fix(backend/chat): Resolve symlinks in session file path for --resume

The CLI resolves symlinks when computing its project directory (e.g. /tmp -> /private/tmp on macOS), so our session file writes must use the resolved path to match. Also adds cwd to ClaudeAgentOptions and debug logging for SDK messages.
fix(backend): Restore PyYAML cp38 wheel entries in poetry.lock
2026-02-10 14:55:16 -05:00 · 2026-02-10 20:11:16 +04:00 · 2026-02-10 20:06:45 +04:00 · 2026-02-10 18:46:33 +04:00 · 2026-02-10 18:19:23 +04:00 · 2026-02-10 18:17:00 +04:00
197 changed files with 15305 additions and 9246 deletions
--- a/.github/workflows/claude-dependabot.yml
+++ b/.github/workflows/claude-dependabot.yml
@@ -78,7 +78,7 @@ jobs:
      # Frontend Node.js/pnpm setup (mirrors platform-frontend-ci.yml)
      - name: Set up Node.js
-        uses: actions/setup-node@v4
+        uses: actions/setup-node@v6
        with:
          node-version: "22"
--- a/.github/workflows/claude.yml
+++ b/.github/workflows/claude.yml
@@ -94,7 +94,7 @@ jobs:
      # Frontend Node.js/pnpm setup (mirrors platform-frontend-ci.yml)
      - name: Set up Node.js
-        uses: actions/setup-node@v4
+        uses: actions/setup-node@v6
        with:
          node-version: "22"
--- a/.github/workflows/copilot-setup-steps.yml
+++ b/.github/workflows/copilot-setup-steps.yml
@@ -76,7 +76,7 @@ jobs:
      # Frontend Node.js/pnpm setup (mirrors platform-frontend-ci.yml)
      - name: Set up Node.js
-        uses: actions/setup-node@v4
+        uses: actions/setup-node@v6
        with:
          node-version: "22"
--- a/.github/workflows/platform-frontend-ci.yml
+++ b/.github/workflows/platform-frontend-ci.yml
@@ -42,7 +42,7 @@ jobs:
              - 'autogpt_platform/frontend/src/components/**'
      - name: Set up Node.js
-        uses: actions/setup-node@v4
+        uses: actions/setup-node@v6
        with:
          node-version: "22.18.0"
@@ -74,7 +74,7 @@ jobs:
        uses: actions/checkout@v4
      - name: Set up Node.js
-        uses: actions/setup-node@v4
+        uses: actions/setup-node@v6
        with:
          node-version: "22.18.0"
@@ -112,7 +112,7 @@ jobs:
          fetch-depth: 0
      - name: Set up Node.js
-        uses: actions/setup-node@v4
+        uses: actions/setup-node@v6
        with:
          node-version: "22.18.0"
@@ -153,7 +153,7 @@ jobs:
          submodules: recursive
      - name: Set up Node.js
-        uses: actions/setup-node@v4
+        uses: actions/setup-node@v6
        with:
          node-version: "22.18.0"
@@ -282,7 +282,7 @@ jobs:
          submodules: recursive
      - name: Set up Node.js
-        uses: actions/setup-node@v4
+        uses: actions/setup-node@v6
        with:
          node-version: "22.18.0"
--- a/.github/workflows/platform-fullstack-ci.yml
+++ b/.github/workflows/platform-fullstack-ci.yml
@@ -32,7 +32,7 @@ jobs:
        uses: actions/checkout@v4
      - name: Set up Node.js
-        uses: actions/setup-node@v4
+        uses: actions/setup-node@v6
        with:
          node-version: "22.18.0"
@@ -56,7 +56,7 @@ jobs:
        run: pnpm install --frozen-lockfile
  types:
-    runs-on: ubuntu-latest
+    runs-on: big-boi
    needs: setup
    strategy:
      fail-fast: false
@@ -68,7 +68,7 @@ jobs:
          submodules: recursive
      - name: Set up Node.js
-        uses: actions/setup-node@v4
+        uses: actions/setup-node@v6
        with:
          node-version: "22.18.0"
@@ -85,7 +85,7 @@ jobs:
      - name: Run docker compose
        run: |
-          docker compose -f ../docker-compose.yml --profile local --profile deps_backend up -d
+          docker compose -f ../docker-compose.yml --profile local up -d deps_backend
      - name: Restore dependencies cache
        uses: actions/cache@v5
--- a/autogpt_platform/autogpt_libs/poetry.lock
+++ b/autogpt_platform/autogpt_libs/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 2.2.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 2.1.1 and should not be changed by hand.
 [[package]]
 name = "annotated-doc"
@@ -67,7 +67,7 @@ description = "Backport of asyncio.Runner, a context manager that controls event
 optional = false
 python-versions = "<3.11,>=3.8"
 groups = ["dev"]
-markers = "python_version == \"3.10\""
+markers = "python_version < \"3.11\""
 files = [
    {file = "backports_asyncio_runner-1.2.0-py3-none-any.whl", hash = "sha256:0da0a936a8aeb554eccb426dc55af3ba63bcdc69fa1a600b5bb305413a4477b5"},
    {file = "backports_asyncio_runner-1.2.0.tar.gz", hash = "sha256:a5aa7b2b7d8f8bfcaa2b57313f70792df84e32a2a746f585213373f900b42162"},
@@ -326,100 +326,118 @@ files = [
 [[package]]
 name = "coverage"
-version = "7.10.5"
+version = "7.13.4"
 description = "Code coverage measurement for Python"
 optional = false
-python-versions = ">=3.9"
+python-versions = ">=3.10"
 groups = ["dev"]
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    {file = "coverage-7.13.4.tar.gz", hash = "sha256:e5c8f6ed1e61a8b2dcdf31eb0b9bbf0130750ca79c1c49eb898e2ad86f5ccc91"},
 ]
 [package.dependencies]
@@ -523,7 +541,7 @@ description = "Backport of PEP 654 (exception groups)"
 optional = false
 python-versions = ">=3.7"
 groups = ["main", "dev"]
-markers = "python_version == \"3.10\""
+markers = "python_version < \"3.11\""
 files = [
    {file = "exceptiongroup-1.3.0-py3-none-any.whl", hash = "sha256:4d111e6e0c13d0644cad6ddaa7ed0261a0b36971f6d23e7ec9b4b9097da78a10"},
    {file = "exceptiongroup-1.3.0.tar.gz", hash = "sha256:b241f5885f560bc56a59ee63ca4c6a8bfa46ae4ad651af316d4e81817bb9fd88"},
@@ -2162,23 +2180,23 @@ testing = ["coverage (>=6.2)", "hypothesis (>=5.7.1)"]
 [[package]]
 name = "pytest-cov"
-version = "6.2.1"
+version = "7.0.0"
 description = "Pytest plugin for measuring coverage."
 optional = false
 python-versions = ">=3.9"
 groups = ["dev"]
 files = [
-    {file = "pytest_cov-6.2.1-py3-none-any.whl", hash = "sha256:f5bc4c23f42f1cdd23c70b1dab1bbaef4fc505ba950d53e0081d0730dd7e86d5"},
+    {file = "pytest_cov-7.0.0-py3-none-any.whl", hash = "sha256:3b8e9558b16cc1479da72058bdecf8073661c7f57f7d3c5f22a1c23507f2d861"},
-    {file = "pytest_cov-6.2.1.tar.gz", hash = "sha256:25cc6cc0a5358204b8108ecedc51a9b57b34cc6b8c967cc2c01a4e00d8a67da2"},
+    {file = "pytest_cov-7.0.0.tar.gz", hash = "sha256:33c97eda2e049a0c5298e91f519302a1334c26ac65c1a483d6206fd458361af1"},
 ]
 [package.dependencies]
-coverage = {version = ">=7.5", extras = ["toml"]}
+coverage = {version = ">=7.10.6", extras = ["toml"]}
 pluggy = ">=1.2"
-pytest = ">=6.2.5"
+pytest = ">=7"
 [package.extras]
-testing = ["fields", "hunter", "process-tests", "pytest-xdist", "virtualenv"]
+testing = ["process-tests", "pytest-xdist", "virtualenv"]
 [[package]]
 name = "pytest-mock"
@@ -2545,7 +2563,7 @@ description = "A lil' TOML parser"
 optional = false
 python-versions = ">=3.8"
 groups = ["dev"]
-markers = "python_version == \"3.10\""
+markers = "python_version < \"3.11\""
 files = [
    {file = "tomli-2.2.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:678e4fa69e4575eb77d103de3df8a895e1591b48e740211bd1067378c69e8249"},
    {file = "tomli-2.2.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:023aa114dd824ade0100497eb2318602af309e5a55595f76b626d6d9f3b7b0a6"},
@@ -2893,4 +2911,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.10,<4.0"
-content-hash = "b7ac335a86aa44c3d7d2802298818b389a6f1286e3e9b7b0edb2ff06377cecaf"
+content-hash = "40eae94995dc0a388fa832ed4af9b6137f28d5b5ced3aaea70d5f91d4d9a179d"
--- a/autogpt_platform/autogpt_libs/pyproject.toml
+++ b/autogpt_platform/autogpt_libs/pyproject.toml
@@ -26,7 +26,7 @@ pyright = "^1.1.408"
 pytest = "^8.4.1"
 pytest-asyncio = "^1.3.0"
 pytest-mock = "^3.15.1"
-pytest-cov = "^6.2.1"
+pytest-cov = "^7.0.0"
 ruff = "^0.15.0"
 [build-system]
--- a/autogpt_platform/backend/backend/api/features/chat/config.py
+++ b/autogpt_platform/backend/backend/api/features/chat/config.py
@@ -27,12 +27,11 @@ class ChatConfig(BaseSettings):
    session_ttl: int = Field(default=43200, description="Session TTL in seconds")
    # Streaming Configuration
    max_context_messages: int = Field(
        default=50, ge=1, le=200, description="Maximum context messages"
    )
    stream_timeout: int = Field(default=300, description="Stream timeout in seconds")
-    max_retries: int = Field(default=3, description="Maximum number of retries")
+    max_retries: int = Field(
        default=3,
        description="Max retries for fallback path (SDK handles retries internally)",
    )
    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"
@@ -93,6 +92,12 @@ 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",
    )
    @field_validator("api_key", mode="before")
    @classmethod
    def get_api_key(cls, v):
@@ -132,6 +137,17 @@ 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",
--- a/autogpt_platform/backend/backend/api/features/chat/db.py
+++ b/autogpt_platform/backend/backend/api/features/chat/db.py
@@ -45,10 +45,7 @@ async def create_chat_session(
        successfulAgentRuns=SafeJson({}),
        successfulAgentSchedules=SafeJson({}),
    )
-    return await PrismaChatSession.prisma().create(
+    return await PrismaChatSession.prisma().create(data=data)
        data=data,
        include={"Messages": True},
    )
 async def update_chat_session(
--- a/autogpt_platform/backend/backend/api/features/chat/model.py
+++ b/autogpt_platform/backend/backend/api/features/chat/model.py
@@ -273,9 +273,8 @@ async def _get_session_from_cache(session_id: str) -> ChatSession | None:
    try:
        session = ChatSession.model_validate_json(raw_session)
        logger.info(
-            f"Loading session {session_id} from cache: "
+            f"[CACHE] Loaded session {session_id}: {len(session.messages)} messages, "
-            f"message_count={len(session.messages)}, "
+            f"last_roles={[m.role for m in session.messages[-3:]]}"  # Last 3 roles
            f"roles={[m.role for m in session.messages]}"
        )
        return session
    except Exception as e:
@@ -317,11 +316,9 @@ async def _get_session_from_db(session_id: str) -> ChatSession | None:
        return None
    messages = prisma_session.Messages
-    logger.info(
+    logger.debug(
-        f"Loading session {session_id} from DB: "
+        f"[DB] Loaded session {session_id}: {len(messages) if messages else 0} messages, "
-        f"has_messages={messages is not None}, "
+        f"roles={[m.role for m in messages[-3:]] if messages else []}"  # Last 3 roles
        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)
@@ -372,10 +369,9 @@ async def _save_session_to_db(
                    "function_call": msg.function_call,
                }
            )
-        logger.info(
+        logger.debug(
-            f"Saving {len(new_messages)} new messages to DB for session {session.session_id}: "
+            f"[DB] Saving {len(new_messages)} messages to 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,
@@ -415,7 +411,7 @@ async def get_chat_session(
        logger.warning(f"Unexpected cache error for session {session_id}: {e}")
    # Fall back to database
-    logger.info(f"Session {session_id} not in cache, checking database")
+    logger.debug(f"Session {session_id} not in cache, checking database")
    session = await _get_session_from_db(session_id)
    if session is None:
@@ -432,7 +428,6 @@ 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}")
@@ -603,13 +598,19 @@ async def update_session_title(session_id: str, title: str) -> bool:
            logger.warning(f"Session {session_id} not found for title update")
            return False
-        # Invalidate cache so next fetch gets updated title
+        # Update title in cache if it exists (instead of invalidating).
        # This prevents race conditions where cache invalidation causes
        # the frontend to see stale DB data while streaming is still in progress.
        try:
-            redis_key = _get_session_cache_key(session_id)
+            cached = await _get_session_from_cache(session_id)
-            async_redis = await get_redis_async()
+            if cached:
-            await async_redis.delete(redis_key)
+                cached.title = title
                await _cache_session(cached)
        except Exception as e:
-            logger.warning(f"Failed to invalidate cache for session {session_id}: {e}")
+            # Not critical - title will be correct on next full cache refresh
            logger.warning(
                f"Failed to update title in cache for session {session_id}: {e}"
            )
        return True
    except Exception as e:
--- a/autogpt_platform/backend/backend/api/features/chat/response_model.py
+++ b/autogpt_platform/backend/backend/api/features/chat/response_model.py
@@ -18,6 +18,10 @@ class ResponseType(str, Enum):
    START = "start"
    FINISH = "finish"
    # Step lifecycle (one LLM API call within a message)
    START_STEP = "start-step"
    FINISH_STEP = "finish-step"
    # Text streaming
    TEXT_START = "text-start"
    TEXT_DELTA = "text-delta"
@@ -57,6 +61,16 @@ class StreamStart(StreamBaseResponse):
        description="Task ID for SSE reconnection. Clients can reconnect using GET /tasks/{taskId}/stream",
    )
    def to_sse(self) -> str:
        """Convert to SSE format, excluding non-protocol fields like taskId."""
        import json
        data: dict[str, Any] = {
            "type": self.type.value,
            "messageId": self.messageId,
        }
        return f"data: {json.dumps(data)}\n\n"
 class StreamFinish(StreamBaseResponse):
    """End of message/stream."""
@@ -64,6 +78,26 @@ class StreamFinish(StreamBaseResponse):
    type: ResponseType = ResponseType.FINISH
 class StreamStartStep(StreamBaseResponse):
    """Start of a step (one LLM API call within a message).
    The AI SDK uses this to add a step-start boundary to message.parts,
    enabling visual separation between multiple LLM calls in a single message.
    """
    type: ResponseType = ResponseType.START_STEP
 class StreamFinishStep(StreamBaseResponse):
    """End of a step (one LLM API call within a message).
    The AI SDK uses this to reset activeTextParts and activeReasoningParts,
    so the next LLM call in a tool-call continuation starts with clean state.
    """
    type: ResponseType = ResponseType.FINISH_STEP
 # ========== Text Streaming ==========
@@ -117,7 +151,7 @@ class StreamToolOutputAvailable(StreamBaseResponse):
    type: ResponseType = ResponseType.TOOL_OUTPUT_AVAILABLE
    toolCallId: str = Field(..., description="Tool call ID this responds to")
    output: str | dict[str, Any] = Field(..., description="Tool execution output")
-    # Additional fields for internal use (not part of AI SDK spec but useful)
+    # Keep these for internal backend use
    toolName: str | None = Field(
        default=None, description="Name of the tool that was executed"
    )
@@ -125,6 +159,17 @@ class StreamToolOutputAvailable(StreamBaseResponse):
        default=True, description="Whether the tool execution succeeded"
    )
    def to_sse(self) -> str:
        """Convert to SSE format, excluding non-spec fields."""
