fix: Address code review feedback - use step title as key and add type safety

Co-authored-by: itsababseh <36419647+itsababseh@users.noreply.github.com>

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

@@ -2,7 +2,7 @@ import asyncio
 import logging
 import uuid
 from datetime import UTC, datetime
-from typing import Any, cast
+from typing import Any
 from weakref import WeakValueDictionary
 
 from openai.types.chat import (
@@ -104,26 +104,6 @@ class ChatSession(BaseModel):
     successful_agent_runs: dict[str, int] = {}
     successful_agent_schedules: dict[str, int] = {}
 
-    def add_tool_call_to_current_turn(self, tool_call: dict) -> None:
-        """Attach a tool_call to the current turn's assistant message.
-
-        Searches backwards for the most recent assistant message (stopping at
-        any user message boundary). If found, appends the tool_call to it.
-        Otherwise creates a new assistant message with the tool_call.
-        """
-        for msg in reversed(self.messages):
-            if msg.role == "user":
-                break
-            if msg.role == "assistant":
-                if not msg.tool_calls:
-                    msg.tool_calls = []
-                msg.tool_calls.append(tool_call)
-                return
-
-        self.messages.append(
-            ChatMessage(role="assistant", content="", tool_calls=[tool_call])
-        )
-
     @staticmethod
     def new(user_id: str) -> "ChatSession":
         return ChatSession(
@@ -192,47 +172,6 @@ class ChatSession(BaseModel):
             successful_agent_schedules=successful_agent_schedules,
         )
 
-    @staticmethod
-    def _merge_consecutive_assistant_messages(
-        messages: list[ChatCompletionMessageParam],
-    ) -> list[ChatCompletionMessageParam]:
-        """Merge consecutive assistant messages into single messages.
-
-        Long-running tool flows can create split assistant messages: one with
-        text content and another with tool_calls. Anthropic's API requires
-        tool_result blocks to reference a tool_use in the immediately preceding
-        assistant message, so these splits cause 400 errors via OpenRouter.
-        """
-        if len(messages) < 2:
-            return messages
-
-        result: list[ChatCompletionMessageParam] = [messages[0]]
-        for msg in messages[1:]:
-            prev = result[-1]
-            if prev.get("role") != "assistant" or msg.get("role") != "assistant":
-                result.append(msg)
-                continue
-
-            prev = cast(ChatCompletionAssistantMessageParam, prev)
-            curr = cast(ChatCompletionAssistantMessageParam, msg)
-
-            curr_content = curr.get("content") or ""
-            if curr_content:
-                prev_content = prev.get("content") or ""
-                prev["content"] = (
-                    f"{prev_content}\n{curr_content}" if prev_content else curr_content
-                )
-
-            curr_tool_calls = curr.get("tool_calls")
-            if curr_tool_calls:
-                prev_tool_calls = prev.get("tool_calls")
-                prev["tool_calls"] = (
-                    list(prev_tool_calls) + list(curr_tool_calls)
-                    if prev_tool_calls
-                    else list(curr_tool_calls)
-                )
-        return result
-
     def to_openai_messages(self) -> list[ChatCompletionMessageParam]:
         messages = []
         for message in self.messages:
@@ -319,7 +258,7 @@ class ChatSession(BaseModel):
                         name=message.name or "",
                     )
                 )
-        return self._merge_consecutive_assistant_messages(messages)
+        return messages
 
 
 async def _get_session_from_cache(session_id: str) -> ChatSession | None:

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

@@ -1,16 +1,4 @@
-from typing import cast
-
 import pytest
-from openai.types.chat import (
-    ChatCompletionAssistantMessageParam,
-    ChatCompletionMessageParam,
-    ChatCompletionToolMessageParam,
-    ChatCompletionUserMessageParam,
-)
-from openai.types.chat.chat_completion_message_tool_call_param import (
-    ChatCompletionMessageToolCallParam,
-    Function,
-)
 
