
+            logger.error(f"Error in task stream {task_id}: {e}", exc_info=True)
+        finally:
+            # Unsubscribe when client disconnects or stream ends
+            try:
+                await stream_registry.unsubscribe_from_task(task_id, subscriber_queue)
+            except Exception as unsub_err:
+                logger.error(
+                    f"Error unsubscribing from task {task_id}: {unsub_err}",
+                    exc_info=True,
+                )
+            # AI SDK protocol termination - always yield even if unsubscribe fails
+            yield "data: [DONE]\n\n"
+
+    return StreamingResponse(
+        event_generator(),
+        media_type="text/event-stream",
+        headers={
+            "Cache-Control": "no-cache",
+            "Connection": "keep-alive",
+            "X-Accel-Buffering": "no",
+            "x-vercel-ai-ui-message-stream": "v1",
+        },
+    )
+
+
+@router.get(
+    "/tasks/{task_id}",
+)
+async def get_task_status(
+    task_id: str,
+    user_id: str | None = Depends(auth.get_user_id),
+) -> dict:
+    """
+    Get the status of a long-running task.
+
+    Args:
+        task_id: The task ID to check.
+        user_id: Authenticated user ID for ownership validation.
+
+    Returns:
+        dict: Task status including task_id, status, tool_name, and operation_id.
+
+    Raises:
+        NotFoundError: If task_id is not found or user doesn't have access.
+    """
+    task = await stream_registry.get_task(task_id)
+
+    if task is None:
+        raise NotFoundError(f"Task {task_id} not found.")
+
+    # Validate ownership - if task has an owner, requester must match
+    if task.user_id and user_id != task.user_id:
+        raise NotFoundError(f"Task {task_id} not found.")
+
+    return {
+        "task_id": task.task_id,
+        "session_id": task.session_id,
+        "status": task.status,
+        "tool_name": task.tool_name,
+        "operation_id": task.operation_id,
+        "created_at": task.created_at.isoformat(),
+    }
+
+
+# ========== External Completion Webhook ==========
+
+
+@router.post(
+    "/operations/{operation_id}/complete",
+    status_code=200,
+)
+async def complete_operation(
+    operation_id: str,
+    request: OperationCompleteRequest,
+    x_api_key: str | None = Header(default=None),
+) -> dict:
+    """
+    External completion webhook for long-running operations.
+
+    Called by Agent Generator (or other services) when an operation completes.
+    This triggers the stream registry to publish completion and continue LLM generation.
+
+    Args:
+        operation_id: The operation ID to complete.
+        request: Completion payload with success status and result/error.
+        x_api_key: Internal API key for authentication.
+
+    Returns:
+        dict: Status of the completion.
+
+    Raises:
+        HTTPException: If API key is invalid or operation not found.
+    """
+    # Validate internal API key - reject if not configured or invalid
+    if not config.internal_api_key:
+        logger.error(
+            "Operation complete webhook rejected: CHAT_INTERNAL_API_KEY not configured"
+        )
+        raise HTTPException(
+            status_code=503,
+            detail="Webhook not available: internal API key not configured",
+        )
+    if x_api_key != config.internal_api_key:
+        raise HTTPException(status_code=401, detail="Invalid API key")
+
+    # Find task by operation_id
+    task = await stream_registry.find_task_by_operation_id(operation_id)
+    if task is None:
+        raise HTTPException(
+            status_code=404,
+            detail=f"Operation {operation_id} not found",
+        )
+
+    logger.info(
+        f"Received completion webhook for operation {operation_id} "
+        f"(task_id={task.task_id}, success={request.success})"
+    )
+
+    if request.success:
+        await process_operation_success(task, request.result)
+    else:
+        await process_operation_failure(task, request.error)
+
+    return {"status": "ok", "task_id": task.task_id}
+
+
+# ========== Configuration ==========
+
+
+@router.get("/config/ttl", status_code=200)
+async def get_ttl_config() -> dict:
+    """
+    Get the stream TTL configuration.
+
+    Returns the Time-To-Live settings for chat streams, which determines
+    how long clients can reconnect to an active stream.
+
+    Returns:
+        dict: TTL configuration with seconds and milliseconds values.
+    """
+    return {
+        "stream_ttl_seconds": config.stream_ttl,
+        "stream_ttl_ms": config.stream_ttl * 1000,
+    }
+
+
 # ========== Health Check ==========
 
 

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

@@ -0,0 +1,704 @@
+"""Stream registry for managing reconnectable SSE streams.
+
+This module provides a registry for tracking active streaming tasks and their
+messages. It uses Redis for all state management (no in-memory state), making
+pods stateless and horizontally scalable.
+
+Architecture:
+- Redis Stream: Persists all messages for replay and real-time delivery
+- Redis Hash: Task metadata (status, session_id, etc.)
+
+Subscribers:
+1. Replay missed messages from Redis Stream (XREAD)
+2. Listen for live updates via blocking XREAD
+3. No in-memory state required on the subscribing pod
+"""
+
+import asyncio
+import logging
+from dataclasses import dataclass, field
+from datetime import datetime, timezone
+from typing import Any, Literal
+
+import orjson
+
+from backend.data.redis_client import get_redis_async
+
+from .config import ChatConfig
+from .response_model import StreamBaseResponse, StreamError, StreamFinish
+
+logger = logging.getLogger(__name__)
+config = ChatConfig()
+
+# Track background tasks for this pod (just the asyncio.Task reference, not subscribers)
+_local_tasks: dict[str, asyncio.Task] = {}
+
+# Track listener tasks per subscriber queue for cleanup
+# Maps queue id() to (task_id, asyncio.Task) for proper cleanup on unsubscribe
+_listener_tasks: dict[int, tuple[str, asyncio.Task]] = {}
+
+# Timeout for putting chunks into subscriber queues (seconds)
+# If the queue is full and doesn't drain within this time, send an overflow error
+QUEUE_PUT_TIMEOUT = 5.0
+
+# Lua script for atomic compare-and-swap status update (idempotent completion)
+# Returns 1 if status was updated, 0 if already completed/failed
+COMPLETE_TASK_SCRIPT = """
+local current = redis.call("HGET", KEYS[1], "status")
+if current == "running" then
+    redis.call("HSET", KEYS[1], "status", ARGV[1])
+    return 1
+end
+return 0
+"""
+
+
+@dataclass
+class ActiveTask:
+    """Represents an active streaming task (metadata only, no in-memory queues)."""
+
+    task_id: str
+    session_id: str
+    user_id: str | None
+    tool_call_id: str
+    tool_name: str
+    operation_id: str
+    status: Literal["running", "completed", "failed"] = "running"
+    created_at: datetime = field(default_factory=lambda: datetime.now(timezone.utc))
+    asyncio_task: asyncio.Task | None = None
+
+
+def _get_task_meta_key(task_id: str) -> str:
+    """Get Redis key for task metadata."""
+    return f"{config.task_meta_prefix}{task_id}"
+
+
+def _get_task_stream_key(task_id: str) -> str:
+    """Get Redis key for task message stream."""
+    return f"{config.task_stream_prefix}{task_id}"
+
+
+def _get_operation_mapping_key(operation_id: str) -> str:
+    """Get Redis key for operation_id to task_id mapping."""
+    return f"{config.task_op_prefix}{operation_id}"
+
+
+async def create_task(
+    task_id: str,
+    session_id: str,
+    user_id: str | None,
+    tool_call_id: str,
+    tool_name: str,
+    operation_id: str,
+) -> ActiveTask:
+    """Create a new streaming task in Redis.
+
+    Args:
+        task_id: Unique identifier for the task
+        session_id: Chat session ID
+        user_id: User ID (may be None for anonymous)
+        tool_call_id: Tool call ID from the LLM
+        tool_name: Name of the tool being executed
+        operation_id: Operation ID for webhook callbacks
+
+    Returns:
+        The created ActiveTask instance (metadata only)
+    """
+    task = ActiveTask(
+        task_id=task_id,
+        session_id=session_id,
+        user_id=user_id,
+        tool_call_id=tool_call_id,
+        tool_name=tool_name,
+        operation_id=operation_id,
+    )
+
+    # Store metadata in Redis
+    redis = await get_redis_async()
+    meta_key = _get_task_meta_key(task_id)
+    op_key = _get_operation_mapping_key(operation_id)
+
+    await redis.hset(  # type: ignore[misc]
+        meta_key,
+        mapping={
+            "task_id": task_id,
+            "session_id": session_id,
+            "user_id": user_id or "",
+            "tool_call_id": tool_call_id,
+            "tool_name": tool_name,
+            "operation_id": operation_id,
+            "status": task.status,
+            "created_at": task.created_at.isoformat(),
+        },
+    )
+    await redis.expire(meta_key, config.stream_ttl)
+
+    # Create operation_id -> task_id mapping for webhook lookups
+    await redis.set(op_key, task_id, ex=config.stream_ttl)
+
+    logger.debug(f"Created task {task_id} for session {session_id}")
+
+    return task
+
+
+async def publish_chunk(
+    task_id: str,
+    chunk: StreamBaseResponse,
+) -> str:
+    """Publish a chunk to Redis Stream.
+
+    All delivery is via Redis Streams - no in-memory state.
+
+    Args:
+        task_id: Task ID to publish to
+        chunk: The stream response chunk to publish
+
+    Returns:
+        The Redis Stream message ID
+    """
+    chunk_json = chunk.model_dump_json()
+    message_id = "0-0"
+
+    try:
+        redis = await get_redis_async()
+        stream_key = _get_task_stream_key(task_id)
+
+        # Write to Redis Stream for persistence and real-time delivery
+        raw_id = await redis.xadd(
+            stream_key,
+            {"data": chunk_json},
+            maxlen=config.stream_max_length,
+        )
+        message_id = raw_id if isinstance(raw_id, str) else raw_id.decode()
+
+        # Set TTL on stream to match task metadata TTL
+        await redis.expire(stream_key, config.stream_ttl)
+    except Exception as e:
+        logger.error(
+            f"Failed to publish chunk for task {task_id}: {e}",
+            exc_info=True,
+        )
+
+    return message_id
+
+
+async def subscribe_to_task(
+    task_id: str,
+    user_id: str | None,
+    last_message_id: str = "0-0",
+) -> asyncio.Queue[StreamBaseResponse] | None:
+    """Subscribe to a task's stream with replay of missed messages.
+
+    This is fully stateless - uses Redis Stream for replay and pub/sub for live updates.
+
+    Args:
+        task_id: Task ID to subscribe to
+        user_id: User ID for ownership validation
+        last_message_id: Last Redis Stream message ID received ("0-0" for full replay)
+
+    Returns:
+        An asyncio Queue that will receive stream chunks, or None if task not found
+        or user doesn't have access
+    """
+    redis = await get_redis_async()
+    meta_key = _get_task_meta_key(task_id)
+    meta: dict[Any, Any] = await redis.hgetall(meta_key)  # type: ignore[misc]
+
+    if not meta:
+        logger.debug(f"Task {task_id} not found in Redis")
+        return None
+
+    # Note: Redis client uses decode_responses=True, so keys are strings
+    task_status = meta.get("status", "")
+    task_user_id = meta.get("user_id", "") or None
+
+    # Validate ownership - if task has an owner, requester must match
+    if task_user_id:
+        if user_id != task_user_id:
+            logger.warning(
+                f"User {user_id} denied access to task {task_id} "
+                f"owned by {task_user_id}"
+            )
+            return None
+
+    subscriber_queue: asyncio.Queue[StreamBaseResponse] = asyncio.Queue()
+    stream_key = _get_task_stream_key(task_id)
+
+    # Step 1: Replay messages from Redis Stream
+    messages = await redis.xread({stream_key: last_message_id}, block=0, count=1000)
+
+    replayed_count = 0
+    replay_last_id = last_message_id
+    if messages:
+        for _stream_name, stream_messages in messages:
+            for msg_id, msg_data in stream_messages:
+                replay_last_id = msg_id if isinstance(msg_id, str) else msg_id.decode()
+                # Note: Redis client uses decode_responses=True, so keys are strings
+                if "data" in msg_data:
+                    try:
+                        chunk_data = orjson.loads(msg_data["data"])
+                        chunk = _reconstruct_chunk(chunk_data)
+                        if chunk:
+                            await subscriber_queue.put(chunk)
+                            replayed_count += 1
+                    except Exception as e:
+                        logger.warning(f"Failed to replay message: {e}")
+
+    logger.debug(f"Task {task_id}: replayed {replayed_count} messages")
+
+    # Step 2: If task is still running, start stream listener for live updates
+    if task_status == "running":
+        listener_task = asyncio.create_task(
+            _stream_listener(task_id, subscriber_queue, replay_last_id)
+        )
+        # Track listener task for cleanup on unsubscribe
+        _listener_tasks[id(subscriber_queue)] = (task_id, listener_task)
+    else:
+        # Task is completed/failed - add finish marker
+        await subscriber_queue.put(StreamFinish())
+
+    return subscriber_queue
+
+
+async def _stream_listener(
+    task_id: str,
+    subscriber_queue: asyncio.Queue[StreamBaseResponse],
+    last_replayed_id: str,
+) -> None:
+    """Listen to Redis Stream for new messages using blocking XREAD.
+
+    This approach avoids the duplicate message issue that can occur with pub/sub
+    when messages are published during the gap between replay and subscription.
+
+    Args:
+        task_id: Task ID to listen for
+        subscriber_queue: Queue to deliver messages to
+        last_replayed_id: Last message ID from replay (continue from here)
+    """
+    queue_id = id(subscriber_queue)
+    # Track the last successfully delivered message ID for recovery hints
+    last_delivered_id = last_replayed_id
+
+    try:
+        redis = await get_redis_async()
+        stream_key = _get_task_stream_key(task_id)
+        current_id = last_replayed_id
+
+        while True:
+            # Block for up to 30 seconds waiting for new messages
+            # This allows periodic checking if task is still running
+            messages = await redis.xread(
+                {stream_key: current_id}, block=30000, count=100
+            )
+
+            if not messages:
+                # Timeout - check if task is still running
+                meta_key = _get_task_meta_key(task_id)
+                status = await redis.hget(meta_key, "status")  # type: ignore[misc]
+                if status and status != "running":
+                    try:
+                        await asyncio.wait_for(
+                            subscriber_queue.put(StreamFinish()),
+                            timeout=QUEUE_PUT_TIMEOUT,
+                        )
+                    except asyncio.TimeoutError:
+                        logger.warning(
+                            f"Timeout delivering finish event for task {task_id}"
+                        )
+                    break
+                continue
+
+            for _stream_name, stream_messages in messages:
+                for msg_id, msg_data in stream_messages:
+                    current_id = msg_id if isinstance(msg_id, str) else msg_id.decode()
+
+                    if "data" not in msg_data:
+                        continue
+
+                    try:
+                        chunk_data = orjson.loads(msg_data["data"])
+                        chunk = _reconstruct_chunk(chunk_data)
+                        if chunk:
+                            try:
+                                await asyncio.wait_for(
+                                    subscriber_queue.put(chunk),
+                                    timeout=QUEUE_PUT_TIMEOUT,
+                                )
+                                # Update last delivered ID on successful delivery
+                                last_delivered_id = current_id
+                            except asyncio.TimeoutError:
+                                logger.warning(
+                                    f"Subscriber queue full for task {task_id}, "
+                                    f"message delivery timed out after {QUEUE_PUT_TIMEOUT}s"
+                                )
+                                # Send overflow error with recovery info
+                                try:
+                                    overflow_error = StreamError(
+                                        errorText="Message delivery timeout - some messages may have been missed",
+                                        code="QUEUE_OVERFLOW",
+                                        details={
+                                            "last_delivered_id": last_delivered_id,
+                                            "recovery_hint": f"Reconnect with last_message_id={last_delivered_id}",
+                                        },
+                                    )
+                                    subscriber_queue.put_nowait(overflow_error)
+                                except asyncio.QueueFull:
+                                    # Queue is completely stuck, nothing more we can do
+                                    logger.error(
+                                        f"Cannot deliver overflow error for task {task_id}, "
+                                        "queue completely blocked"
+                                    )
+
+                            # Stop listening on finish
+                            if isinstance(chunk, StreamFinish):
+                                return
+                    except Exception as e:
+                        logger.warning(f"Error processing stream message: {e}")
+
+    except asyncio.CancelledError:
+        logger.debug(f"Stream listener cancelled for task {task_id}")
+        raise  # Re-raise to propagate cancellation
+    except Exception as e:
+        logger.error(f"Stream listener error for task {task_id}: {e}")
+        # On error, send finish to unblock subscriber
+        try:
+            await asyncio.wait_for(
+                subscriber_queue.put(StreamFinish()),
+                timeout=QUEUE_PUT_TIMEOUT,
+            )
+        except (asyncio.TimeoutError, asyncio.QueueFull):
+            logger.warning(
+                f"Could not deliver finish event for task {task_id} after error"
+            )
+    finally:
+        # Clean up listener task mapping on exit
+        _listener_tasks.pop(queue_id, None)
+
+
+async def mark_task_completed(
+    task_id: str,
+    status: Literal["completed", "failed"] = "completed",
+) -> bool:
+    """Mark a task as completed and publish finish event.
+
+    This is idempotent - calling multiple times with the same task_id is safe.
+    Uses atomic compare-and-swap via Lua script to prevent race conditions.
+    Status is updated first (source of truth), then finish event is published (best-effort).
+
+    Args:
+        task_id: Task ID to mark as completed
+        status: Final status ("completed" or "failed")
+
+    Returns:
+        True if task was newly marked completed, False if already completed/failed
+    """
+    redis = await get_redis_async()
+    meta_key = _get_task_meta_key(task_id)
+
+    # Atomic compare-and-swap: only update if status is "running"
+    # This prevents race conditions when multiple callers try to complete simultaneously
+    result = await redis.eval(COMPLETE_TASK_SCRIPT, 1, meta_key, status)  # type: ignore[misc]
+
+    if result == 0:
+        logger.debug(f"Task {task_id} already completed/failed, skipping")
+        return False
+
+    # THEN publish finish event (best-effort - listeners can detect via status polling)
+    try:
+        await publish_chunk(task_id, StreamFinish())
+    except Exception as e:
+        logger.error(
+            f"Failed to publish finish event for task {task_id}: {e}. "
+            "Listeners will detect completion via status polling."
+        )
+
+    # Clean up local task reference if exists
+    _local_tasks.pop(task_id, None)
+    return True
+
+
+async def find_task_by_operation_id(operation_id: str) -> ActiveTask | None:
+    """Find a task by its operation ID.
+
+    Used by webhook callbacks to locate the task to update.
+
+    Args:
+        operation_id: Operation ID to search for
+
+    Returns:
+        ActiveTask if found, None otherwise
+    """
+    redis = await get_redis_async()
+    op_key = _get_operation_mapping_key(operation_id)
+    task_id = await redis.get(op_key)
+
+    if not task_id:
+        return None
+
+    task_id_str = task_id.decode() if isinstance(task_id, bytes) else task_id
+    return await get_task(task_id_str)
+
+
+async def get_task(task_id: str) -> ActiveTask | None:
+    """Get a task by its ID from Redis.
+
+    Args:
+        task_id: Task ID to look up
+
+    Returns:
+        ActiveTask if found, None otherwise
+    """
+    redis = await get_redis_async()
+    meta_key = _get_task_meta_key(task_id)
+    meta: dict[Any, Any] = await redis.hgetall(meta_key)  # type: ignore[misc]
+
+    if not meta:
+        return None
+
+    # Note: Redis client uses decode_responses=True, so keys/values are strings
+    return ActiveTask(
+        task_id=meta.get("task_id", ""),
+        session_id=meta.get("session_id", ""),
+        user_id=meta.get("user_id", "") or None,
+        tool_call_id=meta.get("tool_call_id", ""),
+        tool_name=meta.get("tool_name", ""),
+        operation_id=meta.get("operation_id", ""),
+        status=meta.get("status", "running"),  # type: ignore[arg-type]
+    )
+
+
+async def get_task_with_expiry_info(
+    task_id: str,
+) -> tuple[ActiveTask | None, str | None]:
+    """Get a task by its ID with expiration detection.
+
+    Returns (task, error_code) where error_code is:
+    - None if task found
+    - "TASK_EXPIRED" if stream exists but metadata is gone (TTL expired)
+    - "TASK_NOT_FOUND" if neither exists
+
+    Args:
+        task_id: Task ID to look up
+
+    Returns:
+        Tuple of (ActiveTask or None, error_code or None)
+    """
+    redis = await get_redis_async()
+    meta_key = _get_task_meta_key(task_id)
+    stream_key = _get_task_stream_key(task_id)
+
+    meta: dict[Any, Any] = await redis.hgetall(meta_key)  # type: ignore[misc]
+
+    if not meta:
+        # Check if stream still has data (metadata expired but stream hasn't)
+        stream_len = await redis.xlen(stream_key)
+        if stream_len > 0:
+            return None, "TASK_EXPIRED"
+        return None, "TASK_NOT_FOUND"
+
+    # Note: Redis client uses decode_responses=True, so keys/values are strings
+    return (
+        ActiveTask(
+            task_id=meta.get("task_id", ""),
+            session_id=meta.get("session_id", ""),
+            user_id=meta.get("user_id", "") or None,
+            tool_call_id=meta.get("tool_call_id", ""),
+            tool_name=meta.get("tool_name", ""),
+            operation_id=meta.get("operation_id", ""),
+            status=meta.get("status", "running"),  # type: ignore[arg-type]
+        ),
+        None,
+    )
+
+
+async def get_active_task_for_session(
+    session_id: str,
+    user_id: str | None = None,
+) -> tuple[ActiveTask | None, str]:
+    """Get the active (running) task for a session, if any.
+
+    Scans Redis for tasks matching the session_id with status="running".
+
+    Args:
+        session_id: Session ID to look up
+        user_id: User ID for ownership validation (optional)
+
+    Returns:
+        Tuple of (ActiveTask if found and running, last_message_id from Redis Stream)
+    """
+
+    redis = await get_redis_async()
+
+    # Scan Redis for task metadata keys
+    cursor = 0
+    tasks_checked = 0
+
+    while True:
+        cursor, keys = await redis.scan(
+            cursor, match=f"{config.task_meta_prefix}*", count=100
+        )
+
+        for key in keys:
+            tasks_checked += 1
+            meta: dict[Any, Any] = await redis.hgetall(key)  # type: ignore[misc]
+            if not meta:
+                continue
+
+            # Note: Redis client uses decode_responses=True, so keys/values are strings
+            task_session_id = meta.get("session_id", "")
+            task_status = meta.get("status", "")
+            task_user_id = meta.get("user_id", "") or None
+            task_id = meta.get("task_id", "")
+
+            if task_session_id == session_id and task_status == "running":
+                # Validate ownership - if task has an owner, requester must match
+                if task_user_id and user_id != task_user_id:
+                    continue
+
+                # Get the last message ID from Redis Stream
+                stream_key = _get_task_stream_key(task_id)
+                last_id = "0-0"
+                try:
+                    messages = await redis.xrevrange(stream_key, count=1)
+                    if messages:
+                        msg_id = messages[0][0]
+                        last_id = msg_id if isinstance(msg_id, str) else msg_id.decode()
+                except Exception as e:
+                    logger.warning(f"Failed to get last message ID: {e}")
+
+                return (
+                    ActiveTask(
+                        task_id=task_id,
+                        session_id=task_session_id,
+                        user_id=task_user_id,
+                        tool_call_id=meta.get("tool_call_id", ""),
+                        tool_name=meta.get("tool_name", ""),
+                        operation_id=meta.get("operation_id", ""),
+                        status="running",
+                    ),
+                    last_id,
+                )
+
+        if cursor == 0:
+            break
+
+    return None, "0-0"
+
+
+def _reconstruct_chunk(chunk_data: dict) -> StreamBaseResponse | None:
+    """Reconstruct a StreamBaseResponse from JSON data.
+
+    Args:
+        chunk_data: Parsed JSON data from Redis
+
+    Returns:
+        Reconstructed response object, or None if unknown type
+    """
+    from .response_model import (
+        ResponseType,
+        StreamError,
+        StreamFinish,
+        StreamHeartbeat,
+        StreamStart,
+        StreamTextDelta,
+        StreamTextEnd,
+        StreamTextStart,
+        StreamToolInputAvailable,
+        StreamToolInputStart,
+        StreamToolOutputAvailable,
+        StreamUsage,
+    )
+
+    # Map response types to their corresponding classes
+    type_to_class: dict[str, type[StreamBaseResponse]] = {
+        ResponseType.START.value: StreamStart,
+        ResponseType.FINISH.value: StreamFinish,
+        ResponseType.TEXT_START.value: StreamTextStart,
+        ResponseType.TEXT_DELTA.value: StreamTextDelta,
+        ResponseType.TEXT_END.value: StreamTextEnd,
+        ResponseType.TOOL_INPUT_START.value: StreamToolInputStart,
+        ResponseType.TOOL_INPUT_AVAILABLE.value: StreamToolInputAvailable,
+        ResponseType.TOOL_OUTPUT_AVAILABLE.value: StreamToolOutputAvailable,
+        ResponseType.ERROR.value: StreamError,
+        ResponseType.USAGE.value: StreamUsage,
+        ResponseType.HEARTBEAT.value: StreamHeartbeat,
+    }
+
+    chunk_type = chunk_data.get("type")
+    chunk_class = type_to_class.get(chunk_type)  # type: ignore[arg-type]
+
+    if chunk_class is None:
+        logger.warning(f"Unknown chunk type: {chunk_type}")
+        return None
+
+    try:
+        return chunk_class(**chunk_data)
+    except Exception as e:
+        logger.warning(f"Failed to reconstruct chunk of type {chunk_type}: {e}")
+        return None
+
+
+async def set_task_asyncio_task(task_id: str, asyncio_task: asyncio.Task) -> None:
+    """Track the asyncio.Task for a task (local reference only).
+
+    This is just for cleanup purposes - the task state is in Redis.
+
+    Args:
+        task_id: Task ID
+        asyncio_task: The asyncio Task to track
+    """
+    _local_tasks[task_id] = asyncio_task
+
+
+async def unsubscribe_from_task(
+    task_id: str,
+    subscriber_queue: asyncio.Queue[StreamBaseResponse],
+) -> None:
+    """Clean up when a subscriber disconnects.
+
+    Cancels the XREAD-based listener task associated with this subscriber queue
+    to prevent resource leaks.
+
+    Args:
+        task_id: Task ID
+        subscriber_queue: The subscriber's queue used to look up the listener task
+    """
+    queue_id = id(subscriber_queue)
+    listener_entry = _listener_tasks.pop(queue_id, None)
+
+    if listener_entry is None:
+        logger.debug(
+            f"No listener task found for task {task_id} queue {queue_id} "
+            "(may have already completed)"
+        )
+        return
+
+    stored_task_id, listener_task = listener_entry
+
+    if stored_task_id != task_id:
+        logger.warning(
+            f"Task ID mismatch in unsubscribe: expected {task_id}, "
+            f"found {stored_task_id}"
+        )
+
+    if listener_task.done():
+        logger.debug(f"Listener task for task {task_id} already completed")
+        return
+
+    # Cancel the listener task
+    listener_task.cancel()
+
+    try:
+        # Wait for the task to be cancelled with a timeout
+        await asyncio.wait_for(listener_task, timeout=5.0)
+    except asyncio.CancelledError:
+        # Expected - the task was successfully cancelled
+        pass
+    except asyncio.TimeoutError:
+        logger.warning(
+            f"Timeout waiting for listener task cancellation for task {task_id}"
+        )
+    except Exception as e:
+        logger.error(f"Error during listener task cancellation for task {task_id}: {e}")
+
+    logger.debug(f"Successfully unsubscribed from task {task_id}")

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

@@ -10,6 +10,7 @@ from .add_understanding import AddUnderstandingTool
 from .agent_output import AgentOutputTool
 from .base import BaseTool
 from .create_agent import CreateAgentTool
+from .customize_agent import CustomizeAgentTool
 from .edit_agent import EditAgentTool
 from .find_agent import FindAgentTool
 from .find_block import FindBlockTool
@@ -34,6 +35,7 @@ logger = logging.getLogger(__name__)
 TOOL_REGISTRY: dict[str, BaseTool] = {
     "add_understanding": AddUnderstandingTool(),
     "create_agent": CreateAgentTool(),
+    "customize_agent": CustomizeAgentTool(),
     "edit_agent": EditAgentTool(),
     "find_agent": FindAgentTool(),
     "find_block": FindBlockTool(),

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

@@ -8,6 +8,7 @@ from .core import (
     DecompositionStep,
     LibraryAgentSummary,
     MarketplaceAgentSummary,
+    customize_template,
     decompose_goal,
     enrich_library_agents_from_steps,
     extract_search_terms_from_steps,
@@ -19,6 +20,7 @@ from .core import (
     get_library_agent_by_graph_id,
     get_library_agent_by_id,
     get_library_agents_for_generation,
+    graph_to_json,
     json_to_graph,
     save_agent_to_library,
     search_marketplace_agents_for_generation,
@@ -36,6 +38,7 @@ __all__ = [
     "LibraryAgentSummary",
     "MarketplaceAgentSummary",
     "check_external_service_health",
+    "customize_template",
     "decompose_goal",
     "enrich_library_agents_from_steps",
     "extract_search_terms_from_steps",
@@ -48,6 +51,7 @@ __all__ = [
     "get_library_agent_by_id",
     "get_library_agents_for_generation",
     "get_user_message_for_error",
+    "graph_to_json",
     "is_external_service_configured",
     "json_to_graph",
     "save_agent_to_library",

autogpt_platform/backend/backend/api/features/chat/tools/agent_generator/core.py

@@ -19,6 +19,7 @@ from backend.data.graph import (
 from backend.util.exceptions import DatabaseError, NotFoundError
 
 from .service import (
+    customize_template_external,
     decompose_goal_external,
     generate_agent_external,
     generate_agent_patch_external,
@@ -549,15 +550,21 @@ async def decompose_goal(
 async def generate_agent(
     instructions: DecompositionResult | dict[str, Any],
     library_agents: list[AgentSummary] | list[dict[str, Any]] | None = None,
+    operation_id: str | None = None,
+    task_id: str | None = None,
 ) -> dict[str, Any] | None:
     """Generate agent JSON from instructions.
 
