chore: steam refresh issue fix

autogpt_platform/backend/backend/blocks/human_in_the_loop.py

@@ -21,71 +21,43 @@ logger = logging.getLogger(__name__)
 
 class HumanInTheLoopBlock(Block):
     """
-    Pauses execution and waits for human approval or rejection of the data.
+    This block pauses execution and waits for human approval or modification of the data.
 
-    When executed, this block creates a pending review entry and sets the node execution
-    status to REVIEW. The execution remains paused until a human user either approves
-    or rejects the data.
+    When executed, it creates a pending review entry and sets the node execution status
+    to REVIEW. The execution will remain paused until a human user either:
+    - Approves the data (with or without modifications)
+    - Rejects the data
 
-    **How it works:**
-    - The input data is presented to a human reviewer
-    - The reviewer can approve or reject (and optionally modify the data if editable)
-    - On approval: the data flows out through the `approved_data` output pin
-    - On rejection: the data flows out through the `rejected_data` output pin
-
-    **Important:** The output pins yield the actual data itself, NOT status strings.
-    The approval/rejection decision determines WHICH output pin fires, not the value.
-    You do NOT need to compare the output to "APPROVED" or "REJECTED" - simply connect
-    downstream blocks to the appropriate output pin for each case.
-
-    **Example usage:**
-    - Connect `approved_data` → next step in your workflow (data was approved)
-    - Connect `rejected_data` → error handling or notification (data was rejected)
+    This is useful for workflows that require human validation or intervention before
+    proceeding to the next steps.
     """
 
     class Input(BlockSchemaInput):
-        data: Any = SchemaField(
-            description="The data to be reviewed by a human user. "
-            "This exact data will be passed through to either approved_data or "
-            "rejected_data output based on the reviewer's decision."
-        )
+        data: Any = SchemaField(description="The data to be reviewed by a human user")
         name: str = SchemaField(
-            description="A descriptive name for what this data represents. "
-            "This helps the reviewer understand what they are reviewing.",
+            description="A descriptive name for what this data represents",
         )
         editable: bool = SchemaField(
-            description="Whether the human reviewer can edit the data before "
-            "approving or rejecting it",
+            description="Whether the human reviewer can edit the data",
             default=True,
             advanced=True,
         )
 
     class Output(BlockSchemaOutput):
         approved_data: Any = SchemaField(
-            description="Outputs the input data when the reviewer APPROVES it. "
-            "The value is the actual data itself (not a status string like 'APPROVED'). "
-            "If the reviewer edited the data, this contains the modified version. "
-            "Connect downstream blocks here for the 'approved' workflow path."
+            description="The data when approved (may be modified by reviewer)"
         )
         rejected_data: Any = SchemaField(
-            description="Outputs the input data when the reviewer REJECTS it. "
-            "The value is the actual data itself (not a status string like 'REJECTED'). "
-            "If the reviewer edited the data, this contains the modified version. "
-            "Connect downstream blocks here for the 'rejected' workflow path."
+            description="The data when rejected (may be modified by reviewer)"
         )
         review_message: str = SchemaField(
-            description="Optional message provided by the reviewer explaining their "
-            "decision. Only outputs when the reviewer provides a message; "
-            "this pin does not fire if no message was given.",
-            default="",
+            description="Any message provided by the reviewer", default=""
         )
 
     def __init__(self):
         super().__init__(
             id="8b2a7b3c-6e9d-4a5f-8c1b-2e3f4a5b6c7d",
-            description="Pause execution for human review. Data flows through "
-            "approved_data or rejected_data output based on the reviewer's decision. "
-            "Outputs contain the actual data, not status strings.",
+            description="Pause execution and wait for human approval or modification of data",
             categories={BlockCategory.BASIC},
             input_schema=HumanInTheLoopBlock.Input,
             output_schema=HumanInTheLoopBlock.Output,

autogpt_platform/frontend/src/app/(platform)/copilot/hooks/Untitled

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

autogpt_platform/frontend/src/app/(platform)/copilot/hooks/useLongRunningToolPolling.ts