 from .model import (
     ChatMessage,
@@ -129,205 +117,3 @@ async def test_chatsession_db_storage(setup_test_user, test_user_id):
                 loaded.tool_calls is not None
             ), f"Tool calls missing for {orig.role} message"
             assert len(orig.tool_calls) == len(loaded.tool_calls)
-
-
-# --------------------------------------------------------------------------- #
-#  _merge_consecutive_assistant_messages                                       #
-# --------------------------------------------------------------------------- #
-
-_tc = ChatCompletionMessageToolCallParam(
-    id="tc1", type="function", function=Function(name="do_stuff", arguments="{}")
-)
-_tc2 = ChatCompletionMessageToolCallParam(
-    id="tc2", type="function", function=Function(name="other", arguments="{}")
-)
-
-
-def test_merge_noop_when_no_consecutive_assistants():
-    """Messages without consecutive assistants are returned unchanged."""
-    msgs = [
-        ChatCompletionUserMessageParam(role="user", content="hi"),
-        ChatCompletionAssistantMessageParam(role="assistant", content="hello"),
-        ChatCompletionUserMessageParam(role="user", content="bye"),
-    ]
-    merged = ChatSession._merge_consecutive_assistant_messages(msgs)
-    assert len(merged) == 3
-    assert [m["role"] for m in merged] == ["user", "assistant", "user"]
-
-
-def test_merge_splits_text_and_tool_calls():
-    """The exact bug scenario: text-only assistant followed by tool_calls-only assistant."""
-    msgs = [
-        ChatCompletionUserMessageParam(role="user", content="build agent"),
-        ChatCompletionAssistantMessageParam(
-            role="assistant", content="Let me build that"
-        ),
-        ChatCompletionAssistantMessageParam(
-            role="assistant", content="", tool_calls=[_tc]
-        ),
-        ChatCompletionToolMessageParam(role="tool", content="ok", tool_call_id="tc1"),
-    ]
-    merged = ChatSession._merge_consecutive_assistant_messages(msgs)
-
-    assert len(merged) == 3
-    assert merged[0]["role"] == "user"
-    assert merged[2]["role"] == "tool"
-    a = cast(ChatCompletionAssistantMessageParam, merged[1])
-    assert a["role"] == "assistant"
-    assert a.get("content") == "Let me build that"
-    assert a.get("tool_calls") == [_tc]
-
-
-def test_merge_combines_tool_calls_from_both():
-    """Both consecutive assistants have tool_calls — they get merged."""
-    msgs: list[ChatCompletionAssistantMessageParam] = [
-        ChatCompletionAssistantMessageParam(
-            role="assistant", content="text", tool_calls=[_tc]
-        ),
-        ChatCompletionAssistantMessageParam(
-            role="assistant", content="", tool_calls=[_tc2]
-        ),
-    ]
-    merged = ChatSession._merge_consecutive_assistant_messages(msgs)  # type: ignore[arg-type]
-
-    assert len(merged) == 1
-    a = cast(ChatCompletionAssistantMessageParam, merged[0])
-    assert a.get("tool_calls") == [_tc, _tc2]
-    assert a.get("content") == "text"
-
-
-def test_merge_three_consecutive_assistants():
-    """Three consecutive assistants collapse into one."""
-    msgs: list[ChatCompletionAssistantMessageParam] = [
-        ChatCompletionAssistantMessageParam(role="assistant", content="a"),
-        ChatCompletionAssistantMessageParam(role="assistant", content="b"),
-        ChatCompletionAssistantMessageParam(
-            role="assistant", content="", tool_calls=[_tc]
-        ),
-    ]
-    merged = ChatSession._merge_consecutive_assistant_messages(msgs)  # type: ignore[arg-type]
-
-    assert len(merged) == 1
-    a = cast(ChatCompletionAssistantMessageParam, merged[0])
-    assert a.get("content") == "a\nb"
-    assert a.get("tool_calls") == [_tc]
-
-
-def test_merge_empty_and_single_message():
-    """Edge cases: empty list and single message."""
-    assert ChatSession._merge_consecutive_assistant_messages([]) == []
-
-    single: list[ChatCompletionMessageParam] = [
-        ChatCompletionUserMessageParam(role="user", content="hi")
-    ]
-    assert ChatSession._merge_consecutive_assistant_messages(single) == single
-
-
-# --------------------------------------------------------------------------- #
-#  add_tool_call_to_current_turn                                               #
-# --------------------------------------------------------------------------- #
-
-_raw_tc = {
-    "id": "tc1",
-    "type": "function",
-    "function": {"name": "f", "arguments": "{}"},
-}
-_raw_tc2 = {
-    "id": "tc2",
-    "type": "function",
-    "function": {"name": "g", "arguments": "{}"},
-}
-
-
-def test_add_tool_call_appends_to_existing_assistant():
-    """When the last assistant is from the current turn, tool_call is added to it."""
-    session = ChatSession.new(user_id="u")
-    session.messages = [
-        ChatMessage(role="user", content="hi"),
-        ChatMessage(role="assistant", content="working on it"),
-    ]
-    session.add_tool_call_to_current_turn(_raw_tc)
-
-    assert len(session.messages) == 2  # no new message created
-    assert session.messages[1].tool_calls == [_raw_tc]
-
-
-def test_add_tool_call_creates_assistant_when_none_exists():
-    """When there's no current-turn assistant, a new one is created."""
-    session = ChatSession.new(user_id="u")
-    session.messages = [
-        ChatMessage(role="user", content="hi"),
-    ]
-    session.add_tool_call_to_current_turn(_raw_tc)
-
-    assert len(session.messages) == 2
-    assert session.messages[1].role == "assistant"
-    assert session.messages[1].tool_calls == [_raw_tc]
-
-
-def test_add_tool_call_does_not_cross_user_boundary():
-    """A user message acts as a boundary — previous assistant is not modified."""
-    session = ChatSession.new(user_id="u")
-    session.messages = [
-        ChatMessage(role="assistant", content="old turn"),
-        ChatMessage(role="user", content="new message"),
-    ]
-    session.add_tool_call_to_current_turn(_raw_tc)
-
-    assert len(session.messages) == 3  # new assistant was created
-    assert session.messages[0].tool_calls is None  # old assistant untouched
-    assert session.messages[2].role == "assistant"
-    assert session.messages[2].tool_calls == [_raw_tc]
-
-
-def test_add_tool_call_multiple_times():
-    """Multiple long-running tool calls accumulate on the same assistant."""
-    session = ChatSession.new(user_id="u")
-    session.messages = [
-        ChatMessage(role="user", content="hi"),
-        ChatMessage(role="assistant", content="doing stuff"),
-    ]
-    session.add_tool_call_to_current_turn(_raw_tc)
-    # Simulate a pending tool result in between (like _yield_tool_call does)
-    session.messages.append(
-        ChatMessage(role="tool", content="pending", tool_call_id="tc1")
-    )
-    session.add_tool_call_to_current_turn(_raw_tc2)
-
-    assert len(session.messages) == 3  # user, assistant, tool — no extra assistant
-    assert session.messages[1].tool_calls == [_raw_tc, _raw_tc2]
-
-
-def test_to_openai_messages_merges_split_assistants():
-    """End-to-end: session with split assistants produces valid OpenAI messages."""
-    session = ChatSession.new(user_id="u")
-    session.messages = [
-        ChatMessage(role="user", content="build agent"),
-        ChatMessage(role="assistant", content="Let me build that"),
-        ChatMessage(
-            role="assistant",
-            content="",
-            tool_calls=[
-                {
-                    "id": "tc1",
-                    "type": "function",
-                    "function": {"name": "create_agent", "arguments": "{}"},
-                }
-            ],
-        ),
-        ChatMessage(role="tool", content="done", tool_call_id="tc1"),
-        ChatMessage(role="assistant", content="Saved!"),
-        ChatMessage(role="user", content="show me an example run"),
-    ]
-    openai_msgs = session.to_openai_messages()
-
-    # The two consecutive assistants at index 1,2 should be merged
-    roles = [m["role"] for m in openai_msgs]
-    assert roles == ["user", "assistant", "tool", "assistant", "user"]
-
-    # The merged assistant should have both content and tool_calls
-    merged = cast(ChatCompletionAssistantMessageParam, openai_msgs[1])
-    assert merged.get("content") == "Let me build that"
-    tc_list = merged.get("tool_calls")
-    assert tc_list is not None and len(list(tc_list)) == 1
-    assert list(tc_list)[0]["id"] == "tc1"

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

@@ -10,8 +10,6 @@ from typing import Any
 
 from pydantic import BaseModel, Field
 
-from backend.util.json import dumps as json_dumps
-
 
 class ResponseType(str, Enum):
     """Types of streaming responses following AI SDK protocol."""
@@ -195,18 +193,6 @@ class StreamError(StreamBaseResponse):
         default=None, description="Additional error details"
     )
 
-    def to_sse(self) -> str:
-        """Convert to SSE format, only emitting fields required by AI SDK protocol.
-
-        The AI SDK uses z.strictObject({type, errorText}) which rejects
-        any extra fields like `code` or `details`.
-        """
-        data = {
-            "type": self.type.value,
-            "errorText": self.errorText,
-        }
-        return f"data: {json_dumps(data)}\n\n"
-
 
 class StreamHeartbeat(StreamBaseResponse):
     """Heartbeat to keep SSE connection alive during long-running operations.