     Args:
         instructions: Structured instructions from decompose_goal
         library_agents: User's library agents available for sub-agent composition
+        operation_id: Operation ID for async processing (enables Redis Streams
+            completion notification)
+        task_id: Task ID for async processing (enables Redis Streams persistence
+            and SSE delivery)
 
     Returns:
-        Agent JSON dict, error dict {"type": "error", ...}, or None on error
+        Agent JSON dict, {"status": "accepted"} for async, error dict {"type": "error", ...}, or None on error
 
     Raises:
         AgentGeneratorNotConfiguredError: If the external service is not configured.
@@ -565,8 +572,13 @@ async def generate_agent(
     _check_service_configured()
     logger.info("Calling external Agent Generator service for generate_agent")
     result = await generate_agent_external(
-        dict(instructions), _to_dict_list(library_agents)
+        dict(instructions), _to_dict_list(library_agents), operation_id, task_id
     )
+
+    # Don't modify async response
+    if result and result.get("status") == "accepted":
+        return result
+
     if result:
         if isinstance(result, dict) and result.get("type") == "error":
             return result
@@ -740,32 +752,15 @@ async def save_agent_to_library(
     return created_graph, library_agents[0]
 
 
-async def get_agent_as_json(
-    agent_id: str, user_id: str | None
-) -> dict[str, Any] | None:
-    """Fetch an agent and convert to JSON format for editing.
+def graph_to_json(graph: Graph) -> dict[str, Any]:
+    """Convert a Graph object to JSON format for the agent generator.
 
     Args:
-        agent_id: Graph ID or library agent ID
-        user_id: User ID
+        graph: Graph object to convert
 
     Returns:
-        Agent as JSON dict or None if not found
+        Agent as JSON dict
     """
-    graph = await get_graph(agent_id, version=None, user_id=user_id)
-
-    if not graph and user_id:
-        try:
-            library_agent = await library_db.get_library_agent(agent_id, user_id)
-            graph = await get_graph(
-                library_agent.graph_id, version=None, user_id=user_id
-            )
-        except NotFoundError:
-            pass
-
-    if not graph:
-        return None
-
     nodes = []
     for node in graph.nodes:
         nodes.append(
@@ -802,10 +797,41 @@ async def get_agent_as_json(
     }
 
 
+async def get_agent_as_json(
+    agent_id: str, user_id: str | None
+) -> dict[str, Any] | None:
+    """Fetch an agent and convert to JSON format for editing.
+
+    Args:
+        agent_id: Graph ID or library agent ID
+        user_id: User ID
+
+    Returns:
+        Agent as JSON dict or None if not found
+    """
+    graph = await get_graph(agent_id, version=None, user_id=user_id)
+
+    if not graph and user_id:
+        try:
+            library_agent = await library_db.get_library_agent(agent_id, user_id)
+            graph = await get_graph(
+                library_agent.graph_id, version=None, user_id=user_id
+            )
+        except NotFoundError:
+            pass
+
+    if not graph:
+        return None
+
+    return graph_to_json(graph)
+
+
 async def generate_agent_patch(
     update_request: str,
     current_agent: dict[str, Any],
     library_agents: list[AgentSummary] | None = None,
+    operation_id: str | None = None,
+    task_id: str | None = None,
 ) -> dict[str, Any] | None:
     """Update an existing agent using natural language.
 
@@ -818,10 +844,12 @@ async def generate_agent_patch(
         update_request: Natural language description of changes
         current_agent: Current agent JSON
         library_agents: User's library agents available for sub-agent composition
+        operation_id: Operation ID for async processing (enables Redis Streams callback)
+        task_id: Task ID for async processing (enables Redis Streams callback)
 
     Returns:
         Updated agent JSON, clarifying questions dict {"type": "clarifying_questions", ...},
-        error dict {"type": "error", ...}, or None on unexpected error
+        {"status": "accepted"} for async, error dict {"type": "error", ...}, or None on error
 
     Raises:
         AgentGeneratorNotConfiguredError: If the external service is not configured.
@@ -829,5 +857,43 @@ async def generate_agent_patch(
     _check_service_configured()
     logger.info("Calling external Agent Generator service for generate_agent_patch")
     return await generate_agent_patch_external(
-        update_request, current_agent, _to_dict_list(library_agents)
+        update_request,
+        current_agent,
+        _to_dict_list(library_agents),
+        operation_id,
+        task_id,
+    )
+
+
+async def customize_template(
+    template_agent: dict[str, Any],
+    modification_request: str,
+    context: str = "",
+) -> dict[str, Any] | None:
+    """Customize a template/marketplace agent using natural language.
+
+    This is used when users want to modify a template or marketplace agent
+    to fit their specific needs before adding it to their library.
+
+    The external Agent Generator service handles:
+    - Understanding the modification request
+    - Applying changes to the template
+    - Fixing and validating the result
+
+    Args:
+        template_agent: The template agent JSON to customize
+        modification_request: Natural language description of customizations
+        context: Additional context (e.g., answers to previous questions)
+
+    Returns:
+        Customized agent JSON, clarifying questions dict {"type": "clarifying_questions", ...},
+        error dict {"type": "error", ...}, or None on unexpected error
+
+    Raises:
+        AgentGeneratorNotConfiguredError: If the external service is not configured.
+    """
+    _check_service_configured()
+    logger.info("Calling external Agent Generator service for customize_template")
+    return await customize_template_external(
+        template_agent, modification_request, context
     )

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

@@ -139,11 +139,10 @@ async def decompose_goal_external(
     """
     client = _get_client()
 
-    # Build the request payload
-    payload: dict[str, Any] = {"description": description}
     if context:
-        # The external service uses user_instruction for additional context
-        payload["user_instruction"] = context
+        description = f"{description}\n\nAdditional context from user:\n{context}"
+
+    payload: dict[str, Any] = {"description": description}
     if library_agents:
         payload["library_agents"] = library_agents
 
@@ -213,24 +212,45 @@ async def decompose_goal_external(
 async def generate_agent_external(
     instructions: dict[str, Any],
     library_agents: list[dict[str, Any]] | None = None,
+    operation_id: str | None = None,
+    task_id: str | None = None,
 ) -> dict[str, Any] | None:
     """Call the external service to generate an agent from instructions.
 
     Args:
         instructions: Structured instructions from decompose_goal
         library_agents: User's library agents available for sub-agent composition
+        operation_id: Operation ID for async processing (enables Redis Streams callback)
+        task_id: Task ID for async processing (enables Redis Streams callback)
 
     Returns:
-        Agent JSON dict on success, or error dict {"type": "error", ...} on error
+        Agent JSON dict, {"status": "accepted"} for async, or error dict {"type": "error", ...} on error
     """
     client = _get_client()
 
+    # Build request payload
     payload: dict[str, Any] = {"instructions": instructions}
     if library_agents:
         payload["library_agents"] = library_agents
+    if operation_id and task_id:
+        payload["operation_id"] = operation_id
+        payload["task_id"] = task_id
 
     try:
         response = await client.post("/api/generate-agent", json=payload)
+
+        # Handle 202 Accepted for async processing
+        if response.status_code == 202:
+            logger.info(
+                f"Agent Generator accepted async request "
+                f"(operation_id={operation_id}, task_id={task_id})"
+            )
+            return {
+                "status": "accepted",
+                "operation_id": operation_id,
+                "task_id": task_id,
+            }
+
         response.raise_for_status()
         data = response.json()
 
@@ -262,6 +282,8 @@ async def generate_agent_patch_external(
     update_request: str,
     current_agent: dict[str, Any],
     library_agents: list[dict[str, Any]] | None = None,
+    operation_id: str | None = None,
+    task_id: str | None = None,
 ) -> dict[str, Any] | None:
     """Call the external service to generate a patch for an existing agent.
 
@@ -269,21 +291,40 @@ async def generate_agent_patch_external(
         update_request: Natural language description of changes
         current_agent: Current agent JSON
         library_agents: User's library agents available for sub-agent composition
+        operation_id: Operation ID for async processing (enables Redis Streams callback)
+        task_id: Task ID for async processing (enables Redis Streams callback)
 
     Returns:
-        Updated agent JSON, clarifying questions dict, or error dict on error
+        Updated agent JSON, clarifying questions dict, {"status": "accepted"} for async, or error dict on error
     """
     client = _get_client()
 
+    # Build request payload
     payload: dict[str, Any] = {
         "update_request": update_request,
         "current_agent_json": current_agent,
     }
     if library_agents:
         payload["library_agents"] = library_agents
+    if operation_id and task_id:
+        payload["operation_id"] = operation_id
+        payload["task_id"] = task_id
 
     try:
         response = await client.post("/api/update-agent", json=payload)
+
+        # Handle 202 Accepted for async processing
+        if response.status_code == 202:
+            logger.info(
+                f"Agent Generator accepted async update request "
+                f"(operation_id={operation_id}, task_id={task_id})"
+            )
+            return {
+                "status": "accepted",
+                "operation_id": operation_id,
+                "task_id": task_id,
+            }
+
         response.raise_for_status()
         data = response.json()
 
@@ -327,6 +368,77 @@ async def generate_agent_patch_external(
         return _create_error_response(error_msg, "unexpected_error")
 
 
+async def customize_template_external(
+    template_agent: dict[str, Any],
+    modification_request: str,
+    context: str = "",
+) -> dict[str, Any] | None:
+    """Call the external service to customize a template/marketplace agent.
+
+    Args:
+        template_agent: The template agent JSON to customize
+        modification_request: Natural language description of customizations
+        context: Additional context (e.g., answers to previous questions)
+
+    Returns:
+        Customized agent JSON, clarifying questions dict, or error dict on error
+    """
+    client = _get_client()
+
+    request = modification_request
+    if context:
+        request = f"{modification_request}\n\nAdditional context from user:\n{context}"
+
+    payload: dict[str, Any] = {
+        "template_agent_json": template_agent,
+        "modification_request": request,
+    }
+
+    try:
+        response = await client.post("/api/template-modification", json=payload)
+        response.raise_for_status()
+        data = response.json()
+
+        if not data.get("success"):
+            error_msg = data.get("error", "Unknown error from Agent Generator")
+            error_type = data.get("error_type", "unknown")
+            logger.error(
+                f"Agent Generator template customization failed: {error_msg} "
+                f"(type: {error_type})"
+            )
+            return _create_error_response(error_msg, error_type)
+
+        # Check if it's clarifying questions
+        if data.get("type") == "clarifying_questions":
+            return {
+                "type": "clarifying_questions",
+                "questions": data.get("questions", []),
+            }
+
+        # Check if it's an error passed through
+        if data.get("type") == "error":
+            return _create_error_response(
+                data.get("error", "Unknown error"),
+                data.get("error_type", "unknown"),
+            )
+
+        # Otherwise return the customized agent JSON
+        return data.get("agent_json")
+
+    except httpx.HTTPStatusError as e:
+        error_type, error_msg = _classify_http_error(e)
+        logger.error(error_msg)
+        return _create_error_response(error_msg, error_type)
+    except httpx.RequestError as e:
+        error_type, error_msg = _classify_request_error(e)
+        logger.error(error_msg)
+        return _create_error_response(error_msg, error_type)
+    except Exception as e:
+        error_msg = f"Unexpected error calling Agent Generator: {e}"
+        logger.error(error_msg)
+        return _create_error_response(error_msg, "unexpected_error")
+
+
 async def get_blocks_external() -> list[dict[str, Any]] | None:
     """Get available blocks from the external service.
 

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

@@ -18,6 +18,7 @@ from .base import BaseTool
 from .models import (
     AgentPreviewResponse,
     AgentSavedResponse,
+    AsyncProcessingResponse,
     ClarificationNeededResponse,
     ClarifyingQuestion,
     ErrorResponse,
@@ -98,6 +99,10 @@ class CreateAgentTool(BaseTool):
         save = kwargs.get("save", True)
         session_id = session.session_id if session else None
 
+        # Extract async processing params (passed by long-running tool handler)
+        operation_id = kwargs.get("_operation_id")
+        task_id = kwargs.get("_task_id")
+
         if not description:
             return ErrorResponse(
                 message="Please provide a description of what the agent should do.",
@@ -219,7 +224,12 @@ class CreateAgentTool(BaseTool):
                 logger.warning(f"Failed to enrich library agents from steps: {e}")
 
         try:
-            agent_json = await generate_agent(decomposition_result, library_agents)
+            agent_json = await generate_agent(
+                decomposition_result,
+                library_agents,
+                operation_id=operation_id,
+                task_id=task_id,
+            )
         except AgentGeneratorNotConfiguredError:
             return ErrorResponse(
                 message=(
@@ -263,6 +273,19 @@ class CreateAgentTool(BaseTool):
                 session_id=session_id,
             )
 
+        # Check if Agent Generator accepted for async processing
+        if agent_json.get("status") == "accepted":
+            logger.info(
+                f"Agent generation delegated to async processing "
+                f"(operation_id={operation_id}, task_id={task_id})"
+            )
+            return AsyncProcessingResponse(
+                message="Agent generation started. You'll be notified when it's complete.",
+                operation_id=operation_id,
+                task_id=task_id,
+                session_id=session_id,
+            )
+
         agent_name = agent_json.get("name", "Generated Agent")
         agent_description = agent_json.get("description", "")
         node_count = len(agent_json.get("nodes", []))

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

@@ -0,0 +1,337 @@
+"""CustomizeAgentTool - Customizes marketplace/template agents using natural language."""
+
+import logging
+from typing import Any
+
+from backend.api.features.chat.model import ChatSession
+from backend.api.features.store import db as store_db
+from backend.api.features.store.exceptions import AgentNotFoundError
+
+from .agent_generator import (
+    AgentGeneratorNotConfiguredError,
+    customize_template,
+    get_user_message_for_error,
+    graph_to_json,
+    save_agent_to_library,
+)
+from .base import BaseTool
+from .models import (
+    AgentPreviewResponse,
+    AgentSavedResponse,
+    ClarificationNeededResponse,
+    ClarifyingQuestion,
+    ErrorResponse,
+    ToolResponseBase,
+)
+
+logger = logging.getLogger(__name__)
+
+
+class CustomizeAgentTool(BaseTool):
+    """Tool for customizing marketplace/template agents using natural language."""
+
+    @property
+    def name(self) -> str:
+        return "customize_agent"
+
+    @property
+    def description(self) -> str:
+        return (
+            "Customize a marketplace or template agent using natural language. "
+            "Takes an existing agent from the marketplace and modifies it based on "
+            "the user's requirements before adding to their library."
+        )
+
+    @property
+    def requires_auth(self) -> bool:
+        return True
+
+    @property
+    def is_long_running(self) -> bool:
+        return True
+
+    @property
+    def parameters(self) -> dict[str, Any]:
+        return {
+            "type": "object",
+            "properties": {
+                "agent_id": {
+                    "type": "string",
+                    "description": (
+                        "The marketplace agent ID in format 'creator/slug' "
+                        "(e.g., 'autogpt/newsletter-writer'). "
+                        "Get this from find_agent results."
+                    ),
+                },
+                "modifications": {
+                    "type": "string",
+                    "description": (
+                        "Natural language description of how to customize the agent. "
+                        "Be specific about what changes you want to make."
+                    ),
+                },
+                "context": {
+                    "type": "string",
+                    "description": (
+                        "Additional context or answers to previous clarifying questions."
+                    ),
+                },
+                "save": {
+                    "type": "boolean",
+                    "description": (
+                        "Whether to save the customized agent to the user's library. "
+                        "Default is true. Set to false for preview only."
+                    ),
+                    "default": True,
+                },
+            },
+            "required": ["agent_id", "modifications"],
+        }
+
+    async def _execute(
+        self,
+        user_id: str | None,
+        session: ChatSession,
+        **kwargs,
+    ) -> ToolResponseBase:
+        """Execute the customize_agent tool.
+
+        Flow:
+        1. Parse the agent ID to get creator/slug
+        2. Fetch the template agent from the marketplace
+        3. Call customize_template with the modification request
+        4. Preview or save based on the save parameter
+        """
+        agent_id = kwargs.get("agent_id", "").strip()
+        modifications = kwargs.get("modifications", "").strip()
+        context = kwargs.get("context", "")
+        save = kwargs.get("save", True)
+        session_id = session.session_id if session else None
+
+        if not agent_id:
+            return ErrorResponse(
+                message="Please provide the marketplace agent ID (e.g., 'creator/agent-name').",
+                error="missing_agent_id",
+                session_id=session_id,
+            )
+
+        if not modifications:
+            return ErrorResponse(
+                message="Please describe how you want to customize this agent.",
+                error="missing_modifications",
+                session_id=session_id,
+            )
+
+        # Parse agent_id in format "creator/slug"
+        parts = [p.strip() for p in agent_id.split("/")]
+        if len(parts) != 2 or not parts[0] or not parts[1]:
+            return ErrorResponse(
+                message=(
+                    f"Invalid agent ID format: '{agent_id}'. "
+                    "Expected format is 'creator/agent-name' "
+                    "(e.g., 'autogpt/newsletter-writer')."
+                ),
+                error="invalid_agent_id_format",
+                session_id=session_id,
+            )
+
+        creator_username, agent_slug = parts
+
+        # Fetch the marketplace agent details
+        try:
+            agent_details = await store_db.get_store_agent_details(
+                username=creator_username, agent_name=agent_slug
+            )
+        except AgentNotFoundError:
+            return ErrorResponse(
+                message=(
+                    f"Could not find marketplace agent '{agent_id}'. "
+                    "Please check the agent ID and try again."
+                ),
+                error="agent_not_found",
+                session_id=session_id,
+            )
+        except Exception as e:
+            logger.error(f"Error fetching marketplace agent {agent_id}: {e}")
+            return ErrorResponse(
+                message="Failed to fetch the marketplace agent. Please try again.",
+                error="fetch_error",
+                session_id=session_id,
+            )
+
+        if not agent_details.store_listing_version_id:
+            return ErrorResponse(
+                message=(
+                    f"The agent '{agent_id}' does not have an available version. "
+                    "Please try a different agent."
+                ),
+                error="no_version_available",
+                session_id=session_id,
+            )
+
+        # Get the full agent graph
+        try:
+            graph = await store_db.get_agent(agent_details.store_listing_version_id)
+            template_agent = graph_to_json(graph)
+        except Exception as e:
+            logger.error(f"Error fetching agent graph for {agent_id}: {e}")
+            return ErrorResponse(
+                message="Failed to fetch the agent configuration. Please try again.",
+                error="graph_fetch_error",
+                session_id=session_id,
+            )
+
+        # Call customize_template
+        try:
+            result = await customize_template(
+                template_agent=template_agent,
+                modification_request=modifications,
+                context=context,
+            )
+        except AgentGeneratorNotConfiguredError:
+            return ErrorResponse(
+                message=(
+                    "Agent customization is not available. "
+                    "The Agent Generator service is not configured."
+                ),
+                error="service_not_configured",
+                session_id=session_id,
+            )
+        except Exception as e:
+            logger.error(f"Error calling customize_template for {agent_id}: {e}")
+            return ErrorResponse(
+                message=(
+                    "Failed to customize the agent due to a service error. "
+                    "Please try again."
+                ),
+                error="customization_service_error",
+                session_id=session_id,
+            )
+
+        if result is None:
+            return ErrorResponse(
+                message=(
+                    "Failed to customize the agent. "
+                    "The agent generation service may be unavailable or timed out. "
+                    "Please try again."
+                ),
+                error="customization_failed",
+                session_id=session_id,
+            )
+
+        # Handle error response
+        if isinstance(result, dict) and result.get("type") == "error":
+            error_msg = result.get("error", "Unknown error")
+            error_type = result.get("error_type", "unknown")
+            user_message = get_user_message_for_error(
+                error_type,
+                operation="customize the agent",
+                llm_parse_message=(
+                    "The AI had trouble customizing the agent. "
+                    "Please try again or simplify your request."
+                ),
+                validation_message=(
+                    "The customized agent failed validation. "
+                    "Please try rephrasing your request."
+                ),
+                error_details=error_msg,
+            )
+            return ErrorResponse(
+                message=user_message,
+                error=f"customization_failed:{error_type}",
+                session_id=session_id,
+            )
+
+        # Handle clarifying questions
+        if isinstance(result, dict) and result.get("type") == "clarifying_questions":
+            questions = result.get("questions") or []
+            if not isinstance(questions, list):
+                logger.error(
+                    f"Unexpected clarifying questions format: {type(questions)}"
+                )
+                questions = []
+            return ClarificationNeededResponse(
+                message=(
+                    "I need some more information to customize this agent. "
+                    "Please answer the following questions:"
+                ),
+                questions=[
+                    ClarifyingQuestion(
+                        question=q.get("question", ""),
+                        keyword=q.get("keyword", ""),
+                        example=q.get("example"),
+                    )
+                    for q in questions
+                    if isinstance(q, dict)
+                ],
+                session_id=session_id,
+            )
+
+        # Result should be the customized agent JSON
+        if not isinstance(result, dict):
+            logger.error(f"Unexpected customize_template response type: {type(result)}")
+            return ErrorResponse(
+                message="Failed to customize the agent due to an unexpected response.",
+                error="unexpected_response_type",
+                session_id=session_id,
+            )
+
+        customized_agent = result
+
+        agent_name = customized_agent.get(
+            "name", f"Customized {agent_details.agent_name}"
+        )
+        agent_description = customized_agent.get("description", "")
+        nodes = customized_agent.get("nodes")
+        links = customized_agent.get("links")
+        node_count = len(nodes) if isinstance(nodes, list) else 0
+        link_count = len(links) if isinstance(links, list) else 0
+
+        if not save:
+            return AgentPreviewResponse(
+                message=(
+                    f"I've customized the agent '{agent_details.agent_name}'. "
+                    f"The customized agent has {node_count} blocks. "
+                    f"Review it and call customize_agent with save=true to save it."
+                ),
+                agent_json=customized_agent,
+                agent_name=agent_name,
+                description=agent_description,
+                node_count=node_count,
+                link_count=link_count,
+                session_id=session_id,
+            )
+
+        if not user_id:
+            return ErrorResponse(
+                message="You must be logged in to save agents.",
+                error="auth_required",
+                session_id=session_id,
+            )
+
+        # Save to user's library
+        try:
+            created_graph, library_agent = await save_agent_to_library(
+                customized_agent, user_id, is_update=False
+            )
+
+            return AgentSavedResponse(
+                message=(
+                    f"Customized agent '{created_graph.name}' "
+                    f"(based on '{agent_details.agent_name}') "
+                    f"has been saved to your library!"
+                ),
+                agent_id=created_graph.id,
+                agent_name=created_graph.name,
+                library_agent_id=library_agent.id,
+                library_agent_link=f"/library/agents/{library_agent.id}",
+                agent_page_link=f"/build?flowID={created_graph.id}",
+                session_id=session_id,
+            )
+        except Exception as e:
+            logger.error(f"Error saving customized agent: {e}")
+            return ErrorResponse(
+                message="Failed to save the customized agent. Please try again.",
+                error="save_failed",
+                session_id=session_id,
+            )

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

@@ -17,6 +17,7 @@ from .base import BaseTool
 from .models import (
     AgentPreviewResponse,
     AgentSavedResponse,
+    AsyncProcessingResponse,
     ClarificationNeededResponse,
     ClarifyingQuestion,
     ErrorResponse,
@@ -104,6 +105,10 @@ class EditAgentTool(BaseTool):
         save = kwargs.get("save", True)
         session_id = session.session_id if session else None
 
+        # Extract async processing params (passed by long-running tool handler)
+        operation_id = kwargs.get("_operation_id")
+        task_id = kwargs.get("_task_id")
+
         if not agent_id:
             return ErrorResponse(
                 message="Please provide the agent ID to edit.",
@@ -149,7 +154,11 @@ class EditAgentTool(BaseTool):
 
         try:
             result = await generate_agent_patch(
-                update_request, current_agent, library_agents
+                update_request,
+                current_agent,
+                library_agents,
+                operation_id=operation_id,
+                task_id=task_id,
             )
         except AgentGeneratorNotConfiguredError:
             return ErrorResponse(
@@ -169,6 +178,20 @@ class EditAgentTool(BaseTool):
                 session_id=session_id,
             )
 
+        # Check if Agent Generator accepted for async processing
+        if result.get("status") == "accepted":
+            logger.info(
+                f"Agent edit delegated to async processing "
+                f"(operation_id={operation_id}, task_id={task_id})"
+            )
+            return AsyncProcessingResponse(
+                message="Agent edit started. You'll be notified when it's complete.",
+                operation_id=operation_id,
+                task_id=task_id,
+                session_id=session_id,
+            )
+
+        # Check if the result is an error from the external service
         if isinstance(result, dict) and result.get("type") == "error":
             error_msg = result.get("error", "Unknown error")
             error_type = result.get("error_type", "unknown")

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

@@ -38,6 +38,8 @@ class ResponseType(str, Enum):
     OPERATION_STARTED = "operation_started"
     OPERATION_PENDING = "operation_pending"
     OPERATION_IN_PROGRESS = "operation_in_progress"
+    # Input validation
+    INPUT_VALIDATION_ERROR = "input_validation_error"
 
 
 # Base response model
@@ -68,6 +70,10 @@ class AgentInfo(BaseModel):
     has_external_trigger: bool | None = None
     new_output: bool | None = None
     graph_id: str | None = None
+    inputs: dict[str, Any] | None = Field(
+        default=None,
+        description="Input schema for the agent, including field names, types, and defaults",
+    )
 
 
 class AgentsFoundResponse(ToolResponseBase):
@@ -194,6 +200,20 @@ class ErrorResponse(ToolResponseBase):
     details: dict[str, Any] | None = None
 
 
+class InputValidationErrorResponse(ToolResponseBase):
+    """Response when run_agent receives unknown input fields."""
+
+    type: ResponseType = ResponseType.INPUT_VALIDATION_ERROR
+    unrecognized_fields: list[str] = Field(
+        description="List of input field names that were not recognized"
+    )
+    inputs: dict[str, Any] = Field(
+        description="The agent's valid input schema for reference"
+    )
+    graph_id: str | None = None
+    graph_version: int | None = None
+
+
 # Agent output models
 class ExecutionOutputInfo(BaseModel):
     """Summary of a single execution's outputs."""
@@ -352,11 +372,15 @@ class OperationStartedResponse(ToolResponseBase):
 
     This is returned immediately to the client while the operation continues
     to execute. The user can close the tab and check back later.
+
+    The task_id can be used to reconnect to the SSE stream via
+    GET /chat/tasks/{task_id}/stream?last_idx=0
     """
 
     type: ResponseType = ResponseType.OPERATION_STARTED
     operation_id: str
     tool_name: str
+    task_id: str | None = None  # For SSE reconnection
 
 
 class OperationPendingResponse(ToolResponseBase):
@@ -380,3 +404,20 @@ class OperationInProgressResponse(ToolResponseBase):
 
     type: ResponseType = ResponseType.OPERATION_IN_PROGRESS
     tool_call_id: str
+
+
+class AsyncProcessingResponse(ToolResponseBase):
+    """Response when an operation has been delegated to async processing.
+
+    This is returned by tools when the external service accepts the request
+    for async processing (HTTP 202 Accepted). The Redis Streams completion
+    consumer will handle the result when the external service completes.
+
+    The status field is specifically "accepted" to allow the long-running tool
+    handler to detect this response and skip LLM continuation.
+    """
+
+    type: ResponseType = ResponseType.OPERATION_STARTED
+    status: str = "accepted"  # Must be "accepted" for detection
+    operation_id: str | None = None
+    task_id: str | None = None

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

@@ -30,6 +30,7 @@ from .models import (
     ErrorResponse,
     ExecutionOptions,
     ExecutionStartedResponse,
+    InputValidationErrorResponse,
     SetupInfo,
     SetupRequirementsResponse,
     ToolResponseBase,
@@ -273,6 +274,22 @@ class RunAgentTool(BaseTool):
             input_properties = graph.input_schema.get("properties", {})
             required_fields = set(graph.input_schema.get("required", []))
             provided_inputs = set(params.inputs.keys())
+            valid_fields = set(input_properties.keys())
+
+            # Check for unknown input fields
+            unrecognized_fields = provided_inputs - valid_fields
+            if unrecognized_fields:
+                return InputValidationErrorResponse(
+                    message=(
+                        f"Unknown input field(s) provided: {', '.join(sorted(unrecognized_fields))}. "
+                        f"Agent was not executed. Please use the correct field names from the schema."
+                    ),
+                    session_id=session_id,
+                    unrecognized_fields=sorted(unrecognized_fields),
+                    inputs=graph.input_schema,
+                    graph_id=graph.id,
+                    graph_version=graph.version,
+                )
 
             # If agent has inputs but none were provided AND use_defaults is not set,
             # always show what's available first so user can decide

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

@@ -402,3 +402,42 @@ async def test_run_agent_schedule_without_name(setup_test_data):
     # Should return error about missing schedule_name
     assert result_data.get("type") == "error"
     assert "schedule_name" in result_data["message"].lower()
+
+
+@pytest.mark.asyncio(loop_scope="session")
+async def test_run_agent_rejects_unknown_input_fields(setup_test_data):
+    """Test that run_agent returns input_validation_error for unknown input fields."""
+    user = setup_test_data["user"]
+    store_submission = setup_test_data["store_submission"]
+
+    tool = RunAgentTool()
+    agent_marketplace_id = f"{user.email.split('@')[0]}/{store_submission.slug}"
+    session = make_session(user_id=user.id)
+
+    # Execute with unknown input field names
+    response = await tool.execute(
+        user_id=user.id,
+        session_id=str(uuid.uuid4()),
+        tool_call_id=str(uuid.uuid4()),
+        username_agent_slug=agent_marketplace_id,
+        inputs={
+            "unknown_field": "some value",
+            "another_unknown": "another value",
+        },
+        session=session,
+    )
+
+    assert response is not None
+    assert hasattr(response, "output")
+    assert isinstance(response.output, str)
+    result_data = orjson.loads(response.output)
+
+    # Should return input_validation_error type with unrecognized fields
+    assert result_data.get("type") == "input_validation_error"
+    assert "unrecognized_fields" in result_data
+    assert set(result_data["unrecognized_fields"]) == {
+        "another_unknown",
+        "unknown_field",
+    }
+    assert "inputs" in result_data  # Contains the valid schema
+    assert "Agent was not executed" in result_data["message"]

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

@@ -5,6 +5,8 @@ import uuid
 from collections import defaultdict
 from typing import Any
 
+from pydantic_core import PydanticUndefined
+
 from backend.api.features.chat.model import ChatSession
 from backend.data.block import get_block
 from backend.data.execution import ExecutionContext
@@ -75,15 +77,22 @@ class RunBlockTool(BaseTool):
         self,
         user_id: str,
         block: Any,
+        input_data: dict[str, Any] | None = None,
     ) -> tuple[dict[str, CredentialsMetaInput], list[CredentialsMetaInput]]:
         """
         Check if user has required credentials for a block.
 