        import json
        data = {
            "type": self.type.value,
            "toolCallId": self.toolCallId,
            "output": self.output,
        }
        return f"data: {json.dumps(data)}\n\n"
 # ========== Other ==========
--- a/autogpt_platform/backend/backend/api/features/chat/routes.py
+++ b/autogpt_platform/backend/backend/api/features/chat/routes.py
@@ -1,12 +1,13 @@
 """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
 from typing import Annotated
 from autogpt_libs import auth
-from fastapi import APIRouter, Depends, Header, HTTPException, Query, Security
+from fastapi import APIRouter, Depends, Header, HTTPException, Query, Response, Security
 from fastapi.responses import StreamingResponse
 from pydantic import BaseModel
@@ -16,8 +17,39 @@ from . import service as chat_service
 from . import stream_registry
 from .completion_handler import process_operation_failure, process_operation_success
 from .config import ChatConfig
-from .model import ChatSession, create_chat_session, get_chat_session, get_user_sessions
+from .model import (
-from .response_model import StreamFinish, StreamHeartbeat, StreamStart
+    ChatMessage,
    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,
    AgentPreviewResponse,
    AgentSavedResponse,
    AgentsFoundResponse,
    BlockListResponse,
    BlockOutputResponse,
    ClarificationNeededResponse,
    DocPageResponse,
    DocSearchResultsResponse,
    ErrorResponse,
    ExecutionStartedResponse,
    InputValidationErrorResponse,
    NeedLoginResponse,
    NoResultsResponse,
    OperationInProgressResponse,
    OperationPendingResponse,
    OperationStartedResponse,
    SetupRequirementsResponse,
    UnderstandingUpdatedResponse,
 )
 from .tracking import track_user_message
 config = ChatConfig()
@@ -209,6 +241,10 @@ 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
@@ -266,12 +302,57 @@ async def stream_chat_post(
    """
    import asyncio
    import time
    stream_start_time = time.perf_counter()
    log_meta = {"component": "ChatStream", "session_id": session_id}
    if user_id:
        log_meta["user_id"] = user_id
    logger.info(
        f"[TIMING] stream_chat_post STARTED, session={session_id}, "
        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",
        extra={
            "json_fields": {
                **log_meta,
                "duration_ms": (time.perf_counter() - stream_start_time) * 1000,
            }
        },
    )
    # 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())
    log_meta["task_id"] = task_id
    task_create_start = time.perf_counter()
    await stream_registry.create_task(
        task_id=task_id,
        session_id=session_id,
@@ -280,40 +361,144 @@ async def stream_chat_post(
        tool_name="chat",
        operation_id=operation_id,
    )
    logger.info(
        f"[TIMING] create_task completed in {(time.perf_counter() - task_create_start) * 1000:.1f}ms",
        extra={
            "json_fields": {
                **log_meta,
                "duration_ms": (time.perf_counter() - task_create_start) * 1000,
            }
        },
    )
    # Background task that runs the AI generation independently of SSE connection
    async def run_ai_generation():
        import time as time_module
        gen_start_time = time_module.perf_counter()
        logger.info(
            f"[TIMING] run_ai_generation STARTED, task={task_id}, session={session_id}, user={user_id}",
            extra={"json_fields": log_meta},
        )
        first_chunk_time, ttfc = None, None
        chunk_count = 0
        try:
            # Emit a start event with task_id for reconnection
            start_chunk = StreamStart(messageId=task_id, taskId=task_id)
            await stream_registry.publish_chunk(task_id, start_chunk)
            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,
                    }
                },
            )
-            async for chunk in chat_service.stream_chat_completion(
+            # 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,
-                request.message,
+                None,  # Message already in session
                is_user_message=request.is_user_message,
                user_id=user_id,
-                session=session,  # Pass pre-fetched session to avoid double-fetch
+                session=session,  # Pass session with message already added
                context=request.context,
            ):
                chunk_count += 1
                if first_chunk_time is None:
                    first_chunk_time = time_module.perf_counter()
                    ttfc = first_chunk_time - gen_start_time
                    logger.info(
                        f"[TIMING] FIRST AI CHUNK at {ttfc:.2f}s, type={type(chunk).__name__}",
                        extra={
                            "json_fields": {
                                **log_meta,
                                "chunk_type": type(chunk).__name__,
                                "time_to_first_chunk_ms": ttfc * 1000,
                            }
                        },
                    )
                # Write to Redis (subscribers will receive via XREAD)
                await stream_registry.publish_chunk(task_id, chunk)
-            # Mark task as completed
+            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"task={task_id}, session={session_id}, "
                f"ttfc={ttfc or -1:.2f}s, n_chunks={chunk_count}",
                extra={
                    "json_fields": {
                        **log_meta,
                        "total_time_ms": total_time,
                        "time_to_first_chunk_ms": (
                            ttfc * 1000 if ttfc is not None else None
                        ),
                        "n_chunks": chunk_count,
                    }
                },
            )
            await stream_registry.mark_task_completed(task_id, "completed")
        except Exception as e:
            elapsed = time_module.perf_counter() - gen_start_time
            logger.error(
-                f"Error in background AI generation for session {session_id}: {e}"
+                f"[TIMING] run_ai_generation ERROR after {elapsed:.2f}s: {e}",
                extra={
                    "json_fields": {
                        **log_meta,
                        "elapsed_ms": elapsed * 1000,
                        "error": str(e),
                    }
                },
            )
            # 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
    bg_task = asyncio.create_task(run_ai_generation())
    await stream_registry.set_task_asyncio_task(task_id, bg_task)
    setup_time = (time.perf_counter() - stream_start_time) * 1000
    logger.info(
        f"[TIMING] Background task started, setup={setup_time:.1f}ms",
        extra={"json_fields": {**log_meta, "setup_time_ms": setup_time}},
    )
    # SSE endpoint that subscribes to the task's stream
    async def event_generator() -> AsyncGenerator[str, None]:
        import time as time_module
        event_gen_start = time_module.perf_counter()
        logger.info(
            f"[TIMING] event_generator STARTED, task={task_id}, session={session_id}, "
            f"user={user_id}",
            extra={"json_fields": log_meta},
        )
        subscriber_queue = None
        first_chunk_yielded = False
        chunks_yielded = 0
        try:
            # Subscribe to the task stream (this replays existing messages + live updates)
            subscriber_queue = await stream_registry.subscribe_to_task(
@@ -328,24 +513,78 @@ async def stream_chat_post(
                return
            # Read from the subscriber queue and yield to SSE
            logger.info(
                "[TIMING] Starting to read from subscriber_queue",
                extra={"json_fields": log_meta},
            )
            while True:
                try:
                    chunk = await asyncio.wait_for(subscriber_queue.get(), timeout=30.0)
                    chunks_yielded += 1
                    if not first_chunk_yielded:
                        first_chunk_yielded = True
                        elapsed = time_module.perf_counter() - event_gen_start
                        logger.info(
                            f"[TIMING] FIRST CHUNK from queue at {elapsed:.2f}s, "
                            f"type={type(chunk).__name__}",
                            extra={
                                "json_fields": {
                                    **log_meta,
                                    "chunk_type": type(chunk).__name__,
                                    "elapsed_ms": elapsed * 1000,
                                }
                            },
                        )
                    yield chunk.to_sse()
                    # Check for finish signal
                    if isinstance(chunk, StreamFinish):
                        total_time = time_module.perf_counter() - event_gen_start
                        logger.info(
                            f"[TIMING] StreamFinish received in {total_time:.2f}s; "
                            f"n_chunks={chunks_yielded}",
                            extra={
                                "json_fields": {
                                    **log_meta,
                                    "chunks_yielded": chunks_yielded,
                                    "total_time_ms": total_time * 1000,
                                }
                            },
                        )
                        break
                except asyncio.TimeoutError:
                    # Send heartbeat to keep connection alive
                    yield StreamHeartbeat().to_sse()
        except GeneratorExit:
            logger.info(
                f"[TIMING] GeneratorExit (client disconnected), chunks={chunks_yielded}",
                extra={
                    "json_fields": {
                        **log_meta,
                        "chunks_yielded": chunks_yielded,
                        "reason": "client_disconnect",
                    }
                },
            )
            pass  # Client disconnected - background task continues
        except Exception as e:
-            logger.error(f"Error in SSE stream for task {task_id}: {e}")
+            elapsed = (time_module.perf_counter() - event_gen_start) * 1000
            logger.error(
                f"[TIMING] event_generator ERROR after {elapsed:.1f}ms: {e}",
                extra={
                    "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 to prevent resource leak
+            # Unsubscribe when client disconnects or stream ends
            if subscriber_queue is not None:
                try:
                    await stream_registry.unsubscribe_from_task(
@@ -357,6 +596,18 @@ async def stream_chat_post(
                        exc_info=True,
                    )
            # AI SDK protocol termination - always yield even if unsubscribe fails
            total_time = time_module.perf_counter() - event_gen_start
            logger.info(
                f"[TIMING] event_generator FINISHED in {total_time:.2f}s; "
                f"task={task_id}, session={session_id}, n_chunks={chunks_yielded}",
                extra={
                    "json_fields": {
                        **log_meta,
                        "total_time_ms": total_time * 1000,
                        "chunks_yielded": chunks_yielded,
                    }
                },
            )
            yield "data: [DONE]\n\n"
    return StreamingResponse(
@@ -374,44 +625,53 @@ async def stream_chat_post(
@router.get(
    "/sessions/{session_id}/stream",
 )
-async def stream_chat_get(
+async def resume_session_stream(
    session_id: str,
    message: Annotated[str, Query(min_length=1, max_length=10000)],
    user_id: str | None = Depends(auth.get_user_id),
    is_user_message: bool = Query(default=True),
 ):
    """
-    Stream chat responses for a session (GET - legacy endpoint).
+    Resume an active stream for a session.
-    Streams the AI/completion responses in real time over Server-Sent Events (SSE), including:
+    Called by the AI SDK's ``useChat(resume: true)`` on page load.
-      - Text fragments as they are generated
+    Checks for an active (in-progress) task on the session and either replays
-      - Tool call UI elements (if invoked)
+    the full SSE stream or returns 204 No Content if nothing is running.
      - Tool execution results
    Args:
-        session_id: The chat session identifier to associate with the streamed messages.
+        session_id: The chat session identifier.
        message: The user's new message to process.
        user_id: Optional authenticated user ID.
        is_user_message: Whether the message is a user message.
    Returns:
        StreamingResponse: SSE-formatted response chunks.
    Returns:
        StreamingResponse (SSE) when an active stream exists,
        or 204 No Content when there is nothing to resume.
    """
-    session = await _validate_and_get_session(session_id, user_id)
+    import asyncio
    active_task, _last_id = await stream_registry.get_active_task_for_session(
        session_id, user_id
    )
    if not active_task:
        return Response(status_code=204)
    subscriber_queue = await stream_registry.subscribe_to_task(
        task_id=active_task.task_id,
        user_id=user_id,
        last_message_id="0-0",  # Full replay so useChat rebuilds the message
    )
    if subscriber_queue is None:
        return Response(status_code=204)
    async def event_generator() -> AsyncGenerator[str, None]:
        chunk_count = 0
        first_chunk_type: str | None = None
-        async for chunk in chat_service.stream_chat_completion(
+        try:
-            session_id,
+            while True:
-            message,
+                try:
-            is_user_message=is_user_message,
+                    chunk = await asyncio.wait_for(subscriber_queue.get(), timeout=30.0)
            user_id=user_id,
            session=session,  # Pass pre-fetched session to avoid double-fetch
        ):
                    if chunk_count < 3:
                        logger.info(
-                    "Chat stream chunk",
+                            "Resume stream chunk",
                            extra={
                                "session_id": session_id,
                                "chunk_type": str(chunk.type),
@@ -421,15 +681,33 @@ async def stream_chat_get(
                        first_chunk_type = str(chunk.type)
                    chunk_count += 1
                    yield chunk.to_sse()
                    if isinstance(chunk, StreamFinish):
                        break
                except asyncio.TimeoutError:
                    yield StreamHeartbeat().to_sse()
        except GeneratorExit:
            pass
        except Exception as e:
            logger.error(f"Error in resume stream for session {session_id}: {e}")
        finally:
            try:
                await stream_registry.unsubscribe_from_task(
                    active_task.task_id, subscriber_queue
                )
            except Exception as unsub_err:
                logger.error(
                    f"Error unsubscribing from task {active_task.task_id}: {unsub_err}",
                    exc_info=True,
                )
            logger.info(
-            "Chat stream completed",
+                "Resume stream completed",
                extra={
                    "session_id": session_id,
-                "chunk_count": chunk_count,
+                    "n_chunks": chunk_count,
                    "first_chunk_type": first_chunk_type,
                },
            )
        # AI SDK protocol termination
            yield "data: [DONE]\n\n"
    return StreamingResponse(
@@ -438,8 +716,8 @@ async def stream_chat_get(
        headers={
            "Cache-Control": "no-cache",
            "Connection": "keep-alive",
-            "X-Accel-Buffering": "no",  # Disable nginx buffering
+            "X-Accel-Buffering": "no",
-            "x-vercel-ai-ui-message-stream": "v1",  # AI SDK protocol header
+            "x-vercel-ai-ui-message-stream": "v1",
        },
    )
@@ -550,8 +828,6 @@ 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:
@@ -751,3 +1027,42 @@ async def health_check() -> dict:
        "service": "chat",
        "version": "0.1.0",
    }
 # ========== Schema Export (for OpenAPI / Orval codegen) ==========
 ToolResponseUnion = (
    AgentsFoundResponse
    | NoResultsResponse
    | AgentDetailsResponse
    | SetupRequirementsResponse
    | ExecutionStartedResponse
    | NeedLoginResponse
    | ErrorResponse
    | InputValidationErrorResponse
    | AgentOutputResponse
    | UnderstandingUpdatedResponse
    | AgentPreviewResponse
    | AgentSavedResponse
    | ClarificationNeededResponse
    | BlockListResponse
    | BlockOutputResponse
    | DocSearchResultsResponse
    | DocPageResponse
    | OperationStartedResponse
    | OperationPendingResponse
    | OperationInProgressResponse
 )
@router.get(
    "/schema/tool-responses",
    response_model=ToolResponseUnion,
    include_in_schema=True,
    summary="[Dummy] Tool response type export for codegen",
    description="This endpoint is not meant to be called. It exists solely to "
    "expose tool response models in the OpenAPI schema for frontend codegen.",
 )
 async def _tool_response_schema() -> ToolResponseUnion:  # type: ignore[return]
    """Never called at runtime. Exists only so Orval generates TS types."""
    raise HTTPException(status_code=501, detail="Schema-only endpoint")
--- a/autogpt_platform/backend/backend/api/features/chat/sdk/init.py
+++ b/autogpt_platform/backend/backend/api/features/chat/sdk/init.py
@@ -0,0 +1,14 @@
 """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",
 ]
--- a/autogpt_platform/backend/backend/api/features/chat/sdk/anthropic_fallback.py
+++ b/autogpt_platform/backend/backend/api/features/chat/sdk/anthropic_fallback.py
@@ -0,0 +1,348 @@
 """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 ..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__)
 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
    max_iterations = 10
    accumulated_text = ""
    accumulated_tool_calls: list[dict[str, Any]] = []
    for _ in range(max_iterations):
        try:
            async with client.messages.stream(
                model="claude-sonnet-4-20250514",
                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
--- a/autogpt_platform/backend/backend/api/features/chat/sdk/response_adapter.py
+++ b/autogpt_platform/backend/backend/api/features/chat/sdk/response_adapter.py
@@ -0,0 +1,174 @@
 """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,
    StreamHeartbeat,
    StreamStart,
    StreamTextDelta,
    StreamTextEnd,
    StreamTextStart,
    StreamToolInputAvailable,
    StreamToolInputStart,
    StreamToolOutputAvailable,
    StreamUsage,
 )
 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
    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)
                )
        elif isinstance(sdk_message, AssistantMessage):
            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)
                    responses.append(
                        StreamToolInputStart(toolCallId=block.id, toolName=block.name)
                    )
                    responses.append(
                        StreamToolInputAvailable(
                            toolCallId=block.id,
                            toolName=block.name,
                            input=block.input,
                        )
                    )
                    self.current_tool_calls[block.id] = {"name": block.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")
                    output = _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),
                        )
                    )
        elif isinstance(sdk_message, ResultMessage):
            if sdk_message.subtype == "success":
                self._end_text_if_open(responses)
                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 create_heartbeat(self, tool_call_id: str | None = None) -> StreamHeartbeat:
        """Create a heartbeat response."""
        return StreamHeartbeat(toolCallId=tool_call_id)
    def create_usage(self, prompt_tokens: int, completion_tokens: int) -> StreamUsage:
        """Create a usage statistics response."""
        return StreamUsage(
            promptTokens=prompt_tokens,
            completionTokens=completion_tokens,
            totalTokens=prompt_tokens + completion_tokens,
        )
 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)
--- a/autogpt_platform/backend/backend/api/features/chat/sdk/response_adapter_test.py
+++ b/autogpt_platform/backend/backend/api/features/chat/sdk/response_adapter_test.py
@@ -0,0 +1,324 @@
 """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,
    StreamStart,
    StreamTextDelta,
    StreamTextEnd,
    StreamTextStart,
    StreamToolInputAvailable,
    StreamToolInputStart,
    StreamToolOutputAvailable,
 )
 from .response_adapter import SDKResponseAdapter
 def _adapter() -> SDKResponseAdapter:
    a = SDKResponseAdapter(message_id="msg-1")
    a.set_task_id("task-1")
    return a
 # -- SystemMessage -----------------------------------------------------------
 def test_system_init_emits_start():
    adapter = _adapter()
    results = adapter.convert_message(SystemMessage(subtype="init", data={}))
    assert len(results) == 1
    assert isinstance(results[0], StreamStart)
    assert results[0].messageId == "msg-1"
    assert results[0].taskId == "task-1"
 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_start_and_delta():
    adapter = _adapter()
    msg = AssistantMessage(content=[TextBlock(text="hello")], model="test")
    results = adapter.convert_message(msg)
    assert len(results) == 2
    assert isinstance(results[0], StreamTextStart)
    assert isinstance(results[1], StreamTextDelta)
    assert results[1].delta == "hello"
 def test_empty_text_block_is_skipped():
    adapter = _adapter()
    msg = AssistantMessage(content=[TextBlock(text="")], model="test")
    results = adapter.convert_message(msg)
    assert results == []
 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 start+delta, second only delta (block already started)
    assert len(r1) == 2
    assert len(r2) == 1
    assert isinstance(r2[0], StreamTextDelta)
    assert isinstance(r1[0], StreamTextStart)
    assert r1[0].id == r2[0].id  # same block ID
 # -- AssistantMessage with ToolUseBlock --------------------------------------
 def test_tool_use_emits_input_start_and_available():
    adapter = _adapter()
    msg = AssistantMessage(
        content=[ToolUseBlock(id="tool-1", name="find_agent", input={"q": "x"})],
        model="test",
    )
    results = adapter.convert_message(msg)
    assert len(results) == 2
    assert isinstance(results[0], StreamToolInputStart)
    assert results[0].toolCallId == "tool-1"
    assert results[0].toolName == "find_agent"
    assert isinstance(results[1], StreamToolInputAvailable)
    assert results[1].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="tool", input={})], model="test"
    )
    adapter.convert_message(text_msg)
    results = adapter.convert_message(tool_msg)
    # Should have: 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():
    adapter = _adapter()
    # First register the tool call
    tool_msg = AssistantMessage(
        content=[ToolUseBlock(id="t1", name="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) == 1
    assert isinstance(results[0], StreamToolOutputAvailable)
    assert results[0].toolCallId == "t1"
    assert results[0].toolName == "find_agent"
    assert results[0].output == "found 3 agents"
    assert results[0].success is True
 def test_tool_result_error():
    adapter = _adapter()
    adapter.convert_message(
        AssistantMessage(
            content=[ToolUseBlock(id="t1", name="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
 def test_tool_result_list_content():
    adapter = _adapter()
    adapter.convert_message(
        AssistantMessage(
            content=[ToolUseBlock(id="t1", name="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"
 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():
    adapter = _adapter()
    # Start some text first
    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 + StreamFinish
    assert len(results) == 2
    assert isinstance(results[0], StreamTextEnd)
    assert isinstance(results[1], 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)
    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 -> Text should get a new text block ID
    adapter.convert_message(
        AssistantMessage(content=[TextBlock(text="before")], model="test")
    )
    adapter.convert_message(
        AssistantMessage(
            content=[ToolUseBlock(id="t1", name="tool", input={})], model="test"
        )
    )
    results = adapter.convert_message(
        AssistantMessage(content=[TextBlock(text="after")], model="test")
    )
    # Should get StreamTextStart (new block) + StreamTextDelta
    assert len(results) == 2
    assert isinstance(results[0], StreamTextStart)
    assert isinstance(results[1], StreamTextDelta)
    assert results[1].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="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",
        "StreamTextStart",
        "StreamTextDelta",  # "Let me search"
        "StreamTextEnd",  # closed before tool
        "StreamToolInputStart",
        "StreamToolInputAvailable",
        "StreamToolOutputAvailable",  # tool result
        "StreamTextStart",  # new block after tool
        "StreamTextDelta",  # "I found 2"
        "StreamTextEnd",  # closed by result
        "StreamFinish",
    ]
--- a/autogpt_platform/backend/backend/api/features/chat/sdk/security_hooks.py
+++ b/autogpt_platform/backend/backend/api/features/chat/sdk/security_hooks.py
@@ -0,0 +1,289 @@
 """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 re
 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",
    "bash",
    "shell",
    "exec",
    "terminal",
    "command",
    "Read",  # Block raw file read - use workspace tools instead
    "Write",  # Block raw file write - use workspace tools instead
    "Edit",  # Block raw file edit - use workspace tools instead
    "Glob",  # Block raw file glob - use workspace tools instead
    "Grep",  # Block raw file grep - use workspace tools instead
 }
 # 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 _validate_tool_access(tool_name: str, tool_input: dict[str, Any]) -> 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 {
            "hookSpecificOutput": {
                "hookEventName": "PreToolUse",
                "permissionDecision": "deny",
                "permissionDecisionReason": (
                    f"Tool '{tool_name}' is not available. "
                    "Use the CoPilot-specific tools instead."
                ),
            }
        }
    # 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 {
                "hookSpecificOutput": {
                    "hookEventName": "PreToolUse",
                    "permissionDecision": "deny",
                    "permissionDecisionReason": "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) -> 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
    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)
                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 {}
 def create_strict_security_hooks(
    user_id: str | None,
    allowed_tools: list[str] | None = None,
 ) -> dict[str, Any]:
    """Create strict security hooks that only allow specific tools.
    Args:
        user_id: Current user ID
        allowed_tools: List of allowed tool names (defaults to CoPilot tools)
    Returns:
        Hooks configuration dict
    """
    try:
        from claude_agent_sdk import HookMatcher
        from claude_agent_sdk.types import HookContext, HookInput, SyncHookJSONOutput
        from .tool_adapter import RAW_TOOL_NAMES
        tools_list = allowed_tools if allowed_tools is not None else RAW_TOOL_NAMES
        allowed_set = set(tools_list)
        async def strict_pre_tool_use(
            input_data: HookInput,
            tool_use_id: str | None,
            context: HookContext,
        ) -> SyncHookJSONOutput:
            """Strict validation that only allows whitelisted tools."""