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

@@ -800,13 +800,9 @@ async def stream_chat_completion(
         # Build the messages list in the correct order
         messages_to_save: list[ChatMessage] = []
 
-        # Add assistant message with tool_calls if any.
-        # Use extend (not assign) to preserve tool_calls already added by
-        # _yield_tool_call for long-running tools.
+        # Add assistant message with tool_calls if any
         if accumulated_tool_calls:
-            if not assistant_response.tool_calls:
-                assistant_response.tool_calls = []
-            assistant_response.tool_calls.extend(accumulated_tool_calls)
+            assistant_response.tool_calls = accumulated_tool_calls
             logger.info(
                 f"Added {len(accumulated_tool_calls)} tool calls to assistant message"
             )
@@ -1408,9 +1404,13 @@ async def _yield_tool_call(
                 operation_id=operation_id,
             )
 
-            # Attach the tool_call to the current turn's assistant message
-            # (or create one if this is a tool-only response with no text).
-            session.add_tool_call_to_current_turn(tool_calls[yield_idx])
+            # Save assistant message with tool_call FIRST (required by LLM)
+            assistant_message = ChatMessage(
+                role="assistant",
+                content="",
+                tool_calls=[tool_calls[yield_idx]],
+            )
+            session.messages.append(assistant_message)
 
             # Then save pending tool result
             pending_message = ChatMessage(

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

@@ -2,7 +2,7 @@
 
 from datetime import datetime
 from enum import Enum
-from typing import Any
+from typing import Any, Literal
 
 from pydantic import BaseModel, Field
 
@@ -40,6 +40,8 @@ class ResponseType(str, Enum):
     OPERATION_IN_PROGRESS = "operation_in_progress"
     # Input validation
     INPUT_VALIDATION_ERROR = "input_validation_error"
+    # Agent creation steps
+    AGENT_CREATION_STEPS = "agent_creation_steps"
 
 
 # Base response model
@@ -286,6 +288,21 @@ class ClarificationNeededResponse(ToolResponseBase):
     questions: list[ClarifyingQuestion] = Field(default_factory=list)
 
 
+class AgentCreationStep(BaseModel):
+    """A single step in the agent creation process."""
+
+    title: str
+    description: str
+    status: Literal["pending", "in_progress", "completed"] = "pending"
+
+
+class AgentCreationStepsResponse(ToolResponseBase):
+    """Response showing the steps for agent creation as a checklist."""
+
+    type: ResponseType = ResponseType.AGENT_CREATION_STEPS
+    steps: list[AgentCreationStep] = Field(default_factory=list)
+
+
 # Documentation search models
 class DocSearchResult(BaseModel):
     """A single documentation search result."""

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/backend/backend/blocks/telegram/__init__.py

@@ -1 +0,0 @@
-# Telegram Bot blocks for AutoGPT Platform

autogpt_platform/backend/backend/blocks/telegram/_api.py

@@ -1,159 +0,0 @@
-"""
-Telegram Bot API helper functions.
-
-Provides utilities for making authenticated requests to the Telegram Bot API.
-"""
-
-import logging
-from io import BytesIO
-from typing import Any, Optional
-
-from backend.data.model import APIKeyCredentials
-from backend.util.request import Requests
-
-logger = logging.getLogger(__name__)
-
-TELEGRAM_API_BASE = "https://api.telegram.org"
-
-
-class TelegramAPIException(Exception):
-    """Exception raised for Telegram API errors."""
-
-    def __init__(self, message: str, error_code: int = 0):
-        super().__init__(message)
-        self.error_code = error_code
-
-
-def get_bot_api_url(bot_token: str, method: str) -> str:
-    """Construct Telegram Bot API URL for a method."""
-    return f"{TELEGRAM_API_BASE}/bot{bot_token}/{method}"
-
-
-def get_file_url(bot_token: str, file_path: str) -> str:
-    """Construct Telegram file download URL."""
-    return f"{TELEGRAM_API_BASE}/file/bot{bot_token}/{file_path}"
-
-
-async def call_telegram_api(
-    credentials: APIKeyCredentials,
-    method: str,
-    data: Optional[dict[str, Any]] = None,
-) -> dict[str, Any]:
-    """
-    Make a request to the Telegram Bot API.
-
-    Args:
-        credentials: Bot token credentials
-        method: API method name (e.g., "sendMessage", "getFile")
-        data: Request parameters
-
-    Returns:
-        API response result
-
-    Raises:
-        TelegramAPIException: If the API returns an error
-    """
-    token = credentials.api_key.get_secret_value()
-    url = get_bot_api_url(token, method)
-
-    response = await Requests().post(url, json=data or {})
-    result = response.json()
-
-    if not result.get("ok"):
-        error_code = result.get("error_code", 0)
-        description = result.get("description", "Unknown error")
-        raise TelegramAPIException(description, error_code)
-
-    return result.get("result", {})
-
-
-async def call_telegram_api_with_file(
-    credentials: APIKeyCredentials,
-    method: str,
-    file_field: str,
-    file_data: bytes,
-    filename: str,
-    content_type: str,
-    data: Optional[dict[str, Any]] = None,
-) -> dict[str, Any]:
-    """
-    Make a multipart/form-data request to the Telegram Bot API with a file upload.
-
-    Args:
-        credentials: Bot token credentials
-        method: API method name (e.g., "sendPhoto", "sendVoice")
-        file_field: Form field name for the file (e.g., "photo", "voice")
-        file_data: Raw file bytes
-        filename: Filename for the upload
-        content_type: MIME type of the file
-        data: Additional form parameters
-
-    Returns:
-        API response result
-
-    Raises:
-        TelegramAPIException: If the API returns an error
-    """
-    token = credentials.api_key.get_secret_value()
-    url = get_bot_api_url(token, method)
-
-    files = [(file_field, (filename, BytesIO(file_data), content_type))]
-
-    response = await Requests().post(url, files=files, data=data or {})
-    result = response.json()
-
-    if not result.get("ok"):
-        error_code = result.get("error_code", 0)
-        description = result.get("description", "Unknown error")
-        raise TelegramAPIException(description, error_code)
-
-    return result.get("result", {})
-
-
-async def get_file_info(credentials: APIKeyCredentials, file_id: str) -> dict[str, Any]:
-    """
-    Get file information from Telegram.
-
-    Args:
-        credentials: Bot token credentials
-        file_id: Telegram file_id from message
-
-    Returns:
-        File info dict containing file_id, file_unique_id, file_size, file_path
-    """
-    return await call_telegram_api(credentials, "getFile", {"file_id": file_id})
-
-
-async def get_file_download_url(credentials: APIKeyCredentials, file_id: str) -> str:
-    """
-    Get the download URL for a Telegram file.
-
-    Args:
-        credentials: Bot token credentials
-        file_id: Telegram file_id from message
-
-    Returns:
-        Full download URL
-    """
-    token = credentials.api_key.get_secret_value()
-    result = await get_file_info(credentials, file_id)
-    file_path = result.get("file_path")
-    if not file_path:
-        raise TelegramAPIException("No file_path returned from getFile")
-    return get_file_url(token, file_path)
-
-
-async def download_telegram_file(credentials: APIKeyCredentials, file_id: str) -> bytes:
-    """
-    Download a file from Telegram servers.
-
-    Args:
-        credentials: Bot token credentials
-        file_id: Telegram file_id
-
-    Returns:
-        File content as bytes
-    """
-    url = await get_file_download_url(credentials, file_id)
-    response = await Requests().get(url)
-    return response.content