+        Args:
+            user_id: User ID
+            block: Block to check credentials for
+            input_data: Input data for the block (used to determine provider via discriminator)
+
         Returns:
             tuple[matched_credentials, missing_credentials]
         """
         matched_credentials: dict[str, CredentialsMetaInput] = {}
         missing_credentials: list[CredentialsMetaInput] = []
+        input_data = input_data or {}
 
         # Get credential field info from block's input schema
         credentials_fields_info = block.input_schema.get_credentials_fields_info()
@@ -96,14 +105,33 @@ class RunBlockTool(BaseTool):
         available_creds = await creds_manager.store.get_all_creds(user_id)
 
         for field_name, field_info in credentials_fields_info.items():
-            # field_info.provider is a frozenset of acceptable providers
-            # field_info.supported_types is a frozenset of acceptable types
+            effective_field_info = field_info
+            if field_info.discriminator and field_info.discriminator_mapping:
+                # Get discriminator from input, falling back to schema default
+                discriminator_value = input_data.get(field_info.discriminator)
+                if discriminator_value is None:
+                    field = block.input_schema.model_fields.get(
+                        field_info.discriminator
+                    )
+                    if field and field.default is not PydanticUndefined:
+                        discriminator_value = field.default
+
+                if (
+                    discriminator_value
+                    and discriminator_value in field_info.discriminator_mapping
+                ):
+                    effective_field_info = field_info.discriminate(discriminator_value)
+                    logger.debug(
+                        f"Discriminated provider for {field_name}: "
+                        f"{discriminator_value} -> {effective_field_info.provider}"
+                    )
+
             matching_cred = next(
                 (
                     cred
                     for cred in available_creds
-                    if cred.provider in field_info.provider
-                    and cred.type in field_info.supported_types
+                    if cred.provider in effective_field_info.provider
+                    and cred.type in effective_field_info.supported_types
                 ),
                 None,
             )
@@ -117,8 +145,8 @@ class RunBlockTool(BaseTool):
                 )
             else:
                 # Create a placeholder for the missing credential
-                provider = next(iter(field_info.provider), "unknown")
-                cred_type = next(iter(field_info.supported_types), "api_key")
+                provider = next(iter(effective_field_info.provider), "unknown")
+                cred_type = next(iter(effective_field_info.supported_types), "api_key")
                 missing_credentials.append(
                     CredentialsMetaInput(
                         id=field_name,
@@ -186,10 +214,9 @@ class RunBlockTool(BaseTool):
 
         logger.info(f"Executing block {block.name} ({block_id}) for user {user_id}")
 
-        # Check credentials
         creds_manager = IntegrationCredentialsManager()
         matched_credentials, missing_credentials = await self._check_block_credentials(
-            user_id, block
+            user_id, block, input_data
         )
 
         if missing_credentials:

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

@@ -454,6 +454,9 @@ async def test_unified_hybrid_search_pagination(
     cleanup_embeddings: list,
 ):
     """Test unified search pagination works correctly."""
+    # Use a unique search term to avoid matching other test data
+    unique_term = f"xyzpagtest{uuid.uuid4().hex[:8]}"
+
     # Create multiple items
     content_ids = []
     for i in range(5):
@@ -465,14 +468,14 @@ async def test_unified_hybrid_search_pagination(
             content_type=ContentType.BLOCK,
             content_id=content_id,
             embedding=mock_embedding,
-            searchable_text=f"pagination test item number {i}",
+            searchable_text=f"{unique_term} item number {i}",
             metadata={"index": i},
             user_id=None,
         )
 
     # Get first page
     page1_results, total1 = await unified_hybrid_search(
-        query="pagination test",
+        query=unique_term,
         content_types=[ContentType.BLOCK],
         page=1,
         page_size=2,
@@ -480,7 +483,7 @@ async def test_unified_hybrid_search_pagination(
 
     # Get second page
     page2_results, total2 = await unified_hybrid_search(
-        query="pagination test",
+        query=unique_term,
         content_types=[ContentType.BLOCK],
         page=2,
         page_size=2,

autogpt_platform/backend/backend/api/rest_api.py

@@ -40,6 +40,10 @@ import backend.data.user
 import backend.integrations.webhooks.utils
 import backend.util.service
 import backend.util.settings
+from backend.api.features.chat.completion_consumer import (
+    start_completion_consumer,
+    stop_completion_consumer,
+)
 from backend.blocks.llm import DEFAULT_LLM_MODEL
 from backend.data.model import Credentials
 from backend.integrations.providers import ProviderName
@@ -118,9 +122,21 @@ async def lifespan_context(app: fastapi.FastAPI):
     await backend.data.graph.migrate_llm_models(DEFAULT_LLM_MODEL)
     await backend.integrations.webhooks.utils.migrate_legacy_triggered_graphs()
 
+    # Start chat completion consumer for Redis Streams notifications
+    try:
+        await start_completion_consumer()
+    except Exception as e:
+        logger.warning(f"Could not start chat completion consumer: {e}")
+
     with launch_darkly_context():
         yield
 
+    # Stop chat completion consumer
+    try:
+        await stop_completion_consumer()
+    except Exception as e:
+        logger.warning(f"Error stopping chat completion consumer: {e}")
+
     try:
         await shutdown_cloud_storage_handler()
     except Exception as e:

autogpt_platform/backend/backend/api/ws_api.py

@@ -66,18 +66,24 @@ async def event_broadcaster(manager: ConnectionManager):
     execution_bus = AsyncRedisExecutionEventBus()
     notification_bus = AsyncRedisNotificationEventBus()
 
-    async def execution_worker():
-        async for event in execution_bus.listen("*"):
-            await manager.send_execution_update(event)
+    try:
 
-    async def notification_worker():
-        async for notification in notification_bus.listen("*"):
-            await manager.send_notification(
-                user_id=notification.user_id,
-                payload=notification.payload,
-            )
+        async def execution_worker():
+            async for event in execution_bus.listen("*"):
+                await manager.send_execution_update(event)
 
-    await asyncio.gather(execution_worker(), notification_worker())
+        async def notification_worker():
+            async for notification in notification_bus.listen("*"):
+                await manager.send_notification(
+                    user_id=notification.user_id,
+                    payload=notification.payload,
+                )
+
+        await asyncio.gather(execution_worker(), notification_worker())
+    finally:
+        # Ensure PubSub connections are closed on any exit to prevent leaks
+        await execution_bus.close()
+        await notification_bus.close()
 
 
 async def authenticate_websocket(websocket: WebSocket) -> str:

autogpt_platform/backend/backend/blocks/llm.py

@@ -32,7 +32,7 @@ from backend.data.model import (
 from backend.integrations.providers import ProviderName
 from backend.util import json
 from backend.util.logging import TruncatedLogger
-from backend.util.prompt import compress_prompt, estimate_token_count
+from backend.util.prompt import compress_context, estimate_token_count
 from backend.util.text import TextFormatter
 
 logger = TruncatedLogger(logging.getLogger(__name__), "[LLM-Block]")
@@ -634,11 +634,18 @@ async def llm_call(
     context_window = llm_model.context_window
 
     if compress_prompt_to_fit:
-        prompt = compress_prompt(
+        result = await compress_context(
             messages=prompt,
             target_tokens=llm_model.context_window // 2,
-            lossy_ok=True,
+            client=None,  # Truncation-only, no LLM summarization
+            reserve=0,  # Caller handles response token budget separately
         )
+        if result.error:
+            logger.warning(
+                f"Prompt compression did not meet target: {result.error}. "
+                f"Proceeding with {result.token_count} tokens."
+            )
+        prompt = result.messages
 
     # Calculate available tokens based on context window and input length
     estimated_input_tokens = estimate_token_count(prompt)

autogpt_platform/backend/backend/data/block.py

@@ -873,14 +873,13 @@ def is_block_auth_configured(
 
 
 async def initialize_blocks() -> None:
-    # First, sync all provider costs to blocks
-    # Imported here to avoid circular import
     from backend.sdk.cost_integration import sync_all_provider_costs
+    from backend.util.retry import func_retry
 
     sync_all_provider_costs()
 
-    for cls in get_blocks().values():
-        block = cls()
+    @func_retry
+    async def sync_block_to_db(block: Block) -> None:
         existing_block = await AgentBlock.prisma().find_first(
             where={"OR": [{"id": block.id}, {"name": block.name}]}
         )
@@ -893,7 +892,7 @@ async def initialize_blocks() -> None:
                     outputSchema=json.dumps(block.output_schema.jsonschema()),
                 )
             )
-            continue
+            return
 
         input_schema = json.dumps(block.input_schema.jsonschema())
         output_schema = json.dumps(block.output_schema.jsonschema())
@@ -913,6 +912,25 @@ async def initialize_blocks() -> None:
                 },
             )
 
+    failed_blocks: list[str] = []
+    for cls in get_blocks().values():
+        block = cls()
+        try:
+            await sync_block_to_db(block)
+        except Exception as e:
+            logger.warning(
+                f"Failed to sync block {block.name} to database: {e}. "
+                "Block is still available in memory.",
+                exc_info=True,
+            )
+            failed_blocks.append(block.name)
+
+    if failed_blocks:
+        logger.error(
+            f"Failed to sync {len(failed_blocks)} block(s) to database: "
+            f"{', '.join(failed_blocks)}. These blocks are still available in memory."
+        )
+
 
 # Note on the return type annotation: https://github.com/microsoft/pyright/issues/10281
 def get_block(block_id: str) -> AnyBlockSchema | None:

autogpt_platform/backend/backend/data/event_bus.py

@@ -133,10 +133,23 @@ class RedisEventBus(BaseRedisEventBus[M], ABC):
 
 
 class AsyncRedisEventBus(BaseRedisEventBus[M], ABC):
+    def __init__(self):
+        self._pubsub: AsyncPubSub | None = None
+
     @property
     async def connection(self) -> redis.AsyncRedis:
         return await redis.get_redis_async()
 
+    async def close(self) -> None:
+        """Close the PubSub connection if it exists."""
+        if self._pubsub is not None:
+            try:
+                await self._pubsub.close()
+            except Exception:
+                logger.warning("Failed to close PubSub connection", exc_info=True)
+            finally:
+                self._pubsub = None
+
     async def publish_event(self, event: M, channel_key: str):
         """
         Publish an event to Redis. Gracefully handles connection failures
@@ -157,6 +170,7 @@ class AsyncRedisEventBus(BaseRedisEventBus[M], ABC):
             await self.connection, channel_key
         )
         assert isinstance(pubsub, AsyncPubSub)
+        self._pubsub = pubsub
 
         if "*" in channel_key:
             await pubsub.psubscribe(full_channel_name)

autogpt_platform/backend/backend/executor/database.py

@@ -17,6 +17,7 @@ from backend.data.analytics import (
     get_accuracy_trends_and_alerts,
     get_marketplace_graphs_for_monitoring,
 )
+from backend.data.auth.oauth import cleanup_expired_oauth_tokens
 from backend.data.credit import UsageTransactionMetadata, get_user_credit_model
 from backend.data.execution import (
     create_graph_execution,
@@ -219,6 +220,9 @@ class DatabaseManager(AppService):
     # Onboarding
     increment_onboarding_runs = _(increment_onboarding_runs)
 
+    # OAuth
+    cleanup_expired_oauth_tokens = _(cleanup_expired_oauth_tokens)
+
     # Store
     get_store_agents = _(get_store_agents)
     get_store_agent_details = _(get_store_agent_details)
@@ -349,6 +353,9 @@ class DatabaseManagerAsyncClient(AppServiceClient):
     # Onboarding
     increment_onboarding_runs = d.increment_onboarding_runs
 
+    # OAuth
+    cleanup_expired_oauth_tokens = d.cleanup_expired_oauth_tokens
+
     # Store
     get_store_agents = d.get_store_agents
     get_store_agent_details = d.get_store_agent_details

autogpt_platform/backend/backend/executor/scheduler.py

@@ -24,11 +24,9 @@ from dotenv import load_dotenv
 from pydantic import BaseModel, Field, ValidationError
 from sqlalchemy import MetaData, create_engine
 
-from backend.data.auth.oauth import cleanup_expired_oauth_tokens
 from backend.data.block import BlockInput
 from backend.data.execution import GraphExecutionWithNodes
 from backend.data.model import CredentialsMetaInput
-from backend.data.onboarding import increment_onboarding_runs
 from backend.executor import utils as execution_utils
 from backend.monitoring import (
     NotificationJobArgs,
@@ -38,7 +36,11 @@ from backend.monitoring import (
     report_execution_accuracy_alerts,
     report_late_executions,
 )
-from backend.util.clients import get_database_manager_client, get_scheduler_client
+from backend.util.clients import (
+    get_database_manager_async_client,
+    get_database_manager_client,
+    get_scheduler_client,
+)
 from backend.util.cloud_storage import cleanup_expired_files_async
 from backend.util.exceptions import (
     GraphNotFoundError,
@@ -148,6 +150,7 @@ def execute_graph(**kwargs):
 async def _execute_graph(**kwargs):
     args = GraphExecutionJobArgs(**kwargs)
     start_time = asyncio.get_event_loop().time()
+    db = get_database_manager_async_client()
     try:
         logger.info(f"Executing recurring job for graph #{args.graph_id}")
         graph_exec: GraphExecutionWithNodes = await execution_utils.add_graph_execution(
@@ -157,7 +160,7 @@ async def _execute_graph(**kwargs):
             inputs=args.input_data,
             graph_credentials_inputs=args.input_credentials,
         )
-        await increment_onboarding_runs(args.user_id)
+        await db.increment_onboarding_runs(args.user_id)
         elapsed = asyncio.get_event_loop().time() - start_time
         logger.info(
             f"Graph execution started with ID {graph_exec.id} for graph {args.graph_id} "
@@ -246,8 +249,13 @@ def cleanup_expired_files():
 
 def cleanup_oauth_tokens():
     """Clean up expired OAuth tokens from the database."""
+
     # Wait for completion
-    run_async(cleanup_expired_oauth_tokens())
+    async def _cleanup():
+        db = get_database_manager_async_client()
+        return await db.cleanup_expired_oauth_tokens()
+
+    run_async(_cleanup())
 
 
 def execution_accuracy_alerts():

autogpt_platform/backend/backend/util/prompt.py

@@ -1,10 +1,19 @@
+from __future__ import annotations
+
+import logging
 from copy import deepcopy
-from typing import Any
+from dataclasses import dataclass
+from typing import TYPE_CHECKING, Any
 
 from tiktoken import encoding_for_model
 
 from backend.util import json
 
+if TYPE_CHECKING:
+    from openai import AsyncOpenAI
+
+logger = logging.getLogger(__name__)
+
 # ---------------------------------------------------------------------------#
 #  CONSTANTS                                                                 #
 # ---------------------------------------------------------------------------#
@@ -100,9 +109,17 @@ def _is_objective_message(msg: dict) -> bool:
 def _truncate_tool_message_content(msg: dict, enc, max_tokens: int) -> None:
     """
     Carefully truncate tool message content while preserving tool structure.
-    Only truncates tool_result content, leaves tool_use intact.
+    Handles both Anthropic-style (list content) and OpenAI-style (string content) tool messages.
     """
     content = msg.get("content")
+
+    # OpenAI-style tool message: role="tool" with string content
+    if msg.get("role") == "tool" and isinstance(content, str):
+        if _tok_len(content, enc) > max_tokens:
+            msg["content"] = _truncate_middle_tokens(content, enc, max_tokens)
+        return
+
+    # Anthropic-style: list content with tool_result items
     if not isinstance(content, list):
         return
 
@@ -140,141 +157,6 @@ def _truncate_middle_tokens(text: str, enc, max_tok: int) -> str:
 # ---------------------------------------------------------------------------#
 
 
-def compress_prompt(
-    messages: list[dict],
-    target_tokens: int,
-    *,
-    model: str = "gpt-4o",
-    reserve: int = 2_048,
-    start_cap: int = 8_192,
-    floor_cap: int = 128,
-    lossy_ok: bool = True,
-) -> list[dict]:
-    """
-    Shrink *messages* so that::
-
-        token_count(prompt) + reserve  ≤  target_tokens
-
-    Strategy
-    --------
-    1. **Token-aware truncation** – progressively halve a per-message cap
-       (`start_cap`, `start_cap/2`, … `floor_cap`) and apply it to the
-       *content* of every message except the first and last.  Tool shells
-       are included: we keep the envelope but shorten huge payloads.
-    2. **Middle-out deletion** – if still over the limit, delete whole
-       messages working outward from the centre, **skipping** any message
-       that contains ``tool_calls`` or has ``role == "tool"``.
-    3. **Last-chance trim** – if still too big, truncate the *first* and
-       *last* message bodies down to `floor_cap` tokens.
-    4. If the prompt is *still* too large:
-         • raise ``ValueError``      when ``lossy_ok == False`` (default)
-         • return the partially-trimmed prompt when ``lossy_ok == True``
-
-    Parameters
-    ----------
-    messages        Complete chat history (will be deep-copied).
-    model           Model name; passed to tiktoken to pick the right
-                    tokenizer (gpt-4o → 'o200k_base', others fallback).
-    target_tokens   Hard ceiling for prompt size **excluding** the model's
-                    forthcoming answer.
-    reserve         How many tokens you want to leave available for that
-                    answer (`max_tokens` in your subsequent completion call).
-    start_cap       Initial per-message truncation ceiling (tokens).
-    floor_cap       Lowest cap we'll accept before moving to deletions.
-    lossy_ok        If *True* return best-effort prompt instead of raising
-                    after all trim passes have been exhausted.
-
-    Returns
-    -------
-    list[dict]  – A *new* messages list that abides by the rules above.
-    """
-    enc = encoding_for_model(model)  # best-match tokenizer
-    msgs = deepcopy(messages)  # never mutate caller
-
-    def total_tokens() -> int:
-        """Current size of *msgs* in tokens."""
-        return sum(_msg_tokens(m, enc) for m in msgs)
-
-    original_token_count = total_tokens()
-
-    if original_token_count + reserve <= target_tokens:
-        return msgs
-
-    # ---- STEP 0 : normalise content --------------------------------------
-    # Convert non-string payloads to strings so token counting is coherent.
-    for i, m in enumerate(msgs):
-        if not isinstance(m.get("content"), str) and m.get("content") is not None:
-            if _is_tool_message(m):
-                continue
-
-            # Keep first and last messages intact (unless they're tool messages)
-            if i == 0 or i == len(msgs) - 1:
-                continue
-
-            # Reasonable 20k-char ceiling prevents pathological blobs
-            content_str = json.dumps(m["content"], separators=(",", ":"))
-            if len(content_str) > 20_000:
-                content_str = _truncate_middle_tokens(content_str, enc, 20_000)
-            m["content"] = content_str
-
-    # ---- STEP 1 : token-aware truncation ---------------------------------
-    cap = start_cap
-    while total_tokens() + reserve > target_tokens and cap >= floor_cap:
-        for m in msgs[1:-1]:  # keep first & last intact
-            if _is_tool_message(m):
-                # For tool messages, only truncate tool result content, preserve structure
-                _truncate_tool_message_content(m, enc, cap)
-                continue
-
-            if _is_objective_message(m):
-                # Never truncate objective messages - they contain the core task
-                continue
-
-            content = m.get("content") or ""
-            if _tok_len(content, enc) > cap:
-                m["content"] = _truncate_middle_tokens(content, enc, cap)
-        cap //= 2  # tighten the screw
-
-    # ---- STEP 2 : middle-out deletion -----------------------------------
-    while total_tokens() + reserve > target_tokens and len(msgs) > 2:
-        # Identify all deletable messages (not first/last, not tool messages, not objective messages)
-        deletable_indices = []
-        for i in range(1, len(msgs) - 1):  # Skip first and last
-            if not _is_tool_message(msgs[i]) and not _is_objective_message(msgs[i]):
-                deletable_indices.append(i)
-
-        if not deletable_indices:
-            break  # nothing more we can drop
-
-        # Delete from center outward - find the index closest to center
-        centre = len(msgs) // 2
-        to_delete = min(deletable_indices, key=lambda i: abs(i - centre))
-        del msgs[to_delete]
-
-    # ---- STEP 3 : final safety-net trim on first & last ------------------
-    cap = start_cap
-    while total_tokens() + reserve > target_tokens and cap >= floor_cap:
-        for idx in (0, -1):  # first and last
-            if _is_tool_message(msgs[idx]):
-                # For tool messages at first/last position, truncate tool result content only
-                _truncate_tool_message_content(msgs[idx], enc, cap)
-                continue
-
-            text = msgs[idx].get("content") or ""
-            if _tok_len(text, enc) > cap:
-                msgs[idx]["content"] = _truncate_middle_tokens(text, enc, cap)
-        cap //= 2  # tighten the screw
-
-    # ---- STEP 4 : success or fail-gracefully -----------------------------
-    if total_tokens() + reserve > target_tokens and not lossy_ok:
-        raise ValueError(
-            "compress_prompt: prompt still exceeds budget "
-            f"({total_tokens() + reserve} > {target_tokens})."
-        )
-
-    return msgs
-
-
 def estimate_token_count(
     messages: list[dict],
     *,
@@ -293,7 +175,8 @@ def estimate_token_count(
     -------
     int  – Token count.
     """
-    enc = encoding_for_model(model)  # best-match tokenizer
+    token_model = _normalize_model_for_tokenizer(model)
+    enc = encoding_for_model(token_model)
     return sum(_msg_tokens(m, enc) for m in messages)
 