            _ = 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", {}))
            # Remove MCP prefix if present
            clean_name = tool_name.removeprefix(MCP_TOOL_PREFIX)
            if clean_name not in allowed_set:
                logger.warning(f"Blocked non-whitelisted tool: {tool_name}")
                return cast(
                    SyncHookJSONOutput,
                    {
                        "hookSpecificOutput": {
                            "hookEventName": "PreToolUse",
                            "permissionDecision": "deny",
                            "permissionDecisionReason": (
                                f"Tool '{tool_name}' is not in the allowed list"
                            ),
                        }
                    },
                )
            # Only run blocklist check for non-CoPilot tools; whitelisted
            # MCP tools are already sandboxed by tool_adapter.
            is_copilot_tool = tool_name.startswith(MCP_TOOL_PREFIX)
            if not is_copilot_tool:
                result = _validate_tool_access(clean_name, tool_input)
                if result:
                    return cast(SyncHookJSONOutput, result)
            result = _validate_user_isolation(clean_name, tool_input, user_id)
            if result:
                return cast(SyncHookJSONOutput, result)
            logger.debug(
                f"[SDK Audit] Tool call: tool={tool_name}, "
                f"user={user_id}, tool_use_id={tool_use_id}"
            )
            return cast(SyncHookJSONOutput, {})
        return {
            "PreToolUse": [
                HookMatcher(matcher="*", hooks=[strict_pre_tool_use]),
            ],
        }
    except ImportError:
        return {}
--- a/autogpt_platform/backend/backend/api/features/chat/sdk/service.py
+++ b/autogpt_platform/backend/backend/api/features/chat/sdk/service.py
@@ -0,0 +1,461 @@
 """Claude Agent SDK service layer for CoPilot chat completions."""
 import asyncio
 import glob
 import json
 import logging
 import os
 import uuid
 from collections.abc import AsyncGenerator
 from typing import Any
 import openai
 from backend.data.understanding import (
    format_understanding_for_prompt,
    get_business_understanding,
 )
 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 ..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 .session_file import cleanup_session_file, write_session_file
 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 tool-results directory pattern
 _SDK_TOOL_RESULTS_GLOB = "/root/.claude/projects/*/tool-results/*"
 def _cleanup_sdk_tool_results() -> None:
    """Remove SDK tool-result files to prevent disk accumulation."""
    for path in glob.glob(_SDK_TOOL_RESULTS_GLOB):
        try:
            os.remove(path)
        except OSError:
            pass
 DEFAULT_SYSTEM_PROMPT = """You are **Otto**, an AI Co-Pilot for AutoGPT and a Forward-Deployed Automation Engineer serving small business owners. Your mission is to help users automate business tasks with AI by delivering tangible value through working automations—not through documentation or lengthy explanations.
 Here is everything you know about the current user from previous interactions:
 <users_information>
 {users_information}
 </users_information>
 ## YOUR CORE MANDATE
 You are action-oriented. Your success is measured by:
 - **Value Delivery**: Does the user think "wow, that was amazing" or "what was the point"?
 - **Demonstrable Proof**: Show working automations, not descriptions of what's possible
 - **Time Saved**: Focus on tangible efficiency gains
 - **Quality Output**: Deliver results that meet or exceed expectations
 ## YOUR WORKFLOW
 Adapt flexibly to the conversation context. Not every interaction requires all stages:
 1. **Explore & Understand**: Learn about the user's business, tasks, and goals. Use `add_understanding` to capture important context that will improve future conversations.
 2. **Assess Automation Potential**: Help the user understand whether and how AI can automate their task.
 3. **Prepare for AI**: Provide brief, actionable guidance on prerequisites (data, access, etc.).
 4. **Discover or Create Agents**:
   - **Always check the user's library first** with `find_library_agent` (these may be customized to their needs)
   - Search the marketplace with `find_agent` for pre-built automations
   - Find reusable components with `find_block`
   - Create custom solutions with `create_agent` if nothing suitable exists
   - Modify existing library agents with `edit_agent`
 5. **Execute**: Run automations immediately, schedule them, or set up webhooks using `run_agent`. Test specific components with `run_block`.
 6. **Show Results**: Display outputs using `agent_output`.
 ## BEHAVIORAL GUIDELINES
 **Be Concise:**
 - Target 2-5 short lines maximum
 - Make every word count—no repetition or filler
 - Use lightweight structure for scannability (bullets, numbered lists, short prompts)
 - Avoid jargon (blocks, slugs, cron) unless the user asks
 **Be Proactive:**
 - Suggest next steps before being asked
 - Anticipate needs based on conversation context and user information
 - Look for opportunities to expand scope when relevant
 - Reveal capabilities through action, not explanation
 **Use Tools Effectively:**
 - Select the right tool for each task
 - **Always check `find_library_agent` before searching the marketplace**
 - Use `add_understanding` to capture valuable business context
 - When tool calls fail, try alternative approaches
 ## CRITICAL REMINDER
 You are NOT a chatbot. You are NOT documentation. You are a partner who helps busy business owners get value quickly by showing proof through working automations. Bias toward action over explanation."""
 async def _build_system_prompt(
    user_id: str | None, has_conversation_history: bool = False
 ) -> tuple[str, Any]:
    """Build the system prompt with user's business understanding context.
    Args:
        user_id: The user ID to fetch understanding for.
        has_conversation_history: Whether there's existing conversation history.
            If True, we don't tell the model to greet/introduce (since they're
            already in a conversation).
    """
    understanding = None
    if user_id:
        try:
            understanding = await get_business_understanding(user_id)
        except Exception as e:
            logger.warning(f"Failed to fetch business understanding: {e}")
    if understanding:
        context = format_understanding_for_prompt(understanding)
    elif has_conversation_history:
        # Don't tell model to greet if there's 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"
    return DEFAULT_SYSTEM_PROMPT.replace("{users_information}", context), understanding
 async def _generate_session_title(
    message: str,
    user_id: str | None = None,
    session_id: str | None = None,
 ) -> str | None:
    """Generate a concise title for a chat session."""
    from backend.util.settings import Settings
    settings = Settings()
    try:
        # Build extra_body for OpenRouter tracing
        extra_body: dict[str, Any] = {
            "posthogProperties": {"environment": settings.config.app_env.value},
        }
        if user_id:
            extra_body["user"] = user_id[:128]
            extra_body["posthogDistinctId"] = user_id
        if session_id:
            extra_body["session_id"] = session_id[:128]
        client = openai.AsyncOpenAI(api_key=config.api_key, base_url=config.base_url)
        response = await client.chat.completions.create(
            model=config.title_model,
            messages=[
                {
                    "role": "system",
                    "content": "Generate a very short title (3-6 words) for a chat conversation based on the user's first message. Return ONLY the title, no quotes or punctuation.",
                },
                {"role": "user", "content": message[:500]},
            ],
            max_tokens=20,
            extra_body=extra_body,
        )
        title = response.choices[0].message.content
        if title:
            title = title.strip().strip("\"'")
            return title[:47] + "..." if len(title) > 50 else title
        return None
    except Exception as e:
        logger.warning(f"Failed to generate session title: {e}")
        return None
 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)
                )
                # Store reference to prevent garbage collection
                _background_tasks.add(task)
                task.add_done_callback(_background_tasks.discard)
    # Check if there's conversation history (more than just the current message)
    has_history = len(session.messages) > 1
    system_prompt, _ = await _build_system_prompt(
        user_id, has_conversation_history=has_history
    )
    set_execution_context(user_id, session, None)
    message_id = str(uuid.uuid4())
    text_block_id = str(uuid.uuid4())
    task_id = str(uuid.uuid4())
    yield StreamStart(messageId=message_id, taskId=task_id)
    # Track whether the stream completed normally via ResultMessage
    stream_completed = False
    try:
        try:
            from claude_agent_sdk import ClaudeAgentOptions, ClaudeSDKClient
            # Create MCP server with CoPilot tools
            mcp_server = create_copilot_mcp_server()
            # For multi-turn conversations, write a session file so the CLI
            # loads full user+assistant context via --resume.  This enables
            # turn-level compaction for long conversations.
            resume_id: str | None = None
            if len(session.messages) > 1:
                resume_id = write_session_file(session)
                if resume_id:
                    logger.info(
                        f"[SDK] Wrote session file for --resume: "
                        f"{len(session.messages) - 1} prior messages"
                    )
            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),  # type: ignore[arg-type]
                cwd="/tmp",
                resume=resume_id,
            )
            adapter = SDKResponseAdapter(message_id=message_id)
            adapter.set_task_id(task_id)
            try:
                async with ClaudeSDKClient(options=options) as client:
                    # Determine the current user message
                    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 ""
                    # Guard against empty messages
                    if not current_message.strip():
                        yield StreamError(
                            errorText="Message cannot be empty.",
                            code="empty_prompt",
                        )
                        yield StreamFinish()
                        return
                    await client.query(current_message, session_id=session_id)
                    logger.info(
                        "[SDK] Query sent"
                        + (" (with --resume)" if resume_id else " (new)")
                    )
                    # Track assistant response to save to session
                    # We may need multiple assistant messages if text comes after tool results
                    assistant_response = ChatMessage(role="assistant", content="")
                    accumulated_tool_calls: list[dict[str, Any]] = []
                    has_appended_assistant = False
                    has_tool_results = False  # Track if we've received tool results
                    # Receive messages from the SDK
                    async for sdk_msg in client.receive_messages():
                        logger.debug(
                            f"[SDK] Received: {type(sdk_msg).__name__} {getattr(sdk_msg, 'subtype', '')}"
                        )
                        for response in adapter.convert_message(sdk_msg):
                            if isinstance(response, StreamStart):
                                continue
                            yield response
                            # Accumulate text deltas into assistant response
                            if isinstance(response, StreamTextDelta):
                                delta = response.delta or ""
                                # After tool results, create new assistant message for post-tool text
                                if has_tool_results and has_appended_assistant:
                                    assistant_response = ChatMessage(
                                        role="assistant", content=delta
                                    )
                                    accumulated_tool_calls = []  # Reset for new message
                                    session.messages.append(assistant_response)
                                    has_tool_results = False
                                else:
                                    assistant_response.content = (
                                        assistant_response.content or ""
                                    ) + delta
                                    if not has_appended_assistant:
                                        session.messages.append(assistant_response)
                                        has_appended_assistant = True
                            # Track tool calls on the assistant message
                            elif isinstance(response, StreamToolInputAvailable):
                                accumulated_tool_calls.append(
                                    {
                                        "id": response.toolCallId,
                                        "type": "function",
                                        "function": {
                                            "name": response.toolName,
                                            "arguments": json.dumps(
                                                response.input or {}
                                            ),
                                        },
                                    }
                                )
                                # Update assistant message with tool calls
                                assistant_response.tool_calls = accumulated_tool_calls
                                # Append assistant message if not already (tool-only response)
                                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
                        # Break out of the message loop if we received finish signal
                        if stream_completed:
                            break
                    # Ensure assistant response is saved even if no text deltas
                    # (e.g., only tool calls were made)
                    if (
                        assistant_response.content or assistant_response.tool_calls
                    ) and not has_appended_assistant:
                        session.messages.append(assistant_response)
            finally:
                # Always clean up SDK tool-result files, even on error
                _cleanup_sdk_tool_results()
                # Clean up session file written for --resume
                if resume_id:
                    cleanup_session_file(resume_id)
        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
        # Save the session with accumulated messages
        await upsert_chat_session(session)
        logger.debug(
            f"[SDK] Session {session_id} saved with {len(session.messages)} messages"
        )
        # Yield StreamFinish to signal completion to the caller (routes.py)
        # Only if one hasn't already been yielded by the stream
        if not stream_completed:
            yield StreamFinish()
    except Exception as e:
        logger.error(f"[SDK] Error: {e}", exc_info=True)
        # Save session even on error to preserve any partial response
        try:
            await upsert_chat_session(session)
        except Exception as save_err:
            logger.error(f"[SDK] Failed to save session on error: {save_err}")
        # Sanitize error message to avoid exposing internal details
        yield StreamError(
            errorText="An error occurred. Please try again.",
            code="sdk_error",
        )
        yield StreamFinish()
 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}")
--- a/autogpt_platform/backend/backend/api/features/chat/sdk/session_file.py
+++ b/autogpt_platform/backend/backend/api/features/chat/sdk/session_file.py
@@ -0,0 +1,119 @@
 """Session file management for Claude Code CLI --resume support.
 Writes conversation history as JSONL files to the CLI's session storage
 directory, enabling --resume to load full user+assistant context with
 turn-level compaction support.
 """
 import json
 import logging
 import uuid
 from datetime import UTC, datetime
 from pathlib import Path
 from ..model import ChatSession
 logger = logging.getLogger(__name__)
 # The CLI stores sessions under ~/.claude/projects/<encoded-cwd>/<session-id>.jsonl
 # The cwd path is encoded by replacing / with - and prefixing with -
 _CLAUDE_PROJECTS_DIR = Path.home() / ".claude" / "projects"
 def _encode_cwd(cwd: str) -> str:
    """Encode a working directory path for the CLI projects dir name."""
    return "-" + cwd.lstrip("/").replace("/", "-")
 def _get_project_dir(cwd: str) -> Path:
    """Get the CLI project directory for a given working directory.
    Resolves symlinks to match the CLI's behavior (e.g. /tmp -> /private/tmp
    on macOS).
    """
    resolved = str(Path(cwd).resolve())
    return _CLAUDE_PROJECTS_DIR / _encode_cwd(resolved)
 def write_session_file(
    session: ChatSession,
    cwd: str = "/tmp",
 ) -> str | None:
    """Write a session's conversation history as a JSONL file for --resume.
    Returns the session ID to pass to --resume, or None if there's not enough
    history to warrant a file (< 2 messages).
    """
    # Only write if there's prior conversation (at least user + assistant)
    prior = [m for m in session.messages[:-1] if m.role in ("user", "assistant")]
    if len(prior) < 2:
        return None
    session_id = session.session_id
    resolved_cwd = str(Path(cwd).resolve())
    project_dir = _get_project_dir(cwd)
    project_dir.mkdir(parents=True, exist_ok=True)
    file_path = project_dir / f"{session_id}.jsonl"
    now = datetime.now(UTC).isoformat()
    lines: list[str] = []
    prev_uuid: str | None = None
    for msg in session.messages[:-1]:
        msg_uuid = str(uuid.uuid4())
        if msg.role == "user" and msg.content:
            line = {
                "parentUuid": prev_uuid,
                "isSidechain": False,
                "userType": "external",
                "cwd": resolved_cwd,
                "sessionId": session_id,
                "type": "user",
                "message": {"role": "user", "content": msg.content},
                "uuid": msg_uuid,
                "timestamp": now,
            }
            lines.append(json.dumps(line))
            prev_uuid = msg_uuid
        elif msg.role == "assistant" and msg.content:
            line = {
                "parentUuid": prev_uuid,
                "isSidechain": False,
                "userType": "external",
                "cwd": resolved_cwd,
                "sessionId": session_id,
                "type": "assistant",
                "message": {
                    "role": "assistant",
                    "content": [{"type": "text", "text": msg.content}],
                    "model": "unknown",
                },
                "uuid": msg_uuid,
                "timestamp": now,
            }
            lines.append(json.dumps(line))
            prev_uuid = msg_uuid
    if not lines:
        return None
    try:
        file_path.write_text("\n".join(lines) + "\n")
        logger.debug(f"[SESSION] Wrote {len(lines)} messages to {file_path}")
        return session_id
    except OSError as e:
        logger.warning(f"[SESSION] Failed to write session file: {e}")
        return None
 def cleanup_session_file(session_id: str, cwd: str = "/tmp") -> None:
    """Remove a session file after use."""
    project_dir = _get_project_dir(cwd)
    file_path = project_dir / f"{session_id}.jsonl"
    try:
        file_path.unlink(missing_ok=True)
    except OSError:
        pass
--- a/autogpt_platform/backend/backend/api/features/chat/sdk/session_file_test.py
+++ b/autogpt_platform/backend/backend/api/features/chat/sdk/session_file_test.py
@@ -0,0 +1,222 @@
 """Unit tests for session file management."""
 import json
 from datetime import UTC, datetime
 from pathlib import Path
 from unittest.mock import patch
 from ..model import ChatMessage, ChatSession
 from .session_file import _get_project_dir, cleanup_session_file, write_session_file
 _NOW = datetime.now(UTC)
 def _make_session(
    messages: list[ChatMessage], session_id: str = "test-session"
 ) -> ChatSession:
    return ChatSession(
        session_id=session_id,
        user_id="test-user",
        messages=messages,
        usage=[],
        started_at=_NOW,
        updated_at=_NOW,
    )
 # -- write_session_file ------------------------------------------------------
 def test_write_returns_none_for_short_history():
    """Sessions with < 2 prior messages shouldn't generate a file."""
    session = _make_session(
        [
            ChatMessage(role="user", content="hello"),
        ]
    )
    assert write_session_file(session) is None
 def test_write_returns_none_for_single_pair():
    """A single user message (the current one) with no prior history."""
    session = _make_session(
        [
            ChatMessage(role="user", content="current message"),
        ]
    )
    assert write_session_file(session) is None
 def test_write_creates_valid_jsonl(tmp_path: Path):
    """Multi-turn session should produce valid JSONL with correct structure."""
    session = _make_session(
        [
            ChatMessage(role="user", content="hello"),
            ChatMessage(role="assistant", content="Hi there!"),
            ChatMessage(role="user", content="how are you"),  # current message
        ],
        session_id="sess-123",
    )
    with patch(
        "backend.api.features.chat.sdk.session_file._get_project_dir",
        return_value=tmp_path,
    ):
        result = write_session_file(session)
    assert result == "sess-123"
    # Verify the file exists and is valid JSONL
    file_path = tmp_path / "sess-123.jsonl"
    assert file_path.exists()
    lines = file_path.read_text().strip().split("\n")
    # Should have 2 lines (prior messages only, not the current/last one)
    assert len(lines) == 2
    # Verify first line (user message)
    line1 = json.loads(lines[0])
    assert line1["type"] == "user"
    assert line1["message"]["role"] == "user"
    assert line1["message"]["content"] == "hello"
    assert line1["sessionId"] == "sess-123"
    assert line1["parentUuid"] is None  # First message has no parent
    assert "uuid" in line1
    assert "timestamp" in line1
    # Verify second line (assistant message)
    line2 = json.loads(lines[1])
    assert line2["type"] == "assistant"
    assert line2["message"]["role"] == "assistant"
    assert line2["message"]["content"] == [{"type": "text", "text": "Hi there!"}]
    assert line2["parentUuid"] == line1["uuid"]  # Chained to previous
 def test_write_skips_tool_messages(tmp_path: Path):
    """Tool messages should be skipped in the session file."""
    session = _make_session(
        [
            ChatMessage(role="user", content="find agents"),
            ChatMessage(role="assistant", content="Let me search."),
            ChatMessage(role="tool", content="found 3", tool_call_id="tc1"),
            ChatMessage(role="assistant", content="I found 3 agents."),
            ChatMessage(role="user", content="run the first one"),
        ],
        session_id="sess-tools",
    )
    with patch(
        "backend.api.features.chat.sdk.session_file._get_project_dir",
        return_value=tmp_path,
    ):
        result = write_session_file(session)
    assert result == "sess-tools"
    file_path = tmp_path / "sess-tools.jsonl"
    lines = file_path.read_text().strip().split("\n")
    # Should have 3 lines: user, assistant, assistant (tool message skipped,
    # last user message excluded as current)
    assert len(lines) == 3
    types = [json.loads(line)["type"] for line in lines]
    assert types == ["user", "assistant", "assistant"]
 def test_write_skips_empty_content(tmp_path: Path):
    """Messages with empty content should be skipped."""
    session = _make_session(
        [
            ChatMessage(role="user", content="hello"),
            ChatMessage(role="assistant", content=""),
            ChatMessage(role="assistant", content="real response"),
            ChatMessage(role="user", content="next"),
        ],
        session_id="sess-empty",
    )
    with patch(
        "backend.api.features.chat.sdk.session_file._get_project_dir",
        return_value=tmp_path,
    ):
        result = write_session_file(session)
    assert result == "sess-empty"
    file_path = tmp_path / "sess-empty.jsonl"
    lines = file_path.read_text().strip().split("\n")
    # user + assistant (non-empty) = 2 lines
    assert len(lines) == 2
 # -- cleanup_session_file ----------------------------------------------------
 def test_cleanup_removes_file(tmp_path: Path):
    """cleanup_session_file should remove the session file."""
    file_path = tmp_path / "sess-cleanup.jsonl"
    file_path.write_text("{}\n")
    assert file_path.exists()
    with patch(
        "backend.api.features.chat.sdk.session_file._get_project_dir",
        return_value=tmp_path,
    ):
        cleanup_session_file("sess-cleanup")
    assert not file_path.exists()
 def test_cleanup_no_error_if_missing(tmp_path: Path):
    """cleanup_session_file should not raise if file doesn't exist."""
    with patch(
        "backend.api.features.chat.sdk.session_file._get_project_dir",
        return_value=tmp_path,
    ):
        cleanup_session_file("nonexistent")  # Should not raise
 # -- _get_project_dir --------------------------------------------------------
 def test_get_project_dir_resolves_symlinks(tmp_path: Path):
    """_get_project_dir should resolve symlinks so the path matches the CLI."""