autogpt_platform/backend/backend/blocks/telegram/_auth.py

@@ -1,43 +0,0 @@
-"""
-Telegram Bot credentials handling.
-
-Telegram bots use an API key (bot token) obtained from @BotFather.
-"""
-
-from typing import Literal
-
-from pydantic import SecretStr
-
-from backend.data.model import APIKeyCredentials, CredentialsField, CredentialsMetaInput
-from backend.integrations.providers import ProviderName
-
-# Bot token credentials (API key style)
-TelegramCredentials = APIKeyCredentials
-TelegramCredentialsInput = CredentialsMetaInput[
-    Literal[ProviderName.TELEGRAM], Literal["api_key"]
-]
-
-
-def TelegramCredentialsField() -> TelegramCredentialsInput:
-    """Creates a Telegram bot token credentials field."""
-    return CredentialsField(
-        description="Telegram Bot API token from @BotFather. "
-        "Create a bot at https://t.me/BotFather to get your token."
-    )
-
-
-# Test credentials for unit tests
-TEST_CREDENTIALS = APIKeyCredentials(
-    id="01234567-89ab-cdef-0123-456789abcdef",
-    provider="telegram",
-    api_key=SecretStr("test_telegram_bot_token"),
-    title="Mock Telegram Bot Token",
-    expires_at=None,
-)
-
-TEST_CREDENTIALS_INPUT = {
-    "provider": TEST_CREDENTIALS.provider,
-    "id": TEST_CREDENTIALS.id,
-    "type": TEST_CREDENTIALS.type,
-    "title": TEST_CREDENTIALS.title,
-}

autogpt_platform/backend/backend/blocks/telegram/triggers.py

@@ -1,352 +0,0 @@
-"""
-Telegram trigger blocks for receiving messages via webhooks.
-"""
-
-import logging
-
-from pydantic import BaseModel
-
-from backend.data.block import (
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
-    BlockWebhookConfig,
-)
-from backend.data.model import SchemaField
-from backend.integrations.providers import ProviderName
-from backend.integrations.webhooks.telegram import TelegramWebhookType
-
-from ._auth import (
-    TEST_CREDENTIALS,
-    TEST_CREDENTIALS_INPUT,
-    TelegramCredentialsField,
-    TelegramCredentialsInput,
-)
-
-logger = logging.getLogger(__name__)
-
-
-# Example payload for testing
-EXAMPLE_MESSAGE_PAYLOAD = {
-    "update_id": 123456789,
-    "message": {
-        "message_id": 1,
-        "from": {
-            "id": 12345678,
-            "is_bot": False,
-            "first_name": "John",
-            "last_name": "Doe",
-            "username": "johndoe",
-            "language_code": "en",
-        },
-        "chat": {
-            "id": 12345678,
-            "first_name": "John",
-            "last_name": "Doe",
-            "username": "johndoe",
-            "type": "private",
-        },
-        "date": 1234567890,
-        "text": "Hello, bot!",
-    },
-}
-
-
-class TelegramTriggerBase:
-    """Base class for Telegram trigger blocks."""
-
-    class Input(BlockSchemaInput):
-        credentials: TelegramCredentialsInput = TelegramCredentialsField()
-        payload: dict = SchemaField(hidden=True, default_factory=dict)
-
-
-class TelegramMessageTriggerBlock(TelegramTriggerBase, Block):
-    """
-    Triggers when a message is received by your Telegram bot.
-
-    Supports text, photos, voice messages, and audio files.
-    Connect the outputs to other blocks to process messages and send responses.
-    """
-
-    class Input(TelegramTriggerBase.Input):
-        class EventsFilter(BaseModel):
-            """Filter for message types to receive."""
-
-            text: bool = True
-            photo: bool = False
-            voice: bool = False
-            audio: bool = False
-            document: bool = False
-            video: bool = False
-
-        events: EventsFilter = SchemaField(
-            title="Message Types", description="Types of messages to receive"
-        )
-
-    class Output(BlockSchemaOutput):
-        payload: dict = SchemaField(
-            description="The complete webhook payload from Telegram"
-        )
-        chat_id: int = SchemaField(
-            description="The chat ID where the message was received. "
-            "Use this to send replies."
-        )
-        message_id: int = SchemaField(description="The unique message ID")
-        user_id: int = SchemaField(description="The user ID who sent the message")
-        username: str = SchemaField(description="Username of the sender (may be empty)")
-        first_name: str = SchemaField(description="First name of the sender")
-        event: str = SchemaField(
-            description="The message type (text, photo, voice, audio, etc.)"
-        )
-        text: str = SchemaField(
-            description="Text content of the message (for text messages)"
-        )
-        photo_file_id: str = SchemaField(
-            description="File ID of the photo (for photo messages). "
-            "Use GetTelegramFileBlock to download."
-        )
-        voice_file_id: str = SchemaField(
-            description="File ID of the voice message (for voice messages). "
-            "Use GetTelegramFileBlock to download."
-        )
-        audio_file_id: str = SchemaField(
-            description="File ID of the audio file (for audio messages). "
-            "Use GetTelegramFileBlock to download."
-        )
-        file_id: str = SchemaField(
-            description="File ID for document/video messages. "
-            "Use GetTelegramFileBlock to download."
-        )
-        file_name: str = SchemaField(
-            description="Original filename (for document/audio messages)"
-        )
-        caption: str = SchemaField(description="Caption for media messages")
-
-    def __init__(self):
-        super().__init__(
-            id="4435e4e0-df6e-4301-8f35-ad70b12fc9ec",
-            description="Triggers when a message is received by your Telegram bot. "
-            "Supports text, photos, voice messages, and audio files.",
-            categories={BlockCategory.SOCIAL},
-            input_schema=TelegramMessageTriggerBlock.Input,
-            output_schema=TelegramMessageTriggerBlock.Output,
-            webhook_config=BlockWebhookConfig(
-                provider=ProviderName.TELEGRAM,
-                webhook_type=TelegramWebhookType.BOT,
-                resource_format="bot",
-                event_filter_input="events",
-                event_format="message.{event}",
-            ),
-            test_input={
-                "events": {"text": True, "photo": True},
-                "credentials": TEST_CREDENTIALS_INPUT,
-                "payload": EXAMPLE_MESSAGE_PAYLOAD,
-            },
-            test_credentials=TEST_CREDENTIALS,
-            test_output=[
-                ("payload", EXAMPLE_MESSAGE_PAYLOAD),
-                ("chat_id", 12345678),
-                ("message_id", 1),
-                ("user_id", 12345678),
-                ("username", "johndoe"),
-                ("first_name", "John"),
-                ("event", "text"),
-                ("text", "Hello, bot!"),
-                ("photo_file_id", ""),
-                ("voice_file_id", ""),
-                ("audio_file_id", ""),
-                ("file_id", ""),
-                ("file_name", ""),
-                ("caption", ""),
-            ],
-        )
-
-    async def run(self, input_data: Input, **kwargs) -> BlockOutput:
-        payload = input_data.payload
-        message = payload.get("message", {})
-
-        # Extract common fields
-        chat = message.get("chat", {})
-        sender = message.get("from", {})
-
-        yield "payload", payload
-        yield "chat_id", chat.get("id", 0)
-        yield "message_id", message.get("message_id", 0)
-        yield "user_id", sender.get("id", 0)
-        yield "username", sender.get("username", "")
-        yield "first_name", sender.get("first_name", "")
-
-        # Determine message type and extract content
-        if "text" in message:
-            yield "event", "text"
-            yield "text", message.get("text", "")
-            yield "photo_file_id", ""
-            yield "voice_file_id", ""
-            yield "audio_file_id", ""
-            yield "file_id", ""
-            yield "file_name", ""
-            yield "caption", ""
-        elif "photo" in message:
-            # Get the largest photo (last in array)
-            photos = message.get("photo", [])
-            photo_fid = photos[-1]["file_id"] if photos else ""
-            yield "event", "photo"
-            yield "text", ""
-            yield "photo_file_id", photo_fid
-            yield "voice_file_id", ""
-            yield "audio_file_id", ""
-            yield "file_id", ""
-            yield "file_name", ""
-            yield "caption", message.get("caption", "")
-        elif "voice" in message:
-            voice = message.get("voice", {})
-            yield "event", "voice"
-            yield "text", ""
-            yield "photo_file_id", ""
-            yield "voice_file_id", voice.get("file_id", "")
-            yield "audio_file_id", ""
-            yield "file_id", ""
-            yield "file_name", ""
-            yield "caption", message.get("caption", "")
-        elif "audio" in message:
-            audio = message.get("audio", {})
-            yield "event", "audio"
-            yield "text", ""
-            yield "photo_file_id", ""
-            yield "voice_file_id", ""
-            yield "audio_file_id", audio.get("file_id", "")
-            yield "file_id", ""
-            yield "file_name", audio.get("file_name", "")
-            yield "caption", message.get("caption", "")
-        elif "document" in message:
-            document = message.get("document", {})
-            yield "event", "document"
-            yield "text", ""
-            yield "photo_file_id", ""
-            yield "voice_file_id", ""
-            yield "audio_file_id", ""
-            yield "file_id", document.get("file_id", "")
-            yield "file_name", document.get("file_name", "")
-            yield "caption", message.get("caption", "")
-        elif "video" in message:
-            video = message.get("video", {})
-            yield "event", "video"
-            yield "text", ""
-            yield "photo_file_id", ""
-            yield "voice_file_id", ""
-            yield "audio_file_id", ""
-            yield "file_id", video.get("file_id", "")
-            yield "file_name", video.get("file_name", "")
-            yield "caption", message.get("caption", "")
-        else:
-            yield "event", "other"
-            yield "text", ""
-            yield "photo_file_id", ""
-            yield "voice_file_id", ""
-            yield "audio_file_id", ""
-            yield "file_id", ""
-            yield "file_name", ""
-            yield "caption", ""
-
-
-# Example payload for reaction trigger testing
-EXAMPLE_REACTION_PAYLOAD = {
-    "update_id": 123456790,
-    "message_reaction": {
-        "chat": {
-            "id": 12345678,
-            "first_name": "John",
-            "last_name": "Doe",
-            "username": "johndoe",
-            "type": "private",
-        },
-        "message_id": 42,
-        "user": {
-            "id": 12345678,
-            "is_bot": False,
-            "first_name": "John",
-            "username": "johndoe",
-        },
-        "date": 1234567890,
-        "new_reaction": [{"type": "emoji", "emoji": "👍"}],
-        "old_reaction": [],
-    },
-}
-
-
-class TelegramMessageReactionTriggerBlock(TelegramTriggerBase, Block):
-    """
-    Triggers when a reaction to a message is changed.
-
-    Works automatically in private chats. In group chats, the bot must be
-    an administrator to receive reaction updates.
-    """
-
-    class Input(TelegramTriggerBase.Input):
-        pass
-
-    class Output(BlockSchemaOutput):
-        payload: dict = SchemaField(
-            description="The complete webhook payload from Telegram"
-        )
-        chat_id: int = SchemaField(
-            description="The chat ID where the reaction occurred"
-        )
-        message_id: int = SchemaField(description="The message ID that was reacted to")
-        user_id: int = SchemaField(description="The user ID who changed the reaction")
-        username: str = SchemaField(description="Username of the user (may be empty)")
-        new_reactions: list = SchemaField(
-            description="List of new reactions on the message"
-        )
-        old_reactions: list = SchemaField(
-            description="List of previous reactions on the message"
-        )
-
-    def __init__(self):
-        super().__init__(
-            id="82525328-9368-4966-8f0c-cd78e80181fd",
-            description="Triggers when a reaction to a message is changed. "
-            "Works in private chats automatically. "
-            "In groups, the bot must be an administrator.",
-            categories={BlockCategory.SOCIAL},
-            input_schema=TelegramMessageReactionTriggerBlock.Input,
-            output_schema=TelegramMessageReactionTriggerBlock.Output,
-            webhook_config=BlockWebhookConfig(
-                provider=ProviderName.TELEGRAM,
-                webhook_type=TelegramWebhookType.BOT,
-                resource_format="bot",
-                event_filter_input="",
-                event_format="message_reaction",
-            ),
-            test_input={
-                "credentials": TEST_CREDENTIALS_INPUT,
-                "payload": EXAMPLE_REACTION_PAYLOAD,
-            },
-            test_credentials=TEST_CREDENTIALS,
-            test_output=[
-                ("payload", EXAMPLE_REACTION_PAYLOAD),
-                ("chat_id", 12345678),
-                ("message_id", 42),
-                ("user_id", 12345678),
-                ("username", "johndoe"),
-                ("new_reactions", [{"type": "emoji", "emoji": "👍"}]),
-                ("old_reactions", []),
-            ],
-        )
-
-    async def run(self, input_data: Input, **kwargs) -> BlockOutput:
-        payload = input_data.payload
-        reaction = payload.get("message_reaction", {})
-
-        chat = reaction.get("chat", {})
-        user = reaction.get("user", {})
-
-        yield "payload", payload
-        yield "chat_id", chat.get("id", 0)
-        yield "message_id", reaction.get("message_id", 0)
-        yield "user_id", user.get("id", 0)
-        yield "username", user.get("username", "")
-        yield "new_reactions", reaction.get("new_reaction", [])
-        yield "old_reactions", reaction.get("old_reaction", [])