 
@@ -315,6 +198,543 @@ def estimate_token_count_str(
     -------
     int  – Token count.
     """
-    enc = encoding_for_model(model)  # best-match tokenizer
+    token_model = _normalize_model_for_tokenizer(model)
+    enc = encoding_for_model(token_model)
     text = json.dumps(text) if not isinstance(text, str) else text
     return _tok_len(text, enc)
+
+
+# ---------------------------------------------------------------------------#
+#  UNIFIED CONTEXT COMPRESSION                                               #
+# ---------------------------------------------------------------------------#
+
+# Default thresholds
+DEFAULT_TOKEN_THRESHOLD = 120_000
+DEFAULT_KEEP_RECENT = 15
+
+
+@dataclass
+class CompressResult:
+    """Result of context compression."""
+
+    messages: list[dict]
+    token_count: int
+    was_compacted: bool
+    error: str | None = None
+    original_token_count: int = 0
+    messages_summarized: int = 0
+    messages_dropped: int = 0
+
+
+def _normalize_model_for_tokenizer(model: str) -> str:
+    """Normalize model name for tiktoken tokenizer selection."""
+    if "/" in model:
+        model = model.split("/")[-1]
+    if "claude" in model.lower() or not any(
+        known in model.lower() for known in ["gpt", "o1", "chatgpt", "text-"]
+    ):
+        return "gpt-4o"
+    return model
+
+
+def _extract_tool_call_ids_from_message(msg: dict) -> set[str]:
+    """
+    Extract tool_call IDs from an assistant message.
+
+    Supports both formats:
+    - OpenAI: {"role": "assistant", "tool_calls": [{"id": "..."}]}
+    - Anthropic: {"role": "assistant", "content": [{"type": "tool_use", "id": "..."}]}
+
+    Returns:
+        Set of tool_call IDs found in the message.
+    """
+    ids: set[str] = set()
+    if msg.get("role") != "assistant":
+        return ids
+
+    # OpenAI format: tool_calls array
+    if msg.get("tool_calls"):
+        for tc in msg["tool_calls"]:
+            tc_id = tc.get("id")
+            if tc_id:
+                ids.add(tc_id)
+
+    # Anthropic format: content list with tool_use blocks
+    content = msg.get("content")
+    if isinstance(content, list):
+        for block in content:
+            if isinstance(block, dict) and block.get("type") == "tool_use":
+                tc_id = block.get("id")
+                if tc_id:
+                    ids.add(tc_id)
+
+    return ids
+
+
+def _extract_tool_response_ids_from_message(msg: dict) -> set[str]:
+    """
+    Extract tool_call IDs that this message is responding to.
+
+    Supports both formats:
+    - OpenAI: {"role": "tool", "tool_call_id": "..."}
+    - Anthropic: {"role": "user", "content": [{"type": "tool_result", "tool_use_id": "..."}]}
+
+    Returns:
+        Set of tool_call IDs this message responds to.
+    """
+    ids: set[str] = set()
+
+    # OpenAI format: role=tool with tool_call_id
+    if msg.get("role") == "tool":
+        tc_id = msg.get("tool_call_id")
+        if tc_id:
+            ids.add(tc_id)
+
+    # Anthropic format: content list with tool_result blocks
+    content = msg.get("content")
+    if isinstance(content, list):
+        for block in content:
+            if isinstance(block, dict) and block.get("type") == "tool_result":
+                tc_id = block.get("tool_use_id")
+                if tc_id:
+                    ids.add(tc_id)
+
+    return ids
+
+
+def _is_tool_response_message(msg: dict) -> bool:
+    """Check if message is a tool response (OpenAI or Anthropic format)."""
+    # OpenAI format
+    if msg.get("role") == "tool":
+        return True
+    # Anthropic format
+    content = msg.get("content")
+    if isinstance(content, list):
+        for block in content:
+            if isinstance(block, dict) and block.get("type") == "tool_result":
+                return True
+    return False
+
+
+def _remove_orphan_tool_responses(
+    messages: list[dict], orphan_ids: set[str]
+) -> list[dict]:
+    """
+    Remove tool response messages/blocks that reference orphan tool_call IDs.
+
+    Supports both OpenAI and Anthropic formats.
+    For Anthropic messages with mixed valid/orphan tool_result blocks,
+    filters out only the orphan blocks instead of dropping the entire message.
+    """
+    result = []
+    for msg in messages:
+        # OpenAI format: role=tool - drop entire message if orphan
+        if msg.get("role") == "tool":
+            tc_id = msg.get("tool_call_id")
+            if tc_id and tc_id in orphan_ids:
+                continue
+            result.append(msg)
+            continue
+
+        # Anthropic format: content list may have mixed tool_result blocks
+        content = msg.get("content")
+        if isinstance(content, list):
+            has_tool_results = any(
+                isinstance(b, dict) and b.get("type") == "tool_result" for b in content
+            )
+            if has_tool_results:
+                # Filter out orphan tool_result blocks, keep valid ones
+                filtered_content = [
+                    block
+                    for block in content
+                    if not (
+                        isinstance(block, dict)
+                        and block.get("type") == "tool_result"
+                        and block.get("tool_use_id") in orphan_ids
+                    )
+                ]
+                # Only keep message if it has remaining content
+                if filtered_content:
+                    msg = msg.copy()
+                    msg["content"] = filtered_content
+                    result.append(msg)
+                continue
+
+        result.append(msg)
+    return result
+
+
+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.
+
+    Supports both formats:
+    - OpenAI: tool_calls array + role="tool" responses
+    - Anthropic: tool_use blocks + tool_result blocks
+
+    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:
+        available_tool_call_ids |= _extract_tool_call_ids_from_message(msg)
+
+    # Find orphan tool responses (responses whose tool_call_id is missing)
+    orphan_tool_call_ids: set[str] = set()
+    for msg in recent_messages:
+        response_ids = _extract_tool_response_ids_from_message(msg)
+        for tc_id in response_ids:
+            if 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]
+        msg_tool_ids = _extract_tool_call_ids_from_message(msg)
+        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]
+                following_response_ids = _extract_tool_response_ids_from_message(
+                    following_msg
+                )
+                if following_response_ids and following_response_ids & msg_tool_ids:
+                    assistant_and_responses.append(following_msg)
+                elif not _is_tool_response_message(following_msg):
+                    # Stop at first non-tool-response 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 = _remove_orphan_tool_responses(
+            recent_messages, 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 _summarize_messages_llm(
+    messages: list[dict],
+    client: AsyncOpenAI,
+    model: str,
+    timeout: float = 30.0,
+) -> str:
+    """Summarize messages using an LLM."""
+    conversation = []
+    for msg in messages:
+        role = msg.get("role", "")
+        content = msg.get("content", "")
+        if content and role in ("user", "assistant", "tool"):
+            conversation.append(f"{role.upper()}: {content}")
+
+    conversation_text = "\n\n".join(conversation)
+
+    if not conversation_text:
+        return "No conversation history available."
+
+    # Limit to ~100k chars for safety
+    MAX_CHARS = 100_000
+    if len(conversation_text) > MAX_CHARS:
+        conversation_text = conversation_text[:MAX_CHARS] + "\n\n[truncated]"
+
+    response = await client.with_options(timeout=timeout).chat.completions.create(
+        model=model,
+        messages=[
+            {
+                "role": "system",
+                "content": (
+                    "Create a detailed summary of the conversation so far. "
+                    "This summary will be used as context when continuing the conversation.\n\n"
+                    "Before writing the summary, analyze each message chronologically to identify:\n"
+                    "- User requests and their explicit goals\n"
+                    "- Your approach and key decisions made\n"
+                    "- Technical specifics (file names, tool outputs, function signatures)\n"
+                    "- Errors encountered and resolutions applied\n\n"
+                    "You MUST include ALL of the following sections:\n\n"
+                    "## 1. Primary Request and Intent\n"
+                    "The user's explicit goals and what they are trying to accomplish.\n\n"
+                    "## 2. Key Technical Concepts\n"
+                    "Technologies, frameworks, tools, and patterns being used or discussed.\n\n"
+                    "## 3. Files and Resources Involved\n"
+                    "Specific files examined or modified, with relevant snippets and identifiers.\n\n"
+                    "## 4. Errors and Fixes\n"
+                    "Problems encountered, error messages, and their resolutions. "
+                    "Include any user feedback on fixes.\n\n"
+                    "## 5. Problem Solving\n"
+                    "Issues that have been resolved and how they were addressed.\n\n"
+                    "## 6. All User Messages\n"
+                    "A complete list of all user inputs (excluding tool outputs) to preserve their exact requests.\n\n"
+                    "## 7. Pending Tasks\n"
+                    "Work items the user explicitly requested that have not yet been completed.\n\n"
+                    "## 8. Current Work\n"
+                    "Precise description of what was being worked on most recently, including relevant context.\n\n"
+                    "## 9. Next Steps\n"
+                    "What should happen next, aligned with the user's most recent requests. "
+                    "Include verbatim quotes of recent instructions if relevant."
+                ),
+            },
+            {"role": "user", "content": f"Summarize:\n\n{conversation_text}"},
+        ],
+        max_tokens=1500,
+        temperature=0.3,
+    )
+
+    return response.choices[0].message.content or "No summary available."
+
+
+async def compress_context(
+    messages: list[dict],
+    target_tokens: int = DEFAULT_TOKEN_THRESHOLD,
+    *,
+    model: str = "gpt-4o",
+    client: AsyncOpenAI | None = None,
+    keep_recent: int = DEFAULT_KEEP_RECENT,
+    reserve: int = 2_048,
+    start_cap: int = 8_192,
+    floor_cap: int = 128,
+) -> CompressResult:
+    """
+    Unified context compression that combines summarization and truncation strategies.
+
+    Strategy (in order):
+    1. **LLM summarization** – If client provided, summarize old messages into a
+       single context message while keeping recent messages intact. This is the
+       primary strategy for chat service.
+    2. **Content truncation** – Progressively halve a per-message cap and truncate
+       bloated message content (tool outputs, large pastes). Preserves all messages
+       but shortens their content. Primary strategy when client=None (LLM blocks).
+    3. **Middle-out deletion** – Delete whole messages one at a time from the center
+       outward, skipping tool messages and objective messages.
+    4. **First/last trim** – Truncate first and last message content as last resort.
+
+    Parameters
+    ----------
+    messages        Complete chat history (will be deep-copied).
+    target_tokens   Hard ceiling for prompt size.
+    model           Model name for tokenization and summarization.
+    client          AsyncOpenAI client. If provided, enables LLM summarization
+                    as the first strategy. If None, skips to truncation strategies.
+    keep_recent     Number of recent messages to preserve during summarization.
+    reserve         Tokens to reserve for model response.
+    start_cap       Initial per-message truncation ceiling (tokens).
+    floor_cap       Lowest cap before moving to deletions.
+
+    Returns
+    -------
+    CompressResult with compressed messages and metadata.
+    """
+    # Guard clause for empty messages
+    if not messages:
+        return CompressResult(
+            messages=[],
+            token_count=0,
+            was_compacted=False,
+            original_token_count=0,
+        )
+
+    token_model = _normalize_model_for_tokenizer(model)
+    enc = encoding_for_model(token_model)
+    msgs = deepcopy(messages)
+
+    def total_tokens() -> int:
+        return sum(_msg_tokens(m, enc) for m in msgs)
+
+    original_count = total_tokens()
+
+    # Already under limit
+    if original_count + reserve <= target_tokens:
+        return CompressResult(
+            messages=msgs,
+            token_count=original_count,
+            was_compacted=False,
+            original_token_count=original_count,
+        )
+
+    messages_summarized = 0
+    messages_dropped = 0
+
+    # ---- STEP 1: LLM summarization (if client provided) -------------------
+    # This is the primary compression strategy for chat service.
+    # Summarize old messages while keeping recent ones intact.
+    if client is not None:
+        has_system = len(msgs) > 0 and msgs[0].get("role") == "system"
+        system_msg = msgs[0] if has_system else None
+
+        # Calculate old vs recent messages
+        if has_system:
+            if len(msgs) > keep_recent + 1:
+                old_msgs = msgs[1:-keep_recent]
+                recent_msgs = msgs[-keep_recent:]
+            else:
+                old_msgs = []
+                recent_msgs = msgs[1:] if len(msgs) > 1 else []
+        else:
+            if len(msgs) > keep_recent:
+                old_msgs = msgs[:-keep_recent]
+                recent_msgs = msgs[-keep_recent:]
+            else:
+                old_msgs = []
+                recent_msgs = msgs
+
+        # Ensure tool pairs stay intact
+        slice_start = max(0, len(msgs) - keep_recent)
+        recent_msgs = _ensure_tool_pairs_intact(recent_msgs, msgs, slice_start)
+
+        if old_msgs:
+            try:
+                summary_text = await _summarize_messages_llm(old_msgs, client, model)
+                summary_msg = {
+                    "role": "assistant",
+                    "content": f"[Previous conversation summary — for context only]: {summary_text}",
+                }
+                messages_summarized = len(old_msgs)
+
+                if has_system:
+                    msgs = [system_msg, summary_msg] + recent_msgs
+                else:
+                    msgs = [summary_msg] + recent_msgs
+
+                logger.info(
+                    f"Context summarized: {original_count} -> {total_tokens()} tokens, "
+                    f"summarized {messages_summarized} messages"
+                )
+            except Exception as e:
+                logger.warning(f"Summarization failed, continuing with truncation: {e}")
+                # Fall through to content truncation
+
+    # ---- STEP 2: Normalize content ----------------------------------------
+    # Convert non-string payloads to strings so token counting is coherent.
+    # Always run this before truncation to ensure consistent token counting.
+    for i, m in enumerate(msgs):
+        if not isinstance(m.get("content"), str) and m.get("content") is not None:
+            if _is_tool_message(m):
+                continue
+            if i == 0 or i == len(msgs) - 1:
+                continue
+            content_str = json.dumps(m["content"], separators=(",", ":"))
+            if len(content_str) > 20_000:
+                content_str = _truncate_middle_tokens(content_str, enc, 20_000)
+            m["content"] = content_str
+
+    # ---- STEP 3: Token-aware content truncation ---------------------------
+    # Progressively halve per-message cap and truncate bloated content.
+    # This preserves all messages but shortens their content.
+    cap = start_cap
+    while total_tokens() + reserve > target_tokens and cap >= floor_cap:
+        for m in msgs[1:-1]:
+            if _is_tool_message(m):
+                _truncate_tool_message_content(m, enc, cap)
+                continue
+            if _is_objective_message(m):
+                continue
+            content = m.get("content") or ""
+            if _tok_len(content, enc) > cap:
+                m["content"] = _truncate_middle_tokens(content, enc, cap)
+        cap //= 2
+
+    # ---- STEP 4: Middle-out deletion --------------------------------------
+    # Delete messages one at a time from the center outward.
+    # This is more granular than dropping all old messages at once.
+    while total_tokens() + reserve > target_tokens and len(msgs) > 2:
+        deletable: list[int] = []
+        for i in range(1, len(msgs) - 1):
+            msg = msgs[i]
+            if (
+                msg is not None
+                and not _is_tool_message(msg)
+                and not _is_objective_message(msg)
+            ):
+                deletable.append(i)
+        if not deletable:
+            break
+        centre = len(msgs) // 2
+        to_delete = min(deletable, key=lambda i: abs(i - centre))
+        del msgs[to_delete]
+        messages_dropped += 1
+
+    # ---- STEP 5: Final trim on first/last ---------------------------------
+    cap = start_cap
+    while total_tokens() + reserve > target_tokens and cap >= floor_cap:
+        for idx in (0, -1):
+            msg = msgs[idx]
+            if msg is None:
+                continue
+            if _is_tool_message(msg):
+                _truncate_tool_message_content(msg, enc, cap)
+                continue
+            text = msg.get("content") or ""
+            if _tok_len(text, enc) > cap:
+                msg["content"] = _truncate_middle_tokens(text, enc, cap)
+        cap //= 2
+
+    # Filter out any None values that may have been introduced
+    final_msgs: list[dict] = [m for m in msgs if m is not None]
+    final_count = sum(_msg_tokens(m, enc) for m in final_msgs)
+    error = None
+    if final_count + reserve > target_tokens:
+        error = f"Could not compress below target ({final_count + reserve} > {target_tokens})"
+        logger.warning(error)
+
+    return CompressResult(
+        messages=final_msgs,
+        token_count=final_count,
+        was_compacted=True,
+        error=error,
+        original_token_count=original_count,
+        messages_summarized=messages_summarized,
+        messages_dropped=messages_dropped,
+    )

autogpt_platform/backend/backend/util/prompt_test.py

@@ -1,10 +1,21 @@
 """Tests for prompt utility functions, especially tool call token counting."""
 
+from unittest.mock import AsyncMock, MagicMock
+
 import pytest
 from tiktoken import encoding_for_model
 
 from backend.util import json
-from backend.util.prompt import _msg_tokens, estimate_token_count
+from backend.util.prompt import (
+    CompressResult,
+    _ensure_tool_pairs_intact,
+    _msg_tokens,
+    _normalize_model_for_tokenizer,
+    _truncate_middle_tokens,
+    _truncate_tool_message_content,
+    compress_context,
+    estimate_token_count,
+)
 
 
 class TestMsgTokens:
@@ -276,3 +287,690 @@ class TestEstimateTokenCount:
 
         assert total_tokens == expected_total
         assert total_tokens > 20  # Should be substantial
+
+
+class TestNormalizeModelForTokenizer:
+    """Test model name normalization for tiktoken."""
+
+    def test_openai_models_unchanged(self):
+        """Test that OpenAI models are returned as-is."""
+        assert _normalize_model_for_tokenizer("gpt-4o") == "gpt-4o"
+        assert _normalize_model_for_tokenizer("gpt-4") == "gpt-4"
+        assert _normalize_model_for_tokenizer("gpt-3.5-turbo") == "gpt-3.5-turbo"
+
+    def test_claude_models_normalized(self):
+        """Test that Claude models are normalized to gpt-4o."""
+        assert _normalize_model_for_tokenizer("claude-3-opus") == "gpt-4o"
+        assert _normalize_model_for_tokenizer("claude-3-sonnet") == "gpt-4o"
+        assert _normalize_model_for_tokenizer("anthropic/claude-3-haiku") == "gpt-4o"
+
+    def test_openrouter_paths_extracted(self):
+        """Test that OpenRouter model paths are handled."""
+        assert _normalize_model_for_tokenizer("openai/gpt-4o") == "gpt-4o"
+        assert _normalize_model_for_tokenizer("anthropic/claude-3-opus") == "gpt-4o"
+
+    def test_unknown_models_default_to_gpt4o(self):
+        """Test that unknown models default to gpt-4o."""
+        assert _normalize_model_for_tokenizer("some-random-model") == "gpt-4o"
+        assert _normalize_model_for_tokenizer("llama-3-70b") == "gpt-4o"
+
+
+class TestTruncateToolMessageContent:
+    """Test tool message content truncation."""
+
+    @pytest.fixture
+    def enc(self):
+        return encoding_for_model("gpt-4o")
+
+    def test_truncate_openai_tool_message(self, enc):
+        """Test truncation of OpenAI-style tool message with string content."""
+        long_content = "x" * 10000
+        msg = {"role": "tool", "tool_call_id": "call_123", "content": long_content}
+
+        _truncate_tool_message_content(msg, enc, max_tokens=100)
+
+        # Content should be truncated
+        assert len(msg["content"]) < len(long_content)
+        assert "…" in msg["content"]  # Has ellipsis marker
+
+    def test_truncate_anthropic_tool_result(self, enc):
+        """Test truncation of Anthropic-style tool_result."""
+        long_content = "y" * 10000
+        msg = {
+            "role": "user",
+            "content": [
+                {
+                    "type": "tool_result",
+                    "tool_use_id": "toolu_123",
+                    "content": long_content,
+                }
+            ],
+        }
+
+        _truncate_tool_message_content(msg, enc, max_tokens=100)
+
+        # Content should be truncated
+        result_content = msg["content"][0]["content"]
+        assert len(result_content) < len(long_content)
+        assert "…" in result_content
+
+    def test_preserve_tool_use_blocks(self, enc):
+        """Test that tool_use blocks are not truncated."""
+        msg = {
+            "role": "assistant",
+            "content": [
+                {
+                    "type": "tool_use",
+                    "id": "toolu_123",
+                    "name": "some_function",
+                    "input": {"key": "value" * 1000},  # Large input
+                }
+            ],
+        }
+
+        original = json.dumps(msg["content"][0]["input"])
+        _truncate_tool_message_content(msg, enc, max_tokens=10)
+
+        # tool_use should be unchanged
+        assert json.dumps(msg["content"][0]["input"]) == original
+
+    def test_no_truncation_when_under_limit(self, enc):
+        """Test that short content is not modified."""
+        msg = {"role": "tool", "tool_call_id": "call_123", "content": "Short content"}
+
+        original = msg["content"]
+        _truncate_tool_message_content(msg, enc, max_tokens=1000)
+
+        assert msg["content"] == original
+
+
+class TestTruncateMiddleTokens:
+    """Test middle truncation of text."""
+
+    @pytest.fixture
+    def enc(self):
+        return encoding_for_model("gpt-4o")
+
+    def test_truncates_long_text(self, enc):
+        """Test that long text is truncated with ellipsis in middle."""
+        long_text = "word " * 1000
+        result = _truncate_middle_tokens(long_text, enc, max_tok=50)
+
+        assert len(enc.encode(result)) <= 52  # Allow some slack for ellipsis
+        assert "…" in result
+        assert result.startswith("word")  # Head preserved
+        assert result.endswith("word ")  # Tail preserved
+
+    def test_preserves_short_text(self, enc):
+        """Test that short text is not modified."""
+        short_text = "Hello world"
+        result = _truncate_middle_tokens(short_text, enc, max_tok=100)
+
+        assert result == short_text
+
+
+class TestEnsureToolPairsIntact:
+    """Test tool call/response pair preservation for both OpenAI and Anthropic formats."""
+
+    # ---- OpenAI Format Tests ----
+
+    def test_openai_adds_missing_tool_call(self):
+        """Test that orphaned OpenAI tool_response gets its tool_call prepended."""
+        all_msgs = [
+            {"role": "system", "content": "You are helpful."},
+            {
+                "role": "assistant",
+                "tool_calls": [
+                    {"id": "call_1", "type": "function", "function": {"name": "f1"}}
+                ],
+            },
+            {"role": "tool", "tool_call_id": "call_1", "content": "result"},
+            {"role": "user", "content": "Thanks!"},
+        ]
+        # Recent messages start at index 2 (the tool response)
+        recent = [all_msgs[2], all_msgs[3]]
+        start_index = 2
+
+        result = _ensure_tool_pairs_intact(recent, all_msgs, start_index)
+
+        # Should prepend the tool_call message
+        assert len(result) == 3
+        assert result[0]["role"] == "assistant"
+        assert "tool_calls" in result[0]
+
+    def test_openai_keeps_complete_pairs(self):
+        """Test that complete OpenAI pairs are unchanged."""
+        all_msgs = [
+            {"role": "system", "content": "System"},
+            {
+                "role": "assistant",
+                "tool_calls": [
+                    {"id": "call_1", "type": "function", "function": {"name": "f1"}}
+                ],
+            },
+            {"role": "tool", "tool_call_id": "call_1", "content": "result"},
+        ]
+        recent = all_msgs[1:]  # Include both tool_call and response
+        start_index = 1
+
+        result = _ensure_tool_pairs_intact(recent, all_msgs, start_index)
+
+        assert len(result) == 2  # No messages added
+
+    def test_openai_multiple_tool_calls(self):
+        """Test multiple OpenAI tool calls in one assistant message."""
+        all_msgs = [
+            {"role": "system", "content": "System"},
+            {
+                "role": "assistant",
+                "tool_calls": [
+                    {"id": "call_1", "type": "function", "function": {"name": "f1"}},
+                    {"id": "call_2", "type": "function", "function": {"name": "f2"}},
+                ],
+            },
+            {"role": "tool", "tool_call_id": "call_1", "content": "result1"},
+            {"role": "tool", "tool_call_id": "call_2", "content": "result2"},
+            {"role": "user", "content": "Thanks!"},
+        ]
+        # Recent messages start at index 2 (first tool response)
+        recent = [all_msgs[2], all_msgs[3], all_msgs[4]]
+        start_index = 2
+
+        result = _ensure_tool_pairs_intact(recent, all_msgs, start_index)
+
+        # Should prepend the assistant message with both tool_calls
+        assert len(result) == 4
+        assert result[0]["role"] == "assistant"
+        assert len(result[0]["tool_calls"]) == 2
+
+    # ---- Anthropic Format Tests ----
+
+    def test_anthropic_adds_missing_tool_use(self):
+        """Test that orphaned Anthropic tool_result gets its tool_use prepended."""
+        all_msgs = [
+            {"role": "system", "content": "You are helpful."},
+            {
+                "role": "assistant",
+                "content": [
+                    {
+                        "type": "tool_use",
+                        "id": "toolu_123",
+                        "name": "get_weather",
+                        "input": {"location": "SF"},
+                    }
+                ],
+            },
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "tool_result",
+                        "tool_use_id": "toolu_123",
+                        "content": "22°C and sunny",
+                    }
+                ],
+            },
+            {"role": "user", "content": "Thanks!"},
+        ]
+        # Recent messages start at index 2 (the tool_result)
+        recent = [all_msgs[2], all_msgs[3]]
+        start_index = 2
+
+        result = _ensure_tool_pairs_intact(recent, all_msgs, start_index)
+
+        # Should prepend the tool_use message
+        assert len(result) == 3
+        assert result[0]["role"] == "assistant"
+        assert result[0]["content"][0]["type"] == "tool_use"
+
+    def test_anthropic_keeps_complete_pairs(self):
+        """Test that complete Anthropic pairs are unchanged."""
+        all_msgs = [
+            {"role": "system", "content": "System"},
+            {
+                "role": "assistant",
+                "content": [
+                    {
+                        "type": "tool_use",
+                        "id": "toolu_456",
+                        "name": "calculator",
+                        "input": {"expr": "2+2"},
+                    }
+                ],
+            },
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "tool_result",
+                        "tool_use_id": "toolu_456",
+                        "content": "4",
+                    }
+                ],
+            },
+        ]
+        recent = all_msgs[1:]  # Include both tool_use and result
+        start_index = 1
+
+        result = _ensure_tool_pairs_intact(recent, all_msgs, start_index)
+
+        assert len(result) == 2  # No messages added
+
+    def test_anthropic_multiple_tool_uses(self):
+        """Test multiple Anthropic tool_use blocks in one message."""
+        all_msgs = [
+            {"role": "system", "content": "System"},
+            {
+                "role": "assistant",
+                "content": [
+                    {"type": "text", "text": "Let me check both..."},
+                    {
+                        "type": "tool_use",
+                        "id": "toolu_1",
+                        "name": "get_weather",
+                        "input": {"city": "NYC"},
+                    },
+                    {
+                        "type": "tool_use",
+                        "id": "toolu_2",
+                        "name": "get_weather",
+                        "input": {"city": "LA"},
+                    },
+                ],
+            },
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "tool_result",
+                        "tool_use_id": "toolu_1",
+                        "content": "Cold",
+                    },
+                    {
+                        "type": "tool_result",
+                        "tool_use_id": "toolu_2",
+                        "content": "Warm",
+                    },
+                ],
+            },
+            {"role": "user", "content": "Thanks!"},
+        ]
+        # Recent messages start at index 2 (tool_result)
+        recent = [all_msgs[2], all_msgs[3]]
+        start_index = 2
+
+        result = _ensure_tool_pairs_intact(recent, all_msgs, start_index)
+
+        # Should prepend the assistant message with both tool_uses
+        assert len(result) == 3
+        assert result[0]["role"] == "assistant"
+        tool_use_count = sum(
+            1 for b in result[0]["content"] if b.get("type") == "tool_use"
+        )
+        assert tool_use_count == 2
+
+    # ---- Mixed/Edge Case Tests ----
+
+    def test_anthropic_with_type_message_field(self):
+        """Test Anthropic format with 'type': 'message' field (smart_decision_maker style)."""
+        all_msgs = [
+            {"role": "system", "content": "You are helpful."},
+            {
+                "role": "assistant",
+                "content": [
+                    {
+                        "type": "tool_use",
+                        "id": "toolu_abc",
+                        "name": "search",
+                        "input": {"q": "test"},
+                    }
+                ],
+            },
+            {
+                "role": "user",
+                "type": "message",  # Extra field from smart_decision_maker
+                "content": [
+                    {
+                        "type": "tool_result",
+                        "tool_use_id": "toolu_abc",
+                        "content": "Found results",
+                    }
+                ],
+            },
+            {"role": "user", "content": "Thanks!"},
+        ]
+        # Recent messages start at index 2 (the tool_result with 'type': 'message')
+        recent = [all_msgs[2], all_msgs[3]]
+        start_index = 2
+
+        result = _ensure_tool_pairs_intact(recent, all_msgs, start_index)
+
+        # Should prepend the tool_use message
+        assert len(result) == 3
+        assert result[0]["role"] == "assistant"
+        assert result[0]["content"][0]["type"] == "tool_use"
+
+    def test_handles_no_tool_messages(self):
+        """Test messages without tool calls."""
+        all_msgs = [
+            {"role": "user", "content": "Hello"},
+            {"role": "assistant", "content": "Hi there!"},
+        ]
+        recent = all_msgs
+        start_index = 0
+
+        result = _ensure_tool_pairs_intact(recent, all_msgs, start_index)
+
+        assert result == all_msgs
+
+    def test_handles_empty_messages(self):
+        """Test empty message list."""
+        result = _ensure_tool_pairs_intact([], [], 0)
+        assert result == []
+
+    def test_mixed_text_and_tool_content(self):
+        """Test Anthropic message with mixed text and tool_use content."""
+        all_msgs = [
+            {
+                "role": "assistant",
+                "content": [
+                    {"type": "text", "text": "I'll help you with that."},
+                    {
+                        "type": "tool_use",
+                        "id": "toolu_mixed",
+                        "name": "search",
+                        "input": {"q": "test"},
+                    },
+                ],
+            },
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "tool_result",
+                        "tool_use_id": "toolu_mixed",
+                        "content": "Found results",
+                    }
+                ],
+            },
+            {"role": "assistant", "content": "Here are the results..."},
+        ]
+        # Start from tool_result
+        recent = [all_msgs[1], all_msgs[2]]
+        start_index = 1
+
+        result = _ensure_tool_pairs_intact(recent, all_msgs, start_index)
+
+        # Should prepend the assistant message with tool_use
+        assert len(result) == 3
+        assert result[0]["content"][0]["type"] == "text"
+        assert result[0]["content"][1]["type"] == "tool_use"
+
+
+class TestCompressContext:
+    """Test the async compress_context function."""
+
+    @pytest.mark.asyncio
+    async def test_no_compression_needed(self):
+        """Test messages under limit return without compression."""
+        messages = [
+            {"role": "system", "content": "You are helpful."},
+            {"role": "user", "content": "Hello!"},
+        ]
+
+        result = await compress_context(messages, target_tokens=100000)
+
+        assert isinstance(result, CompressResult)
+        assert result.was_compacted is False
+        assert len(result.messages) == 2
+        assert result.error is None
+
+    @pytest.mark.asyncio
+    async def test_truncation_without_client(self):
+        """Test that truncation works without LLM client."""
+        long_content = "x" * 50000
+        messages = [
+            {"role": "system", "content": "System"},
+            {"role": "user", "content": long_content},
+            {"role": "assistant", "content": "Response"},
+        ]
+
+        result = await compress_context(
+            messages, target_tokens=1000, client=None, reserve=100
+        )
+
+        assert result.was_compacted is True
+        # Should have truncated without summarization
+        assert result.messages_summarized == 0
+
+    @pytest.mark.asyncio
+    async def test_with_mocked_llm_client(self):
+        """Test summarization with mocked LLM client."""
+        # Create many messages to trigger summarization
+        messages = [{"role": "system", "content": "System prompt"}]
+        for i in range(30):
+            messages.append({"role": "user", "content": f"User message {i} " * 100})
+            messages.append(
+                {"role": "assistant", "content": f"Assistant response {i} " * 100}
+            )
+
+        # Mock the AsyncOpenAI client
+        mock_client = AsyncMock()
+        mock_response = MagicMock()
+        mock_response.choices = [MagicMock()]
+        mock_response.choices[0].message.content = "Summary of conversation"
+        mock_client.with_options.return_value.chat.completions.create = AsyncMock(
+            return_value=mock_response
+        )
+
+        result = await compress_context(
+            messages,
+            target_tokens=5000,
+            client=mock_client,
+            keep_recent=5,
+            reserve=500,
+        )
+
+        assert result.was_compacted is True
+        # Should have attempted summarization
+        assert mock_client.with_options.called or result.messages_summarized > 0
+
+    @pytest.mark.asyncio
+    async def test_preserves_tool_pairs(self):
+        """Test that tool call/response pairs stay together."""
+        messages = [
+            {"role": "system", "content": "System"},
+            {"role": "user", "content": "Do something"},
+            {
+                "role": "assistant",
+                "tool_calls": [
+                    {"id": "call_1", "type": "function", "function": {"name": "func"}}
+                ],
+            },
+            {"role": "tool", "tool_call_id": "call_1", "content": "Result " * 1000},
+            {"role": "assistant", "content": "Done!"},
+        ]
+
+        result = await compress_context(
+            messages, target_tokens=500, client=None, reserve=50
+        )
+
+        # Check that if tool response exists, its call exists too
+        tool_call_ids = set()
+        tool_response_ids = set()
+        for msg in result.messages:
+            if "tool_calls" in msg:
+                for tc in msg["tool_calls"]:
+                    tool_call_ids.add(tc["id"])
+            if msg.get("role") == "tool":
+                tool_response_ids.add(msg.get("tool_call_id"))
+
+        # All tool responses should have their calls
+        assert tool_response_ids <= tool_call_ids
+
+    @pytest.mark.asyncio
+    async def test_returns_error_when_cannot_compress(self):
+        """Test that error is returned when compression fails."""
+        # Single huge message that can't be compressed enough
+        messages = [
+            {"role": "user", "content": "x" * 100000},
+        ]
+
+        result = await compress_context(
+            messages, target_tokens=100, client=None, reserve=50
+        )
+
+        # Should have an error since we can't get below 100 tokens
+        assert result.error is not None
+        assert result.was_compacted is True
+
+    @pytest.mark.asyncio
+    async def test_empty_messages(self):
+        """Test that empty messages list returns early without error."""
+        result = await compress_context([], target_tokens=1000)
+
+        assert result.messages == []
+        assert result.token_count == 0
+        assert result.was_compacted is False
+        assert result.error is None
+
+
+class TestRemoveOrphanToolResponses:
+    """Test _remove_orphan_tool_responses helper function."""
+
+    def test_removes_openai_orphan(self):
+        """Test removal of orphan OpenAI tool response."""
+        from backend.util.prompt import _remove_orphan_tool_responses
+
+        messages = [
+            {"role": "tool", "tool_call_id": "call_orphan", "content": "result"},
+            {"role": "user", "content": "Hello"},
+        ]
+        orphan_ids = {"call_orphan"}
+
+        result = _remove_orphan_tool_responses(messages, orphan_ids)
+
+        assert len(result) == 1
+        assert result[0]["role"] == "user"
+
+    def test_keeps_valid_openai_tool(self):
+        """Test that valid OpenAI tool responses are kept."""
+        from backend.util.prompt import _remove_orphan_tool_responses
+
+        messages = [
+            {"role": "tool", "tool_call_id": "call_valid", "content": "result"},
+        ]
+        orphan_ids = {"call_other"}
+
+        result = _remove_orphan_tool_responses(messages, orphan_ids)
+
+        assert len(result) == 1
+        assert result[0]["tool_call_id"] == "call_valid"
+
+    def test_filters_anthropic_mixed_blocks(self):
+        """Test filtering individual orphan blocks from Anthropic message with mixed valid/orphan."""
+        from backend.util.prompt import _remove_orphan_tool_responses
+
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "tool_result",
+                        "tool_use_id": "toolu_valid",
+                        "content": "valid result",
+                    },
+                    {
+                        "type": "tool_result",
+                        "tool_use_id": "toolu_orphan",
+                        "content": "orphan result",
+                    },
+                ],
+            },
+        ]
+        orphan_ids = {"toolu_orphan"}
+
+        result = _remove_orphan_tool_responses(messages, orphan_ids)
+
+        assert len(result) == 1
+        # Should only have the valid tool_result, orphan filtered out
+        assert len(result[0]["content"]) == 1
+        assert result[0]["content"][0]["tool_use_id"] == "toolu_valid"
+
+    def test_removes_anthropic_all_orphan(self):
+        """Test removal of Anthropic message when all tool_results are orphans."""
+        from backend.util.prompt import _remove_orphan_tool_responses
+
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "tool_result",
+                        "tool_use_id": "toolu_orphan1",
+                        "content": "result1",
+                    },
+                    {
+                        "type": "tool_result",
+                        "tool_use_id": "toolu_orphan2",
+                        "content": "result2",
+                    },
+                ],
+            },
+        ]
+        orphan_ids = {"toolu_orphan1", "toolu_orphan2"}
+
+        result = _remove_orphan_tool_responses(messages, orphan_ids)
+
+        # Message should be completely removed since no content left
+        assert len(result) == 0
+
+    def test_preserves_non_tool_messages(self):
+        """Test that non-tool messages are preserved."""
+        from backend.util.prompt import _remove_orphan_tool_responses
+
+        messages = [
+            {"role": "user", "content": "Hello"},
+            {"role": "assistant", "content": "Hi there!"},
+        ]
+        orphan_ids = {"some_id"}
+
+        result = _remove_orphan_tool_responses(messages, orphan_ids)
+
+        assert result == messages
+
+
+class TestCompressResultDataclass:
+    """Test CompressResult dataclass."""
+
+    def test_default_values(self):
+        """Test default values are set correctly."""
+        result = CompressResult(
+            messages=[{"role": "user", "content": "test"}],
+            token_count=10,
+            was_compacted=False,
+        )
+
+        assert result.error is None
+        assert result.original_token_count == 0  # Defaults to 0, not None
+        assert result.messages_summarized == 0
+        assert result.messages_dropped == 0
+
+    def test_all_fields(self):
+        """Test all fields can be set."""
+        result = CompressResult(
+            messages=[{"role": "user", "content": "test"}],
+            token_count=100,
+            was_compacted=True,
+            error="Some error",
+            original_token_count=500,
+            messages_summarized=10,
+            messages_dropped=5,
+        )
+
+        assert result.token_count == 100
+        assert result.was_compacted is True
+        assert result.error == "Some error"
+        assert result.original_token_count == 500
+        assert result.messages_summarized == 10
+        assert result.messages_dropped == 5