    # Create a symlink: tmp_path/link -> tmp_path/real
    real_dir = tmp_path / "real"
    real_dir.mkdir()
    link = tmp_path / "link"
    link.symlink_to(real_dir)
    with patch(
        "backend.api.features.chat.sdk.session_file._CLAUDE_PROJECTS_DIR",
        tmp_path / "projects",
    ):
        result = _get_project_dir(str(link))
    # Should resolve the symlink and encode the real path
    expected_encoded = "-" + str(real_dir).lstrip("/").replace("/", "-")
    assert result.name == expected_encoded
 def test_write_uses_resolved_cwd_in_messages(tmp_path: Path):
    """The cwd field in JSONL messages should use the resolved path."""
    session = _make_session(
        [
            ChatMessage(role="user", content="hello"),
            ChatMessage(role="assistant", content="Hi!"),
            ChatMessage(role="user", content="current"),
        ],
        session_id="sess-cwd",
    )
    with patch(
        "backend.api.features.chat.sdk.session_file._get_project_dir",
        return_value=tmp_path,
    ):
        write_session_file(session, cwd="/tmp")
    file_path = tmp_path / "sess-cwd.jsonl"
    lines = file_path.read_text().strip().split("\n")
    for line in lines:
        obj = json.loads(line)
        # On macOS /tmp resolves to /private/tmp; on Linux stays /tmp
        resolved = str(Path("/tmp").resolve())
        assert obj["cwd"] == resolved
--- a/autogpt_platform/backend/backend/api/features/chat/sdk/tool_adapter.py
+++ b/autogpt_platform/backend/backend/api/features/chat/sdk/tool_adapter.py
@@ -0,0 +1,287 @@
 """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 = "/root/.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
 )
 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)
 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 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)
            )
            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
 # List of tool names for allowed_tools configuration
 # Include the Read tool so the SDK can use it for oversized tool results
 COPILOT_TOOL_NAMES = [
    *[f"{MCP_TOOL_PREFIX}{name}" for name in TOOL_REGISTRY.keys()],
    f"{MCP_TOOL_PREFIX}{_READ_TOOL_NAME}",
 ]
 # Also export the raw tool names for flexibility
 RAW_TOOL_NAMES = list(TOOL_REGISTRY.keys())
--- a/autogpt_platform/backend/backend/api/features/chat/service.py
+++ b/autogpt_platform/backend/backend/api/features/chat/service.py
@@ -52,8 +52,10 @@ from .response_model import (
    StreamBaseResponse,
    StreamError,
    StreamFinish,
    StreamFinishStep,
    StreamHeartbeat,
    StreamStart,
    StreamStartStep,
    StreamTextDelta,
    StreamTextEnd,
    StreamTextStart,
@@ -351,6 +353,10 @@ async def stream_chat_completion(
    retry_count: int = 0,
    session: ChatSession | None = None,
    context: dict[str, str] | None = None,  # {url: str, content: str}
    _continuation_message_id: (
        str | None
    ) = None,  # Internal: reuse message ID for tool call continuations
    _task_id: str | None = None,  # Internal: task ID for SSE reconnection support
 ) -> AsyncGenerator[StreamBaseResponse, None]:
    """Main entry point for streaming chat completions with database handling.
@@ -368,24 +374,47 @@ async def stream_chat_completion(
    Raises:
        NotFoundError: If session_id is invalid
        ValueError: If max_context_messages is exceeded
    """
    completion_start = time.monotonic()
    # Build log metadata for structured logging
    log_meta = {"component": "ChatService", "session_id": session_id}
    if user_id:
        log_meta["user_id"] = user_id
    logger.info(
-        f"Streaming chat completion for session {session_id} for message {message} and user id {user_id}. Message is user message: {is_user_message}"
+        f"[TIMING] stream_chat_completion STARTED, session={session_id}, user={user_id}, "
        f"message_len={len(message) if message else 0}, is_user={is_user_message}",
        extra={
            "json_fields": {
                **log_meta,
                "message_len": len(message) if message else 0,
                "is_user_message": is_user_message,
            }
        },
    )
    # Only fetch from Redis if session not provided (initial call)
    if session is None:
        fetch_start = time.monotonic()
        session = await get_chat_session(session_id, user_id)
        fetch_time = (time.monotonic() - fetch_start) * 1000
        logger.info(
-            f"Fetched session from Redis: {session.session_id if session else 'None'}, "
+            f"[TIMING] get_chat_session took {fetch_time:.1f}ms, "
-            f"message_count={len(session.messages) if session else 0}"
+            f"n_messages={len(session.messages) if session else 0}",
            extra={
                "json_fields": {
                    **log_meta,
                    "duration_ms": fetch_time,
                    "n_messages": len(session.messages) if session else 0,
                }
            },
        )
    else:
        logger.info(
-            f"Using provided session object: {session.session_id}, "
+            f"[TIMING] Using provided session, messages={len(session.messages)}",
-            f"message_count={len(session.messages)}"
+            extra={"json_fields": {**log_meta, "n_messages": len(session.messages)}},
        )
    if not session:
@@ -406,23 +435,32 @@ async def stream_chat_completion(
        # Track user message in PostHog
        if is_user_message:
            posthog_start = time.monotonic()
            track_user_message(
                user_id=user_id,
                session_id=session_id,
                message_length=len(message),
            )
-
+            posthog_time = (time.monotonic() - posthog_start) * 1000
            logger.info(
-        f"Upserting session: {session.session_id} with user id {session.user_id}, "
+                f"[TIMING] track_user_message took {posthog_time:.1f}ms",
-        f"message_count={len(session.messages)}"
+                extra={"json_fields": {**log_meta, "duration_ms": posthog_time}},
            )
    upsert_start = time.monotonic()
    session = await upsert_chat_session(session)
    upsert_time = (time.monotonic() - upsert_start) * 1000
    logger.info(
        f"[TIMING] upsert_chat_session took {upsert_time:.1f}ms",
        extra={"json_fields": {**log_meta, "duration_ms": upsert_time}},
    )
    assert session, "Session not found"
    # 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
@@ -430,7 +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 = message
+            captured_message = first_user_msg
            captured_user_id = user_id
            async def _update_title():
@@ -454,7 +492,13 @@ async def stream_chat_completion(
            asyncio.create_task(_update_title())
    # Build system prompt with business understanding
    prompt_start = time.monotonic()
    system_prompt, understanding = await _build_system_prompt(user_id)
    prompt_time = (time.monotonic() - prompt_start) * 1000
    logger.info(
        f"[TIMING] _build_system_prompt took {prompt_time:.1f}ms",
        extra={"json_fields": {**log_meta, "duration_ms": prompt_time}},
    )
    # Initialize variables for streaming
    assistant_response = ChatMessage(
@@ -479,13 +523,27 @@ async def stream_chat_completion(
    # Generate unique IDs for AI SDK protocol
    import uuid as uuid_module
-    message_id = str(uuid_module.uuid4())
+    is_continuation = _continuation_message_id is not None
    message_id = _continuation_message_id or str(uuid_module.uuid4())
    text_block_id = str(uuid_module.uuid4())
-    # Yield message start
+    # Only yield message start for the initial call, not for continuations.
-    yield StreamStart(messageId=message_id)
+    setup_time = (time.monotonic() - completion_start) * 1000
    logger.info(
        f"[TIMING] Setup complete, yielding StreamStart at {setup_time:.1f}ms",
        extra={"json_fields": {**log_meta, "setup_time_ms": setup_time}},
    )
    if not is_continuation:
        yield StreamStart(messageId=message_id, taskId=_task_id)
    # Emit start-step before each LLM call (AI SDK uses this to add step boundaries)
    yield StreamStartStep()
    try:
        logger.info(
            "[TIMING] Calling _stream_chat_chunks",
            extra={"json_fields": log_meta},
        )
        async for chunk in _stream_chat_chunks(
            session=session,
            tools=tools,
@@ -585,6 +643,10 @@ async def stream_chat_completion(
                    )
                yield chunk
            elif isinstance(chunk, StreamFinish):
                if has_done_tool_call:
                    # Tool calls happened — close the step but don't send message-level finish.
                    # The continuation will open a new step, and finish will come at the end.
                    yield StreamFinishStep()
                if not has_done_tool_call:
                    # Emit text-end before finish if we received text but haven't closed it
                    if has_received_text and not text_streaming_ended:
@@ -616,6 +678,8 @@ async def stream_chat_completion(
                            has_saved_assistant_message = True
                    has_yielded_end = True
                    # Emit finish-step before finish (resets AI SDK text/reasoning state)
                    yield StreamFinishStep()
                    yield chunk
            elif isinstance(chunk, StreamError):
                has_yielded_error = True
@@ -665,6 +729,10 @@ async def stream_chat_completion(
            logger.info(
                f"Retryable error encountered. Attempt {retry_count + 1}/{config.max_retries}"
            )
            # Close the current step before retrying so the recursive call's
            # StreamStartStep doesn't produce unbalanced step events.
            if not has_yielded_end:
                yield StreamFinishStep()
            should_retry = True
        else:
            # Non-retryable error or max retries exceeded
@@ -700,6 +768,7 @@ async def stream_chat_completion(
                error_response = StreamError(errorText=error_message)
                yield error_response
            if not has_yielded_end:
                yield StreamFinishStep()
                yield StreamFinish()
            return
@@ -714,6 +783,8 @@ async def stream_chat_completion(
            retry_count=retry_count + 1,
            session=session,
            context=context,
            _continuation_message_id=message_id,  # Reuse message ID since start was already sent
            _task_id=_task_id,
        ):
            yield chunk
        return  # Exit after retry to avoid double-saving in finally block
@@ -783,6 +854,8 @@ async def stream_chat_completion(
            session=session,  # Pass session object to avoid Redis refetch
            context=context,
            tool_call_response=str(tool_response_messages),
            _continuation_message_id=message_id,  # Reuse message ID to avoid duplicates
            _task_id=_task_id,
        ):
            yield chunk
@@ -893,9 +966,21 @@ async def _stream_chat_chunks(
        SSE formatted JSON response objects
    """
    import time as time_module
    stream_chunks_start = time_module.perf_counter()
    model = config.model
-    logger.info("Starting pure chat stream")
+    # Build log metadata for structured logging
    log_meta = {"component": "ChatService", "session_id": session.session_id}
    if session.user_id:
        log_meta["user_id"] = session.user_id
    logger.info(
        f"[TIMING] _stream_chat_chunks STARTED, session={session.session_id}, "
        f"user={session.user_id}, n_messages={len(session.messages)}",
        extra={"json_fields": {**log_meta, "n_messages": len(session.messages)}},
    )
    messages = session.to_openai_messages()
    if system_prompt:
@@ -906,12 +991,18 @@ async def _stream_chat_chunks(
        messages = [system_message] + messages
    # Apply context window management
    context_start = time_module.perf_counter()
    context_result = await _manage_context_window(
        messages=messages,
        model=model,
        api_key=config.api_key,
        base_url=config.base_url,
    )
    context_time = (time_module.perf_counter() - context_start) * 1000
    logger.info(
        f"[TIMING] _manage_context_window took {context_time:.1f}ms",
        extra={"json_fields": {**log_meta, "duration_ms": context_time}},
    )
    if context_result.error:
        if "System prompt dropped" in context_result.error:
@@ -946,9 +1037,19 @@ async def _stream_chat_chunks(
        while retry_count <= MAX_RETRIES:
            try:
                elapsed = (time_module.perf_counter() - stream_chunks_start) * 1000
                retry_info = (
                    f" (retry {retry_count}/{MAX_RETRIES})" if retry_count > 0 else ""
                )
                logger.info(
-                    f"Creating OpenAI chat completion stream..."
+                    f"[TIMING] Creating OpenAI stream at {elapsed:.1f}ms{retry_info}",
-                    f"{f' (retry {retry_count}/{MAX_RETRIES})' if retry_count > 0 else ''}"
+                    extra={
                        "json_fields": {
                            **log_meta,
                            "elapsed_ms": elapsed,
                            "retry_count": retry_count,
                        }
                    },
                )
                # Build extra_body for OpenRouter tracing and PostHog analytics
@@ -965,6 +1066,7 @@ async def _stream_chat_chunks(
                        :128
                    ]  # OpenRouter limit
                api_call_start = time_module.perf_counter()
                stream = await client.chat.completions.create(
                    model=model,
                    messages=cast(list[ChatCompletionMessageParam], messages),
@@ -974,6 +1076,11 @@ async def _stream_chat_chunks(
                    stream_options=ChatCompletionStreamOptionsParam(include_usage=True),
                    extra_body=extra_body,
                )
                api_init_time = (time_module.perf_counter() - api_call_start) * 1000
                logger.info(
                    f"[TIMING] OpenAI stream object returned in {api_init_time:.1f}ms",
                    extra={"json_fields": {**log_meta, "duration_ms": api_init_time}},
                )
                # Variables to accumulate tool calls
                tool_calls: list[dict[str, Any]] = []
@@ -984,10 +1091,13 @@ async def _stream_chat_chunks(
                # Track if we've started the text block
                text_started = False
                first_content_chunk = True
                chunk_count = 0
                # Process the stream
                chunk: ChatCompletionChunk
                async for chunk in stream:
                    chunk_count += 1
                    if chunk.usage:
                        yield StreamUsage(
                            promptTokens=chunk.usage.prompt_tokens,
@@ -1010,6 +1120,23 @@ async def _stream_chat_chunks(
                            if not text_started and text_block_id:
                                yield StreamTextStart(id=text_block_id)
                                text_started = True
                            # Log timing for first content chunk
                            if first_content_chunk:
                                first_content_chunk = False
                                ttfc = (
                                    time_module.perf_counter() - api_call_start
                                ) * 1000
                                logger.info(
                                    f"[TIMING] FIRST CONTENT CHUNK at {ttfc:.1f}ms "
                                    f"(since API call), n_chunks={chunk_count}",
                                    extra={
                                        "json_fields": {
                                            **log_meta,
                                            "time_to_first_chunk_ms": ttfc,
                                            "n_chunks": chunk_count,
                                        }
                                    },
                                )
                            # Stream the text delta
                            text_response = StreamTextDelta(
                                id=text_block_id or "",
@@ -1066,7 +1193,21 @@ async def _stream_chat_chunks(
                                        toolName=tool_calls[idx]["function"]["name"],
                                    )
                                    emitted_start_for_idx.add(idx)
-                logger.info(f"Stream complete. Finish reason: {finish_reason}")
+                stream_duration = time_module.perf_counter() - api_call_start
                logger.info(
                    f"[TIMING] OpenAI stream COMPLETE, finish_reason={finish_reason}, "
                    f"duration={stream_duration:.2f}s, "
                    f"n_chunks={chunk_count}, n_tool_calls={len(tool_calls)}",
                    extra={
                        "json_fields": {
                            **log_meta,
                            "stream_duration_ms": stream_duration * 1000,
                            "finish_reason": finish_reason,
                            "n_chunks": chunk_count,
                            "n_tool_calls": len(tool_calls),
                        }
                    },
                )
                # Yield all accumulated tool calls after the stream is complete
                # This ensures all tool call arguments have been fully received
@@ -1086,11 +1227,16 @@ async def _stream_chat_chunks(
                        # Re-raise to trigger retry logic in the parent function
                        raise
                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"session={session.session_id}, user={session.user_id}",
                    extra={"json_fields": {**log_meta, "total_time_ms": total_time}},
                )
                yield StreamFinish()
                return
            except Exception as e:
                last_error = e
                if _is_retryable_error(e) and retry_count < MAX_RETRIES:
                    retry_count += 1
                    # Calculate delay with exponential backoff
@@ -1106,26 +1252,12 @@ async def _stream_chat_chunks(
                    continue  # Retry the stream
                else:
                    # Non-retryable error or max retries exceeded
-                    _log_api_error(
+                    logger.error(
-                        error=e,
+                        f"Error in stream (not retrying): {e!s}",
-                        session_id=session.session_id if session else None,
+                        exc_info=True,
                        message_count=len(messages) if messages else None,
                        model=model,
                        retry_count=retry_count,
                    )
                    error_code = None
                    error_text = str(e)
                    error_details = _extract_api_error_details(e)
                    if error_details.get("response_body"):
                        body = error_details["response_body"]
                        if isinstance(body, dict):
                            err = body.get("error")
                            if isinstance(err, dict) and err.get("message"):
                                error_text = err["message"]
                            elif body.get("message"):
                                error_text = body["message"]
                    if _is_region_blocked_error(e):
                        error_code = "MODEL_NOT_AVAILABLE_REGION"
                        error_text = (
@@ -1142,12 +1274,9 @@ async def _stream_chat_chunks(
        # If we exit the retry loop without returning, it means we exhausted retries
        if last_error:
-            _log_api_error(
+            logger.error(
-                error=last_error,
+                f"Max retries ({MAX_RETRIES}) exceeded. Last error: {last_error!s}",
-                session_id=session.session_id if session else None,
+                exc_info=True,
                message_count=len(messages) if messages else None,
                model=model,
                retry_count=MAX_RETRIES,
            )
            yield StreamError(errorText=f"Max retries exceeded: {last_error!s}")
            yield StreamFinish()
@@ -1583,6 +1712,7 @@ async def _execute_long_running_tool_with_streaming(
            task_id,
            StreamError(errorText=str(e)),
        )
        await stream_registry.publish_chunk(task_id, StreamFinishStep())
        await stream_registry.publish_chunk(task_id, StreamFinish())
        await _update_pending_operation(
@@ -1719,7 +1849,6 @@ async def _generate_llm_continuation(
                break  # Success, exit retry loop
            except Exception as e:
                last_error = e
                if _is_retryable_error(e) and retry_count < MAX_RETRIES:
                    retry_count += 1
                    delay = min(
@@ -1733,23 +1862,17 @@ async def _generate_llm_continuation(
                    await asyncio.sleep(delay)
                    continue
                else:
-                    # Non-retryable error - log details and exit gracefully
+                    # Non-retryable error - log and exit gracefully
-                    _log_api_error(
+                    logger.error(
-                        error=e,
+                        f"Non-retryable error in LLM continuation: {e!s}",
-                        session_id=session_id,
+                        exc_info=True,
                        message_count=len(messages) if messages else None,
                        model=config.model,
                        retry_count=retry_count,
                    )
                    return
        if last_error:
-            _log_api_error(
+            logger.error(
-                error=last_error,
+                f"Max retries ({MAX_RETRIES}) exceeded for LLM continuation. "
-                session_id=session_id,
+                f"Last error: {last_error!s}"
                message_count=len(messages) if messages else None,
                model=config.model,
                retry_count=MAX_RETRIES,
            )
            return
@@ -1789,89 +1912,6 @@ async def _generate_llm_continuation(
        logger.error(f"Failed to generate LLM continuation: {e}", exc_info=True)
 def _log_api_error(
    error: Exception,
    session_id: str | None = None,
    message_count: int | None = None,
    model: str | None = None,
    retry_count: int = 0,
 ) -> None:
    """Log detailed API error information for debugging."""
    details = _extract_api_error_details(error)
    details["session_id"] = session_id
    details["message_count"] = message_count
    details["model"] = model
    details["retry_count"] = retry_count
    if isinstance(error, RateLimitError):
        logger.warning(f"Rate limit error: {details}")
    elif isinstance(error, APIConnectionError):
        logger.warning(f"API connection error: {details}")
    elif isinstance(error, APIStatusError) and error.status_code >= 500:
        logger.error(f"API server error (5xx): {details}")
    else:
        logger.error(f"API error: {details}")
 def _extract_api_error_details(error: Exception) -> dict[str, Any]:
    """Extract detailed information from OpenAI/OpenRouter API errors."""