@@ -0,0 +1,131 @@
+import { getGetV2GetSessionQueryKey } from "@/app/api/__generated__/endpoints/chat/chat";
+import { useQueryClient } from "@tanstack/react-query";
+import type { UIDataTypes, UIMessage, UITools } from "ai";
+import { useCallback, useEffect, useRef } from "react";
+import { convertChatSessionMessagesToUiMessages } from "../helpers/convertChatSessionToUiMessages";
+
+const OPERATING_TYPES = new Set([
+  "operation_started",
+  "operation_pending",
+  "operation_in_progress",
+]);
+
+const POLL_INTERVAL_MS = 5_000;
+
+/**
+ * Detects whether any message contains a tool part whose output indicates
+ * a long-running operation is still in progress.
+ */
+function hasOperatingTool(
+  messages: UIMessage<unknown, UIDataTypes, UITools>[],
+): boolean {
+  for (const msg of messages) {
+    for (const part of msg.parts) {
+      if (!part.type.startsWith("tool-")) continue;
+      const toolPart = part as { output?: unknown };
+      if (!toolPart.output) continue;
+      const output =
+        typeof toolPart.output === "string"
+          ? safeParse(toolPart.output)
+          : toolPart.output;
+      if (
+        output &&
+        typeof output === "object" &&
+        "type" in output &&
+        OPERATING_TYPES.has((output as { type: string }).type)
+      ) {
+        return true;
+      }
+    }
+  }
+  return false;
+}
+
+function safeParse(value: string): unknown {
+  try {
+    return JSON.parse(value);
+  } catch {
+    return null;
+  }
+}
+
+/**
+ * Polls the session endpoint while any tool is in an "operating" state
+ * (operation_started / operation_pending / operation_in_progress).
+ *
+ * When the session data shows the tool output has changed (e.g. to
+ * agent_saved), it calls `setMessages` with the updated messages.
+ */
+export function useLongRunningToolPolling(
+  sessionId: string | null,
+  messages: UIMessage<unknown, UIDataTypes, UITools>[],
+  setMessages: (
+    updater: (
+      prev: UIMessage<unknown, UIDataTypes, UITools>[],
+    ) => UIMessage<unknown, UIDataTypes, UITools>[],
+  ) => void,
+) {
+  const queryClient = useQueryClient();
+  const isPollingRef = useRef(false);
+  const intervalRef = useRef<ReturnType<typeof setInterval> | null>(null);
+
+  const stopPolling = useCallback(() => {
+    if (intervalRef.current) {
+      clearInterval(intervalRef.current);
+      intervalRef.current = null;
+    }
+    isPollingRef.current = false;
+  }, []);
+
+  const poll = useCallback(async () => {
+    if (!sessionId) return;
+
+    // Invalidate the query cache so the next fetch gets fresh data
+    await queryClient.invalidateQueries({
+      queryKey: getGetV2GetSessionQueryKey(sessionId),
+    });
+
+    // Fetch fresh session data
+    const data = queryClient.getQueryData<{
+      status: number;
+      data: { messages?: unknown[] };
+    }>(getGetV2GetSessionQueryKey(sessionId));
+
+    if (data?.status !== 200 || !data.data.messages) return;
+
+    const freshMessages = convertChatSessionMessagesToUiMessages(
+      sessionId,
+      data.data.messages,
+    );
+
+    if (!freshMessages || freshMessages.length === 0) return;
+
+    // Only update if the fresh data no longer has operating tools
+    // (meaning the long-running tool completed)
+    if (!hasOperatingTool(freshMessages)) {
+      setMessages(() => freshMessages);
+      stopPolling();
+    }
+  }, [sessionId, queryClient, setMessages, stopPolling]);
+
+  useEffect(() => {
+    const shouldPoll = hasOperatingTool(messages);
+
+    if (shouldPoll && !isPollingRef.current && sessionId) {
+      isPollingRef.current = true;
+      intervalRef.current = setInterval(() => {
+        poll();
+      }, POLL_INTERVAL_MS);
+    } else if (!shouldPoll && isPollingRef.current) {
+      stopPolling();
+    }
+
+    return () => {
+      // Cleanup on unmount or dependency change
+      if (intervalRef.current) {
+        clearInterval(intervalRef.current);
+        intervalRef.current = null;
+      }
+    };
+  }, [messages, sessionId, poll, stopPolling]);
+}