autogpt_platform/backend/test/agent_generator/test_core_integration.py

@@ -111,9 +111,7 @@ class TestGenerateAgent:
             instructions = {"type": "instructions", "steps": ["Step 1"]}
             result = await core.generate_agent(instructions)
 
-            # library_agents defaults to None
-            mock_external.assert_called_once_with(instructions, None)
-            # Result should have id, version, is_active added if not present
+            mock_external.assert_called_once_with(instructions, None, None, None)
             assert result is not None
             assert result["name"] == "Test Agent"
             assert "id" in result
@@ -177,8 +175,9 @@ class TestGenerateAgentPatch:
             current_agent = {"nodes": [], "links": []}
             result = await core.generate_agent_patch("Add a node", current_agent)
 
-            # library_agents defaults to None
-            mock_external.assert_called_once_with("Add a node", current_agent, None)
+            mock_external.assert_called_once_with(
+                "Add a node", current_agent, None, None, None
+            )
             assert result == expected_result
 
     @pytest.mark.asyncio

autogpt_platform/backend/test/agent_generator/test_service.py

@@ -102,7 +102,7 @@ class TestDecomposeGoalExternal:
 
     @pytest.mark.asyncio
     async def test_decompose_goal_with_context(self):
-        """Test decomposition with additional context."""
+        """Test decomposition with additional context enriched into description."""
         mock_response = MagicMock()
         mock_response.json.return_value = {
             "success": True,
@@ -119,9 +119,12 @@ class TestDecomposeGoalExternal:
                 "Build a chatbot", context="Use Python"
             )
 
+        expected_description = (
+            "Build a chatbot\n\nAdditional context from user:\nUse Python"
+        )
         mock_client.post.assert_called_once_with(
             "/api/decompose-description",
-            json={"description": "Build a chatbot", "user_instruction": "Use Python"},
+            json={"description": expected_description},
         )
 
     @pytest.mark.asyncio

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

@@ -1,10 +1,9 @@
 "use client";
+import { getV1OnboardingState } from "@/app/api/__generated__/endpoints/onboarding/onboarding";
+import { getOnboardingStatus, resolveResponse } from "@/app/api/helpers";
 import { LoadingSpinner } from "@/components/atoms/LoadingSpinner/LoadingSpinner";
 import { useRouter } from "next/navigation";
 import { useEffect } from "react";
-import { resolveResponse, getOnboardingStatus } from "@/app/api/helpers";
-import { getV1OnboardingState } from "@/app/api/__generated__/endpoints/onboarding/onboarding";
-import { getHomepageRoute } from "@/lib/constants";
 
 export default function OnboardingPage() {
   const router = useRouter();
@@ -13,12 +12,10 @@ export default function OnboardingPage() {
     async function redirectToStep() {
       try {
         // Check if onboarding is enabled (also gets chat flag for redirect)
-        const { shouldShowOnboarding, isChatEnabled } =
-          await getOnboardingStatus();
-        const homepageRoute = getHomepageRoute(isChatEnabled);
+        const { shouldShowOnboarding } = await getOnboardingStatus();
 
         if (!shouldShowOnboarding) {
-          router.replace(homepageRoute);
+          router.replace("/");
           return;
         }
 
@@ -26,7 +23,7 @@ export default function OnboardingPage() {
 
         // Handle completed onboarding
         if (onboarding.completedSteps.includes("GET_RESULTS")) {
-          router.replace(homepageRoute);
+          router.replace("/");
           return;
         }
 

autogpt_platform/frontend/src/app/(platform)/auth/callback/route.ts

@@ -1,9 +1,8 @@
-import { getServerSupabase } from "@/lib/supabase/server/getServerSupabase";
-import { getHomepageRoute } from "@/lib/constants";
-import BackendAPI from "@/lib/autogpt-server-api";
-import { NextResponse } from "next/server";
-import { revalidatePath } from "next/cache";
 import { getOnboardingStatus } from "@/app/api/helpers";
+import BackendAPI from "@/lib/autogpt-server-api";
+import { getServerSupabase } from "@/lib/supabase/server/getServerSupabase";
+import { revalidatePath } from "next/cache";
+import { NextResponse } from "next/server";
 
 // Handle the callback to complete the user session login
 export async function GET(request: Request) {
@@ -27,13 +26,12 @@ export async function GET(request: Request) {
         await api.createUser();
 
         // Get onboarding status from backend (includes chat flag evaluated for this user)
-        const { shouldShowOnboarding, isChatEnabled } =
-          await getOnboardingStatus();
+        const { shouldShowOnboarding } = await getOnboardingStatus();
         if (shouldShowOnboarding) {
           next = "/onboarding";
           revalidatePath("/onboarding", "layout");
         } else {
-          next = getHomepageRoute(isChatEnabled);
+          next = "/";
           revalidatePath(next, "layout");
         }
       } catch (createUserError) {

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

@@ -11,7 +11,6 @@ import { useBreakpoint } from "@/lib/hooks/useBreakpoint";
 import { useSupabase } from "@/lib/supabase/hooks/useSupabase";
 import { useQueryClient } from "@tanstack/react-query";
 import { usePathname, useSearchParams } from "next/navigation";
-import { useRef } from "react";
 import { useCopilotStore } from "../../copilot-page-store";
 import { useCopilotSessionId } from "../../useCopilotSessionId";
 import { useMobileDrawer } from "./components/MobileDrawer/useMobileDrawer";
@@ -70,41 +69,16 @@ export function useCopilotShell() {
   });
 
   const stopStream = useChatStore((s) => s.stopStream);
-  const onStreamComplete = useChatStore((s) => s.onStreamComplete);
-  const isStreaming = useCopilotStore((s) => s.isStreaming);
   const isCreatingSession = useCopilotStore((s) => s.isCreatingSession);
-  const setIsSwitchingSession = useCopilotStore((s) => s.setIsSwitchingSession);
-  const openInterruptModal = useCopilotStore((s) => s.openInterruptModal);
 
-  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);
-  }
-
-  function selectSession(sessionId: string) {
+  function handleSessionClick(sessionId: string) {
     if (sessionId === currentSessionId) return;
+
+    // Stop current stream - SSE reconnection allows resuming later
+    if (currentSessionId) {
+      stopStream(currentSessionId);
+    }
+
     if (recentlyCreatedSessionsRef.current.has(sessionId)) {
       queryClient.invalidateQueries({
         queryKey: getGetV2GetSessionQueryKey(sessionId),
@@ -114,7 +88,12 @@ export function useCopilotShell() {
     if (isMobile) handleCloseDrawer();
   }
 
-  function startNewChat() {
+  function handleNewChatClick() {
+    // Stop current stream - SSE reconnection allows resuming later
+    if (currentSessionId) {
+      stopStream(currentSessionId);
+    }
+
     resetPagination();
     queryClient.invalidateQueries({
       queryKey: getGetV2ListSessionsQueryKey(),
@@ -123,32 +102,6 @@ export function useCopilotShell() {
     if (isMobile) handleCloseDrawer();
   }
 
-  function handleSessionClick(sessionId: string) {
-    if (sessionId === currentSessionId) return;
-
-    if (isStreaming) {
-      pendingActionRef.current = async () => {
-        await stopCurrentStream();
-        selectSession(sessionId);
-      };
-      openInterruptModal(pendingActionRef.current);
-    } else {
-      selectSession(sessionId);
-    }
-  }
-
-  function handleNewChatClick() {
-    if (isStreaming) {
-      pendingActionRef.current = async () => {
-        await stopCurrentStream();
-        startNewChat();
-      };
-      openInterruptModal(pendingActionRef.current);
-    } else {
-      startNewChat();
-    }
-  }
-
   return {
     isMobile,
     isDrawerOpen,

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

@@ -1,6 +1,13 @@
-import type { ReactNode } from "react";
+"use client";
+import { FeatureFlagPage } from "@/services/feature-flags/FeatureFlagPage";
+import { Flag } from "@/services/feature-flags/use-get-flag";
+import { type ReactNode } from "react";
 import { CopilotShell } from "./components/CopilotShell/CopilotShell";
 
 export default function CopilotLayout({ children }: { children: ReactNode }) {
-  return <CopilotShell>{children}</CopilotShell>;
+  return (
+    <FeatureFlagPage flag={Flag.CHAT} whenDisabled="/library">
+      <CopilotShell>{children}</CopilotShell>
+    </FeatureFlagPage>
+  );
 }

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

@@ -14,14 +14,8 @@ export default function CopilotPage() {
   const isInterruptModalOpen = useCopilotStore((s) => s.isInterruptModalOpen);
   const confirmInterrupt = useCopilotStore((s) => s.confirmInterrupt);
   const cancelInterrupt = useCopilotStore((s) => s.cancelInterrupt);
-  const {
-    greetingName,
-    quickActions,
-    isLoading,
-    hasSession,
-    initialPrompt,
-    isReady,
-  } = state;
+  const { greetingName, quickActions, isLoading, hasSession, initialPrompt } =
+    state;
   const {
     handleQuickAction,
     startChatWithPrompt,
@@ -29,8 +23,6 @@ export default function CopilotPage() {
     handleStreamingChange,
   } = handlers;
 
-  if (!isReady) return null;
-
   if (hasSession) {
     return (
       <div className="flex h-full flex-col">

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

@@ -3,18 +3,11 @@ import {
   postV2CreateSession,
 } from "@/app/api/__generated__/endpoints/chat/chat";
 import { useToast } from "@/components/molecules/Toast/use-toast";
-import { getHomepageRoute } from "@/lib/constants";
 import { useSupabase } from "@/lib/supabase/hooks/useSupabase";
 import { useOnboarding } from "@/providers/onboarding/onboarding-provider";
-import {
-  Flag,
-  type FlagValues,
-  useGetFlag,
-} from "@/services/feature-flags/use-get-flag";
 import { SessionKey, sessionStorage } from "@/services/storage/session-storage";
 import * as Sentry from "@sentry/nextjs";
 import { useQueryClient } from "@tanstack/react-query";
-import { useFlags } from "launchdarkly-react-client-sdk";
 import { useRouter } from "next/navigation";
 import { useEffect } from "react";
 import { useCopilotStore } from "./copilot-page-store";
@@ -33,22 +26,6 @@ export function useCopilotPage() {
   const isCreating = useCopilotStore((s) => s.isCreatingSession);
   const setIsCreating = useCopilotStore((s) => s.setIsCreatingSession);
 
-  // Complete VISIT_COPILOT onboarding step to grant $5 welcome bonus
-  useEffect(() => {
-    if (isLoggedIn) {
-      completeStep("VISIT_COPILOT");
-    }
-  }, [completeStep, isLoggedIn]);
-
-  const isChatEnabled = useGetFlag(Flag.CHAT);
-  const flags = useFlags<FlagValues>();
-  const homepageRoute = getHomepageRoute(isChatEnabled);
-  const envEnabled = process.env.NEXT_PUBLIC_LAUNCHDARKLY_ENABLED === "true";
-  const clientId = process.env.NEXT_PUBLIC_LAUNCHDARKLY_CLIENT_ID;
-  const isLaunchDarklyConfigured = envEnabled && Boolean(clientId);
-  const isFlagReady =
-    !isLaunchDarklyConfigured || flags[Flag.CHAT] !== undefined;
-
   const greetingName = getGreetingName(user);
   const quickActions = getQuickActions();
 
@@ -58,11 +35,8 @@ export function useCopilotPage() {
     : undefined;
 
   useEffect(() => {
-    if (!isFlagReady) return;
-    if (isChatEnabled === false) {
-      router.replace(homepageRoute);
-    }
-  }, [homepageRoute, isChatEnabled, isFlagReady, router]);
+    if (isLoggedIn) completeStep("VISIT_COPILOT");
+  }, [completeStep, isLoggedIn]);
 
   async function startChatWithPrompt(prompt: string) {
     if (!prompt?.trim()) return;
@@ -116,7 +90,6 @@ export function useCopilotPage() {
       isLoading: isUserLoading,
       hasSession,
       initialPrompt,
-      isReady: isFlagReady && isChatEnabled !== false && isLoggedIn,
     },
     handlers: {
       handleQuickAction,

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

@@ -1,8 +1,6 @@
 "use client";
 
 import { ErrorCard } from "@/components/molecules/ErrorCard/ErrorCard";
-import { getHomepageRoute } from "@/lib/constants";
-import { Flag, useGetFlag } from "@/services/feature-flags/use-get-flag";
 import { useSearchParams } from "next/navigation";
 import { Suspense } from "react";
 import { getErrorDetails } from "./helpers";
@@ -11,8 +9,6 @@ function ErrorPageContent() {
   const searchParams = useSearchParams();
   const errorMessage = searchParams.get("message");
   const errorDetails = getErrorDetails(errorMessage);
-  const isChatEnabled = useGetFlag(Flag.CHAT);
-  const homepageRoute = getHomepageRoute(isChatEnabled);
 
   function handleRetry() {
     // Auth-related errors should redirect to login
@@ -30,7 +26,7 @@ function ErrorPageContent() {
       }, 2000);
     } else {
       // For server/network errors, go to home
-      window.location.href = homepageRoute;
+      window.location.href = "/";
     }
   }
 

autogpt_platform/frontend/src/app/(platform)/login/actions.ts

@@ -1,6 +1,5 @@
 "use server";
 
-import { getHomepageRoute } from "@/lib/constants";
 import BackendAPI from "@/lib/autogpt-server-api";
 import { getServerSupabase } from "@/lib/supabase/server/getServerSupabase";
 import { loginFormSchema } from "@/types/auth";
@@ -38,10 +37,8 @@ export async function login(email: string, password: string) {
     await api.createUser();
 
     // Get onboarding status from backend (includes chat flag evaluated for this user)
-    const { shouldShowOnboarding, isChatEnabled } = await getOnboardingStatus();
-    const next = shouldShowOnboarding
-      ? "/onboarding"
-      : getHomepageRoute(isChatEnabled);
+    const { shouldShowOnboarding } = await getOnboardingStatus();
+    const next = shouldShowOnboarding ? "/onboarding" : "/";
 
     return {
       success: true,

autogpt_platform/frontend/src/app/(platform)/login/useLoginPage.ts

@@ -1,8 +1,6 @@
 import { useToast } from "@/components/molecules/Toast/use-toast";
-import { getHomepageRoute } from "@/lib/constants";
 import { useSupabase } from "@/lib/supabase/hooks/useSupabase";
 import { environment } from "@/services/environment";
-import { Flag, useGetFlag } from "@/services/feature-flags/use-get-flag";
 import { loginFormSchema, LoginProvider } from "@/types/auth";
 import { zodResolver } from "@hookform/resolvers/zod";
 import { useRouter, useSearchParams } from "next/navigation";
@@ -22,17 +20,15 @@ export function useLoginPage() {
   const [isGoogleLoading, setIsGoogleLoading] = useState(false);
   const [showNotAllowedModal, setShowNotAllowedModal] = useState(false);
   const isCloudEnv = environment.isCloud();
-  const isChatEnabled = useGetFlag(Flag.CHAT);
-  const homepageRoute = getHomepageRoute(isChatEnabled);
 
   // Get redirect destination from 'next' query parameter
   const nextUrl = searchParams.get("next");
 
   useEffect(() => {
     if (isLoggedIn && !isLoggingIn) {
-      router.push(nextUrl || homepageRoute);
+      router.push(nextUrl || "/");
     }
-  }, [homepageRoute, isLoggedIn, isLoggingIn, nextUrl, router]);
+  }, [isLoggedIn, isLoggingIn, nextUrl, router]);
 
   const form = useForm<z.infer<typeof loginFormSchema>>({
     resolver: zodResolver(loginFormSchema),
@@ -98,7 +94,7 @@ export function useLoginPage() {
       }
 
       // Prefer URL's next parameter, then use backend-determined route
-      router.replace(nextUrl || result.next || homepageRoute);
+      router.replace(nextUrl || result.next || "/");
     } catch (error) {
       toast({
         title:

autogpt_platform/frontend/src/app/(platform)/signup/actions.ts

@@ -1,6 +1,5 @@
 "use server";
 
-import { getHomepageRoute } from "@/lib/constants";
 import { getServerSupabase } from "@/lib/supabase/server/getServerSupabase";
 import { signupFormSchema } from "@/types/auth";
 import * as Sentry from "@sentry/nextjs";
@@ -59,10 +58,8 @@ export async function signup(
     }
 
     // Get onboarding status from backend (includes chat flag evaluated for this user)
-    const { shouldShowOnboarding, isChatEnabled } = await getOnboardingStatus();
-    const next = shouldShowOnboarding
-      ? "/onboarding"
-      : getHomepageRoute(isChatEnabled);
+    const { shouldShowOnboarding } = await getOnboardingStatus();
+    const next = shouldShowOnboarding ? "/onboarding" : "/";
 
     return { success: true, next };
   } catch (err) {

autogpt_platform/frontend/src/app/(platform)/signup/useSignupPage.ts

@@ -1,8 +1,6 @@
 import { useToast } from "@/components/molecules/Toast/use-toast";
-import { getHomepageRoute } from "@/lib/constants";
 import { useSupabase } from "@/lib/supabase/hooks/useSupabase";
 import { environment } from "@/services/environment";
-import { Flag, useGetFlag } from "@/services/feature-flags/use-get-flag";
 import { LoginProvider, signupFormSchema } from "@/types/auth";
 import { zodResolver } from "@hookform/resolvers/zod";
 import { useRouter, useSearchParams } from "next/navigation";
@@ -22,17 +20,15 @@ export function useSignupPage() {
   const [isGoogleLoading, setIsGoogleLoading] = useState(false);
   const [showNotAllowedModal, setShowNotAllowedModal] = useState(false);
   const isCloudEnv = environment.isCloud();
-  const isChatEnabled = useGetFlag(Flag.CHAT);
-  const homepageRoute = getHomepageRoute(isChatEnabled);
 
   // Get redirect destination from 'next' query parameter
   const nextUrl = searchParams.get("next");
 
   useEffect(() => {
     if (isLoggedIn && !isSigningUp) {
-      router.push(nextUrl || homepageRoute);
+      router.push(nextUrl || "/");
     }
-  }, [homepageRoute, isLoggedIn, isSigningUp, nextUrl, router]);
+  }, [isLoggedIn, isSigningUp, nextUrl, router]);
 
   const form = useForm<z.infer<typeof signupFormSchema>>({
     resolver: zodResolver(signupFormSchema),
@@ -133,7 +129,7 @@ export function useSignupPage() {
       }
 
       // Prefer the URL's next parameter, then result.next (for onboarding), then default
-      const redirectTo = nextUrl || result.next || homepageRoute;
+      const redirectTo = nextUrl || result.next || "/";
       router.replace(redirectTo);
     } catch (error) {
       setIsLoading(false);

autogpt_platform/frontend/src/app/api/chat/tasks/[taskId]/stream/route.ts

@@ -0,0 +1,81 @@
+import { environment } from "@/services/environment";
+import { getServerAuthToken } from "@/lib/autogpt-server-api/helpers";
+import { NextRequest } from "next/server";
+
+/**
+ * SSE Proxy for task stream reconnection.
+ *
+ * This endpoint allows clients to reconnect to an ongoing or recently completed
+ * background task's stream. It replays missed messages from Redis Streams and
+ * subscribes to live updates if the task is still running.
+ *
+ * Client contract:
+ * 1. When receiving an operation_started event, store the task_id
+ * 2. To reconnect: GET /api/chat/tasks/{taskId}/stream?last_message_id={idx}
+ * 3. Messages are replayed from the last_message_id position
+ * 4. Stream ends when "finish" event is received
+ */
+export async function GET(
+  request: NextRequest,
+  { params }: { params: Promise<{ taskId: string }> },
+) {
+  const { taskId } = await params;
+  const searchParams = request.nextUrl.searchParams;
+  const lastMessageId = searchParams.get("last_message_id") || "0-0";
+
+  try {
+    // Get auth token from server-side session
+    const token = await getServerAuthToken();
+
+    // Build backend URL
+    const backendUrl = environment.getAGPTServerBaseUrl();
+    const streamUrl = new URL(`/api/chat/tasks/${taskId}/stream`, backendUrl);
+    streamUrl.searchParams.set("last_message_id", lastMessageId);
+
+    // Forward request to backend with auth header
+    const headers: Record<string, string> = {
+      Accept: "text/event-stream",
+      "Cache-Control": "no-cache",
+      Connection: "keep-alive",
+    };
+
+    if (token) {
+      headers["Authorization"] = `Bearer ${token}`;
+    }
+
+    const response = await fetch(streamUrl.toString(), {
+      method: "GET",
+      headers,
+    });
+
+    if (!response.ok) {
+      const error = await response.text();
+      return new Response(error, {
+        status: response.status,
+        headers: { "Content-Type": "application/json" },
+      });
+    }
+
+    // Return the SSE stream directly
+    return new Response(response.body, {
+      headers: {
+        "Content-Type": "text/event-stream",
+        "Cache-Control": "no-cache, no-transform",
+        Connection: "keep-alive",
+        "X-Accel-Buffering": "no",
+      },
+    });
+  } catch (error) {
+    console.error("Task stream proxy error:", error);
+    return new Response(
+      JSON.stringify({
+        error: "Failed to connect to task stream",
+        detail: error instanceof Error ? error.message : String(error),
+      }),
+      {
+        status: 500,
+        headers: { "Content-Type": "application/json" },
+      },
+    );
+  }
+}

autogpt_platform/frontend/src/app/api/helpers.ts

@@ -181,6 +181,5 @@ export async function getOnboardingStatus() {
   const isCompleted = onboarding.completedSteps.includes("CONGRATS");
   return {
     shouldShowOnboarding: status.is_onboarding_enabled && !isCompleted,
-    isChatEnabled: status.is_chat_enabled,
   };
 }