    error_msg = str(error)
    details: dict[str, Any] = {
        "error_type": type(error).__name__,
        "error_message": error_msg[:500] + "..." if len(error_msg) > 500 else error_msg,
    }
    if hasattr(error, "code"):
        details["code"] = getattr(error, "code", None)
    if hasattr(error, "param"):
        details["param"] = getattr(error, "param", None)
    if isinstance(error, APIStatusError):
        details["status_code"] = error.status_code
        details["request_id"] = getattr(error, "request_id", None)
        if hasattr(error, "body") and error.body:
            details["response_body"] = _sanitize_error_body(error.body)
        if hasattr(error, "response") and error.response:
            headers = error.response.headers
            details["openrouter_provider"] = headers.get("x-openrouter-provider")
            details["openrouter_model"] = headers.get("x-openrouter-model")
            details["retry_after"] = headers.get("retry-after")
            details["rate_limit_remaining"] = headers.get("x-ratelimit-remaining")
    return details
 def _sanitize_error_body(
    body: Any, max_length: int = 2000
 ) -> dict[str, Any] | str | None:
    """Extract only safe fields from error response body to avoid logging sensitive data."""
    if not isinstance(body, dict):
        # Non-dict bodies (e.g., HTML error pages) - return truncated string
        if body is not None:
            body_str = str(body)
            if len(body_str) > max_length:
                return body_str[:max_length] + "...[truncated]"
            return body_str
        return None
    safe_fields = ("message", "type", "code", "param", "error")
    sanitized: dict[str, Any] = {}
    for field in safe_fields:
        if field in body:
            value = body[field]
            if field == "error" and isinstance(value, dict):
                sanitized[field] = _sanitize_error_body(value, max_length)
            elif isinstance(value, str) and len(value) > max_length:
                sanitized[field] = value[:max_length] + "...[truncated]"
            else:
                sanitized[field] = value
    return sanitized if sanitized else None
 async def _generate_llm_continuation_with_streaming(
    session_id: str,
    user_id: str | None,
@@ -1930,6 +1970,7 @@ async def _generate_llm_continuation_with_streaming(
        # Publish start event
        await stream_registry.publish_chunk(task_id, StreamStart(messageId=message_id))
        await stream_registry.publish_chunk(task_id, StreamStartStep())
        await stream_registry.publish_chunk(task_id, StreamTextStart(id=text_block_id))
        # Stream the response
@@ -1953,6 +1994,7 @@ async def _generate_llm_continuation_with_streaming(
        # Publish end events
        await stream_registry.publish_chunk(task_id, StreamTextEnd(id=text_block_id))
        await stream_registry.publish_chunk(task_id, StreamFinishStep())
        if assistant_content:
            # Reload session from DB to avoid race condition with user messages
@@ -1994,4 +2036,5 @@ async def _generate_llm_continuation_with_streaming(
            task_id,
            StreamError(errorText=f"Failed to generate response: {e}"),
        )
        await stream_registry.publish_chunk(task_id, StreamFinishStep())
        await stream_registry.publish_chunk(task_id, StreamFinish())
--- a/autogpt_platform/backend/backend/api/features/chat/stream_registry.py
+++ b/autogpt_platform/backend/backend/api/features/chat/stream_registry.py
@@ -104,6 +104,24 @@ async def create_task(
    Returns:
        The created ActiveTask instance (metadata only)
    """
    import time
    start_time = time.perf_counter()
    # Build log metadata for structured logging
    log_meta = {
        "component": "StreamRegistry",
        "task_id": task_id,
        "session_id": session_id,
    }
    if user_id:
        log_meta["user_id"] = user_id
    logger.info(
        f"[TIMING] create_task STARTED, task={task_id}, session={session_id}, user={user_id}",
        extra={"json_fields": log_meta},
    )
    task = ActiveTask(
        task_id=task_id,
        session_id=session_id,
@@ -114,10 +132,18 @@ async def create_task(
    )
    # Store metadata in Redis
    redis_start = time.perf_counter()
    redis = await get_redis_async()
    redis_time = (time.perf_counter() - redis_start) * 1000
    logger.info(
        f"[TIMING] get_redis_async took {redis_time:.1f}ms",
        extra={"json_fields": {**log_meta, "duration_ms": redis_time}},
    )
    meta_key = _get_task_meta_key(task_id)
    op_key = _get_operation_mapping_key(operation_id)
    hset_start = time.perf_counter()
    await redis.hset(  # type: ignore[misc]
        meta_key,
        mapping={
@@ -131,12 +157,22 @@ async def create_task(
            "created_at": task.created_at.isoformat(),
        },
    )
    hset_time = (time.perf_counter() - hset_start) * 1000
    logger.info(
        f"[TIMING] redis.hset took {hset_time:.1f}ms",
        extra={"json_fields": {**log_meta, "duration_ms": hset_time}},
    )
    await redis.expire(meta_key, config.stream_ttl)
    # Create operation_id -> task_id mapping for webhook lookups
    await redis.set(op_key, task_id, ex=config.stream_ttl)
-    logger.debug(f"Created task {task_id} for session {session_id}")
+    total_time = (time.perf_counter() - start_time) * 1000
    logger.info(
        f"[TIMING] create_task COMPLETED in {total_time:.1f}ms; task={task_id}, session={session_id}",
        extra={"json_fields": {**log_meta, "total_time_ms": total_time}},
    )
    return task
@@ -156,26 +192,60 @@ async def publish_chunk(
    Returns:
        The Redis Stream message ID
    """
    import time
    start_time = time.perf_counter()
    chunk_type = type(chunk).__name__
    chunk_json = chunk.model_dump_json()
    message_id = "0-0"
    # Build log metadata
    log_meta = {
        "component": "StreamRegistry",
        "task_id": task_id,
        "chunk_type": chunk_type,
    }
    try:
        redis = await get_redis_async()
        stream_key = _get_task_stream_key(task_id)
        # Write to Redis Stream for persistence and real-time delivery
        xadd_start = time.perf_counter()
        raw_id = await redis.xadd(
            stream_key,
            {"data": chunk_json},
            maxlen=config.stream_max_length,
        )
        xadd_time = (time.perf_counter() - xadd_start) * 1000
        message_id = raw_id if isinstance(raw_id, str) else raw_id.decode()
        # Set TTL on stream to match task metadata TTL
        await redis.expire(stream_key, config.stream_ttl)
        total_time = (time.perf_counter() - start_time) * 1000
        # Only log timing for significant chunks or slow operations
        if (
            chunk_type
            in ("StreamStart", "StreamFinish", "StreamTextStart", "StreamTextEnd")
            or total_time > 50
        ):
            logger.info(
                f"[TIMING] publish_chunk {chunk_type} in {total_time:.1f}ms (xadd={xadd_time:.1f}ms)",
                extra={
                    "json_fields": {
                        **log_meta,
                        "total_time_ms": total_time,
                        "xadd_time_ms": xadd_time,
                        "message_id": message_id,
                    }
                },
            )
    except Exception as e:
        elapsed = (time.perf_counter() - start_time) * 1000
        logger.error(
-            f"Failed to publish chunk for task {task_id}: {e}",
+            f"[TIMING] Failed to publish chunk {chunk_type} after {elapsed:.1f}ms: {e}",
            extra={"json_fields": {**log_meta, "elapsed_ms": elapsed, "error": str(e)}},
            exc_info=True,
        )
@@ -200,24 +270,61 @@ async def subscribe_to_task(
        An asyncio Queue that will receive stream chunks, or None if task not found
        or user doesn't have access
    """
    import time
    start_time = time.perf_counter()
    # Build log metadata
    log_meta = {"component": "StreamRegistry", "task_id": task_id}
    if user_id:
        log_meta["user_id"] = user_id
    logger.info(
        f"[TIMING] subscribe_to_task STARTED, task={task_id}, user={user_id}, last_msg={last_message_id}",
        extra={"json_fields": {**log_meta, "last_message_id": last_message_id}},
    )
    redis_start = time.perf_counter()
    redis = await get_redis_async()
    meta_key = _get_task_meta_key(task_id)
    meta: dict[Any, Any] = await redis.hgetall(meta_key)  # type: ignore[misc]
    hgetall_time = (time.perf_counter() - redis_start) * 1000
    logger.info(
        f"[TIMING] Redis hgetall took {hgetall_time:.1f}ms",
        extra={"json_fields": {**log_meta, "duration_ms": hgetall_time}},
    )
    if not meta:
-        logger.debug(f"Task {task_id} not found in Redis")
+        elapsed = (time.perf_counter() - start_time) * 1000
        logger.info(
            f"[TIMING] Task not found in Redis after {elapsed:.1f}ms",
            extra={
                "json_fields": {
                    **log_meta,
                    "elapsed_ms": elapsed,
                    "reason": "task_not_found",
                }
            },
        )
        return None
    # Note: Redis client uses decode_responses=True, so keys are strings
    task_status = meta.get("status", "")
    task_user_id = meta.get("user_id", "") or None
    log_meta["session_id"] = meta.get("session_id", "")
    # Validate ownership - if task has an owner, requester must match
    if task_user_id:
        if user_id != task_user_id:
            logger.warning(
-                f"User {user_id} denied access to task {task_id} "
+                f"[TIMING] Access denied: user {user_id} tried to access task owned by {task_user_id}",
-                f"owned by {task_user_id}"
+                extra={
                    "json_fields": {
                        **log_meta,
                        "task_owner": task_user_id,
                        "reason": "access_denied",
                    }
                },
            )
            return None
@@ -225,7 +332,19 @@ async def subscribe_to_task(
    stream_key = _get_task_stream_key(task_id)
    # Step 1: Replay messages from Redis Stream
    xread_start = time.perf_counter()
    messages = await redis.xread({stream_key: last_message_id}, block=0, count=1000)
    xread_time = (time.perf_counter() - xread_start) * 1000
    logger.info(
        f"[TIMING] Redis xread (replay) took {xread_time:.1f}ms, status={task_status}",
        extra={
            "json_fields": {
                **log_meta,
                "duration_ms": xread_time,
                "task_status": task_status,
            }
        },
    )
    replayed_count = 0
    replay_last_id = last_message_id
@@ -244,19 +363,48 @@ async def subscribe_to_task(
                    except Exception as e:
                        logger.warning(f"Failed to replay message: {e}")
-    logger.debug(f"Task {task_id}: replayed {replayed_count} messages")
+    logger.info(
        f"[TIMING] Replayed {replayed_count} messages, last_id={replay_last_id}",
        extra={
            "json_fields": {
                **log_meta,
                "n_messages_replayed": replayed_count,
                "replay_last_id": replay_last_id,
            }
        },
    )
    # Step 2: If task is still running, start stream listener for live updates
    if task_status == "running":
        logger.info(
            "[TIMING] Task still running, starting _stream_listener",
            extra={"json_fields": {**log_meta, "task_status": task_status}},
        )
        listener_task = asyncio.create_task(
-            _stream_listener(task_id, subscriber_queue, replay_last_id)
+            _stream_listener(task_id, subscriber_queue, replay_last_id, log_meta)
        )
        # Track listener task for cleanup on unsubscribe
        _listener_tasks[id(subscriber_queue)] = (task_id, listener_task)
    else:
        # Task is completed/failed - add finish marker
        logger.info(
            f"[TIMING] Task already {task_status}, adding StreamFinish",
            extra={"json_fields": {**log_meta, "task_status": task_status}},
        )
        await subscriber_queue.put(StreamFinish())
    total_time = (time.perf_counter() - start_time) * 1000
    logger.info(
        f"[TIMING] subscribe_to_task COMPLETED in {total_time:.1f}ms; task={task_id}, "
        f"n_messages_replayed={replayed_count}",
        extra={
            "json_fields": {
                **log_meta,
                "total_time_ms": total_time,
                "n_messages_replayed": replayed_count,
            }
        },
    )
    return subscriber_queue
@@ -264,6 +412,7 @@ async def _stream_listener(
    task_id: str,
    subscriber_queue: asyncio.Queue[StreamBaseResponse],
    last_replayed_id: str,
    log_meta: dict | None = None,
 ) -> None:
    """Listen to Redis Stream for new messages using blocking XREAD.
@@ -274,10 +423,27 @@ async def _stream_listener(
        task_id: Task ID to listen for
        subscriber_queue: Queue to deliver messages to
        last_replayed_id: Last message ID from replay (continue from here)
        log_meta: Structured logging metadata
    """
    import time
    start_time = time.perf_counter()
    # Use provided log_meta or build minimal one
    if log_meta is None:
        log_meta = {"component": "StreamRegistry", "task_id": task_id}
    logger.info(
        f"[TIMING] _stream_listener STARTED, task={task_id}, last_id={last_replayed_id}",
        extra={"json_fields": {**log_meta, "last_replayed_id": last_replayed_id}},
    )
    queue_id = id(subscriber_queue)
    # Track the last successfully delivered message ID for recovery hints
    last_delivered_id = last_replayed_id
    messages_delivered = 0
    first_message_time = None
    xread_count = 0
    try:
        redis = await get_redis_async()
@@ -287,9 +453,39 @@ async def _stream_listener(
        while True:
            # Block for up to 30 seconds waiting for new messages
            # This allows periodic checking if task is still running
            xread_start = time.perf_counter()
            xread_count += 1
            messages = await redis.xread(
                {stream_key: current_id}, block=30000, count=100
            )
            xread_time = (time.perf_counter() - xread_start) * 1000
            if messages:
                msg_count = sum(len(msgs) for _, msgs in messages)
                logger.info(
                    f"[TIMING] xread #{xread_count} returned {msg_count} messages in {xread_time:.1f}ms",
                    extra={
                        "json_fields": {
                            **log_meta,
                            "xread_count": xread_count,
                            "n_messages": msg_count,
                            "duration_ms": xread_time,
                        }
                    },
                )
            elif xread_time > 1000:
                # Only log timeouts (30s blocking)
                logger.info(
                    f"[TIMING] xread #{xread_count} timeout after {xread_time:.1f}ms",
                    extra={
                        "json_fields": {
                            **log_meta,
                            "xread_count": xread_count,
                            "duration_ms": xread_time,
                            "reason": "timeout",
                        }
                    },
                )
            if not messages:
                # Timeout - check if task is still running
@@ -326,10 +522,30 @@ async def _stream_listener(
                                )
                                # Update last delivered ID on successful delivery
                                last_delivered_id = current_id
                                messages_delivered += 1
                                if first_message_time is None:
                                    first_message_time = time.perf_counter()
                                    elapsed = (first_message_time - start_time) * 1000
                                    logger.info(
                                        f"[TIMING] FIRST live message at {elapsed:.1f}ms, type={type(chunk).__name__}",
                                        extra={
                                            "json_fields": {
                                                **log_meta,
                                                "elapsed_ms": elapsed,
                                                "chunk_type": type(chunk).__name__,
                                            }
                                        },
                                    )
                            except asyncio.TimeoutError:
                                logger.warning(
-                                    f"Subscriber queue full for task {task_id}, "
+                                    f"[TIMING] Subscriber queue full, delivery timed out after {QUEUE_PUT_TIMEOUT}s",
-                                    f"message delivery timed out after {QUEUE_PUT_TIMEOUT}s"
+                                    extra={
                                        "json_fields": {
                                            **log_meta,
                                            "timeout_s": QUEUE_PUT_TIMEOUT,
                                            "reason": "queue_full",
                                        }
                                    },
                                )
                                # Send overflow error with recovery info
                                try:
@@ -351,15 +567,44 @@ async def _stream_listener(
                            # Stop listening on finish
                            if isinstance(chunk, StreamFinish):
                                total_time = (time.perf_counter() - start_time) * 1000
                                logger.info(
                                    f"[TIMING] StreamFinish received in {total_time/1000:.1f}s; delivered={messages_delivered}",
                                    extra={
                                        "json_fields": {
                                            **log_meta,
                                            "total_time_ms": total_time,
                                            "messages_delivered": messages_delivered,
                                        }
                                    },
                                )
                                return
                    except Exception as e:
-                        logger.warning(f"Error processing stream message: {e}")
+                        logger.warning(
                            f"Error processing stream message: {e}",
                            extra={"json_fields": {**log_meta, "error": str(e)}},
                        )
    except asyncio.CancelledError:
-        logger.debug(f"Stream listener cancelled for task {task_id}")
+        elapsed = (time.perf_counter() - start_time) * 1000
        logger.info(
            f"[TIMING] _stream_listener CANCELLED after {elapsed:.1f}ms, delivered={messages_delivered}",
            extra={
                "json_fields": {
                    **log_meta,
                    "elapsed_ms": elapsed,
                    "messages_delivered": messages_delivered,
                    "reason": "cancelled",
                }
            },
        )
        raise  # Re-raise to propagate cancellation
    except Exception as e:
-        logger.error(f"Stream listener error for task {task_id}: {e}")
+        elapsed = (time.perf_counter() - start_time) * 1000
        logger.error(
            f"[TIMING] _stream_listener ERROR after {elapsed:.1f}ms: {e}",
            extra={"json_fields": {**log_meta, "elapsed_ms": elapsed, "error": str(e)}},
        )
        # On error, send finish to unblock subscriber
        try:
            await asyncio.wait_for(
@@ -368,10 +613,24 @@ async def _stream_listener(
            )
        except (asyncio.TimeoutError, asyncio.QueueFull):
            logger.warning(
-                f"Could not deliver finish event for task {task_id} after error"
+                "Could not deliver finish event after error",
                extra={"json_fields": log_meta},
            )
    finally:
        # Clean up listener task mapping on exit
        total_time = (time.perf_counter() - start_time) * 1000
        logger.info(
            f"[TIMING] _stream_listener FINISHED in {total_time/1000:.1f}s; task={task_id}, "
            f"delivered={messages_delivered}, xread_count={xread_count}",
            extra={
                "json_fields": {
                    **log_meta,
                    "total_time_ms": total_time,
                    "messages_delivered": messages_delivered,
                    "xread_count": xread_count,
                }
            },
        )
        _listener_tasks.pop(queue_id, None)
@@ -555,6 +814,28 @@ 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"
@@ -598,8 +879,10 @@ def _reconstruct_chunk(chunk_data: dict) -> StreamBaseResponse | None:
        ResponseType,
        StreamError,
        StreamFinish,
        StreamFinishStep,
        StreamHeartbeat,
        StreamStart,
        StreamStartStep,
        StreamTextDelta,
        StreamTextEnd,
        StreamTextStart,
@@ -613,6 +896,8 @@ def _reconstruct_chunk(chunk_data: dict) -> StreamBaseResponse | None:
    type_to_class: dict[str, type[StreamBaseResponse]] = {
        ResponseType.START.value: StreamStart,
        ResponseType.FINISH.value: StreamFinish,
        ResponseType.START_STEP.value: StreamStartStep,
        ResponseType.FINISH_STEP.value: StreamFinishStep,
        ResponseType.TEXT_START.value: StreamTextStart,
        ResponseType.TEXT_DELTA.value: StreamTextDelta,
        ResponseType.TEXT_END.value: StreamTextEnd,
--- a/autogpt_platform/backend/backend/api/features/chat/tools/find_block.py
+++ b/autogpt_platform/backend/backend/api/features/chat/tools/find_block.py
@@ -13,10 +13,32 @@ from backend.api.features.chat.tools.models import (
    NoResultsResponse,
 )
 from backend.api.features.store.hybrid_search import unified_hybrid_search
-from backend.data.block import get_block
+from backend.data.block import BlockType, get_block
 logger = logging.getLogger(__name__)
 _TARGET_RESULTS = 10
 # Over-fetch to compensate for post-hoc filtering of graph-only blocks.
 # 40 is 2x current removed; speed of query 10 vs 40 is minimial
 _OVERFETCH_PAGE_SIZE = 40
 # Block types that only work within graphs and cannot run standalone in CoPilot.
 COPILOT_EXCLUDED_BLOCK_TYPES = {
    BlockType.INPUT,  # Graph interface definition - data enters via chat, not graph inputs
    BlockType.OUTPUT,  # Graph interface definition - data exits via chat, not graph outputs
    BlockType.WEBHOOK,  # Wait for external events - would hang forever in CoPilot
    BlockType.WEBHOOK_MANUAL,  # Same as WEBHOOK
    BlockType.NOTE,  # Visual annotation only - no runtime behavior
    BlockType.HUMAN_IN_THE_LOOP,  # Pauses for human approval - CoPilot IS human-in-the-loop
    BlockType.AGENT,  # AgentExecutorBlock requires execution_context - use run_agent tool
 }
 # Specific block IDs excluded from CoPilot (STANDARD type but still require graph context)
 COPILOT_EXCLUDED_BLOCK_IDS = {
    # SmartDecisionMakerBlock - dynamically discovers downstream blocks via graph topology
    "3b191d9f-356f-482d-8238-ba04b6d18381",
 }
 class FindBlockTool(BaseTool):
    """Tool for searching available blocks."""