autogpt_platform/backend/backend/integrations/providers.py

@@ -46,7 +46,6 @@ class ProviderName(str, Enum):
     SLANT3D = "slant3d"
     SMARTLEAD = "smartlead"
     SMTP = "smtp"
-    TELEGRAM = "telegram"
     TWITTER = "twitter"
     TODOIST = "todoist"
     UNREAL_SPEECH = "unreal_speech"

autogpt_platform/backend/backend/integrations/webhooks/__init__.py

@@ -15,7 +15,6 @@ def load_webhook_managers() -> dict["ProviderName", type["BaseWebhooksManager"]]
     from .compass import CompassWebhookManager
     from .github import GithubWebhooksManager
     from .slant3d import Slant3DWebhooksManager
-    from .telegram import TelegramWebhooksManager
 
     webhook_managers.update(
         {
@@ -24,7 +23,6 @@ def load_webhook_managers() -> dict["ProviderName", type["BaseWebhooksManager"]]
                 CompassWebhookManager,
                 GithubWebhooksManager,
                 Slant3DWebhooksManager,
-                TelegramWebhooksManager,
             ]
         }
     )

autogpt_platform/backend/backend/integrations/webhooks/telegram.py

@@ -1,166 +0,0 @@
-"""
-Telegram Bot API Webhooks Manager.
-
-Handles webhook registration and validation for Telegram bots.
-"""
-
-import logging
-
-from fastapi import HTTPException, Request
-from strenum import StrEnum
-
-from backend.data import integrations
-from backend.data.model import APIKeyCredentials, Credentials
-from backend.integrations.providers import ProviderName
-from backend.util.request import Requests
-
-from ._base import BaseWebhooksManager
-
-logger = logging.getLogger(__name__)
-
-
-class TelegramWebhookType(StrEnum):
-    BOT = "bot"
-
-
-class TelegramWebhooksManager(BaseWebhooksManager[TelegramWebhookType]):
-    """
-    Manages Telegram bot webhooks.
-
-    Telegram webhooks are registered via the setWebhook API method.
-    Incoming requests are validated using the secret_token header.
-    """
-
-    PROVIDER_NAME = ProviderName.TELEGRAM
-    WebhookType = TelegramWebhookType
-
-    TELEGRAM_API_BASE = "https://api.telegram.org"
-
-    @classmethod
-    async def validate_payload(
-        cls,
-        webhook: integrations.Webhook,
-        request: Request,
-        credentials: Credentials | None,
-    ) -> tuple[dict, str]:
-        """
-        Validates incoming Telegram webhook request.
-
-        Telegram sends X-Telegram-Bot-Api-Secret-Token header when secret_token
-        was set in setWebhook call.
-
-        Returns:
-            tuple: (payload dict, event_type string)
-        """
-        # Verify secret token header
-        secret_header = request.headers.get("X-Telegram-Bot-Api-Secret-Token")
-        if not secret_header or secret_header != webhook.secret:
-            raise HTTPException(
-                status_code=403,
-                detail="Invalid or missing X-Telegram-Bot-Api-Secret-Token",
-            )
-
-        payload = await request.json()
-
-        # Determine event type based on update content
-        # Telegram updates can contain: message, edited_message,
-        # channel_post, callback_query, etc.
-        if "message" in payload:
-            message = payload["message"]
-            if "text" in message:
-                event_type = "message.text"
-            elif "photo" in message:
-                event_type = "message.photo"
-            elif "voice" in message:
-                event_type = "message.voice"
-            elif "audio" in message:
-                event_type = "message.audio"
-            elif "document" in message:
-                event_type = "message.document"
-            elif "video" in message:
-                event_type = "message.video"
-            else:
-                event_type = "message.other"
-        elif "edited_message" in payload:
-            event_type = "edited_message"
-        elif "message_reaction" in payload:
-            event_type = "message_reaction"
-        elif "callback_query" in payload:
-            event_type = "callback_query"
-        else:
-            event_type = "unknown"
-
-        return payload, event_type
-
-    async def _register_webhook(
-        self,
-        credentials: Credentials,
-        webhook_type: TelegramWebhookType,
-        resource: str,
-        events: list[str],
-        ingress_url: str,
-        secret: str,
-    ) -> tuple[str, dict]:
-        """
-        Register webhook with Telegram using setWebhook API.
-
-        Args:
-            credentials: Bot token credentials
-            webhook_type: Type of webhook (always BOT for Telegram)
-            resource: Resource identifier (unused for Telegram, bots are global)
-            events: Events to subscribe to
-            ingress_url: URL to receive webhook payloads
-            secret: Secret token for request validation
-
-        Returns:
-            tuple: (provider_webhook_id, config dict)
-        """
-        if not isinstance(credentials, APIKeyCredentials):
-            raise ValueError("API key (bot token) is required for Telegram webhooks")
-
-        token = credentials.api_key.get_secret_value()
-        url = f"{self.TELEGRAM_API_BASE}/bot{token}/setWebhook"
-
-        # Telegram setWebhook parameters
-        webhook_data = {
-            "url": ingress_url,
-            "secret_token": secret,
-            "allowed_updates": ["message", "edited_message", "message_reaction"],
-        }
-
-        response = await Requests().post(url, json=webhook_data)
-        result = response.json()
-
-        if not result.get("ok"):
-            error_desc = result.get("description", "Unknown error")
-            raise ValueError(f"Failed to set Telegram webhook: {error_desc}")
-
-        # Telegram doesn't return a webhook ID, use empty string
-        config = {
-            "url": ingress_url,
-            "allowed_updates": webhook_data["allowed_updates"],
-        }
-
-        return "", config
-
-    async def _deregister_webhook(
-        self, webhook: integrations.Webhook, credentials: Credentials
-    ) -> None:
-        """
-        Deregister webhook by calling setWebhook with empty URL.
-
-        This removes the webhook from Telegram's servers.
-        """
-        if not isinstance(credentials, APIKeyCredentials):
-            raise ValueError("API key (bot token) is required for Telegram webhooks")
-
-        token = credentials.api_key.get_secret_value()
-        url = f"{self.TELEGRAM_API_BASE}/bot{token}/setWebhook"
-
-        # Setting empty URL removes the webhook
-        response = await Requests().post(url, json={"url": ""})
-        result = response.json()
-
-        if not result.get("ok"):
-            error_desc = result.get("description", "Unknown error")
-            logger.warning(f"Failed to deregister Telegram webhook: {error_desc}")

autogpt_platform/backend/backend/util/prompt.py

@@ -364,44 +364,6 @@ def _remove_orphan_tool_responses(
     return result
 
 
-def validate_and_remove_orphan_tool_responses(
-    messages: list[dict],
-    log_warning: bool = True,
-) -> list[dict]:
-    """
-    Validate tool_call/tool_response pairs and remove orphaned responses.
-
-    Scans messages in order, tracking all tool_call IDs. Any tool response
-    referencing an ID not seen in a preceding message is considered orphaned
-    and removed. This prevents API errors like Anthropic's "unexpected tool_use_id".
-
-    Args:
-        messages: List of messages to validate (OpenAI or Anthropic format)
-        log_warning: Whether to log a warning when orphans are found
-
-    Returns:
-        A new list with orphaned tool responses removed
-    """
-    available_ids: set[str] = set()
-    orphan_ids: set[str] = set()
-
-    for msg in messages:
-        available_ids |= _extract_tool_call_ids_from_message(msg)
-        for resp_id in _extract_tool_response_ids_from_message(msg):
-            if resp_id not in available_ids:
-                orphan_ids.add(resp_id)
-
-    if not orphan_ids:
-        return messages
-
-    if log_warning:
-        logger.warning(
-            f"Removing {len(orphan_ids)} orphan tool response(s): {orphan_ids}"
-        )
-
-    return _remove_orphan_tool_responses(messages, orphan_ids)
-
-
 def _ensure_tool_pairs_intact(
     recent_messages: list[dict],
     all_messages: list[dict],
@@ -761,13 +723,6 @@ async def compress_context(
 