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

@@ -917,6 +917,28 @@
         "security": [{ "HTTPBearerJWT": [] }]
       }
     },
+    "/api/chat/config/ttl": {
+      "get": {
+        "tags": ["v2", "chat", "chat"],
+        "summary": "Get Ttl Config",
+        "description": "Get the stream TTL configuration.\n\nReturns the Time-To-Live settings for chat streams, which determines\nhow long clients can reconnect to an active stream.\n\nReturns:\n    dict: TTL configuration with seconds and milliseconds values.",
+        "operationId": "getV2GetTtlConfig",
+        "responses": {
+          "200": {
+            "description": "Successful Response",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "additionalProperties": true,
+                  "type": "object",
+                  "title": "Response Getv2Getttlconfig"
+                }
+              }
+            }
+          }
+        }
+      }
+    },
     "/api/chat/health": {
       "get": {
         "tags": ["v2", "chat", "chat"],
@@ -939,6 +961,63 @@
         }
       }
     },
+    "/api/chat/operations/{operation_id}/complete": {
+      "post": {
+        "tags": ["v2", "chat", "chat"],
+        "summary": "Complete Operation",
+        "description": "External completion webhook for long-running operations.\n\nCalled by Agent Generator (or other services) when an operation completes.\nThis triggers the stream registry to publish completion and continue LLM generation.\n\nArgs:\n    operation_id: The operation ID to complete.\n    request: Completion payload with success status and result/error.\n    x_api_key: Internal API key for authentication.\n\nReturns:\n    dict: Status of the completion.\n\nRaises:\n    HTTPException: If API key is invalid or operation not found.",
+        "operationId": "postV2CompleteOperation",
+        "parameters": [
+          {
+            "name": "operation_id",
+            "in": "path",
+            "required": true,
+            "schema": { "type": "string", "title": "Operation Id" }
+          },
+          {
+            "name": "x-api-key",
+            "in": "header",
+            "required": false,
+            "schema": {
+              "anyOf": [{ "type": "string" }, { "type": "null" }],
+              "title": "X-Api-Key"
+            }
+          }
+        ],
+        "requestBody": {
+          "required": true,
+          "content": {
+            "application/json": {
+              "schema": {
+                "$ref": "#/components/schemas/OperationCompleteRequest"
+              }
+            }
+          }
+        },
+        "responses": {
+          "200": {
+            "description": "Successful Response",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "type": "object",
+                  "additionalProperties": true,
+                  "title": "Response Postv2Completeoperation"
+                }
+              }
+            }
+          },
+          "422": {
+            "description": "Validation Error",
+            "content": {
+              "application/json": {
+                "schema": { "$ref": "#/components/schemas/HTTPValidationError" }
+              }
+            }
+          }
+        }
+      }
+    },
     "/api/chat/sessions": {
       "get": {
         "tags": ["v2", "chat", "chat"],
@@ -1022,7 +1101,7 @@
       "get": {
         "tags": ["v2", "chat", "chat"],
         "summary": "Get Session",
-        "description": "Retrieve the details of a specific chat session.\n\nLooks up a chat session by ID for the given user (if authenticated) and returns all session data including messages.\n\nArgs:\n    session_id: The unique identifier for the desired chat session.\n    user_id: The optional authenticated user ID, or None for anonymous access.\n\nReturns:\n    SessionDetailResponse: Details for the requested session, or None if not found.",
+        "description": "Retrieve the details of a specific chat session.\n\nLooks up a chat session by ID for the given user (if authenticated) and returns all session data including messages.\nIf there's an active stream for this session, returns the task_id for reconnection.\n\nArgs:\n    session_id: The unique identifier for the desired chat session.\n    user_id: The optional authenticated user ID, or None for anonymous access.\n\nReturns:\n    SessionDetailResponse: Details for the requested session, including active_stream info if applicable.",
         "operationId": "getV2GetSession",
         "security": [{ "HTTPBearerJWT": [] }],
         "parameters": [
@@ -1157,7 +1236,7 @@
       "post": {
         "tags": ["v2", "chat", "chat"],
         "summary": "Stream Chat Post",
-        "description": "Stream chat responses for a session (POST with context support).\n\nStreams the AI/completion responses in real time over Server-Sent Events (SSE), including:\n  - Text fragments as they are generated\n  - Tool call UI elements (if invoked)\n  - Tool execution results\n\nArgs:\n    session_id: The chat session identifier to associate with the streamed messages.\n    request: Request body containing message, is_user_message, and optional context.\n    user_id: Optional authenticated user ID.\nReturns:\n    StreamingResponse: SSE-formatted response chunks.",
+        "description": "Stream chat responses for a session (POST with context support).\n\nStreams the AI/completion responses in real time over Server-Sent Events (SSE), including:\n  - Text fragments as they are generated\n  - Tool call UI elements (if invoked)\n  - Tool execution results\n\nThe AI generation runs in a background task that continues even if the client disconnects.\nAll chunks are written to Redis for reconnection support. If the client disconnects,\nthey can reconnect using GET /tasks/{task_id}/stream to resume from where they left off.\n\nArgs:\n    session_id: The chat session identifier to associate with the streamed messages.\n    request: Request body containing message, is_user_message, and optional context.\n    user_id: Optional authenticated user ID.\nReturns:\n    StreamingResponse: SSE-formatted response chunks. First chunk is a \"start\" event\n    containing the task_id for reconnection.",
         "operationId": "postV2StreamChatPost",
         "security": [{ "HTTPBearerJWT": [] }],
         "parameters": [
@@ -1195,6 +1274,94 @@
         }
       }
     },
+    "/api/chat/tasks/{task_id}": {
+      "get": {
+        "tags": ["v2", "chat", "chat"],
+        "summary": "Get Task Status",
+        "description": "Get the status of a long-running task.\n\nArgs:\n    task_id: The task ID to check.\n    user_id: Authenticated user ID for ownership validation.\n\nReturns:\n    dict: Task status including task_id, status, tool_name, and operation_id.\n\nRaises:\n    NotFoundError: If task_id is not found or user doesn't have access.",
+        "operationId": "getV2GetTaskStatus",
+        "security": [{ "HTTPBearerJWT": [] }],
+        "parameters": [
+          {
+            "name": "task_id",
+            "in": "path",
+            "required": true,
+            "schema": { "type": "string", "title": "Task Id" }
+          }
+        ],
+        "responses": {
+          "200": {
+            "description": "Successful Response",
+            "content": {
+              "application/json": {
+                "schema": {
+                  "type": "object",
+                  "additionalProperties": true,
+                  "title": "Response Getv2Gettaskstatus"
+                }
+              }
+            }
+          },
+          "401": {
+            "$ref": "#/components/responses/HTTP401NotAuthenticatedError"
+          },
+          "422": {
+            "description": "Validation Error",
+            "content": {
+              "application/json": {
+                "schema": { "$ref": "#/components/schemas/HTTPValidationError" }
+              }
+            }
+          }
+        }
+      }
+    },
+    "/api/chat/tasks/{task_id}/stream": {
+      "get": {
+        "tags": ["v2", "chat", "chat"],
+        "summary": "Stream Task",
+        "description": "Reconnect to a long-running task's SSE stream.\n\nWhen a long-running operation (like agent generation) starts, the client\nreceives a task_id. If the connection drops, the client can reconnect\nusing this endpoint to resume receiving updates.\n\nArgs:\n    task_id: The task ID from the operation_started response.\n    user_id: Authenticated user ID for ownership validation.\n    last_message_id: Last Redis Stream message ID received (\"0-0\" for full replay).\n\nReturns:\n    StreamingResponse: SSE-formatted response chunks starting after last_message_id.\n\nRaises:\n    HTTPException: 404 if task not found, 410 if task expired, 403 if access denied.",
+        "operationId": "getV2StreamTask",
+        "security": [{ "HTTPBearerJWT": [] }],
+        "parameters": [
+          {
+            "name": "task_id",
+            "in": "path",
+            "required": true,
+            "schema": { "type": "string", "title": "Task Id" }
+          },
+          {
+            "name": "last_message_id",
+            "in": "query",
+            "required": false,
+            "schema": {
+              "type": "string",
+              "description": "Last Redis Stream message ID received (e.g., '1706540123456-0'). Use '0-0' for full replay.",
+              "default": "0-0",
+              "title": "Last Message Id"
+            },
+            "description": "Last Redis Stream message ID received (e.g., '1706540123456-0'). Use '0-0' for full replay."
+          }
+        ],
+        "responses": {
+          "200": {
+            "description": "Successful Response",
+            "content": { "application/json": { "schema": {} } }
+          },
+          "401": {
+            "$ref": "#/components/responses/HTTP401NotAuthenticatedError"
+          },
+          "422": {
+            "description": "Validation Error",
+            "content": {
+              "application/json": {
+                "schema": { "$ref": "#/components/schemas/HTTPValidationError" }
+              }
+            }
+          }
+        }
+      }
+    },
     "/api/credits": {
       "get": {
         "tags": ["v1", "credits"],
@@ -6168,6 +6335,18 @@
         "title": "AccuracyTrendsResponse",
         "description": "Response model for accuracy trends and alerts."
       },
+      "ActiveStreamInfo": {
+        "properties": {
+          "task_id": { "type": "string", "title": "Task Id" },
+          "last_message_id": { "type": "string", "title": "Last Message Id" },
+          "operation_id": { "type": "string", "title": "Operation Id" },
+          "tool_name": { "type": "string", "title": "Tool Name" }
+        },
+        "type": "object",
+        "required": ["task_id", "last_message_id", "operation_id", "tool_name"],
+        "title": "ActiveStreamInfo",
+        "description": "Information about an active stream for reconnection."
+      },
       "AddUserCreditsResponse": {
         "properties": {
           "new_balance": { "type": "integer", "title": "New Balance" },
@@ -8823,6 +9002,27 @@
         ],
         "title": "OnboardingStep"
       },
+      "OperationCompleteRequest": {
+        "properties": {
+          "success": { "type": "boolean", "title": "Success" },
+          "result": {
+            "anyOf": [
+              { "additionalProperties": true, "type": "object" },
+              { "type": "string" },
+              { "type": "null" }
+            ],
+            "title": "Result"
+          },
+          "error": {
+            "anyOf": [{ "type": "string" }, { "type": "null" }],
+            "title": "Error"
+          }
+        },
+        "type": "object",
+        "required": ["success"],
+        "title": "OperationCompleteRequest",
+        "description": "Request model for external completion webhook."
+      },
       "Pagination": {
         "properties": {
           "total_items": {
@@ -9678,6 +9878,12 @@
             "items": { "additionalProperties": true, "type": "object" },
             "type": "array",
             "title": "Messages"
+          },
+          "active_stream": {
+            "anyOf": [
+              { "$ref": "#/components/schemas/ActiveStreamInfo" },
+              { "type": "null" }
+            ]
           }
         },
         "type": "object",

autogpt_platform/frontend/src/app/page.tsx

@@ -1,27 +1,15 @@
 "use client";
 
-import { getHomepageRoute } from "@/lib/constants";
-import { Flag, useGetFlag } from "@/services/feature-flags/use-get-flag";
+import { LoadingSpinner } from "@/components/atoms/LoadingSpinner/LoadingSpinner";
 import { useRouter } from "next/navigation";
 import { useEffect } from "react";
 
 export default function Page() {
-  const isChatEnabled = useGetFlag(Flag.CHAT);
   const router = useRouter();
-  const homepageRoute = getHomepageRoute(isChatEnabled);
-  const envEnabled = process.env.NEXT_PUBLIC_LAUNCHDARKLY_ENABLED === "true";
-  const clientId = process.env.NEXT_PUBLIC_LAUNCHDARKLY_CLIENT_ID;
-  const isLaunchDarklyConfigured = envEnabled && Boolean(clientId);
-  const isFlagReady =
-    !isLaunchDarklyConfigured || typeof isChatEnabled === "boolean";
 
-  useEffect(
-    function redirectToHomepage() {
-      if (!isFlagReady) return;
-      router.replace(homepageRoute);
-    },
-    [homepageRoute, isFlagReady, router],
-  );
+  useEffect(() => {
+    router.replace("/copilot");
+  }, [router]);
 
-  return null;
+  return <LoadingSpinner size="large" cover />;
 }

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

@@ -1,7 +1,6 @@
 "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";
@@ -25,8 +24,8 @@ export function Chat({
 }: ChatProps) {
   const { urlSessionId } = useCopilotSessionId();
   const hasHandledNotFoundRef = useRef(false);
-  const isSwitchingSession = useCopilotStore((s) => s.isSwitchingSession);
   const {
+    session,
     messages,
     isLoading,
     isCreating,
@@ -38,6 +37,18 @@ export function Chat({
     startPollingForOperation,
   } = useChat({ urlSessionId });
 
+  // Extract active stream info for reconnection
+  const activeStream = (
+    session as {
+      active_stream?: {
+        task_id: string;
+        last_message_id: string;
+        operation_id: string;
+        tool_name: string;
+      };
+    }
+  )?.active_stream;
+
   useEffect(() => {
     if (!onSessionNotFound) return;
     if (!urlSessionId) return;
@@ -53,8 +64,7 @@ export function Chat({
     isCreating,
   ]);
 
-  const shouldShowLoader =
-    (showLoader && (isLoading || isCreating)) || isSwitchingSession;
+  const shouldShowLoader = showLoader && (isLoading || isCreating);
 
   return (
     <div className={cn("flex h-full flex-col", className)}>
@@ -66,21 +76,19 @@ export function Chat({
             <div className="flex flex-col items-center gap-3">
               <LoadingSpinner size="large" className="text-neutral-400" />
               <Text variant="body" className="text-zinc-500">
-                {isSwitchingSession
-                  ? "Switching chat..."
-                  : "Loading your chat..."}
+                Loading your chat...
               </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}
@@ -88,6 +96,16 @@ export function Chat({
             className="flex-1"
             onStreamingChange={onStreamingChange}
             onOperationStarted={startPollingForOperation}
+            activeStream={
+              activeStream
+                ? {
+                    taskId: activeStream.task_id,
+                    lastMessageId: activeStream.last_message_id,
+                    operationId: activeStream.operation_id,
+                    toolName: activeStream.tool_name,
+                  }
+                : undefined
+            }
           />
         )}
       </main>

autogpt_platform/frontend/src/components/contextual/Chat/SSE_RECONNECTION.md

@@ -0,0 +1,159 @@
+# SSE Reconnection Contract for Long-Running Operations
+
+This document describes the client-side contract for handling SSE (Server-Sent Events) disconnections and reconnecting to long-running background tasks.
+
+## Overview
+
+When a user triggers a long-running operation (like agent generation), the backend:
+
+1. Spawns a background task that survives SSE disconnections
+2. Returns an `operation_started` response with a `task_id`
+3. Stores stream messages in Redis Streams for replay
+
+Clients can reconnect to the task stream at any time to receive missed messages.
+
+## Client-Side Flow
+
+### 1. Receiving Operation Started
+
+When you receive an `operation_started` tool response:
+
+```typescript
+// The response includes a task_id for reconnection
+{
+  type: "operation_started",
+  tool_name: "generate_agent",
+  operation_id: "uuid-...",
+  task_id: "task-uuid-...",  // <-- Store this for reconnection
+  message: "Operation started. You can close this tab."
+}
+```
+
+### 2. Storing Task Info
+
+Use the chat store to track the active task:
+
+```typescript
+import { useChatStore } from "./chat-store";
+
+// When operation_started is received:
+useChatStore.getState().setActiveTask(sessionId, {
+  taskId: response.task_id,
+  operationId: response.operation_id,
+  toolName: response.tool_name,
+  lastMessageId: "0",
+});
+```
+
+### 3. Reconnecting to a Task
+
+To reconnect (e.g., after page refresh or tab reopen):
+
+```typescript
+const { reconnectToTask, getActiveTask } = useChatStore.getState();
+
+// Check if there's an active task for this session
+const activeTask = getActiveTask(sessionId);
+
+if (activeTask) {
+  // Reconnect to the task stream
+  await reconnectToTask(
+    sessionId,
+    activeTask.taskId,
+    activeTask.lastMessageId, // Resume from last position
+    (chunk) => {
+      // Handle incoming chunks
+      console.log("Received chunk:", chunk);
+    },
+  );
+}
+```
+
+### 4. Tracking Message Position
+
+To enable precise replay, update the last message ID as chunks arrive:
+
+```typescript
+const { updateTaskLastMessageId } = useChatStore.getState();
+
+function handleChunk(chunk: StreamChunk) {
+  // If chunk has an index/id, track it
+  if (chunk.idx !== undefined) {
+    updateTaskLastMessageId(sessionId, String(chunk.idx));
+  }
+}
+```
+
+## API Endpoints
+
+### Task Stream Reconnection
+
+```
+GET /api/chat/tasks/{taskId}/stream?last_message_id={idx}
+```
+
+- `taskId`: The task ID from `operation_started`
+- `last_message_id`: Last received message index (default: "0" for full replay)
+
+Returns: SSE stream of missed messages + live updates
+
+## Chunk Types
+
+The reconnected stream follows the same Vercel AI SDK protocol:
+
+| Type                    | Description             |
+| ----------------------- | ----------------------- |
+| `start`                 | Message lifecycle start |
+| `text-delta`            | Streaming text content  |
+| `text-end`              | Text block completed    |
+| `tool-output-available` | Tool result available   |
+| `finish`                | Stream completed        |
+| `error`                 | Error occurred          |
+
+## Error Handling
+
+If reconnection fails:
+
+1. Check if task still exists (may have expired - default TTL: 1 hour)
+2. Fall back to polling the session for final state
+3. Show appropriate UI message to user
+
+## Persistence Considerations
+
+For robust reconnection across browser restarts:
+
+```typescript
+// Store in localStorage/sessionStorage
+const ACTIVE_TASKS_KEY = "chat_active_tasks";
+
+function persistActiveTask(sessionId: string, task: ActiveTaskInfo) {
+  const tasks = JSON.parse(localStorage.getItem(ACTIVE_TASKS_KEY) || "{}");
+  tasks[sessionId] = task;
+  localStorage.setItem(ACTIVE_TASKS_KEY, JSON.stringify(tasks));
+}
+
+function loadPersistedTasks(): Record<string, ActiveTaskInfo> {
+  return JSON.parse(localStorage.getItem(ACTIVE_TASKS_KEY) || "{}");
+}
+```
+
+## Backend Configuration
+
+The following backend settings affect reconnection behavior:
+
+| Setting             | Default | Description                        |
+| ------------------- | ------- | ---------------------------------- |
+| `stream_ttl`        | 3600s   | How long streams are kept in Redis |
+| `stream_max_length` | 1000    | Max messages per stream            |
+
+## Testing
+
+To test reconnection locally:
+
+1. Start a long-running operation (e.g., agent generation)
+2. Note the `task_id` from the `operation_started` response
+3. Close the browser tab
+4. Reopen and call `reconnectToTask` with the saved `task_id`
+5. Verify that missed messages are replayed
+
+See the main README for full local development setup.

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

@@ -0,0 +1,16 @@
+/**
+ * Constants for the chat system.
+ *
+ * Centralizes magic strings and values used across chat components.
+ */
+
+// LocalStorage keys
+export const STORAGE_KEY_ACTIVE_TASKS = "chat_active_tasks";
+
+// Redis Stream IDs
+export const INITIAL_MESSAGE_ID = "0";
+export const INITIAL_STREAM_ID = "0-0";
+
+// TTL values (in milliseconds)
+export const COMPLETED_STREAM_TTL_MS = 5 * 60 * 1000; // 5 minutes
+export const ACTIVE_TASK_TTL_MS = 60 * 60 * 1000; // 1 hour

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

@@ -1,6 +1,12 @@
 "use client";
 
 import { create } from "zustand";
+import {
+  ACTIVE_TASK_TTL_MS,
+  COMPLETED_STREAM_TTL_MS,
+  INITIAL_STREAM_ID,
+  STORAGE_KEY_ACTIVE_TASKS,
+} from "./chat-constants";
 import type {
   ActiveStream,
   StreamChunk,
@@ -8,15 +14,59 @@ import type {
   StreamResult,
   StreamStatus,
 } from "./chat-types";
-import { executeStream } from "./stream-executor";
+import { executeStream, executeTaskReconnect } from "./stream-executor";
 
-const COMPLETED_STREAM_TTL = 5 * 60 * 1000; // 5 minutes
+export interface ActiveTaskInfo {
+  taskId: string;
+  sessionId: string;
+  operationId: string;
+  toolName: string;
+  lastMessageId: string;
+  startedAt: number;
+}
+
+/** Load active tasks from localStorage */
+function loadPersistedTasks(): Map<string, ActiveTaskInfo> {
+  if (typeof window === "undefined") return new Map();
+  try {
+    const stored = localStorage.getItem(STORAGE_KEY_ACTIVE_TASKS);
+    if (!stored) return new Map();
+    const parsed = JSON.parse(stored) as Record<string, ActiveTaskInfo>;
+    const now = Date.now();
+    const tasks = new Map<string, ActiveTaskInfo>();
+    // Filter out expired tasks
+    for (const [sessionId, task] of Object.entries(parsed)) {
+      if (now - task.startedAt < ACTIVE_TASK_TTL_MS) {
+        tasks.set(sessionId, task);
+      }
+    }
+    return tasks;
+  } catch {
+    return new Map();
+  }
+}
+
+/** Save active tasks to localStorage */
+function persistTasks(tasks: Map<string, ActiveTaskInfo>): void {
+  if (typeof window === "undefined") return;
+  try {
+    const obj: Record<string, ActiveTaskInfo> = {};
+    for (const [sessionId, task] of tasks) {
+      obj[sessionId] = task;
+    }
+    localStorage.setItem(STORAGE_KEY_ACTIVE_TASKS, JSON.stringify(obj));
+  } catch {
+    // Ignore storage errors
+  }
+}
 
 interface ChatStoreState {
   activeStreams: Map<string, ActiveStream>;
   completedStreams: Map<string, StreamResult>;
   activeSessions: Set<string>;
   streamCompleteCallbacks: Set<StreamCompleteCallback>;
+  /** Active tasks for SSE reconnection - keyed by sessionId */
+  activeTasks: Map<string, ActiveTaskInfo>;
 }
 
 interface ChatStoreActions {
@@ -41,6 +91,24 @@ interface ChatStoreActions {
   unregisterActiveSession: (sessionId: string) => void;
   isSessionActive: (sessionId: string) => boolean;
   onStreamComplete: (callback: StreamCompleteCallback) => () => void;
+  /** Track active task for SSE reconnection */
+  setActiveTask: (
+    sessionId: string,
+    taskInfo: Omit<ActiveTaskInfo, "sessionId" | "startedAt">,
+  ) => void;
+  /** Get active task for a session */
+  getActiveTask: (sessionId: string) => ActiveTaskInfo | undefined;
+  /** Clear active task when operation completes */
+  clearActiveTask: (sessionId: string) => void;
+  /** Reconnect to an existing task stream */
+  reconnectToTask: (
+    sessionId: string,
+    taskId: string,
+    lastMessageId?: string,
+    onChunk?: (chunk: StreamChunk) => void,
+  ) => Promise<void>;
+  /** Update last message ID for a task (for tracking replay position) */
+  updateTaskLastMessageId: (sessionId: string, lastMessageId: string) => void;
 }
 
 type ChatStore = ChatStoreState & ChatStoreActions;
@@ -64,18 +132,126 @@ function cleanupExpiredStreams(
   const now = Date.now();
   const cleaned = new Map(completedStreams);
   for (const [sessionId, result] of cleaned) {
-    if (now - result.completedAt > COMPLETED_STREAM_TTL) {
+    if (now - result.completedAt > COMPLETED_STREAM_TTL_MS) {
       cleaned.delete(sessionId);
     }
   }
   return cleaned;
 }
 
+/**
+ * Finalize a stream by moving it from activeStreams to completedStreams.
+ * Also handles cleanup and notifications.
+ */
+function finalizeStream(
+  sessionId: string,
+  stream: ActiveStream,
+  onChunk: ((chunk: StreamChunk) => void) | undefined,
+  get: () => ChatStoreState & ChatStoreActions,
+  set: (state: Partial<ChatStoreState>) => void,
+): void {
+  if (onChunk) stream.onChunkCallbacks.delete(onChunk);
+
+  if (stream.status !== "streaming") {
+    const currentState = get();
+    const finalActiveStreams = new Map(currentState.activeStreams);
+    let finalCompletedStreams = new Map(currentState.completedStreams);
+
+    const storedStream = finalActiveStreams.get(sessionId);
+    if (storedStream === stream) {
+      const result: StreamResult = {
+        sessionId,
+        status: stream.status,
+        chunks: stream.chunks,
+        completedAt: Date.now(),
+        error: stream.error,
+      };
+      finalCompletedStreams.set(sessionId, result);
+      finalActiveStreams.delete(sessionId);
+      finalCompletedStreams = cleanupExpiredStreams(finalCompletedStreams);
+      set({
+        activeStreams: finalActiveStreams,
+        completedStreams: finalCompletedStreams,
+      });
+
+      if (stream.status === "completed" || stream.status === "error") {
+        notifyStreamComplete(currentState.streamCompleteCallbacks, sessionId);
+      }
+    }
+  }
+}
+
+/**
+ * Clean up an existing stream for a session and move it to completed streams.
+ * Returns updated maps for both active and completed streams.
+ */
+function cleanupExistingStream(
+  sessionId: string,
+  activeStreams: Map<string, ActiveStream>,
+  completedStreams: Map<string, StreamResult>,
+  callbacks: Set<StreamCompleteCallback>,
+): {
+  activeStreams: Map<string, ActiveStream>;
+  completedStreams: Map<string, StreamResult>;
+} {
+  const newActiveStreams = new Map(activeStreams);
+  let newCompletedStreams = new Map(completedStreams);
+
+  const existingStream = newActiveStreams.get(sessionId);
+  if (existingStream) {
+    existingStream.abortController.abort();
+    const normalizedStatus =
+      existingStream.status === "streaming"
+        ? "completed"
+        : existingStream.status;
+    const result: StreamResult = {
+      sessionId,
+      status: normalizedStatus,
+      chunks: existingStream.chunks,
+      completedAt: Date.now(),
+      error: existingStream.error,
+    };
+    newCompletedStreams.set(sessionId, result);
+    newActiveStreams.delete(sessionId);
+    newCompletedStreams = cleanupExpiredStreams(newCompletedStreams);
+    if (normalizedStatus === "completed" || normalizedStatus === "error") {
+      notifyStreamComplete(callbacks, sessionId);
+    }
+  }
+
+  return {
+    activeStreams: newActiveStreams,
+    completedStreams: newCompletedStreams,
+  };
+}
+
+/**
+ * Create a new active stream with initial state.
+ */
+function createActiveStream(
+  sessionId: string,
+  onChunk?: (chunk: StreamChunk) => void,
+): ActiveStream {
+  const abortController = new AbortController();
+  const initialCallbacks = new Set<(chunk: StreamChunk) => void>();
+  if (onChunk) initialCallbacks.add(onChunk);
+
+  return {
+    sessionId,
+    abortController,
+    status: "streaming",
+    startedAt: Date.now(),
+    chunks: [],
+    onChunkCallbacks: initialCallbacks,
+  };
+}
+
 export const useChatStore = create<ChatStore>((set, get) => ({
   activeStreams: new Map(),
   completedStreams: new Map(),
   activeSessions: new Set(),
   streamCompleteCallbacks: new Set(),
+  activeTasks: loadPersistedTasks(),
 
   startStream: async function startStream(
     sessionId,
@@ -85,45 +261,21 @@ export const useChatStore = create<ChatStore>((set, get) => ({
     onChunk,
   ) {
     const state = get();
-    const newActiveStreams = new Map(state.activeStreams);
-    let newCompletedStreams = new Map(state.completedStreams);
     const callbacks = state.streamCompleteCallbacks;
 