@@ -88,7 +110,7 @@ class FindBlockTool(BaseTool):
                query=query,
                content_types=[ContentType.BLOCK],
                page=1,
-                page_size=10,
+                page_size=_OVERFETCH_PAGE_SIZE,
            )
            if not results:
@@ -108,18 +130,35 @@ class FindBlockTool(BaseTool):
                block = get_block(block_id)
                # Skip disabled blocks
-                if block and not block.disabled:
+                if not block or block.disabled:
                    continue
                # Skip blocks excluded from CoPilot (graph-only blocks)
                if (
                    block.block_type in COPILOT_EXCLUDED_BLOCK_TYPES
                    or block.id in COPILOT_EXCLUDED_BLOCK_IDS
                ):
                    continue
                # Get input/output schemas
                input_schema = {}
                output_schema = {}
                try:
                    input_schema = block.input_schema.jsonschema()
-                    except Exception:
+                except Exception as e:
-                        pass
+                    logger.debug(
                        "Failed to generate input schema for block %s: %s",
                        block_id,
                        e,
                    )
                try:
                    output_schema = block.output_schema.jsonschema()
-                    except Exception:
+                except Exception as e:
-                        pass
+                    logger.debug(
                        "Failed to generate output schema for block %s: %s",
                        block_id,
                        e,
                    )
                # Get categories from block instance
                categories = []
@@ -163,6 +202,19 @@ class FindBlockTool(BaseTool):
                    )
                )
                if len(blocks) >= _TARGET_RESULTS:
                    break
            if blocks and len(blocks) < _TARGET_RESULTS:
                logger.debug(
                    "find_block returned %d/%d results for query '%s' "
                    "(filtered %d excluded/disabled blocks)",
                    len(blocks),
                    _TARGET_RESULTS,
                    query,
                    len(results) - len(blocks),
                )
            if not blocks:
                return NoResultsResponse(
                    message=f"No blocks found for '{query}'",
--- a/autogpt_platform/backend/backend/api/features/chat/tools/find_block_test.py
+++ b/autogpt_platform/backend/backend/api/features/chat/tools/find_block_test.py
@@ -0,0 +1,139 @@
 """Tests for block filtering in FindBlockTool."""
 from unittest.mock import AsyncMock, MagicMock, patch
 import pytest
 from backend.api.features.chat.tools.find_block import (
    COPILOT_EXCLUDED_BLOCK_IDS,
    COPILOT_EXCLUDED_BLOCK_TYPES,
    FindBlockTool,
 )
 from backend.api.features.chat.tools.models import BlockListResponse
 from backend.data.block import BlockType
 from ._test_data import make_session
 _TEST_USER_ID = "test-user-find-block"
 def make_mock_block(
    block_id: str, name: str, block_type: BlockType, disabled: bool = False
 ):
    """Create a mock block for testing."""
    mock = MagicMock()
    mock.id = block_id
    mock.name = name
    mock.description = f"{name} description"
    mock.block_type = block_type
    mock.disabled = disabled
    mock.input_schema = MagicMock()
    mock.input_schema.jsonschema.return_value = {"properties": {}, "required": []}
    mock.input_schema.get_credentials_fields.return_value = {}
    mock.output_schema = MagicMock()
    mock.output_schema.jsonschema.return_value = {}
    mock.categories = []
    return mock
 class TestFindBlockFiltering:
    """Tests for block filtering in FindBlockTool."""
    def test_excluded_block_types_contains_expected_types(self):
        """Verify COPILOT_EXCLUDED_BLOCK_TYPES contains all graph-only types."""
        assert BlockType.INPUT in COPILOT_EXCLUDED_BLOCK_TYPES
        assert BlockType.OUTPUT in COPILOT_EXCLUDED_BLOCK_TYPES
        assert BlockType.WEBHOOK in COPILOT_EXCLUDED_BLOCK_TYPES
        assert BlockType.WEBHOOK_MANUAL in COPILOT_EXCLUDED_BLOCK_TYPES
        assert BlockType.NOTE in COPILOT_EXCLUDED_BLOCK_TYPES
        assert BlockType.HUMAN_IN_THE_LOOP in COPILOT_EXCLUDED_BLOCK_TYPES
        assert BlockType.AGENT in COPILOT_EXCLUDED_BLOCK_TYPES
    def test_excluded_block_ids_contains_smart_decision_maker(self):
        """Verify SmartDecisionMakerBlock is in COPILOT_EXCLUDED_BLOCK_IDS."""
        assert "3b191d9f-356f-482d-8238-ba04b6d18381" in COPILOT_EXCLUDED_BLOCK_IDS
    @pytest.mark.asyncio(loop_scope="session")
    async def test_excluded_block_type_filtered_from_results(self):
        """Verify blocks with excluded BlockTypes are filtered from search results."""
        session = make_session(user_id=_TEST_USER_ID)
        # Mock search returns an INPUT block (excluded) and a STANDARD block (included)
        search_results = [
            {"content_id": "input-block-id", "score": 0.9},
            {"content_id": "standard-block-id", "score": 0.8},
        ]
        input_block = make_mock_block("input-block-id", "Input Block", BlockType.INPUT)
        standard_block = make_mock_block(
            "standard-block-id", "HTTP Request", BlockType.STANDARD
        )
        def mock_get_block(block_id):
            return {
                "input-block-id": input_block,
                "standard-block-id": standard_block,
            }.get(block_id)
        with patch(
            "backend.api.features.chat.tools.find_block.unified_hybrid_search",
            new_callable=AsyncMock,
            return_value=(search_results, 2),
        ):
            with patch(
                "backend.api.features.chat.tools.find_block.get_block",
                side_effect=mock_get_block,
            ):
                tool = FindBlockTool()
                response = await tool._execute(
                    user_id=_TEST_USER_ID, session=session, query="test"
                )
        # Should only return the standard block, not the INPUT block
        assert isinstance(response, BlockListResponse)
        assert len(response.blocks) == 1
        assert response.blocks[0].id == "standard-block-id"
    @pytest.mark.asyncio(loop_scope="session")
    async def test_excluded_block_id_filtered_from_results(self):
        """Verify SmartDecisionMakerBlock is filtered from search results."""
        session = make_session(user_id=_TEST_USER_ID)
        smart_decision_id = "3b191d9f-356f-482d-8238-ba04b6d18381"
        search_results = [
            {"content_id": smart_decision_id, "score": 0.9},
            {"content_id": "normal-block-id", "score": 0.8},
        ]
        # SmartDecisionMakerBlock has STANDARD type but is excluded by ID
        smart_block = make_mock_block(
            smart_decision_id, "Smart Decision Maker", BlockType.STANDARD
        )
        normal_block = make_mock_block(
            "normal-block-id", "Normal Block", BlockType.STANDARD
        )
        def mock_get_block(block_id):
            return {
                smart_decision_id: smart_block,
                "normal-block-id": normal_block,
            }.get(block_id)
        with patch(
            "backend.api.features.chat.tools.find_block.unified_hybrid_search",
            new_callable=AsyncMock,
            return_value=(search_results, 2),
        ):
            with patch(
                "backend.api.features.chat.tools.find_block.get_block",
                side_effect=mock_get_block,
            ):
                tool = FindBlockTool()
                response = await tool._execute(
                    user_id=_TEST_USER_ID, session=session, query="decision"
                )
        # Should only return normal block, not SmartDecisionMakerBlock
        assert isinstance(response, BlockListResponse)
        assert len(response.blocks) == 1
        assert response.blocks[0].id == "normal-block-id"
--- a/autogpt_platform/backend/backend/api/features/chat/tools/helpers.py
+++ b/autogpt_platform/backend/backend/api/features/chat/tools/helpers.py
@@ -0,0 +1,29 @@
 """Shared helpers for chat tools."""
 from typing import Any
 def get_inputs_from_schema(
    input_schema: dict[str, Any],
    exclude_fields: set[str] | None = None,
 ) -> list[dict[str, Any]]:
    """Extract input field info from JSON schema."""
    if not isinstance(input_schema, dict):
        return []
    exclude = exclude_fields or set()
    properties = input_schema.get("properties", {})
    required = set(input_schema.get("required", []))
    return [
        {
            "name": name,
            "title": schema.get("title", name),
            "type": schema.get("type", "string"),
            "description": schema.get("description", ""),
            "required": name in required,
            "default": schema.get("default"),
        }
        for name, schema in properties.items()
        if name not in exclude
    ]
--- a/autogpt_platform/backend/backend/api/features/chat/tools/run_agent.py
+++ b/autogpt_platform/backend/backend/api/features/chat/tools/run_agent.py
@@ -24,6 +24,7 @@ from backend.util.timezone_utils import (
 )
 from .base import BaseTool
 from .helpers import get_inputs_from_schema
 from .models import (
    AgentDetails,
    AgentDetailsResponse,
@@ -261,7 +262,7 @@ class RunAgentTool(BaseTool):
                        ),
                        requirements={
                            "credentials": requirements_creds_list,
-                            "inputs": self._get_inputs_list(graph.input_schema),
+                            "inputs": get_inputs_from_schema(graph.input_schema),
                            "execution_modes": self._get_execution_modes(graph),
                        },
                    ),
@@ -369,22 +370,6 @@ class RunAgentTool(BaseTool):
                session_id=session_id,
            )
    def _get_inputs_list(self, input_schema: dict[str, Any]) -> list[dict[str, Any]]:
        """Extract inputs list from schema."""
        inputs_list = []
        if isinstance(input_schema, dict) and "properties" in input_schema:
            for field_name, field_schema in input_schema["properties"].items():
                inputs_list.append(
                    {
                        "name": field_name,
                        "title": field_schema.get("title", field_name),
                        "type": field_schema.get("type", "string"),
                        "description": field_schema.get("description", ""),
                        "required": field_name in input_schema.get("required", []),
                    }
                )
        return inputs_list
    def _get_execution_modes(self, graph: GraphModel) -> list[str]:
        """Get available execution modes for the graph."""
        trigger_info = graph.trigger_setup_info
@@ -398,7 +383,7 @@ class RunAgentTool(BaseTool):
        suffix: str,
    ) -> str:
        """Build a message describing available inputs for an agent."""
-        inputs_list = self._get_inputs_list(graph.input_schema)
+        inputs_list = get_inputs_from_schema(graph.input_schema)
        required_names = [i["name"] for i in inputs_list if i["required"]]
        optional_names = [i["name"] for i in inputs_list if not i["required"]]
--- a/autogpt_platform/backend/backend/api/features/chat/tools/run_block.py
+++ b/autogpt_platform/backend/backend/api/features/chat/tools/run_block.py
@@ -8,14 +8,19 @@ from typing import Any
 from pydantic_core import PydanticUndefined
 from backend.api.features.chat.model import ChatSession
-from backend.data.block import get_block
+from backend.api.features.chat.tools.find_block import (
    COPILOT_EXCLUDED_BLOCK_IDS,
    COPILOT_EXCLUDED_BLOCK_TYPES,
 )
 from backend.data.block import AnyBlockSchema, get_block
 from backend.data.execution import ExecutionContext
-from backend.data.model import CredentialsMetaInput
+from backend.data.model import CredentialsFieldInfo, CredentialsMetaInput
 from backend.data.workspace import get_or_create_workspace
 from backend.integrations.creds_manager import IntegrationCredentialsManager
 from backend.util.exceptions import BlockError
 from .base import BaseTool
 from .helpers import get_inputs_from_schema
 from .models import (
    BlockOutputResponse,
    ErrorResponse,
@@ -24,7 +29,10 @@ from .models import (
    ToolResponseBase,
    UserReadiness,
 )
-from .utils import build_missing_credentials_from_field_info
+from .utils import (
    build_missing_credentials_from_field_info,
    match_credentials_to_requirements,
 )
 logger = logging.getLogger(__name__)
@@ -73,91 +81,6 @@ class RunBlockTool(BaseTool):
    def requires_auth(self) -> bool:
        return True
    async def _check_block_credentials(
        self,
        user_id: str,
        block: Any,
        input_data: dict[str, Any] | None = None,
    ) -> tuple[dict[str, CredentialsMetaInput], list[CredentialsMetaInput]]:
        """
        Check if user has required credentials for a block.
        Args:
            user_id: User ID
            block: Block to check credentials for
            input_data: Input data for the block (used to determine provider via discriminator)
        Returns:
            tuple[matched_credentials, missing_credentials]
        """
        matched_credentials: dict[str, CredentialsMetaInput] = {}
        missing_credentials: list[CredentialsMetaInput] = []
        input_data = input_data or {}
        # Get credential field info from block's input schema
        credentials_fields_info = block.input_schema.get_credentials_fields_info()
        if not credentials_fields_info:
            return matched_credentials, missing_credentials
        # Get user's available credentials
        creds_manager = IntegrationCredentialsManager()
        available_creds = await creds_manager.store.get_all_creds(user_id)
        for field_name, field_info in credentials_fields_info.items():
            effective_field_info = field_info
            if field_info.discriminator and field_info.discriminator_mapping:
                # Get discriminator from input, falling back to schema default
                discriminator_value = input_data.get(field_info.discriminator)
                if discriminator_value is None:
                    field = block.input_schema.model_fields.get(
                        field_info.discriminator
                    )
                    if field and field.default is not PydanticUndefined:
                        discriminator_value = field.default
                if (
                    discriminator_value
                    and discriminator_value in field_info.discriminator_mapping
                ):
                    effective_field_info = field_info.discriminate(discriminator_value)
                    logger.debug(
                        f"Discriminated provider for {field_name}: "
                        f"{discriminator_value} -> {effective_field_info.provider}"
                    )
            matching_cred = next(
                (
                    cred
                    for cred in available_creds
                    if cred.provider in effective_field_info.provider
                    and cred.type in effective_field_info.supported_types
                ),
                None,
            )
            if matching_cred:
                matched_credentials[field_name] = CredentialsMetaInput(
                    id=matching_cred.id,
                    provider=matching_cred.provider,  # type: ignore
                    type=matching_cred.type,
                    title=matching_cred.title,
                )
            else:
                # Create a placeholder for the missing credential
                provider = next(iter(effective_field_info.provider), "unknown")
                cred_type = next(iter(effective_field_info.supported_types), "api_key")
                missing_credentials.append(
                    CredentialsMetaInput(
                        id=field_name,
                        provider=provider,  # type: ignore
                        type=cred_type,  # type: ignore
                        title=field_name.replace("_", " ").title(),
                    )
                )
        return matched_credentials, missing_credentials
    async def _execute(
        self,
        user_id: str | None,
@@ -212,11 +135,24 @@ class RunBlockTool(BaseTool):
                session_id=session_id,
            )
        # Check if block is excluded from CoPilot (graph-only blocks)
        if (
            block.block_type in COPILOT_EXCLUDED_BLOCK_TYPES
            or block.id in COPILOT_EXCLUDED_BLOCK_IDS
        ):
            return ErrorResponse(
                message=(
                    f"Block '{block.name}' cannot be run directly in CoPilot. "
                    "This block is designed for use within graphs only."
                ),
                session_id=session_id,
            )
        logger.info(f"Executing block {block.name} ({block_id}) for user {user_id}")
        creds_manager = IntegrationCredentialsManager()
-        matched_credentials, missing_credentials = await self._check_block_credentials(
+        matched_credentials, missing_credentials = (
-            user_id, block, input_data
+            await self._resolve_block_credentials(user_id, block, input_data)
        )
        if missing_credentials:
@@ -345,29 +281,75 @@ class RunBlockTool(BaseTool):
                session_id=session_id,
            )
-    def _get_inputs_list(self, block: Any) -> list[dict[str, Any]]:
+    async def _resolve_block_credentials(
        self,
        user_id: str,
        block: AnyBlockSchema,
        input_data: dict[str, Any] | None = None,
    ) -> tuple[dict[str, CredentialsMetaInput], list[CredentialsMetaInput]]:
        """
        Resolve credentials for a block by matching user's available credentials.
        Args:
            user_id: User ID
            block: Block to resolve credentials for
            input_data: Input data for the block (used to determine provider via discriminator)
        Returns:
            tuple of (matched_credentials, missing_credentials) - matched credentials
            are used for block execution, missing ones indicate setup requirements.
        """
        input_data = input_data or {}
        requirements = self._resolve_discriminated_credentials(block, input_data)
        if not requirements:
            return {}, []
        return await match_credentials_to_requirements(user_id, requirements)
    def _get_inputs_list(self, block: AnyBlockSchema) -> list[dict[str, Any]]:
        """Extract non-credential inputs from block schema."""
        inputs_list = []
        schema = block.input_schema.jsonschema()
        properties = schema.get("properties", {})
        required_fields = set(schema.get("required", []))
        # Get credential field names to exclude
        credentials_fields = set(block.input_schema.get_credentials_fields().keys())
        return get_inputs_from_schema(schema, exclude_fields=credentials_fields)
-        for field_name, field_schema in properties.items():
+    def _resolve_discriminated_credentials(
-            # Skip credential fields
+        self,
-            if field_name in credentials_fields:
+        block: AnyBlockSchema,
-                continue
+        input_data: dict[str, Any],
    ) -> dict[str, CredentialsFieldInfo]:
        """Resolve credential requirements, applying discriminator logic where needed."""
        credentials_fields_info = block.input_schema.get_credentials_fields_info()
        if not credentials_fields_info:
            return {}
-            inputs_list.append(
+        resolved: dict[str, CredentialsFieldInfo] = {}
-                {
+
-                    "name": field_name,
+        for field_name, field_info in credentials_fields_info.items():
-                    "title": field_schema.get("title", field_name),
+            effective_field_info = field_info
-                    "type": field_schema.get("type", "string"),
+
-                    "description": field_schema.get("description", ""),
+            if field_info.discriminator and field_info.discriminator_mapping:
-                    "required": field_name in required_fields,
+                discriminator_value = input_data.get(field_info.discriminator)
-                }
+                if discriminator_value is None:
                    field = block.input_schema.model_fields.get(
                        field_info.discriminator
                    )
                    if field and field.default is not PydanticUndefined:
                        discriminator_value = field.default
                if (
                    discriminator_value
                    and discriminator_value in field_info.discriminator_mapping
                ):
                    effective_field_info = field_info.discriminate(discriminator_value)
                    # For host-scoped credentials, add the discriminator value
                    # (e.g., URL) so _credential_is_for_host can match it
                    effective_field_info.discriminator_values.add(discriminator_value)
                    logger.debug(
                        f"Discriminated provider for {field_name}: "
                        f"{discriminator_value} -> {effective_field_info.provider}"
                    )
-        return inputs_list
+            resolved[field_name] = effective_field_info
        return resolved
--- a/autogpt_platform/backend/backend/api/features/chat/tools/run_block_test.py
+++ b/autogpt_platform/backend/backend/api/features/chat/tools/run_block_test.py
@@ -0,0 +1,106 @@
 """Tests for block execution guards in RunBlockTool."""
 from unittest.mock import MagicMock, patch
 import pytest
 from backend.api.features.chat.tools.models import ErrorResponse
 from backend.api.features.chat.tools.run_block import RunBlockTool
 from backend.data.block import BlockType
 from ._test_data import make_session
 _TEST_USER_ID = "test-user-run-block"
 def make_mock_block(
    block_id: str, name: str, block_type: BlockType, disabled: bool = False
 ):
    """Create a mock block for testing."""
    mock = MagicMock()
    mock.id = block_id
    mock.name = name
    mock.block_type = block_type
    mock.disabled = disabled
    mock.input_schema = MagicMock()
    mock.input_schema.jsonschema.return_value = {"properties": {}, "required": []}
    mock.input_schema.get_credentials_fields_info.return_value = []
    return mock
 class TestRunBlockFiltering:
    """Tests for block execution guards in RunBlockTool."""