     # Filter out any None values that may have been introduced
     final_msgs: list[dict] = [m for m in msgs if m is not None]
-
-    # ---- STEP 6: Final tool-pair validation ---------------------------------
-    # After all compression steps, verify that every tool response has a
-    # matching tool_call in a preceding assistant message. Remove orphans
-    # to prevent API errors (e.g., Anthropic's "unexpected tool_use_id").
-    final_msgs = validate_and_remove_orphan_tool_responses(final_msgs)
-
     final_count = sum(_msg_tokens(m, enc) for m in final_msgs)
     error = None
     if final_count + reserve > target_tokens:

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

@@ -10,9 +10,8 @@ import {
   MessageResponse,
 } from "@/components/ai-elements/message";
 import { LoadingSpinner } from "@/components/atoms/LoadingSpinner/LoadingSpinner";
-import { toast } from "@/components/molecules/Toast/use-toast";
 import { ToolUIPart, UIDataTypes, UIMessage, UITools } from "ai";
-import { useEffect, useRef, useState } from "react";
+import { useEffect, useState } from "react";
 import { CreateAgentTool } from "../../tools/CreateAgent/CreateAgent";
 import { EditAgentTool } from "../../tools/EditAgent/EditAgent";
 import { FindAgentsTool } from "../../tools/FindAgents/FindAgents";
@@ -122,7 +121,6 @@ export const ChatMessagesContainer = ({
   isLoading,
 }: ChatMessagesContainerProps) => {
   const [thinkingPhrase, setThinkingPhrase] = useState(getRandomPhrase);
-  const lastToastTimeRef = useRef(0);
 
   useEffect(() => {
     if (status === "submitted") {
@@ -130,20 +128,6 @@ export const ChatMessagesContainer = ({
     }
   }, [status]);
 
-  // Show a toast when a new error occurs, debounced to avoid spam
-  useEffect(() => {
-    if (!error) return;
-    const now = Date.now();
-    if (now - lastToastTimeRef.current < 3_000) return;
-    lastToastTimeRef.current = now;
-    toast({
-      variant: "destructive",
-      title: "Something went wrong",
-      description:
-        "The assistant encountered an error. Please try sending your message again.",
-    });
-  }, [error]);
-
   const lastMessage = messages[messages.length - 1];
   const lastAssistantHasVisibleContent =
     lastMessage?.role === "assistant" &&
@@ -279,12 +263,8 @@ export const ChatMessagesContainer = ({
           </Message>
         )}
         {error && (
-          <div className="rounded-lg bg-red-50 p-4 text-sm text-red-700">
-            <p className="font-medium">Something went wrong</p>
-            <p className="mt-1 text-red-600">
-              The assistant encountered an error. Please try sending your
-              message again.
-            </p>
+          <div className="rounded-lg bg-red-50 p-3 text-red-600">
+            Error: {error.message}
           </div>
         )}
       </ConversationContent>

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

@@ -30,7 +30,7 @@ export function ContentCard({
   return (
     <div
       className={cn(
-        "min-w-0 rounded-lg bg-gradient-to-r from-purple-500/30 to-blue-500/30 p-[1px]",
+        "rounded-lg bg-gradient-to-r from-purple-500/30 to-blue-500/30 p-[1px]",
         className,
       )}
     >

autogpt_platform/frontend/src/app/(platform)/copilot/tools/CreateAgent/CreateAgent.tsx

@@ -4,6 +4,7 @@ import { WarningDiamondIcon } from "@phosphor-icons/react";
 import type { ToolUIPart } from "ai";
 import { useCopilotChatActions } from "../../components/CopilotChatActionsProvider/useCopilotChatActions";
 import { MorphingTextAnimation } from "../../components/MorphingTextAnimation/MorphingTextAnimation";
+import { OrbitLoader } from "../../components/OrbitLoader/OrbitLoader";
 import { ProgressBar } from "../../components/ProgressBar/ProgressBar";
 import {
   ContentCardDescription,
@@ -15,6 +16,10 @@ import {
 } from "../../components/ToolAccordion/AccordionContent";
 import { ToolAccordion } from "../../components/ToolAccordion/ToolAccordion";
 import { useAsymptoticProgress } from "../../hooks/useAsymptoticProgress";
+import {
+  AgentCreationChecklistCard,
+  AgentCreationChecklistStep,
+} from "./components/AgentCreationChecklistCard";
 import {
   ClarificationQuestionsCard,
   ClarifyingQuestion,
@@ -76,7 +81,7 @@ function getAccordionMeta(output: CreateAgentToolOutput) {
     isOperationInProgressOutput(output)
   ) {
     return {
-      icon,
+      icon: <OrbitLoader size={32} />,
       title: "Creating agent, this may take a few minutes. Sit back and relax.",
     };
   }
@@ -152,6 +157,9 @@ export function CreateAgentTool({ part }: Props) {
         <ToolAccordion {...getAccordionMeta(output)}>
           {isOperating && (
             <ContentGrid>
+              <AgentCreationChecklistCard
+                steps={getAgentCreationSteps(progress)}
+              />
               <ProgressBar value={progress} className="max-w-[280px]" />
               <ContentHint>
                 This could take a few minutes, grab a coffee ☕
@@ -232,3 +240,32 @@ export function CreateAgentTool({ part }: Props) {
     </div>
   );
 }
+
+function getAgentCreationSteps(progress: number): AgentCreationChecklistStep[] {
+  return [
+    {
+      title: "Analyzing your request",
+      description:
+        "Understanding your requirements and breaking down the goal into actionable steps",
+      status: progress > 10 ? "completed" : "pending",
+    },
+    {
+      title: "Searching for relevant blocks",
+      description:
+        "Finding the best building blocks to accomplish your agent's tasks",
+      status: progress > 30 ? "completed" : "pending",
+    },
+    {
+      title: "Designing the workflow",
+      description:
+        "Creating the agent structure and connecting blocks together",
+      status: progress > 60 ? "completed" : "pending",
+    },
+    {
+      title: "Validating and optimizing",
+      description:
+        "Ensuring the agent works correctly and optimizing the workflow",
+      status: progress > 85 ? "completed" : "pending",
+    },
+  ];
+}

autogpt_platform/frontend/src/app/(platform)/copilot/tools/CreateAgent/components/AgentCreationChecklistCard.tsx

@@ -0,0 +1,56 @@
+import { CheckCircleIcon, CircleIcon } from "@phosphor-icons/react";
+
+export interface AgentCreationChecklistStep {
+  title: string;
+  description: string;
+  status: "pending" | "in_progress" | "completed";
+}
+
+interface Props {
+  steps: AgentCreationChecklistStep[];
+}
+
+export function AgentCreationChecklistCard({ steps }: Props) {
+  return (
+    <div className="space-y-3">
+      <p className="text-sm text-muted-foreground">
+        Creating your custom agent...
+      </p>
+      <div className="space-y-2">
+        {steps.map((step) => (
+          <div key={step.title} className="flex items-start gap-3">
+            <div className="mt-0.5 flex-shrink-0">
+              {step.status === "completed" ? (
+                <CheckCircleIcon
+                  size={20}
+                  weight="fill"
+                  className="text-green-500"
+                />
+              ) : (
+                <CircleIcon
+                  size={20}
+                  weight="regular"
+                  className="text-neutral-400"
+                />
+              )}
+            </div>
+            <div className="flex-1 space-y-1">
+              <div
+                className={`text-sm font-medium ${
+                  step.status === "completed"
+                    ? "text-neutral-900 dark:text-neutral-100"
+                    : "text-muted-foreground"
+                }`}
+              >
+                {step.title}
+              </div>
+              <div className="text-xs text-muted-foreground">
+                {step.description}
+              </div>
+            </div>
+          </div>
+        ))}
+      </div>
+    </div>
+  );
+}