-    const existingStream = newActiveStreams.get(sessionId);
-    if (existingStream) {
-      existingStream.abortController.abort();
-      const normalizedStatus =
-        existingStream.status === "streaming"
-          ? "completed"
-          : existingStream.status;
-      const result: StreamResult = {
-        sessionId,
-        status: normalizedStatus,
-        chunks: existingStream.chunks,
-        completedAt: Date.now(),
-        error: existingStream.error,
-      };
-      newCompletedStreams.set(sessionId, result);
-      newActiveStreams.delete(sessionId);
-      newCompletedStreams = cleanupExpiredStreams(newCompletedStreams);
-      if (normalizedStatus === "completed" || normalizedStatus === "error") {
-        notifyStreamComplete(callbacks, sessionId);
-      }
-    }
-
-    const abortController = new AbortController();
-    const initialCallbacks = new Set<(chunk: StreamChunk) => void>();
-    if (onChunk) initialCallbacks.add(onChunk);
-
-    const stream: ActiveStream = {
+    // Clean up any existing stream for this session
+    const {
+      activeStreams: newActiveStreams,
+      completedStreams: newCompletedStreams,
+    } = cleanupExistingStream(
       sessionId,
-      abortController,
-      status: "streaming",
-      startedAt: Date.now(),
-      chunks: [],
-      onChunkCallbacks: initialCallbacks,
-    };
+      state.activeStreams,
+      state.completedStreams,
+      callbacks,
+    );
 
+    // Create new stream
+    const stream = createActiveStream(sessionId, onChunk);
     newActiveStreams.set(sessionId, stream);
     set({
       activeStreams: newActiveStreams,
@@ -133,36 +285,7 @@ export const useChatStore = create<ChatStore>((set, get) => ({
     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,
-            );
-          }
-        }
-      }
+      finalizeStream(sessionId, stream, onChunk, get, set);
     }
   },
 
@@ -286,4 +409,93 @@ export const useChatStore = create<ChatStore>((set, get) => ({
       set({ streamCompleteCallbacks: cleanedCallbacks });
     };
   },
+
+  setActiveTask: function setActiveTask(sessionId, taskInfo) {
+    const state = get();
+    const newActiveTasks = new Map(state.activeTasks);
+    newActiveTasks.set(sessionId, {
+      ...taskInfo,
+      sessionId,
+      startedAt: Date.now(),
+    });
+    set({ activeTasks: newActiveTasks });
+    persistTasks(newActiveTasks);
+  },
+
+  getActiveTask: function getActiveTask(sessionId) {
+    return get().activeTasks.get(sessionId);
+  },
+
+  clearActiveTask: function clearActiveTask(sessionId) {
+    const state = get();
+    if (!state.activeTasks.has(sessionId)) return;
+
+    const newActiveTasks = new Map(state.activeTasks);
+    newActiveTasks.delete(sessionId);
+    set({ activeTasks: newActiveTasks });
+    persistTasks(newActiveTasks);
+  },
+
+  reconnectToTask: async function reconnectToTask(
+    sessionId,
+    taskId,
+    lastMessageId = INITIAL_STREAM_ID,
+    onChunk,
+  ) {
+    const state = get();
+    const callbacks = state.streamCompleteCallbacks;
+
+    // Clean up any existing stream for this session
+    const {
+      activeStreams: newActiveStreams,
+      completedStreams: newCompletedStreams,
+    } = cleanupExistingStream(
+      sessionId,
+      state.activeStreams,
+      state.completedStreams,
+      callbacks,
+    );
+
+    // Create new stream for reconnection
+    const stream = createActiveStream(sessionId, onChunk);
+    newActiveStreams.set(sessionId, stream);
+    set({
+      activeStreams: newActiveStreams,
+      completedStreams: newCompletedStreams,
+    });
+
+    try {
+      await executeTaskReconnect(stream, taskId, lastMessageId);
+    } finally {
+      finalizeStream(sessionId, stream, onChunk, get, set);
+
+      // Clear active task on completion
+      if (stream.status === "completed" || stream.status === "error") {
+        const taskState = get();
+        if (taskState.activeTasks.has(sessionId)) {
+          const newActiveTasks = new Map(taskState.activeTasks);
+          newActiveTasks.delete(sessionId);
+          set({ activeTasks: newActiveTasks });
+          persistTasks(newActiveTasks);
+        }
+      }
+    }
+  },
+
+  updateTaskLastMessageId: function updateTaskLastMessageId(
+    sessionId,
+    lastMessageId,
+  ) {
+    const state = get();
+    const task = state.activeTasks.get(sessionId);
+    if (!task) return;
+
+    const newActiveTasks = new Map(state.activeTasks);
+    newActiveTasks.set(sessionId, {
+      ...task,
+      lastMessageId,
+    });
+    set({ activeTasks: newActiveTasks });
+    persistTasks(newActiveTasks);
+  },
 }));

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

@@ -4,6 +4,7 @@ export type StreamStatus = "idle" | "streaming" | "completed" | "error";
 
 export interface StreamChunk {
   type:
+    | "stream_start"
     | "text_chunk"
     | "text_ended"
     | "tool_call"
@@ -15,6 +16,7 @@ export interface StreamChunk {
     | "error"
     | "usage"
     | "stream_end";
+  taskId?: string;
   timestamp?: string;
   content?: string;
   message?: string;
@@ -41,7 +43,7 @@ export interface StreamChunk {
 }
 
 export type VercelStreamChunk =
-  | { type: "start"; messageId: string }
+  | { type: "start"; messageId: string; taskId?: string }
   | { type: "finish" }
   | { type: "text-start"; id: string }
   | { type: "text-delta"; id: string; delta: string }
@@ -92,3 +94,70 @@ export interface StreamResult {
 }
 
 export type StreamCompleteCallback = (sessionId: string) => void;
+
+// Type guards for message types
+
+/**
+ * Check if a message has a toolId property.
+ */
+export function hasToolId<T extends { type: string }>(
+  msg: T,
+): msg is T & { toolId: string } {
+  return (
+    "toolId" in msg &&
+    typeof (msg as Record<string, unknown>).toolId === "string"
+  );
+}
+
+/**
+ * Check if a message has an operationId property.
+ */
+export function hasOperationId<T extends { type: string }>(
+  msg: T,
+): msg is T & { operationId: string } {
+  return (
+    "operationId" in msg &&
+    typeof (msg as Record<string, unknown>).operationId === "string"
+  );
+}
+
+/**
+ * Check if a message has a toolCallId property.
+ */
+export function hasToolCallId<T extends { type: string }>(
+  msg: T,
+): msg is T & { toolCallId: string } {
+  return (
+    "toolCallId" in msg &&
+    typeof (msg as Record<string, unknown>).toolCallId === "string"
+  );
+}
+
+/**
+ * Check if a message is an operation message type.
+ */
+export function isOperationMessage<T extends { type: string }>(
+  msg: T,
+): msg is T & {
+  type: "operation_started" | "operation_pending" | "operation_in_progress";
+} {
+  return (
+    msg.type === "operation_started" ||
+    msg.type === "operation_pending" ||
+    msg.type === "operation_in_progress"
+  );
+}
+
+/**
+ * Get the tool ID from a message if available.
+ * Checks toolId, operationId, and toolCallId properties.
+ */
+export function getToolIdFromMessage<T extends { type: string }>(
+  msg: T,
+): string | undefined {
+  const record = msg as Record<string, unknown>;
+  if (typeof record.toolId === "string") return record.toolId;
+  if (typeof record.operationId === "string") return record.operationId;
+  if (typeof record.toolCallId === "string") return record.toolCallId;
+  return undefined;
+}

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

@@ -17,6 +17,13 @@ export interface ChatContainerProps {
   className?: string;
   onStreamingChange?: (isStreaming: boolean) => void;
   onOperationStarted?: () => void;
+  /** Active stream info from the server for reconnection */
+  activeStream?: {
+    taskId: string;
+    lastMessageId: string;
+    operationId: string;
+    toolName: string;
+  };
 }
 
 export function ChatContainer({
@@ -26,6 +33,7 @@ export function ChatContainer({
   className,
   onStreamingChange,
   onOperationStarted,
+  activeStream,
 }: ChatContainerProps) {
   const {
     messages,
@@ -41,6 +49,7 @@ export function ChatContainer({
     initialMessages,
     initialPrompt,
     onOperationStarted,
+    activeStream,
   });
 
   useEffect(() => {

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

@@ -2,6 +2,7 @@ import { toast } from "sonner";
 import type { StreamChunk } from "../../chat-types";
 import type { HandlerDependencies } from "./handlers";
 import {
+  getErrorDisplayMessage,
   handleError,
   handleLoginNeeded,
   handleStreamEnd,
@@ -24,16 +25,22 @@ export function createStreamEventDispatcher(
       chunk.type === "need_login" ||
       chunk.type === "error"
     ) {
-      if (!deps.hasResponseRef.current) {
-        console.info("[ChatStream] First response chunk:", {
-          type: chunk.type,
-          sessionId: deps.sessionId,
-        });
-      }
       deps.hasResponseRef.current = true;
     }
 
     switch (chunk.type) {
+      case "stream_start":
+        // Store task ID for SSE reconnection
+        if (chunk.taskId && deps.onActiveTaskStarted) {
+          deps.onActiveTaskStarted({
+            taskId: chunk.taskId,
+            operationId: chunk.taskId,
+            toolName: "chat",
+            toolCallId: "chat_stream",
+          });
+        }
+        break;
+
       case "text_chunk":
         handleTextChunk(chunk, deps);
         break;
@@ -56,11 +63,7 @@ export function createStreamEventDispatcher(
         break;
 
       case "stream_end":
-        console.info("[ChatStream] Stream ended:", {
-          sessionId: deps.sessionId,
-          hasResponse: deps.hasResponseRef.current,
-          chunkCount: deps.streamingChunksRef.current.length,
-        });
+        // Note: "finish" type from backend gets normalized to "stream_end" by normalizeStreamChunk
         handleStreamEnd(chunk, deps);
         break;
 
@@ -70,7 +73,7 @@ export function createStreamEventDispatcher(
         // Show toast at dispatcher level to avoid circular dependencies
         if (!isRegionBlocked) {
           toast.error("Chat Error", {
-            description: chunk.message || chunk.content || "An error occurred",
+            description: getErrorDisplayMessage(chunk),
           });
         }
         break;

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

@@ -18,11 +18,19 @@ export interface HandlerDependencies {
   setStreamingChunks: Dispatch<SetStateAction<string[]>>;
   streamingChunksRef: MutableRefObject<string[]>;
   hasResponseRef: MutableRefObject<boolean>;
+  textFinalizedRef: MutableRefObject<boolean>;
+  streamEndedRef: MutableRefObject<boolean>;
   setMessages: Dispatch<SetStateAction<ChatMessageData[]>>;
   setIsStreamingInitiated: Dispatch<SetStateAction<boolean>>;
   setIsRegionBlockedModalOpen: Dispatch<SetStateAction<boolean>>;
   sessionId: string;
   onOperationStarted?: () => void;
+  onActiveTaskStarted?: (taskInfo: {
+    taskId: string;
+    operationId: string;
+    toolName: string;
+    toolCallId: string;
+  }) => void;
 }
 
 export function isRegionBlockedError(chunk: StreamChunk): boolean {
@@ -32,6 +40,25 @@ export function isRegionBlockedError(chunk: StreamChunk): boolean {
   return message.toLowerCase().includes("not available in your region");
 }
 
+export function getUserFriendlyErrorMessage(
+  code: string | undefined,
+): string | undefined {
+  switch (code) {
+    case "TASK_EXPIRED":
+      return "This operation has expired. Please try again.";
+    case "TASK_NOT_FOUND":
+      return "Could not find the requested operation.";
+    case "ACCESS_DENIED":
+      return "You do not have access to this operation.";
+    case "QUEUE_OVERFLOW":
+      return "Connection was interrupted. Please refresh to continue.";
+    case "MODEL_NOT_AVAILABLE_REGION":
+      return "This model is not available in your region.";
+    default:
+      return undefined;
+  }
+}
+
 export function handleTextChunk(chunk: StreamChunk, deps: HandlerDependencies) {
   if (!chunk.content) return;
   deps.setHasTextChunks(true);
@@ -46,10 +73,15 @@ export function handleTextEnded(
   _chunk: StreamChunk,
   deps: HandlerDependencies,
 ) {
+  if (deps.textFinalizedRef.current) {
+    return;
+  }
+
   const completedText = deps.streamingChunksRef.current.join("");
   if (completedText.trim()) {
+    deps.textFinalizedRef.current = true;
+
     deps.setMessages((prev) => {
-      // Check if this exact message already exists to prevent duplicates
       const exists = prev.some(
         (msg) =>
           msg.type === "message" &&
@@ -76,9 +108,14 @@ export function handleToolCallStart(
   chunk: StreamChunk,
   deps: HandlerDependencies,
 ) {
+  // Use deterministic fallback instead of Date.now() to ensure same ID on replay
+  const toolId =
+    chunk.tool_id ||
+    `tool-${deps.sessionId}-${chunk.idx ?? "unknown"}-${chunk.tool_name || "unknown"}`;
+
   const toolCallMessage: Extract<ChatMessageData, { type: "tool_call" }> = {
     type: "tool_call",
-    toolId: chunk.tool_id || `tool-${Date.now()}-${chunk.idx || 0}`,
+    toolId,
     toolName: chunk.tool_name || "Executing",
     arguments: chunk.arguments || {},
     timestamp: new Date(),
@@ -111,6 +148,29 @@ export function handleToolCallStart(
   deps.setMessages(updateToolCallMessages);
 }
 
+const TOOL_RESPONSE_TYPES = new Set([
+  "tool_response",
+  "operation_started",
+  "operation_pending",
+  "operation_in_progress",
+  "execution_started",
+  "agent_carousel",
+  "clarification_needed",
+]);
+
+function hasResponseForTool(
+  messages: ChatMessageData[],
+  toolId: string,
+): boolean {
+  return messages.some((msg) => {
+    if (!TOOL_RESPONSE_TYPES.has(msg.type)) return false;
+    const msgToolId =
+      (msg as { toolId?: string }).toolId ||
+      (msg as { toolCallId?: string }).toolCallId;
+    return msgToolId === toolId;
+  });
+}
+
 export function handleToolResponse(
   chunk: StreamChunk,
   deps: HandlerDependencies,
@@ -152,31 +212,49 @@ export function handleToolResponse(
     ) {
       const inputsMessage = extractInputsNeeded(parsedResult, chunk.tool_name);
       if (inputsMessage) {
-        deps.setMessages((prev) => [...prev, inputsMessage]);
+        deps.setMessages((prev) => {
+          // Check for duplicate inputs_needed message
+          const exists = prev.some((msg) => msg.type === "inputs_needed");
+          if (exists) return prev;
+          return [...prev, inputsMessage];
+        });
       }
       const credentialsMessage = extractCredentialsNeeded(
         parsedResult,
         chunk.tool_name,
       );
       if (credentialsMessage) {
-        deps.setMessages((prev) => [...prev, credentialsMessage]);
+        deps.setMessages((prev) => {
+          // Check for duplicate credentials_needed message
+          const exists = prev.some((msg) => msg.type === "credentials_needed");
+          if (exists) return prev;
+          return [...prev, credentialsMessage];
+        });
       }
     }
     return;
   }
-  // Trigger polling when operation_started is received
   if (responseMessage.type === "operation_started") {
     deps.onOperationStarted?.();
+    const taskId = (responseMessage as { taskId?: string }).taskId;
+    if (taskId && deps.onActiveTaskStarted) {
+      deps.onActiveTaskStarted({
+        taskId,
+        operationId:
+          (responseMessage as { operationId?: string }).operationId || "",
+        toolName: (responseMessage as { toolName?: string }).toolName || "",
+        toolCallId: (responseMessage as { toolId?: string }).toolId || "",
+      });
+    }
   }
 
   deps.setMessages((prev) => {
     const toolCallIndex = prev.findIndex(
       (msg) => msg.type === "tool_call" && msg.toolId === chunk.tool_id,
     );
-    const hasResponse = prev.some(
-      (msg) => msg.type === "tool_response" && msg.toolId === chunk.tool_id,
-    );
-    if (hasResponse) return prev;
+    if (hasResponseForTool(prev, chunk.tool_id!)) {
+      return prev;
+    }
     if (toolCallIndex !== -1) {
       const newMessages = [...prev];
       newMessages.splice(toolCallIndex + 1, 0, responseMessage);
@@ -198,28 +276,48 @@ export function handleLoginNeeded(
     agentInfo: chunk.agent_info,
     timestamp: new Date(),
   };
-  deps.setMessages((prev) => [...prev, loginNeededMessage]);
+  deps.setMessages((prev) => {
+    // Check for duplicate login_needed message
+    const exists = prev.some((msg) => msg.type === "login_needed");
+    if (exists) return prev;
+    return [...prev, loginNeededMessage];
+  });
 }
 
 export function handleStreamEnd(
   _chunk: StreamChunk,
   deps: HandlerDependencies,
 ) {
+  if (deps.streamEndedRef.current) {
+    return;
+  }
+  deps.streamEndedRef.current = true;
+
   const completedContent = deps.streamingChunksRef.current.join("");
   if (!completedContent.trim() && !deps.hasResponseRef.current) {
-    deps.setMessages((prev) => [
-      ...prev,
-      {
-        type: "message",
-        role: "assistant",
-        content: "No response received. Please try again.",
-        timestamp: new Date(),
-      },
-    ]);
-  }
-  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 === "No response received. Please try again.",
+      );
+      if (exists) return prev;
+      return [
+        ...prev,
+        {
+          type: "message",
+          role: "assistant",
+          content: "No response received. Please try again.",
+          timestamp: new Date(),
+        },
+      ];
+    });
+  }
+  if (completedContent.trim() && !deps.textFinalizedRef.current) {
+    deps.textFinalizedRef.current = true;
+
+    deps.setMessages((prev) => {
       const exists = prev.some(
         (msg) =>
           msg.type === "message" &&
@@ -244,8 +342,6 @@ export function handleStreamEnd(
 }
 
 export function handleError(chunk: StreamChunk, deps: HandlerDependencies) {
-  const errorMessage = chunk.message || chunk.content || "An error occurred";
-  console.error("Stream error:", errorMessage);
   if (isRegionBlockedError(chunk)) {
     deps.setIsRegionBlockedModalOpen(true);
   }
@@ -253,4 +349,14 @@ export function handleError(chunk: StreamChunk, deps: HandlerDependencies) {
   deps.setHasTextChunks(false);
   deps.setStreamingChunks([]);
   deps.streamingChunksRef.current = [];
+  deps.textFinalizedRef.current = false;
+  deps.streamEndedRef.current = true;
+}
+
+export function getErrorDisplayMessage(chunk: StreamChunk): string {
+  const friendlyMessage = getUserFriendlyErrorMessage(chunk.code);
+  if (friendlyMessage) {
+    return friendlyMessage;
+  }
+  return chunk.message || chunk.content || "An error occurred";
 }

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

@@ -349,6 +349,7 @@ export function parseToolResponse(
         toolName: (parsedResult.tool_name as string) || toolName,
         toolId,
         operationId: (parsedResult.operation_id as string) || "",
+        taskId: (parsedResult.task_id as string) || undefined, // For SSE reconnection
         message:
           (parsedResult.message as string) ||
           "Operation started. You can close this tab.",

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

@@ -1,10 +1,17 @@
 import type { SessionDetailResponse } from "@/app/api/__generated__/models/sessionDetailResponse";
 import { useEffect, useMemo, useRef, useState } from "react";
+import { INITIAL_STREAM_ID } from "../../chat-constants";
 import { useChatStore } from "../../chat-store";
 import { toast } from "sonner";
 import { useChatStream } from "../../useChatStream";
 import { usePageContext } from "../../usePageContext";
 import type { ChatMessageData } from "../ChatMessage/useChatMessage";
+import {
+  getToolIdFromMessage,
+  hasToolId,
+  isOperationMessage,
+  type StreamChunk,
+} from "../../chat-types";
 import { createStreamEventDispatcher } from "./createStreamEventDispatcher";
 import {
   createUserMessage,
@@ -14,6 +21,13 @@ import {
   processInitialMessages,
 } from "./helpers";
 
+const TOOL_RESULT_TYPES = new Set([
+  "tool_response",
+  "agent_carousel",
+  "execution_started",
+  "clarification_needed",
+]);
+
 // Helper to generate deduplication key for a message
 function getMessageKey(msg: ChatMessageData): string {
   if (msg.type === "message") {
@@ -23,14 +37,18 @@ function getMessageKey(msg: ChatMessageData): string {
     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 if (TOOL_RESULT_TYPES.has(msg.type)) {
+    // Unified key for all tool result types - same toolId with different types
+    // (tool_response vs agent_carousel) should deduplicate to the same key
+    const toolId = getToolIdFromMessage(msg);
+    // If no toolId, fall back to content-based key to avoid empty key collisions
+    if (!toolId) {
+      return `toolresult:content:${JSON.stringify(msg).slice(0, 200)}`;
+    }
+    return `toolresult:${toolId}`;
+  } else if (isOperationMessage(msg)) {
+    const toolId = getToolIdFromMessage(msg) || "";
+    return `op:${toolId}:${msg.toolName}`;
   } else {
     return `${msg.type}:${JSON.stringify(msg).slice(0, 100)}`;
   }
@@ -41,6 +59,13 @@ interface Args {
   initialMessages: SessionDetailResponse["messages"];
   initialPrompt?: string;
   onOperationStarted?: () => void;
+  /** Active stream info from the server for reconnection */
+  activeStream?: {
+    taskId: string;
+    lastMessageId: string;
+    operationId: string;
+    toolName: string;
+  };
 }
 
 export function useChatContainer({
@@ -48,6 +73,7 @@ export function useChatContainer({
   initialMessages,
   initialPrompt,
   onOperationStarted,
+  activeStream,
 }: Args) {
   const [messages, setMessages] = useState<ChatMessageData[]>([]);
   const [streamingChunks, setStreamingChunks] = useState<string[]>([]);
@@ -57,6 +83,8 @@ export function useChatContainer({
     useState(false);
   const hasResponseRef = useRef(false);
   const streamingChunksRef = useRef<string[]>([]);
+  const textFinalizedRef = useRef(false);
+  const streamEndedRef = useRef(false);
   const previousSessionIdRef = useRef<string | null>(null);
   const {
     error,
@@ -65,44 +93,182 @@ export function useChatContainer({
   } = useChatStream();
   const activeStreams = useChatStore((s) => s.activeStreams);
   const subscribeToStream = useChatStore((s) => s.subscribeToStream);
+  const setActiveTask = useChatStore((s) => s.setActiveTask);
+  const getActiveTask = useChatStore((s) => s.getActiveTask);
+  const reconnectToTask = useChatStore((s) => s.reconnectToTask);
   const isStreaming = isStreamingInitiated || hasTextChunks;
+  // Track whether we've already connected to this activeStream to avoid duplicate connections
+  const connectedActiveStreamRef = useRef<string | null>(null);
+  // Track if component is mounted to prevent state updates after unmount
+  const isMountedRef = useRef(true);
+  // Track current dispatcher to prevent multiple dispatchers from adding messages
+  const currentDispatcherIdRef = useRef(0);
+
+  // Set mounted flag - reset on every mount, cleanup on unmount
+  useEffect(function trackMountedState() {
+    isMountedRef.current = true;
+    return function cleanup() {
+      isMountedRef.current = false;
+    };
+  }, []);
+
+  // Callback to store active task info for SSE reconnection
+  function handleActiveTaskStarted(taskInfo: {
+    taskId: string;
+    operationId: string;
+    toolName: string;
+    toolCallId: string;
+  }) {
+    if (!sessionId) return;
+    setActiveTask(sessionId, {
+      taskId: taskInfo.taskId,
+      operationId: taskInfo.operationId,
+      toolName: taskInfo.toolName,
+      lastMessageId: INITIAL_STREAM_ID,
+    });
+  }
+
+  // Create dispatcher for stream events - stable reference for current sessionId
+  // Each dispatcher gets a unique ID to prevent stale dispatchers from updating state
+  function createDispatcher() {
+    if (!sessionId) return () => {};
+    // Increment dispatcher ID - only the most recent dispatcher should update state
+    const dispatcherId = ++currentDispatcherIdRef.current;
+
+    const baseDispatcher = createStreamEventDispatcher({
+      setHasTextChunks,
+      setStreamingChunks,
+      streamingChunksRef,
+      hasResponseRef,
+      textFinalizedRef,
+      streamEndedRef,
+      setMessages,
+      setIsRegionBlockedModalOpen,
+      sessionId,
+      setIsStreamingInitiated,
+      onOperationStarted,
+      onActiveTaskStarted: handleActiveTaskStarted,
+    });
+
+    // Wrap dispatcher to check if it's still the current one
+    return function guardedDispatcher(chunk: StreamChunk) {
+      // Skip if component unmounted or this is a stale dispatcher
+      if (!isMountedRef.current) {
+        return;
+      }
+      if (dispatcherId !== currentDispatcherIdRef.current) {
+        return;
+      }
+      baseDispatcher(chunk);
+    };
+  }
 
   useEffect(
     function handleSessionChange() {
-      if (sessionId === previousSessionIdRef.current) return;
+      const isSessionChange = sessionId !== previousSessionIdRef.current;
 
-      const prevSession = previousSessionIdRef.current;
-      if (prevSession) {
-        stopStreaming(prevSession);
+      // Handle session change - reset state
+      if (isSessionChange) {
+        const prevSession = previousSessionIdRef.current;
+        if (prevSession) {
+          stopStreaming(prevSession);
+        }
+        previousSessionIdRef.current = sessionId;
+        connectedActiveStreamRef.current = null;
+        setMessages([]);
+        setStreamingChunks([]);
+        streamingChunksRef.current = [];
+        setHasTextChunks(false);
+        setIsStreamingInitiated(false);
+        hasResponseRef.current = false;
+        textFinalizedRef.current = false;
+        streamEndedRef.current = false;
       }
-      previousSessionIdRef.current = sessionId;
-      setMessages([]);
-      setStreamingChunks([]);
-      streamingChunksRef.current = [];
-      setHasTextChunks(false);
-      setIsStreamingInitiated(false);
-      hasResponseRef.current = false;
 
       if (!sessionId) return;
 
-      const activeStream = activeStreams.get(sessionId);
-      if (!activeStream || activeStream.status !== "streaming") return;
+      // Priority 1: Check if server told us there's an active stream (most authoritative)
+      if (activeStream) {
+        const streamKey = `${sessionId}:${activeStream.taskId}`;
 
-      const dispatcher = createStreamEventDispatcher({
-        setHasTextChunks,
-        setStreamingChunks,
-        streamingChunksRef,
-        hasResponseRef,
-        setMessages,
-        setIsRegionBlockedModalOpen,
-        sessionId,
-        setIsStreamingInitiated,
-        onOperationStarted,
-      });
+        if (connectedActiveStreamRef.current === streamKey) {
+          return;
+        }
+
+        // Skip if there's already an active stream for this session in the store
+        const existingStream = activeStreams.get(sessionId);
+        if (existingStream && existingStream.status === "streaming") {
+          connectedActiveStreamRef.current = streamKey;
+          return;
+        }
+
+        connectedActiveStreamRef.current = streamKey;
+
+        // Clear all state before reconnection to prevent duplicates
+        // Server's initialMessages is authoritative; local state will be rebuilt from SSE replay
+        setMessages([]);
+        setStreamingChunks([]);
+        streamingChunksRef.current = [];
+        setHasTextChunks(false);
+        textFinalizedRef.current = false;
+        streamEndedRef.current = false;
+        hasResponseRef.current = false;
+
+        setIsStreamingInitiated(true);
+        setActiveTask(sessionId, {
+          taskId: activeStream.taskId,
+          operationId: activeStream.operationId,
+          toolName: activeStream.toolName,
+          lastMessageId: activeStream.lastMessageId,
+        });
+        reconnectToTask(
+          sessionId,
+          activeStream.taskId,
+          activeStream.lastMessageId,
+          createDispatcher(),
+        );
+        // Don't return cleanup here - the guarded dispatcher handles stale events
+        // and the stream will complete naturally. Cleanup would prematurely stop
+        // the stream when effect re-runs due to activeStreams changing.
+        return;
+      }
+
+      // Only check localStorage/in-memory on session change
+      if (!isSessionChange) return;
+
+      // Priority 2: Check localStorage for active task
+      const activeTask = getActiveTask(sessionId);
+      if (activeTask) {
+        // Clear all state before reconnection to prevent duplicates
+        // Server's initialMessages is authoritative; local state will be rebuilt from SSE replay
+        setMessages([]);
+        setStreamingChunks([]);
+        streamingChunksRef.current = [];
+        setHasTextChunks(false);
+        textFinalizedRef.current = false;
+        streamEndedRef.current = false;
+        hasResponseRef.current = false;
+
+        setIsStreamingInitiated(true);
+        reconnectToTask(
+          sessionId,
+          activeTask.taskId,
+          activeTask.lastMessageId,
+          createDispatcher(),
+        );
+        // Don't return cleanup here - the guarded dispatcher handles stale events
+        return;
+      }
+
+      // Priority 3: Check for an in-memory active stream (same-tab scenario)
+      const inMemoryStream = activeStreams.get(sessionId);
+      if (!inMemoryStream || inMemoryStream.status !== "streaming") {
+        return;
+      }
 
       setIsStreamingInitiated(true);
       const skipReplay = initialMessages.length > 0;
-      return subscribeToStream(sessionId, dispatcher, skipReplay);
+      return subscribeToStream(sessionId, createDispatcher(), skipReplay);
     },
     [
       sessionId,
@@ -110,6 +276,10 @@ export function useChatContainer({
       activeStreams,
       subscribeToStream,
       onOperationStarted,
+      getActiveTask,
+      reconnectToTask,
+      activeStream,
+      setActiveTask,
     ],
   );
 