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

@@ -5,6 +5,7 @@ import { useChat } from "@ai-sdk/react";
 import { DefaultChatTransport } from "ai";
 import { useEffect, useMemo, useState } from "react";
 import { useChatSession } from "./useChatSession";
+import { useLongRunningToolPolling } from "./hooks/useLongRunningToolPolling";
 
 export function useCopilotPage() {
   const { isUserLoading, isLoggedIn } = useSupabase();
@@ -60,6 +61,11 @@ export function useCopilotPage() {
     });
   }, [hydratedMessages, setMessages]);
 
+  // Poll session endpoint when a long-running tool (create_agent, edit_agent)
+  // is in progress. When the backend completes, the session data will contain
+  // the final tool output — this hook detects the change and updates messages.
+  useLongRunningToolPolling(sessionId, messages, setMessages);
+
   // Clear messages when session is null
   useEffect(() => {
     if (!sessionId) setMessages([]);

docs/integrations/README.md

@@ -61,7 +61,7 @@ Below is a comprehensive list of all available blocks, categorized by their prim
 | [Get List Item](block-integrations/basic.md#get-list-item) | Returns the element at the given index |
 | [Get Store Agent Details](block-integrations/system/store_operations.md#get-store-agent-details) | Get detailed information about an agent from the store |
 | [Get Weather Information](block-integrations/basic.md#get-weather-information) | Retrieves weather information for a specified location using OpenWeatherMap API |
-| [Human In The Loop](block-integrations/basic.md#human-in-the-loop) | Pause execution for human review |
+| [Human In The Loop](block-integrations/basic.md#human-in-the-loop) | Pause execution and wait for human approval or modification of data |
 | [List Is Empty](block-integrations/basic.md#list-is-empty) | Checks if a list is empty |
 | [List Library Agents](block-integrations/system/library_operations.md#list-library-agents) | List all agents in your personal library |
 | [Note](block-integrations/basic.md#note) | A visual annotation block that displays a sticky note in the workflow editor for documentation and organization purposes |

docs/integrations/block-integrations/basic.md

@@ -975,7 +975,7 @@ A travel planning application could use this block to provide users with current
 ## Human In The Loop
 
 ### What it is
-Pause execution for human review. Data flows through approved_data or rejected_data output based on the reviewer's decision. Outputs contain the actual data, not status strings.
+Pause execution and wait for human approval or modification of data
 
 ### How it works
 <!-- MANUAL: how_it_works -->
@@ -988,18 +988,18 @@ This enables human oversight at critical points in automated workflows, ensuring
 
 | Input | Description | Type | Required |
 |-------|-------------|------|----------|
-| data | The data to be reviewed by a human user. This exact data will be passed through to either approved_data or rejected_data output based on the reviewer's decision. | Data | Yes |
-| name | A descriptive name for what this data represents. This helps the reviewer understand what they are reviewing. | str | Yes |
-| editable | Whether the human reviewer can edit the data before approving or rejecting it | bool | No |
+| data | The data to be reviewed by a human user | Data | Yes |
+| name | A descriptive name for what this data represents | str | Yes |
+| editable | Whether the human reviewer can edit the data | bool | No |
 
 ### Outputs
 
 | Output | Description | Type |
 |--------|-------------|------|
 | error | Error message if the operation failed | str |
-| approved_data | Outputs the input data when the reviewer APPROVES it. The value is the actual data itself (not a status string like 'APPROVED'). If the reviewer edited the data, this contains the modified version. Connect downstream blocks here for the 'approved' workflow path. | Approved Data |
-| rejected_data | Outputs the input data when the reviewer REJECTS it. The value is the actual data itself (not a status string like 'REJECTED'). If the reviewer edited the data, this contains the modified version. Connect downstream blocks here for the 'rejected' workflow path. | Rejected Data |
-| review_message | Optional message provided by the reviewer explaining their decision. Only outputs when the reviewer provides a message; this pin does not fire if no message was given. | str |
+| approved_data | The data when approved (may be modified by reviewer) | Approved Data |
+| rejected_data | The data when rejected (may be modified by reviewer) | Rejected Data |
+| review_message | Any message provided by the reviewer | str |
 
 ### Possible use case
 <!-- MANUAL: use_case -->