    @pytest.mark.asyncio(loop_scope="session")
    async def test_excluded_block_type_returns_error(self):
        """Attempting to execute a block with excluded BlockType returns error."""
        session = make_session(user_id=_TEST_USER_ID)
        input_block = make_mock_block("input-block-id", "Input Block", BlockType.INPUT)
        with patch(
            "backend.api.features.chat.tools.run_block.get_block",
            return_value=input_block,
        ):
            tool = RunBlockTool()
            response = await tool._execute(
                user_id=_TEST_USER_ID,
                session=session,
                block_id="input-block-id",
                input_data={},
            )
        assert isinstance(response, ErrorResponse)
        assert "cannot be run directly in CoPilot" in response.message
        assert "designed for use within graphs only" in response.message
    @pytest.mark.asyncio(loop_scope="session")
    async def test_excluded_block_id_returns_error(self):
        """Attempting to execute SmartDecisionMakerBlock returns error."""
        session = make_session(user_id=_TEST_USER_ID)
        smart_decision_id = "3b191d9f-356f-482d-8238-ba04b6d18381"
        smart_block = make_mock_block(
            smart_decision_id, "Smart Decision Maker", BlockType.STANDARD
        )
        with patch(
            "backend.api.features.chat.tools.run_block.get_block",
            return_value=smart_block,
        ):
            tool = RunBlockTool()
            response = await tool._execute(
                user_id=_TEST_USER_ID,
                session=session,
                block_id=smart_decision_id,
                input_data={},
            )
        assert isinstance(response, ErrorResponse)
        assert "cannot be run directly in CoPilot" in response.message
    @pytest.mark.asyncio(loop_scope="session")
    async def test_non_excluded_block_passes_guard(self):
        """Non-excluded blocks pass the filtering guard (may fail later for other reasons)."""
        session = make_session(user_id=_TEST_USER_ID)
        standard_block = make_mock_block(
            "standard-id", "HTTP Request", BlockType.STANDARD
        )
        with patch(
            "backend.api.features.chat.tools.run_block.get_block",
            return_value=standard_block,
        ):
            tool = RunBlockTool()
            response = await tool._execute(
                user_id=_TEST_USER_ID,
                session=session,
                block_id="standard-id",
                input_data={},
            )
        # Should NOT be an ErrorResponse about CoPilot exclusion
        # (may be other errors like missing credentials, but not the exclusion guard)
        if isinstance(response, ErrorResponse):
            assert "cannot be run directly in CoPilot" not in response.message
--- a/autogpt_platform/backend/backend/api/features/chat/tools/utils.py
+++ b/autogpt_platform/backend/backend/api/features/chat/tools/utils.py
@@ -8,6 +8,7 @@ from backend.api.features.library import model as library_model
 from backend.api.features.store import db as store_db
 from backend.data.graph import GraphModel
 from backend.data.model import (
    Credentials,
    CredentialsFieldInfo,
    CredentialsMetaInput,
    HostScopedCredentials,
@@ -223,6 +224,99 @@ async def get_or_create_library_agent(
    return library_agents[0]
 async def match_credentials_to_requirements(
    user_id: str,
    requirements: dict[str, CredentialsFieldInfo],
 ) -> tuple[dict[str, CredentialsMetaInput], list[CredentialsMetaInput]]:
    """
    Match user's credentials against a dictionary of credential requirements.
    This is the core matching logic shared by both graph and block credential matching.
    """
    matched: dict[str, CredentialsMetaInput] = {}
    missing: list[CredentialsMetaInput] = []
    if not requirements:
        return matched, missing
    available_creds = await get_user_credentials(user_id)
    for field_name, field_info in requirements.items():
        matching_cred = find_matching_credential(available_creds, field_info)
        if matching_cred:
            try:
                matched[field_name] = create_credential_meta_from_match(matching_cred)
            except Exception as e:
                logger.error(
                    f"Failed to create CredentialsMetaInput for field '{field_name}': "
                    f"provider={matching_cred.provider}, type={matching_cred.type}, "
                    f"credential_id={matching_cred.id}",
                    exc_info=True,
                )
                provider = next(iter(field_info.provider), "unknown")
                cred_type = next(iter(field_info.supported_types), "api_key")
                missing.append(
                    CredentialsMetaInput(
                        id=field_name,
                        provider=provider,  # type: ignore
                        type=cred_type,  # type: ignore
                        title=f"{field_name} (validation failed: {e})",
                    )
                )
        else:
            provider = next(iter(field_info.provider), "unknown")
            cred_type = next(iter(field_info.supported_types), "api_key")
            missing.append(
                CredentialsMetaInput(
                    id=field_name,
                    provider=provider,  # type: ignore
                    type=cred_type,  # type: ignore
                    title=field_name.replace("_", " ").title(),
                )
            )
    return matched, missing
 async def get_user_credentials(user_id: str) -> list[Credentials]:
    """Get all available credentials for a user."""
    creds_manager = IntegrationCredentialsManager()
    return await creds_manager.store.get_all_creds(user_id)
 def find_matching_credential(
    available_creds: list[Credentials],
    field_info: CredentialsFieldInfo,
 ) -> Credentials | None:
    """Find a credential that matches the required provider, type, scopes, and host."""
    for cred in available_creds:
        if cred.provider not in field_info.provider:
            continue
        if cred.type not in field_info.supported_types:
            continue
        if cred.type == "oauth2" and not _credential_has_required_scopes(
            cred, field_info
        ):
            continue
        if cred.type == "host_scoped" and not _credential_is_for_host(cred, field_info):
            continue
        return cred
    return None
 def create_credential_meta_from_match(
    matching_cred: Credentials,
 ) -> CredentialsMetaInput:
    """Create a CredentialsMetaInput from a matched credential."""
    return CredentialsMetaInput(
        id=matching_cred.id,
        provider=matching_cred.provider,  # type: ignore
        type=matching_cred.type,
        title=matching_cred.title,
    )
 async def match_user_credentials_to_graph(
    user_id: str,
    graph: GraphModel,
@@ -331,8 +425,6 @@ def _credential_has_required_scopes(
    # If no scopes are required, any credential matches
    if not requirements.required_scopes:
        return True
    # Check that credential scopes are a superset of required scopes
    return set(credential.scopes).issuperset(requirements.required_scopes)
--- a/autogpt_platform/backend/backend/api/features/store/hybrid_search.py
+++ b/autogpt_platform/backend/backend/api/features/store/hybrid_search.py
@@ -8,6 +8,7 @@ Includes BM25 reranking for improved lexical relevance.
 import logging
 import re
 import time
 from dataclasses import dataclass
 from typing import Any, Literal
@@ -362,7 +363,11 @@ async def unified_hybrid_search(
        LIMIT {limit_param} OFFSET {offset_param}
    """
    try:
        results = await query_raw_with_schema(sql_query, *params)
    except Exception as e:
        await _log_vector_error_diagnostics(e)
        raise
    total = results[0]["total_count"] if results else 0
    # Apply BM25 reranking
@@ -686,7 +691,11 @@ async def hybrid_search(
        LIMIT {limit_param} OFFSET {offset_param}
    """
    try:
        results = await query_raw_with_schema(sql_query, *params)
    except Exception as e:
        await _log_vector_error_diagnostics(e)
        raise
    total = results[0]["total_count"] if results else 0
@@ -718,6 +727,87 @@ async def hybrid_search_simple(
    return await hybrid_search(query=query, page=page, page_size=page_size)
 # ============================================================================
 # Diagnostics
 # ============================================================================
 # Rate limit: only log vector error diagnostics once per this interval
 _VECTOR_DIAG_INTERVAL_SECONDS = 60
 _last_vector_diag_time: float = 0
 async def _log_vector_error_diagnostics(error: Exception) -> None:
    """Log diagnostic info when 'type vector does not exist' error occurs.
    Note: Diagnostic queries use query_raw_with_schema which may run on a different
    pooled connection than the one that failed. Session-level search_path can differ,
    so these diagnostics show cluster-wide state, not necessarily the failed session.
    Includes rate limiting to avoid log spam - only logs once per minute.
    Caller should re-raise the error after calling this function.
    """
    global _last_vector_diag_time
    # Check if this is the vector type error
    error_str = str(error).lower()
    if not (
        "type" in error_str and "vector" in error_str and "does not exist" in error_str
    ):
        return
    # Rate limit: only log once per interval
    now = time.time()
    if now - _last_vector_diag_time < _VECTOR_DIAG_INTERVAL_SECONDS:
        return
    _last_vector_diag_time = now
    try:
        diagnostics: dict[str, object] = {}
        try:
            search_path_result = await query_raw_with_schema("SHOW search_path")
            diagnostics["search_path"] = search_path_result
        except Exception as e:
            diagnostics["search_path"] = f"Error: {e}"
        try:
            schema_result = await query_raw_with_schema("SELECT current_schema()")
            diagnostics["current_schema"] = schema_result
        except Exception as e:
            diagnostics["current_schema"] = f"Error: {e}"
        try:
            user_result = await query_raw_with_schema(
                "SELECT current_user, session_user, current_database()"
            )
            diagnostics["user_info"] = user_result
        except Exception as e:
            diagnostics["user_info"] = f"Error: {e}"
        try:
            # Check pgvector extension installation (cluster-wide, stable info)
            ext_result = await query_raw_with_schema(
                "SELECT extname, extversion, nspname as schema "
                "FROM pg_extension e "
                "JOIN pg_namespace n ON e.extnamespace = n.oid "
                "WHERE extname = 'vector'"
            )
            diagnostics["pgvector_extension"] = ext_result
        except Exception as e:
            diagnostics["pgvector_extension"] = f"Error: {e}"
        logger.error(
            f"Vector type error diagnostics:\n"
            f"  Error: {error}\n"
            f"  search_path: {diagnostics.get('search_path')}\n"
            f"  current_schema: {diagnostics.get('current_schema')}\n"
            f"  user_info: {diagnostics.get('user_info')}\n"
            f"  pgvector_extension: {diagnostics.get('pgvector_extension')}"
        )
    except Exception as diag_error:
        logger.error(f"Failed to collect vector error diagnostics: {diag_error}")
 # Backward compatibility alias - HybridSearchWeights maps to StoreAgentSearchWeights
 # for existing code that expects the popularity parameter
 HybridSearchWeights = StoreAgentSearchWeights
--- a/autogpt_platform/backend/backend/data/rabbitmq.py
+++ b/autogpt_platform/backend/backend/data/rabbitmq.py
@@ -1,3 +1,4 @@
 import asyncio
 import logging
 from abc import ABC, abstractmethod
 from enum import Enum
@@ -225,6 +226,10 @@ class SyncRabbitMQ(RabbitMQBase):
 class AsyncRabbitMQ(RabbitMQBase):
    """Asynchronous RabbitMQ client"""
    def __init__(self, config: RabbitMQConfig):
        super().__init__(config)
        self._reconnect_lock: asyncio.Lock | None = None
    @property
    def is_connected(self) -> bool:
        return bool(self._connection and not self._connection.is_closed)
@@ -235,7 +240,17 @@ class AsyncRabbitMQ(RabbitMQBase):
    @conn_retry("AsyncRabbitMQ", "Acquiring async connection")
    async def connect(self):
-        if self.is_connected:
+        if self.is_connected and self._channel and not self._channel.is_closed:
            return
        if (
            self.is_connected
            and self._connection
            and (self._channel is None or self._channel.is_closed)
        ):
            self._channel = await self._connection.channel()
            await self._channel.set_qos(prefetch_count=1)
            await self.declare_infrastructure()
            return
        self._connection = await aio_pika.connect_robust(
@@ -291,24 +306,46 @@ class AsyncRabbitMQ(RabbitMQBase):
                    exchange, routing_key=queue.routing_key or queue.name
                )
-    @func_retry
+    @property
-    async def publish_message(
+    def _lock(self) -> asyncio.Lock:
        if self._reconnect_lock is None:
            self._reconnect_lock = asyncio.Lock()
        return self._reconnect_lock
    async def _ensure_channel(self) -> aio_pika.abc.AbstractChannel:
        """Get a valid channel, reconnecting if the current one is stale.
        Uses a lock to prevent concurrent reconnection attempts from racing.
        """
        if self.is_ready:
            return self._channel  # type: ignore  # is_ready guarantees non-None
        async with self._lock:
            # Double-check after acquiring lock
            if self.is_ready:
                return self._channel  # type: ignore
            self._channel = None
            await self.connect()
            if self._channel is None:
                raise RuntimeError("Channel should be established after connect")
            return self._channel
    async def _publish_once(
        self,
        routing_key: str,
        message: str,
        exchange: Optional[Exchange] = None,
        persistent: bool = True,
    ) -> None:
-        if not self.is_ready:
+        channel = await self._ensure_channel()
            await self.connect()
        if self._channel is None:
            raise RuntimeError("Channel should be established after connect")
        if exchange:
-            exchange_obj = await self._channel.get_exchange(exchange.name)
+            exchange_obj = await channel.get_exchange(exchange.name)
        else:
-            exchange_obj = self._channel.default_exchange
+            exchange_obj = channel.default_exchange
        await exchange_obj.publish(
            aio_pika.Message(
@@ -322,9 +359,23 @@ class AsyncRabbitMQ(RabbitMQBase):
            routing_key=routing_key,
        )
    @func_retry
    async def publish_message(
        self,
        routing_key: str,
        message: str,
        exchange: Optional[Exchange] = None,
        persistent: bool = True,
    ) -> None:
        try:
            await self._publish_once(routing_key, message, exchange, persistent)
        except aio_pika.exceptions.ChannelInvalidStateError:
            logger.warning(
                "RabbitMQ channel invalid, forcing reconnect and retrying publish"
            )
            async with self._lock:
                self._channel = None
            await self._publish_once(routing_key, message, exchange, persistent)
    async def get_channel(self) -> aio_pika.abc.AbstractChannel:
-        if not self.is_ready:
+        return await self._ensure_channel()
            await self.connect()
        if self._channel is None:
            raise RuntimeError("Channel should be established after connect")
        return self._channel
--- a/autogpt_platform/backend/poetry.lock
+++ b/autogpt_platform/backend/poetry.lock
@@ -897,6 +897,29 @@ files = [
    {file = "charset_normalizer-3.4.4.tar.gz", hash = "sha256:94537985111c35f28720e43603b8e7b43a6ecfb2ce1d3058bbe955b73404e21a"},
 ]
 [[package]]
 name = "claude-agent-sdk"
 version = "0.1.33"
 description = "Python SDK for Claude Code"
 optional = false
 python-versions = ">=3.10"
 groups = ["main"]
 files = [
    {file = "claude_agent_sdk-0.1.33-py3-none-macosx_11_0_arm64.whl", hash = "sha256:57886a2dd124e5b3c9e12ec3e4841742ab3444d1e428b45ceaec8841c96698fa"},
    {file = "claude_agent_sdk-0.1.33-py3-none-manylinux_2_17_aarch64.whl", hash = "sha256:ea0f1e4fadeec766000122723c406a6f47c6210ea11bb5cc0c88af11ef7c940c"},
    {file = "claude_agent_sdk-0.1.33-py3-none-manylinux_2_17_x86_64.whl", hash = "sha256:0ecd822c577b4ea2a52e51146a24dcea73eb69ff366bdb875785dadb116d593b"},
    {file = "claude_agent_sdk-0.1.33-py3-none-win_amd64.whl", hash = "sha256:a9fbd09d8f947005e087340ecd0706ed35639c946b4bd49429d3132db4cb3751"},
    {file = "claude_agent_sdk-0.1.33.tar.gz", hash = "sha256:134bf403bb7553d829dadec42c30ecef340f5d4ad1595c1bdef933a9ca3129cf"},
 ]
 [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"
@@ -1382,14 +1405,14 @@ tzdata = "*"
 [[package]]
 name = "fastapi"
-version = "0.128.5"
+version = "0.128.6"
 description = "FastAPI framework, high performance, easy to learn, fast to code, ready for production"
 optional = false
 python-versions = ">=3.9"
 groups = ["main"]
 files = [
-    {file = "fastapi-0.128.5-py3-none-any.whl", hash = "sha256:bceec0de8aa6564599c5bcc0593b0d287703562c848271fca8546fd2c87bf4dd"},
+    {file = "fastapi-0.128.6-py3-none-any.whl", hash = "sha256:bb1c1ef87d6086a7132d0ab60869d6f1ee67283b20fbf84ec0003bd335099509"},
-    {file = "fastapi-0.128.5.tar.gz", hash = "sha256:a7173579fc162d6471e3c6fbd9a4b7610c7a3b367bcacf6c4f90d5d022cab711"},
+    {file = "fastapi-0.128.6.tar.gz", hash = "sha256:0cb3946557e792d731b26a42b04912f16367e3c3135ea8290f620e234f2b604f"},
 ]
 [package.dependencies]
@@ -2593,6 +2616,18 @@ 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"
@@ -3078,14 +3113,14 @@ type = ["pygobject-stubs", "pytest-mypy (>=1.0.1)", "shtab", "types-pywin32"]
 [[package]]
 name = "langfuse"
-version = "3.13.0"
+version = "3.14.1"
 description = "A client library for accessing langfuse"
 optional = false
 python-versions = "<4.0,>=3.10"
 groups = ["main"]
 files = [
-    {file = "langfuse-3.13.0-py3-none-any.whl", hash = "sha256:71912ddac1cc831a65df895eae538a556f564c094ae51473e747426e9ded1a9d"},
+    {file = "langfuse-3.14.1-py3-none-any.whl", hash = "sha256:17bed605dbfc9947cbd1738a715f6d27c1b80b6da9f2946586171958fa5820d0"},
-    {file = "langfuse-3.13.0.tar.gz", hash = "sha256:dacea8111ca4442e97dbfec4f8d676cf9709b35357a26e468f8887b95de0012f"},
+    {file = "langfuse-3.14.1.tar.gz", hash = "sha256:404a6104cd29353d7829aa417ec46565b04917e5599afdda96c5b0865f4bc991"},
 ]
 [package.dependencies]
@@ -3310,6 +3345,39 @@ 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"
@@ -5994,7 +6062,7 @@ description = "Python for Window Extensions"
 optional = false
 python-versions = "*"
 groups = ["main"]
-markers = "platform_system == \"Windows\""
+markers = "sys_platform == \"win32\" or 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"},
@@ -6974,6 +7042,28 @@ 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"
@@ -8440,4 +8530,4 @@ 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 = "14686ee0e2dc446a75d0db145b08dc410dc31c357e25085bb0f9b0174711c4b1"
+content-hash = "c15100f0726e7e42e0a69b50474ef23c8b90125b1da6cc0c6afca47f6c453a86"
--- a/autogpt_platform/backend/pyproject.toml
+++ b/autogpt_platform/backend/pyproject.toml
@@ -16,12 +16,13 @@ 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"
 e2b-code-interpreter = "^1.5.2"
 elevenlabs = "^1.50.0"
-fastapi = "^0.128.5"
+fastapi = "^0.128.6"
 feedparser = "^6.0.11"
 flake8 = "^7.3.0"
 google-api-python-client = "^2.177.0"
@@ -34,7 +35,7 @@ html2text = "^2024.2.26"
 jinja2 = "^3.1.6"
 jsonref = "^1.1.0"
 jsonschema = "^4.25.0"
-langfuse = "^3.11.0"
+langfuse = "^3.14.1"
 launchdarkly-server-sdk = "^9.14.1"
 mem0ai = "^0.1.115"
 moviepy = "^2.1.2"
--- a/autogpt_platform/frontend/Dockerfile
+++ b/autogpt_platform/frontend/Dockerfile
@@ -25,8 +25,12 @@ RUN if [ -f .env.production ]; then \
      cp .env.default .env; \
    fi
 RUN pnpm run generate:api
 # Disable source-map generation in Docker builds to halve webpack memory usage.
 # Source maps are only useful when SENTRY_AUTH_TOKEN is set (Vercel deploys);
 # the Docker image never uploads them, so generating them just wastes RAM.
 ENV NEXT_PUBLIC_SOURCEMAPS="false"
 # In CI, we want NEXT_PUBLIC_PW_TEST=true during build so Next.js inlines it
-RUN if [ "$NEXT_PUBLIC_PW_TEST" = "true" ]; then NEXT_PUBLIC_PW_TEST=true NODE_OPTIONS="--max-old-space-size=4096" pnpm build; else NODE_OPTIONS="--max-old-space-size=4096" pnpm build; fi
+RUN if [ "$NEXT_PUBLIC_PW_TEST" = "true" ]; then NEXT_PUBLIC_PW_TEST=true NODE_OPTIONS="--max-old-space-size=8192" pnpm build; else NODE_OPTIONS="--max-old-space-size=8192" pnpm build; fi
 # Prod stage - based on NextJS reference Dockerfile https://github.com/vercel/next.js/blob/64271354533ed16da51be5dce85f0dbd15f17517/examples/with-docker/Dockerfile
 FROM node:21-alpine AS prod
--- a/autogpt_platform/frontend/next.config.mjs
+++ b/autogpt_platform/frontend/next.config.mjs
@@ -1,8 +1,12 @@
 import { withSentryConfig } from "@sentry/nextjs";
 // Allow Docker builds to skip source-map generation (halves memory usage).