@@ -124,7 +294,7 @@ export function useChatContainer({
         msg.type === "agent_carousel" ||
         msg.type === "execution_started"
       ) {
-        const toolId = (msg as any).toolId;
+        const toolId = hasToolId(msg) ? msg.toolId : undefined;
         if (toolId) {
           ids.add(toolId);
         }
@@ -141,12 +311,8 @@ export function useChatContainer({
 
       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 (isOperationMessage(msg)) {
+            const toolId = getToolIdFromMessage(msg);
             if (toolId && completedToolIds.has(toolId)) {
               return false; // Remove - operation completed
             }
@@ -174,12 +340,8 @@ export function useChatContainer({
     // 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 (isOperationMessage(msg)) {
+        const toolId = getToolIdFromMessage(msg);
         if (toolId && completedToolIds.has(toolId)) {
           return false;
         }
@@ -190,7 +352,70 @@ export function useChatContainer({
     });
 
     // Server messages first (correct order), then new local messages
-    return [...processedInitial, ...newLocalMessages];
+    const combined = [...processedInitial, ...newLocalMessages];
+
+    // Post-processing: Remove duplicate assistant messages that can occur during
+    // race conditions (e.g., rapid screen switching during SSE reconnection).
+    // Two assistant messages are considered duplicates if:
+    // - They are both text messages with role "assistant"
+    // - One message's content starts with the other's content (partial vs complete)
+    // - Or they have very similar content (>80% overlap at the start)
+    const deduplicated: ChatMessageData[] = [];
+    for (let i = 0; i < combined.length; i++) {
+      const current = combined[i];
+
+      // Check if this is an assistant text message
+      if (current.type !== "message" || current.role !== "assistant") {
+        deduplicated.push(current);
+        continue;
+      }
+
+      // Look for duplicate assistant messages in the rest of the array
+      let dominated = false;
+      for (let j = 0; j < combined.length; j++) {
+        if (i === j) continue;
+        const other = combined[j];
+        if (other.type !== "message" || other.role !== "assistant") continue;
+
+        const currentContent = current.content || "";
+        const otherContent = other.content || "";
+
+        // Skip empty messages
+        if (!currentContent.trim() || !otherContent.trim()) continue;
+
+        // Check if current is a prefix of other (current is incomplete version)
+        if (
+          otherContent.length > currentContent.length &&
+          otherContent.startsWith(currentContent.slice(0, 100))
+        ) {
+          // Current is a shorter/incomplete version of other - skip it
+          dominated = true;
+          break;
+        }
+
+        // Check if messages are nearly identical (within a small difference)
+        // This catches cases where content differs only slightly
+        const minLen = Math.min(currentContent.length, otherContent.length);
+        const compareLen = Math.min(minLen, 200); // Compare first 200 chars
+        if (
+          compareLen > 50 &&
+          currentContent.slice(0, compareLen) ===
+            otherContent.slice(0, compareLen)
+        ) {
+          // Same prefix - keep the longer one
+          if (otherContent.length > currentContent.length) {
+            dominated = true;
+            break;
+          }
+        }
+      }
+
+      if (!dominated) {
+        deduplicated.push(current);
+      }
+    }
+
+    return deduplicated;
   }, [initialMessages, messages, completedToolIds]);
 
   async function sendMessage(
@@ -198,10 +423,8 @@ export function useChatContainer({
     isUserMessage: boolean = true,
     context?: { url: string; content: string },
   ) {
-    if (!sessionId) {
-      console.error("[useChatContainer] Cannot send message: no session ID");
-      return;
-    }
+    if (!sessionId) return;
+
     setIsRegionBlockedModalOpen(false);
     if (isUserMessage) {
       const userMessage = createUserMessage(content);
@@ -214,31 +437,19 @@ export function useChatContainer({
     setHasTextChunks(false);
     setIsStreamingInitiated(true);
     hasResponseRef.current = false;
-
-    const dispatcher = createStreamEventDispatcher({
-      setHasTextChunks,
-      setStreamingChunks,
-      streamingChunksRef,
-      hasResponseRef,
-      setMessages,
-      setIsRegionBlockedModalOpen,
-      sessionId,
-      setIsStreamingInitiated,
-      onOperationStarted,
-    });
+    textFinalizedRef.current = false;
+    streamEndedRef.current = false;
 
     try {
       await sendStreamMessage(
         sessionId,
         content,
-        dispatcher,
+        createDispatcher(),
         isUserMessage,
         context,
       );
     } catch (err) {
-      console.error("[useChatContainer] Failed to send message:", err);
       setIsStreamingInitiated(false);
-
       if (err instanceof Error && err.name === "AbortError") return;
 
       const errorMessage =

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

@@ -111,6 +111,7 @@ export type ChatMessageData =
       toolName: string;
       toolId: string;
       operationId: string;
+      taskId?: string; // For SSE reconnection
       message: string;
       timestamp?: string | Date;
     }

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

@@ -31,11 +31,6 @@ export function MessageList({
     isStreaming,
   });
 
-  /**
-   * Keeps this for debugging purposes 💆🏽
-   */
-  console.log(messages);
-
   return (
     <div className="relative flex min-h-0 flex-1 flex-col">
       {/* Top fade shadow */}

autogpt_platform/frontend/src/components/contextual/Chat/stream-executor.ts

@@ -1,3 +1,4 @@
+import { INITIAL_STREAM_ID } from "./chat-constants";
 import type {
   ActiveStream,
   StreamChunk,
@@ -10,8 +11,14 @@ import {
   parseSSELine,
 } from "./stream-utils";
 
-function notifySubscribers(stream: ActiveStream, chunk: StreamChunk) {
-  stream.chunks.push(chunk);
+function notifySubscribers(
+  stream: ActiveStream,
+  chunk: StreamChunk,
+  skipStore = false,
+) {
+  if (!skipStore) {
+    stream.chunks.push(chunk);
+  }
   for (const callback of stream.onChunkCallbacks) {
     try {
       callback(chunk);
@@ -21,36 +28,114 @@ function notifySubscribers(stream: ActiveStream, chunk: StreamChunk) {
   }
 }
 
-export async function executeStream(
-  stream: ActiveStream,
-  message: string,
-  isUserMessage: boolean,
-  context?: { url: string; content: string },
-  retryCount: number = 0,
+interface StreamExecutionOptions {
+  stream: ActiveStream;
+  mode: "new" | "reconnect";
+  message?: string;
+  isUserMessage?: boolean;
+  context?: { url: string; content: string };
+  taskId?: string;
+  lastMessageId?: string;
+  retryCount?: number;
+}
+
+async function executeStreamInternal(
+  options: StreamExecutionOptions,
 ): Promise<void> {
+  const {
+    stream,
+    mode,
+    message,
+    isUserMessage,
+    context,
+    taskId,
+    lastMessageId = INITIAL_STREAM_ID,
+    retryCount = 0,
+  } = options;
+
   const { sessionId, abortController } = stream;
+  const isReconnect = mode === "reconnect";
+
+  if (isReconnect) {
+    if (!taskId) {
+      throw new Error("taskId is required for reconnect mode");
+    }
+    if (lastMessageId === null || lastMessageId === undefined) {
+      throw new Error("lastMessageId is required for reconnect mode");
+    }
+  } else {
+    if (!message) {
+      throw new Error("message is required for new stream mode");
+    }
+    if (isUserMessage === undefined) {
+      throw new Error("isUserMessage is required for new stream mode");
+    }
+  }
 
   try {
-    const url = `/api/chat/sessions/${sessionId}/stream`;
-    const body = JSON.stringify({
-      message,
-      is_user_message: isUserMessage,
-      context: context || null,
-    });
+    let url: string;
+    let fetchOptions: RequestInit;
 
-    const response = await fetch(url, {
-      method: "POST",
-      headers: {
-        "Content-Type": "application/json",
-        Accept: "text/event-stream",
-      },
-      body,
-      signal: abortController.signal,
-    });
+    if (isReconnect) {
+      url = `/api/chat/tasks/${taskId}/stream?last_message_id=${encodeURIComponent(lastMessageId)}`;
+      fetchOptions = {
+        method: "GET",
+        headers: {
+          Accept: "text/event-stream",
+        },
+        signal: abortController.signal,
+      };
+    } else {
+      url = `/api/chat/sessions/${sessionId}/stream`;
+      fetchOptions = {
+        method: "POST",
+        headers: {
+          "Content-Type": "application/json",
+          Accept: "text/event-stream",
+        },
+        body: JSON.stringify({
+          message,
+          is_user_message: isUserMessage,
+          context: context || null,
+        }),
+        signal: abortController.signal,
+      };
+    }
+
+    const response = await fetch(url, fetchOptions);
 
     if (!response.ok) {
       const errorText = await response.text();
-      throw new Error(errorText || `HTTP ${response.status}`);
+      let errorCode: string | undefined;
+      let errorMessage = errorText || `HTTP ${response.status}`;
+      try {
+        const parsed = JSON.parse(errorText);
+        if (parsed.detail) {
+          const detail =
+            typeof parsed.detail === "string"
+              ? parsed.detail
+              : parsed.detail.message || JSON.stringify(parsed.detail);
+          errorMessage = detail;
+          errorCode =
+            typeof parsed.detail === "object" ? parsed.detail.code : undefined;
+        }
+      } catch {}
+
+      const isPermanentError =
+        isReconnect &&
+        (response.status === 404 ||
+          response.status === 403 ||
+          response.status === 410);
+
+      const error = new Error(errorMessage) as Error & {
+        status?: number;
+        isPermanent?: boolean;
+        taskErrorCode?: string;
+      };
+      error.status = response.status;
+      error.isPermanent = isPermanentError;
+      error.taskErrorCode = errorCode;
+      throw error;
     }
 
     if (!response.body) {
@@ -104,9 +189,7 @@ export async function executeStream(
               );
               return;
             }
-          } catch (err) {
-            console.warn("[StreamExecutor] Failed to parse SSE chunk:", err);
-          }
+          } catch {}
         }
       }
     }
@@ -117,19 +200,17 @@ export async function executeStream(
       return;
     }
 
-    if (retryCount < MAX_RETRIES) {
+    const isPermanentError =
+      err instanceof Error &&
+      (err as Error & { isPermanent?: boolean }).isPermanent;
+
+    if (!isPermanentError && retryCount < MAX_RETRIES) {
       const retryDelay = INITIAL_RETRY_DELAY * Math.pow(2, retryCount);
-      console.log(
-        `[StreamExecutor] Retrying in ${retryDelay}ms (attempt ${retryCount + 1}/${MAX_RETRIES})`,
-      );
       await new Promise((resolve) => setTimeout(resolve, retryDelay));
-      return executeStream(
-        stream,
-        message,
-        isUserMessage,
-        context,
-        retryCount + 1,
-      );
+      return executeStreamInternal({
+        ...options,
+        retryCount: retryCount + 1,
+      });
     }
 
     stream.status = "error";
@@ -140,3 +221,35 @@ export async function executeStream(
     });
   }
 }
+
+export async function executeStream(
+  stream: ActiveStream,
+  message: string,
+  isUserMessage: boolean,
+  context?: { url: string; content: string },
+  retryCount: number = 0,
+): Promise<void> {
+  return executeStreamInternal({
+    stream,
+    mode: "new",
+    message,
+    isUserMessage,
+    context,
+    retryCount,
+  });
+}
+
+export async function executeTaskReconnect(
+  stream: ActiveStream,
+  taskId: string,
+  lastMessageId: string = INITIAL_STREAM_ID,
+  retryCount: number = 0,
+): Promise<void> {
+  return executeStreamInternal({
+    stream,
+    mode: "reconnect",
+    taskId,
+    lastMessageId,
+    retryCount,
+  });
+}

autogpt_platform/frontend/src/components/contextual/Chat/stream-utils.ts

@@ -28,6 +28,7 @@ export function normalizeStreamChunk(
 
   switch (chunk.type) {
     case "text-delta":
+      // Vercel AI SDK sends "delta" for text content
       return { type: "text_chunk", content: chunk.delta };
     case "text-end":
       return { type: "text_ended" };
@@ -63,6 +64,10 @@ export function normalizeStreamChunk(
     case "finish":
       return { type: "stream_end" };
     case "start":
+      // Start event with optional taskId for reconnection
+      return chunk.taskId
+        ? { type: "stream_start", taskId: chunk.taskId }
+        : null;
     case "text-start":
       return null;
     case "tool-input-start":

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

@@ -1,7 +1,6 @@
 "use client";
 
 import { IconLaptop } from "@/components/__legacy__/ui/icons";
-import { getHomepageRoute } from "@/lib/constants";
 import { cn } from "@/lib/utils";
 import { Flag, useGetFlag } from "@/services/feature-flags/use-get-flag";
 import { ListChecksIcon } from "@phosphor-icons/react/dist/ssr";
@@ -24,11 +23,11 @@ interface Props {
 export function NavbarLink({ name, href }: Props) {
   const pathname = usePathname();
   const isChatEnabled = useGetFlag(Flag.CHAT);
-  const homepageRoute = getHomepageRoute(isChatEnabled);
+  const expectedHomeRoute = isChatEnabled ? "/copilot" : "/library";
 
   const isActive =
-    href === homepageRoute
-      ? pathname === "/" || pathname.startsWith(homepageRoute)
+    href === expectedHomeRoute
+      ? pathname === "/" || pathname.startsWith(expectedHomeRoute)
       : pathname.includes(href);
 
   return (

autogpt_platform/frontend/src/hooks/useAgentGraph.tsx

@@ -66,7 +66,7 @@ export default function useAgentGraph(
   >(null);
   const [xyNodes, setXYNodes] = useState<CustomNode[]>([]);
   const [xyEdges, setXYEdges] = useState<CustomEdge[]>([]);
-  const betaBlocks = useGetFlag(Flag.BETA_BLOCKS);
+  const betaBlocks = useGetFlag(Flag.BETA_BLOCKS) as string[];
 
   // Filter blocks based on beta flags
   const availableBlocks = useMemo(() => {

autogpt_platform/frontend/src/lib/constants.ts

@@ -11,10 +11,3 @@ export const API_KEY_HEADER_NAME = "X-API-Key";
 
 // Layout
 export const NAVBAR_HEIGHT_PX = 60;
-
-// Routes
-export function getHomepageRoute(isChatEnabled?: boolean | null): string {
-  if (isChatEnabled === true) return "/copilot";
-  if (isChatEnabled === false) return "/library";
-  return "/";
-}

autogpt_platform/frontend/src/lib/supabase/helpers.ts

@@ -1,4 +1,3 @@
-import { getHomepageRoute } from "@/lib/constants";
 import { environment } from "@/services/environment";
 import { Key, storage } from "@/services/storage/local-storage";
 import { type CookieOptions } from "@supabase/ssr";
@@ -71,7 +70,7 @@ export function getRedirectPath(
   }
 
   if (isAdminPage(path) && userRole !== "admin") {
-    return getHomepageRoute();
+    return "/";
   }
 
   return null;

autogpt_platform/frontend/src/lib/supabase/middleware.ts

@@ -1,4 +1,3 @@
-import { getHomepageRoute } from "@/lib/constants";
 import { environment } from "@/services/environment";
 import { createServerClient } from "@supabase/ssr";
 import { NextResponse, type NextRequest } from "next/server";
@@ -67,7 +66,7 @@ export async function updateSession(request: NextRequest) {
 
     // 2. Check if user is authenticated but lacks admin role when accessing admin pages
     if (user && userRole !== "admin" && isAdminPage(pathname)) {
-      url.pathname = getHomepageRoute();
+      url.pathname = "/";
       return NextResponse.redirect(url);
     }
 

autogpt_platform/frontend/src/providers/onboarding/onboarding-provider.tsx

@@ -23,9 +23,7 @@ import {
   WebSocketNotification,
 } from "@/lib/autogpt-server-api";
 import { useBackendAPI } from "@/lib/autogpt-server-api/context";
-import { getHomepageRoute } from "@/lib/constants";
 import { useSupabase } from "@/lib/supabase/hooks/useSupabase";
-import { Flag, useGetFlag } from "@/services/feature-flags/use-get-flag";
 import Link from "next/link";
 import { usePathname, useRouter } from "next/navigation";
 import {
@@ -104,8 +102,6 @@ export default function OnboardingProvider({
   const pathname = usePathname();
   const router = useRouter();
   const { isLoggedIn } = useSupabase();
-  const isChatEnabled = useGetFlag(Flag.CHAT);
-  const homepageRoute = getHomepageRoute(isChatEnabled);
 
   useOnboardingTimezoneDetection();
 
@@ -150,7 +146,7 @@ export default function OnboardingProvider({
         if (isOnOnboardingRoute) {
           const enabled = await resolveResponse(getV1IsOnboardingEnabled());
           if (!enabled) {
-            router.push(homepageRoute);
+            router.push("/");
             return;
           }
         }
@@ -162,7 +158,7 @@ export default function OnboardingProvider({
           isOnOnboardingRoute &&
           shouldRedirectFromOnboarding(onboarding.completedSteps, pathname)
         ) {
-          router.push(homepageRoute);
+          router.push("/");
         }
       } catch (error) {
         console.error("Failed to initialize onboarding:", error);
@@ -177,7 +173,7 @@ export default function OnboardingProvider({
     }
 
     initializeOnboarding();
-  }, [api, homepageRoute, isOnOnboardingRoute, router, isLoggedIn, pathname]);
+  }, [api, isOnOnboardingRoute, router, isLoggedIn, pathname]);
 
   const handleOnboardingNotification = useCallback(
     (notification: WebSocketNotification) => {

autogpt_platform/frontend/src/services/environment/index.ts

@@ -83,6 +83,10 @@ function getPostHogCredentials() {
   };
 }
 
+function getLaunchDarklyClientId() {
+  return process.env.NEXT_PUBLIC_LAUNCHDARKLY_CLIENT_ID;
+}
+
 function isProductionBuild() {
   return process.env.NODE_ENV === "production";
 }
@@ -120,7 +124,10 @@ function isVercelPreview() {
 }
 
 function areFeatureFlagsEnabled() {
-  return process.env.NEXT_PUBLIC_LAUNCHDARKLY_ENABLED === "enabled";
+  return (
+    process.env.NEXT_PUBLIC_LAUNCHDARKLY_ENABLED === "true" &&
+    Boolean(process.env.NEXT_PUBLIC_LAUNCHDARKLY_CLIENT_ID)
+  );
 }
 
 function isPostHogEnabled() {
@@ -143,6 +150,7 @@ export const environment = {
   getSupabaseAnonKey,
   getPreviewStealingDev,
   getPostHogCredentials,
+  getLaunchDarklyClientId,
   // Assertions
   isServerSide,
   isClientSide,

autogpt_platform/frontend/src/services/feature-flags/FeatureFlagPage.tsx

@@ -0,0 +1,59 @@
+"use client";
+
+import { LoadingSpinner } from "@/components/atoms/LoadingSpinner/LoadingSpinner";
+import { useLDClient } from "launchdarkly-react-client-sdk";
+import { useRouter } from "next/navigation";
+import { ReactNode, useEffect, useState } from "react";
+import { environment } from "../environment";
+import { Flag, useGetFlag } from "./use-get-flag";
+
+interface FeatureFlagRedirectProps {
+  flag: Flag;
+  whenDisabled: string;
+  children: ReactNode;
+}
+
+export function FeatureFlagPage({
+  flag,
+  whenDisabled,
+  children,
+}: FeatureFlagRedirectProps) {
+  const [isLoading, setIsLoading] = useState(true);
+  const router = useRouter();
+  const flagValue = useGetFlag(flag);
+  const ldClient = useLDClient();
+  const ldEnabled = environment.areFeatureFlagsEnabled();
+  const ldReady = Boolean(ldClient);
+  const flagEnabled = Boolean(flagValue);
+
+  useEffect(() => {
+    const initialize = async () => {
+      if (!ldEnabled) {
+        router.replace(whenDisabled);
+        setIsLoading(false);
+        return;
+      }
+
+      // Wait for LaunchDarkly to initialize when enabled to prevent race conditions
+      if (ldEnabled && !ldReady) return;
+
+      try {
+        await ldClient?.waitForInitialization();
+        if (!flagEnabled) router.replace(whenDisabled);
+      } catch (error) {
+        console.error(error);
+        router.replace(whenDisabled);
+      } finally {
+        setIsLoading(false);
+      }
+    };
+
+    initialize();
+  }, [ldReady, flagEnabled]);
+
+  return isLoading || !flagEnabled ? (
+    <LoadingSpinner size="large" cover />
+  ) : (
+    <>{children}</>
+  );
+}

autogpt_platform/frontend/src/services/feature-flags/FeatureFlagRedirect.tsx

@@ -0,0 +1,51 @@
+"use client";
+
+import { LoadingSpinner } from "@/components/atoms/LoadingSpinner/LoadingSpinner";
+import { useLDClient } from "launchdarkly-react-client-sdk";
+import { useRouter } from "next/navigation";
+import { useEffect } from "react";
+import { environment } from "../environment";
+import { Flag, useGetFlag } from "./use-get-flag";
+
+interface FeatureFlagRedirectProps {
+  flag: Flag;
+  whenEnabled: string;
+  whenDisabled: string;
+}
+
+export function FeatureFlagRedirect({
+  flag,
+  whenEnabled,
+  whenDisabled,
+}: FeatureFlagRedirectProps) {
+  const router = useRouter();
+  const flagValue = useGetFlag(flag);
+  const ldEnabled = environment.areFeatureFlagsEnabled();
+  const ldClient = useLDClient();
+  const ldReady = Boolean(ldClient);
+  const flagEnabled = Boolean(flagValue);
+
+  useEffect(() => {
+    const initialize = async () => {
+      if (!ldEnabled) {
+        router.replace(whenDisabled);
+        return;
+      }
+
+      // Wait for LaunchDarkly to initialize when enabled to prevent race conditions
+      if (ldEnabled && !ldReady) return;
+
+      try {
+        await ldClient?.waitForInitialization();
+        router.replace(flagEnabled ? whenEnabled : whenDisabled);
+      } catch (error) {
+        console.error(error);
+        router.replace(whenDisabled);
+      }
+    };
+
+    initialize();
+  }, [ldReady, flagEnabled]);
+
+  return <LoadingSpinner size="large" cover />;
+}

autogpt_platform/frontend/src/services/feature-flags/feature-flag-provider.tsx

@@ -1,5 +1,6 @@
 "use client";
 
+import { LoadingSpinner } from "@/components/atoms/LoadingSpinner/LoadingSpinner";
 import { useSupabase } from "@/lib/supabase/hooks/useSupabase";
 import * as Sentry from "@sentry/nextjs";
 import { LDProvider } from "launchdarkly-react-client-sdk";
@@ -7,17 +8,17 @@ import type { ReactNode } from "react";
 import { useMemo } from "react";
 import { environment } from "../environment";
 
-const clientId = process.env.NEXT_PUBLIC_LAUNCHDARKLY_CLIENT_ID;
-const envEnabled = process.env.NEXT_PUBLIC_LAUNCHDARKLY_ENABLED === "true";
 const LAUNCHDARKLY_INIT_TIMEOUT_MS = 5000;
 
 export function LaunchDarklyProvider({ children }: { children: ReactNode }) {
   const { user, isUserLoading } = useSupabase();
-  const isCloud = environment.isCloud();
-  const isLaunchDarklyConfigured = isCloud && envEnabled && clientId;
+  const envEnabled = environment.areFeatureFlagsEnabled();
+  const clientId = environment.getLaunchDarklyClientId();
 
   const context = useMemo(() => {
-    if (isUserLoading || !user) {
+    if (isUserLoading) return;
+
+    if (!user) {
       return {
         kind: "user" as const,
         key: "anonymous",
@@ -36,15 +37,17 @@ export function LaunchDarklyProvider({ children }: { children: ReactNode }) {
     };
   }, [user, isUserLoading]);
 
-  if (!isLaunchDarklyConfigured) {
+  if (!envEnabled) {
     return <>{children}</>;
   }
 
+  if (isUserLoading) {
+    return <LoadingSpinner size="large" cover />;
+  }
+
   return (
     <LDProvider
-      // Add this key prop. It will be 'anonymous' when logged out,
-      key={context.key}
-      clientSideID={clientId}
+      clientSideID={clientId ?? ""}
       context={context}
       timeout={LAUNCHDARKLY_INIT_TIMEOUT_MS}
       reactOptions={{ useCamelCaseFlagKeys: false }}

autogpt_platform/frontend/src/services/feature-flags/use-get-flag.ts

@@ -1,6 +1,7 @@
 "use client";
 
 import { DEFAULT_SEARCH_TERMS } from "@/app/(platform)/marketplace/components/HeroSection/helpers";
+import { environment } from "@/services/environment";
 import { useFlags } from "launchdarkly-react-client-sdk";
 
 export enum Flag {
@@ -18,24 +19,9 @@ export enum Flag {
   CHAT = "chat",
 }
 
-export type FlagValues = {
-  [Flag.BETA_BLOCKS]: string[];
-  [Flag.NEW_BLOCK_MENU]: boolean;
-  [Flag.NEW_AGENT_RUNS]: boolean;
-  [Flag.GRAPH_SEARCH]: boolean;
-  [Flag.ENABLE_ENHANCED_OUTPUT_HANDLING]: boolean;
-  [Flag.NEW_FLOW_EDITOR]: boolean;
-  [Flag.BUILDER_VIEW_SWITCH]: boolean;
-  [Flag.SHARE_EXECUTION_RESULTS]: boolean;
-  [Flag.AGENT_FAVORITING]: boolean;
-  [Flag.MARKETPLACE_SEARCH_TERMS]: string[];
-  [Flag.ENABLE_PLATFORM_PAYMENT]: boolean;
-  [Flag.CHAT]: boolean;
-};
-
 const isPwMockEnabled = process.env.NEXT_PUBLIC_PW_TEST === "true";
 
-const mockFlags = {
+const defaultFlags = {
   [Flag.BETA_BLOCKS]: [],
   [Flag.NEW_BLOCK_MENU]: false,
   [Flag.NEW_AGENT_RUNS]: false,
@@ -50,17 +36,16 @@ const mockFlags = {
   [Flag.CHAT]: false,
 };
 
-export function useGetFlag<T extends Flag>(flag: T): FlagValues[T] | null {
+type FlagValues = typeof defaultFlags;
+
+export function useGetFlag<T extends Flag>(flag: T): FlagValues[T] {
   const currentFlags = useFlags<FlagValues>();
   const flagValue = currentFlags[flag];
+  const areFlagsEnabled = environment.areFeatureFlagsEnabled();
 
-  const envEnabled = process.env.NEXT_PUBLIC_LAUNCHDARKLY_ENABLED === "true";
-  const clientId = process.env.NEXT_PUBLIC_LAUNCHDARKLY_CLIENT_ID;
-  const isLaunchDarklyConfigured = envEnabled && Boolean(clientId);
-
-  if (!isLaunchDarklyConfigured || isPwMockEnabled) {
-    return mockFlags[flag];
+  if (!areFlagsEnabled || isPwMockEnabled) {
+    return defaultFlags[flag];
   }
 
-  return flagValue ?? mockFlags[flag];
+  return flagValue ?? defaultFlags[flag];
 }

classic/frontend/.gitignore

@@ -8,6 +8,7 @@
 .buildlog/
 .history
 .svn/
+.next/
 migrate_working_dir/
 
 # IntelliJ related