autogpt_platform/frontend/src/app/(platform)/copilot/tools/RunAgent/helpers.tsx

@@ -203,7 +203,7 @@ export function getAccordionMeta(output: RunAgentToolOutput): {
         ? output.status.trim()
         : "started";
     return {
-      icon,
+      icon: <OrbitLoader size={28} className="text-neutral-700" />,
       title: output.graph_name,
       description: `Status: ${statusText}`,
     };

autogpt_platform/frontend/src/app/(platform)/copilot/tools/RunBlock/helpers.tsx

@@ -149,7 +149,7 @@ export function getAccordionMeta(output: RunBlockToolOutput): {
   if (isRunBlockBlockOutput(output)) {
     const keys = Object.keys(output.outputs ?? {});
     return {
-      icon,
+      icon: <OrbitLoader size={24} className="text-neutral-700" />,
       title: output.block_name,
       description:
         keys.length > 0

autogpt_platform/frontend/src/app/api/chat/sessions/[sessionId]/stream/route.ts

@@ -1,8 +1,11 @@
 import { environment } from "@/services/environment";
 import { getServerAuthToken } from "@/lib/autogpt-server-api/helpers";
 import { NextRequest } from "next/server";
-import { normalizeSSEStream, SSE_HEADERS } from "../../../sse-helpers";
 
+/**
+ * SSE Proxy for chat streaming.
+ * Supports POST with context (page content + URL) in the request body.
+ */
 export async function POST(
   request: NextRequest,
   { params }: { params: Promise<{ sessionId: string }> },
@@ -20,14 +23,17 @@ export async function POST(
       );
     }
 
+    // Get auth token from server-side session
     const token = await getServerAuthToken();
 
+    // Build backend URL
     const backendUrl = environment.getAGPTServerBaseUrl();
     const streamUrl = new URL(
       `/api/chat/sessions/${sessionId}/stream`,
       backendUrl,
     );
 
+    // Forward request to backend with auth header
     const headers: Record<string, string> = {
       "Content-Type": "application/json",
       Accept: "text/event-stream",
@@ -57,15 +63,14 @@ export async function POST(
       });
     }
 
-    if (!response.body) {
-      return new Response(
-        JSON.stringify({ error: "Empty response from chat service" }),
-        { status: 502, headers: { "Content-Type": "application/json" } },
-      );
-    }
-
-    return new Response(normalizeSSEStream(response.body), {
-      headers: SSE_HEADERS,
+    // 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("SSE proxy error:", error);
@@ -82,6 +87,13 @@ export async function POST(
   }
 }
 
+/**
+ * Resume an active stream for a session.
+ *
+ * Called by the AI SDK's `useChat(resume: true)` on page load.
+ * Proxies to the backend which checks for an active stream and either
+ * replays it (200 + SSE) or returns 204 No Content.
+ */
 export async function GET(
   _request: NextRequest,
   { params }: { params: Promise<{ sessionId: string }> },
@@ -112,6 +124,7 @@ export async function GET(
       headers,
     });
 
+    // 204 = no active stream to resume
     if (response.status === 204) {
       return new Response(null, { status: 204 });
     }
@@ -124,13 +137,12 @@ export async function GET(
       });
     }
 
-    if (!response.body) {
-      return new Response(null, { status: 204 });
-    }
-
-    return new Response(normalizeSSEStream(response.body), {
+    return new Response(response.body, {
       headers: {
-        ...SSE_HEADERS,
+        "Content-Type": "text/event-stream",
+        "Cache-Control": "no-cache, no-transform",
+        Connection: "keep-alive",
+        "X-Accel-Buffering": "no",
         "x-vercel-ai-ui-message-stream": "v1",
       },
     });

autogpt_platform/frontend/src/app/api/chat/sse-helpers.ts

@@ -1,72 +0,0 @@
-export const SSE_HEADERS = {
-  "Content-Type": "text/event-stream",
-  "Cache-Control": "no-cache, no-transform",
-  Connection: "keep-alive",
-  "X-Accel-Buffering": "no",
-} as const;
-
-export function normalizeSSEStream(
-  input: ReadableStream<Uint8Array>,
-): ReadableStream<Uint8Array> {
-  const decoder = new TextDecoder();
-  const encoder = new TextEncoder();
-  let buffer = "";
-
-  return input.pipeThrough(
-    new TransformStream<Uint8Array, Uint8Array>({
-      transform(chunk, controller) {
-        buffer += decoder.decode(chunk, { stream: true });
-
-        const parts = buffer.split("\n\n");
-        buffer = parts.pop() ?? "";
-
-        for (const part of parts) {
-          const normalized = normalizeSSEEvent(part);
-          controller.enqueue(encoder.encode(normalized + "\n\n"));
-        }
-      },
-      flush(controller) {
-        if (buffer.trim()) {
-          const normalized = normalizeSSEEvent(buffer);
-          controller.enqueue(encoder.encode(normalized + "\n\n"));
-        }
-      },
-    }),
-  );
-}
-
-function normalizeSSEEvent(event: string): string {
-  const lines = event.split("\n");
-  const dataLines: string[] = [];
-  const otherLines: string[] = [];
-
-  for (const line of lines) {
-    if (line.startsWith("data: ")) {
-      dataLines.push(line.slice(6));
-    } else {
-      otherLines.push(line);
-    }
-  }
-
-  if (dataLines.length === 0) return event;
-
-  const dataStr = dataLines.join("\n");
-  try {
-    const parsed = JSON.parse(dataStr) as Record<string, unknown>;
-    if (parsed.type === "error") {
-      const normalized = {
-        type: "error",
-        errorText:
-          typeof parsed.errorText === "string"
-            ? parsed.errorText
-            : "An unexpected error occurred",
-      };
-      const newData = `data: ${JSON.stringify(normalized)}`;
-      return [...otherLines.filter((l) => l.length > 0), newData].join("\n");
-    }
-  } catch {
-    // Not valid JSON — pass through as-is
-  }
-
-  return event;
-}

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

@@ -1,8 +1,20 @@
 import { environment } from "@/services/environment";
 import { getServerAuthToken } from "@/lib/autogpt-server-api/helpers";
 import { NextRequest } from "next/server";
-import { normalizeSSEStream, SSE_HEADERS } from "../../../sse-helpers";
 
+/**
+ * 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 }> },
@@ -12,12 +24,15 @@ export async function GET(
   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",
@@ -41,12 +56,14 @@ export async function GET(
       });
     }
 
-    if (!response.body) {
-      return new Response(null, { status: 204 });
-    }
-
-    return new Response(normalizeSSEStream(response.body), {
-      headers: SSE_HEADERS,
+    // 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);

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 -->