 // Defaults to true so Vercel/local builds are unaffected.
 const enableSourceMaps = process.env.NEXT_PUBLIC_SOURCEMAPS !== "false";
 /** @type {import('next').NextConfig} */
 const nextConfig = {
-  productionBrowserSourceMaps: true,
+  productionBrowserSourceMaps: enableSourceMaps,
  // Externalize OpenTelemetry packages to fix Turbopack HMR issues
  serverExternalPackages: [
    "@opentelemetry/instrumentation",
@@ -14,9 +18,37 @@ const nextConfig = {
    serverActions: {
      bodySizeLimit: "256mb",
    },
    // Increase body size limit for API routes (file uploads) - 256MB to match backend limit
    proxyClientMaxBodySize: "256mb",
    middlewareClientMaxBodySize: "256mb",
    // Limit parallel webpack workers to reduce peak memory during builds.
    cpus: 2,
  },
  // Work around cssnano "Invalid array length" bug in Next.js's bundled
  // cssnano-simple comment parser when processing very large CSS chunks.
  // CSS is still bundled correctly; gzip handles most of the size savings anyway.
  webpack: (config, { dev }) => {
    if (!dev) {
      // Next.js adds CssMinimizerPlugin internally (after user config), so we
      // can't filter it from config.plugins. Instead, intercept the webpack
      // compilation hooks and replace the buggy plugin's tap with a no-op.
      config.plugins.push({
        apply(compiler) {
          compiler.hooks.compilation.tap(
            "DisableCssMinimizer",
            (compilation) => {
              compilation.hooks.processAssets.intercept({
                register: (tap) => {
                  if (tap.name === "CssMinimizerPlugin") {
                    return { ...tap, fn: async () => {} };
                  }
                  return tap;
                },
              });
            },
          );
        },
      });
    }
    return config;
  },
  images: {
    domains: [
@@ -54,9 +86,16 @@ const nextConfig = {
  transpilePackages: ["geist"],
 };
-const isDevelopmentBuild = process.env.NODE_ENV !== "production";
+// Only run the Sentry webpack plugin when we can actually upload source maps
 // (i.e. on Vercel with SENTRY_AUTH_TOKEN set). The Sentry *runtime* SDK
 // (imported in app code) still captures errors without the plugin.
 // Skipping the plugin saves ~1 GB of peak memory during `next build`.
 const skipSentryPlugin =
  process.env.NODE_ENV !== "production" ||
  !enableSourceMaps ||
  !process.env.SENTRY_AUTH_TOKEN;
-export default isDevelopmentBuild
+export default skipSentryPlugin
  ? nextConfig
  : withSentryConfig(nextConfig, {
      // For all available options, see:
@@ -96,7 +135,7 @@ export default isDevelopmentBuild
      // This helps Sentry with sourcemaps... https://docs.sentry.io/platforms/javascript/guides/nextjs/sourcemaps/
      sourcemaps: {
-        disable: false,
+        disable: !enableSourceMaps,
        assets: [".next/**/*.js", ".next/**/*.js.map"],
        ignore: ["**/node_modules/**"],
        deleteSourcemapsAfterUpload: false, // Source is public anyway :)
--- a/autogpt_platform/frontend/package.json
+++ b/autogpt_platform/frontend/package.json
@@ -7,7 +7,7 @@
  },
  "scripts": {
    "dev": "pnpm run generate:api:force && next dev --turbo",
-    "build": "next build",
+    "build": "cross-env NODE_OPTIONS=--max-old-space-size=16384 next build",
    "start": "next start",
    "start:standalone": "cd .next/standalone && node server.js",
    "lint": "next lint && prettier --check .",
@@ -30,6 +30,7 @@
    "defaults"
  ],
  "dependencies": {
    "@ai-sdk/react": "3.0.61",
    "@faker-js/faker": "10.0.0",
    "@hookform/resolvers": "5.2.2",
    "@next/third-parties": "15.4.6",
@@ -60,6 +61,10 @@
    "@rjsf/utils": "6.1.2",
    "@rjsf/validator-ajv8": "6.1.2",
    "@sentry/nextjs": "10.27.0",
    "@streamdown/cjk": "1.0.1",
    "@streamdown/code": "1.0.1",
    "@streamdown/math": "1.0.1",
    "@streamdown/mermaid": "1.0.1",
    "@supabase/ssr": "0.7.0",
    "@supabase/supabase-js": "2.78.0",
    "@tanstack/react-query": "5.90.6",
@@ -68,6 +73,7 @@
    "@vercel/analytics": "1.5.0",
    "@vercel/speed-insights": "1.2.0",
    "@xyflow/react": "12.9.2",
    "ai": "6.0.59",
    "boring-avatars": "1.11.2",
    "class-variance-authority": "0.7.1",
    "clsx": "2.1.1",
@@ -87,7 +93,6 @@
    "launchdarkly-react-client-sdk": "3.9.0",
    "lodash": "4.17.21",
    "lucide-react": "0.552.0",
    "moment": "2.30.1",
    "next": "15.4.10",
    "next-themes": "0.4.6",
    "nuqs": "2.7.2",
@@ -112,9 +117,11 @@
    "remark-math": "6.0.0",
    "shepherd.js": "14.5.1",
    "sonner": "2.0.7",
    "streamdown": "2.1.0",
    "tailwind-merge": "2.6.0",
    "tailwind-scrollbar": "3.1.0",
    "tailwindcss-animate": "1.0.7",
    "use-stick-to-bottom": "1.1.2",
    "uuid": "11.1.0",
    "vaul": "1.1.2",
    "zod": "3.25.76",
@@ -172,7 +179,8 @@
  },
  "pnpm": {
    "overrides": {
-      "@opentelemetry/instrumentation": "0.209.0"
+      "@opentelemetry/instrumentation": "0.209.0",
      "lodash-es": "4.17.23"
    }
  },
  "packageManager": "pnpm@10.20.0+sha512.cf9998222162dd85864d0a8102e7892e7ba4ceadebbf5a31f9c2fce48dfce317a9c53b9f6464d1ef9042cba2e02ae02a9f7c143a2b438cd93c91840f0192b9dd"
--- a/autogpt_platform/frontend/pnpm-lock.yaml
+++ b/autogpt_platform/frontend/pnpm-lock.yaml
--- a/autogpt_platform/frontend/src/app/(platform)/build/components/NewControlPanel/NewBlockMenu/BlockMenuSearchBar/useBlockMenuSearchBar.ts
+++ b/autogpt_platform/frontend/src/app/(platform)/build/components/NewControlPanel/NewBlockMenu/BlockMenuSearchBar/useBlockMenuSearchBar.ts
@@ -1,4 +1,4 @@
-import { debounce } from "lodash";
+import debounce from "lodash/debounce";
 import { useCallback, useEffect, useRef, useState } from "react";
 import { useBlockMenuStore } from "../../../../stores/blockMenuStore";
 import { getQueryClient } from "@/lib/react-query/queryClient";
--- a/autogpt_platform/frontend/src/app/(platform)/build/components/NewControlPanel/NewBlockMenu/HorizontalScroll.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/build/components/NewControlPanel/NewBlockMenu/HorizontalScroll.tsx
@@ -70,10 +70,10 @@ export const HorizontalScroll: React.FC<HorizontalScrollAreaProps> = ({
          {children}
        </div>
        {canScrollLeft && (
-          <div className="pointer-events-none absolute inset-y-0 left-0 w-8 bg-gradient-to-r from-white via-white/80 to-white/0" />
+          <div className="pointer-events-none absolute inset-y-0 left-0 w-8 bg-gradient-to-r from-background via-background/80 to-background/0" />
        )}
        {canScrollRight && (
-          <div className="pointer-events-none absolute inset-y-0 right-0 w-8 bg-gradient-to-l from-white via-white/80 to-white/0" />
+          <div className="pointer-events-none absolute inset-y-0 right-0 w-8 bg-gradient-to-l from-background via-background/80 to-background/0" />
        )}
        {canScrollLeft && (
          <button
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/CopilotPage.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/CopilotPage.tsx
@@ -0,0 +1,76 @@
 "use client";
 import { LoadingSpinner } from "@/components/atoms/LoadingSpinner/LoadingSpinner";
 import { SidebarProvider } from "@/components/ui/sidebar";
 import { ChatContainer } from "./components/ChatContainer/ChatContainer";
 import { ChatSidebar } from "./components/ChatSidebar/ChatSidebar";
 import { MobileDrawer } from "./components/MobileDrawer/MobileDrawer";
 import { MobileHeader } from "./components/MobileHeader/MobileHeader";
 import { useCopilotPage } from "./useCopilotPage";
 export function CopilotPage() {
  const {
    sessionId,
    messages,
    status,
    error,
    stop,
    createSession,
    onSend,
    isLoadingSession,
    isCreatingSession,
    isUserLoading,
    isLoggedIn,
    // Mobile drawer
    isMobile,
    isDrawerOpen,
    sessions,
    isLoadingSessions,
    handleOpenDrawer,
    handleCloseDrawer,
    handleDrawerOpenChange,
    handleSelectSession,
    handleNewChat,
  } = useCopilotPage();
  if (isUserLoading || !isLoggedIn) {
    return <LoadingSpinner size="large" cover />;
  }
  return (
    <SidebarProvider
      defaultOpen={true}
      className="h-[calc(100vh-72px)] min-h-0"
    >
      {!isMobile && <ChatSidebar />}
      <div className="relative flex h-full w-full flex-col overflow-hidden bg-[#f8f8f9] px-0">
        {isMobile && <MobileHeader onOpenDrawer={handleOpenDrawer} />}
        <div className="flex-1 overflow-hidden">
          <ChatContainer
            messages={messages}
            status={status}
            error={error}
            sessionId={sessionId}
            isLoadingSession={isLoadingSession}
            isCreatingSession={isCreatingSession}
            onCreateSession={createSession}
            onSend={onSend}
            onStop={stop}
          />
        </div>
      </div>
      {isMobile && (
        <MobileDrawer
          isOpen={isDrawerOpen}
          sessions={sessions}
          currentSessionId={sessionId}
          isLoading={isLoadingSessions}
          onSelectSession={handleSelectSession}
          onNewChat={handleNewChat}
          onClose={handleCloseDrawer}
          onOpenChange={handleDrawerOpenChange}
        />
      )}
    </SidebarProvider>
  );
 }
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/ChatContainer/ChatContainer.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/ChatContainer/ChatContainer.tsx
@@ -0,0 +1,74 @@
 "use client";
 import { ChatInput } from "@/app/(platform)/copilot/components/ChatInput/ChatInput";
 import { UIDataTypes, UIMessage, UITools } from "ai";
 import { LayoutGroup, motion } from "framer-motion";
 import { ChatMessagesContainer } from "../ChatMessagesContainer/ChatMessagesContainer";
 import { CopilotChatActionsProvider } from "../CopilotChatActionsProvider/CopilotChatActionsProvider";
 import { EmptySession } from "../EmptySession/EmptySession";
 export interface ChatContainerProps {
  messages: UIMessage<unknown, UIDataTypes, UITools>[];
  status: string;
  error: Error | undefined;
  sessionId: string | null;
  isLoadingSession: boolean;
  isCreatingSession: boolean;
  onCreateSession: () => void | Promise<string>;
  onSend: (message: string) => void | Promise<void>;
  onStop: () => void;
 }
 export const ChatContainer = ({
  messages,
  status,
  error,
  sessionId,
  isLoadingSession,
  isCreatingSession,
  onCreateSession,
  onSend,
  onStop,
 }: ChatContainerProps) => {
  const inputLayoutId = "copilot-2-chat-input";
  return (
    <CopilotChatActionsProvider onSend={onSend}>
      <LayoutGroup id="copilot-2-chat-layout">
        <div className="flex h-full min-h-0 w-full flex-col bg-[#f8f8f9] px-2 lg:px-0">
          {sessionId ? (
            <div className="mx-auto flex h-full min-h-0 w-full max-w-3xl flex-col">
              <ChatMessagesContainer
                messages={messages}
                status={status}
                error={error}
                isLoading={isLoadingSession}
              />
              <motion.div
                initial={{ opacity: 0 }}
                animate={{ opacity: 1 }}
                transition={{ duration: 0.3 }}
                className="relative px-3 pb-2 pt-2"
              >
                <div className="pointer-events-none absolute left-0 right-0 top-[-18px] z-10 h-6 bg-gradient-to-b from-transparent to-[#f8f8f9]" />
                <ChatInput
                  inputId="chat-input-session"
                  onSend={onSend}
                  disabled={status === "streaming"}
                  isStreaming={status === "streaming"}
                  onStop={onStop}
                  placeholder="What else can I help with?"
                />
              </motion.div>
            </div>
          ) : (
            <EmptySession
              inputLayoutId={inputLayoutId}
              isCreatingSession={isCreatingSession}
              onCreateSession={onCreateSession}
              onSend={onSend}
            />
          )}
        </div>
      </LayoutGroup>
    </CopilotChatActionsProvider>
  );
 };
--- a/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatInput/ChatInput.tsx
+++ b/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatInput/ChatInput.tsx
@@ -6,17 +6,19 @@ import {
  MicrophoneIcon,
  StopIcon,
 } from "@phosphor-icons/react";
 import { ChangeEvent, useCallback } from "react";
 import { RecordingIndicator } from "./components/RecordingIndicator";
 import { useChatInput } from "./useChatInput";
 import { useVoiceRecording } from "./useVoiceRecording";
 export interface Props {
-  onSend: (message: string) => void;
+  onSend: (message: string) => void | Promise<void>;
  disabled?: boolean;
  isStreaming?: boolean;
  onStop?: () => void;
  placeholder?: string;
  className?: string;
  inputId?: string;
 }
 export function ChatInput({
@@ -26,14 +28,14 @@ export function ChatInput({
  onStop,
  placeholder = "Type your message...",
  className,
  inputId = "chat-input",
 }: Props) {
  const inputId = "chat-input";
  const {
    value,
    setValue,
    handleKeyDown: baseHandleKeyDown,
    handleSubmit,
-    handleChange,
+    handleChange: baseHandleChange,
    hasMultipleLines,
  } = useChatInput({
    onSend,
@@ -60,6 +62,15 @@ export function ChatInput({
    inputId,
  });
  // Block text changes when recording
  const handleChange = useCallback(
    (e: ChangeEvent<HTMLTextAreaElement>) => {
      if (isRecording) return;
      baseHandleChange(e);
    },
    [isRecording, baseHandleChange],
  );
  return (
    <form onSubmit={handleSubmit} className={cn("relative flex-1", className)}>
      <div className="relative">
--- a/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatInput/components/AudioWaveform.tsx
+++ b/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatInput/components/AudioWaveform.tsx
--- a/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatInput/components/RecordingIndicator.tsx
+++ b/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatInput/components/RecordingIndicator.tsx
--- a/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatInput/helpers.ts
+++ b/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatInput/helpers.ts
--- a/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatInput/useChatInput.ts
+++ b/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatInput/useChatInput.ts
@@ -21,6 +21,7 @@ export function useChatInput({
 }: Args) {
  const [value, setValue] = useState("");
  const [hasMultipleLines, setHasMultipleLines] = useState(false);
  const [isSending, setIsSending] = useState(false);
  useEffect(
    function focusOnMount() {
@@ -100,9 +101,12 @@ export function useChatInput({
    }
  }, [value, maxRows, inputId]);
-  const handleSend = () => {
+  async function handleSend() {
-    if (disabled || !value.trim()) return;
+    if (disabled || isSending || !value.trim()) return;
-    onSend(value.trim());
+
    setIsSending(true);
    try {
      await onSend(value.trim());
      setValue("");
      setHasMultipleLines(false);
      const textarea = document.getElementById(inputId) as HTMLTextAreaElement;
@@ -116,18 +120,21 @@ export function useChatInput({
        wrapper.style.height = "";
        wrapper.style.maxHeight = "";
      }
-  };
+    } finally {
      setIsSending(false);
    }
  }
  function handleKeyDown(event: KeyboardEvent<HTMLTextAreaElement>) {
    if (event.key === "Enter" && !event.shiftKey) {
      event.preventDefault();
-      handleSend();
+      void handleSend();
    }
  }
  function handleSubmit(e: FormEvent<HTMLFormElement>) {
    e.preventDefault();
-    handleSend();
+    void handleSend();
  }
  function handleChange(e: ChangeEvent<HTMLTextAreaElement>) {
@@ -142,5 +149,6 @@ export function useChatInput({
    handleSubmit,
    handleChange,
    hasMultipleLines,
    isSending,
  };
 }
--- a/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatInput/useVoiceRecording.ts
+++ b/autogpt_platform/frontend/src/components/contextual/Chat/components/ChatInput/useVoiceRecording.ts
@@ -38,9 +38,13 @@ export function useVoiceRecording({
  const streamRef = useRef<MediaStream | null>(null);
  const isRecordingRef = useRef(false);
-  const isSupported =
+  const [isSupported, setIsSupported] = useState(false);
-    typeof window !== "undefined" &&
+
-    !!(navigator.mediaDevices && navigator.mediaDevices.getUserMedia);
+  useEffect(() => {
    setIsSupported(
      !!(navigator.mediaDevices && navigator.mediaDevices.getUserMedia),
    );
  }, []);
  const clearTimer = useCallback(() => {
    if (timerRef.current) {
@@ -214,17 +218,33 @@ export function useVoiceRecording({
  const handleKeyDown = useCallback(
    (event: KeyboardEvent<HTMLTextAreaElement>) => {
-      if (event.key === " " && !value.trim() && !isTranscribing) {
+      // Allow space to toggle recording (start when empty, stop when recording)
      if (event.key === " " && !isTranscribing) {
        if (isRecordingRef.current) {
          // Stop recording on space
          event.preventDefault();
          stopRecording();
          return;
        } else if (!value.trim()) {
          // Start recording on space when input is empty
          event.preventDefault();
          void startRecording();
          return;
        }
      }
      // Block all key events when recording (except space handled above)
      if (isRecordingRef.current) {
        event.preventDefault();
        toggleRecording();
        return;
      }
      baseHandleKeyDown(event);
    },
-    [value, isTranscribing, toggleRecording, baseHandleKeyDown],
+    [value, isTranscribing, stopRecording, startRecording, baseHandleKeyDown],
  );
  const showMicButton = isSupported;
  // Don't include isRecording in disabled state - we need key events to work
  // Text input is blocked via handleKeyDown instead
  const isInputDisabled = disabled || isStreaming || isTranscribing;
  // Cleanup on unmount
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/ChatMessagesContainer/ChatMessagesContainer.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/ChatMessagesContainer/ChatMessagesContainer.tsx
@@ -0,0 +1,274 @@
 import { getGetWorkspaceDownloadFileByIdUrl } from "@/app/api/__generated__/endpoints/workspace/workspace";
 import {
  Conversation,
  ConversationContent,
  ConversationScrollButton,
 } from "@/components/ai-elements/conversation";
 import {
  Message,
  MessageContent,
  MessageResponse,
 } from "@/components/ai-elements/message";
 import { LoadingSpinner } from "@/components/atoms/LoadingSpinner/LoadingSpinner";
 import { ToolUIPart, UIDataTypes, UIMessage, UITools } from "ai";
 import { useEffect, useState } from "react";
 import { CreateAgentTool } from "../../tools/CreateAgent/CreateAgent";
 import { EditAgentTool } from "../../tools/EditAgent/EditAgent";
 import { FindAgentsTool } from "../../tools/FindAgents/FindAgents";
 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 { ViewAgentOutputTool } from "../../tools/ViewAgentOutput/ViewAgentOutput";
 // ---------------------------------------------------------------------------
 // Workspace media support
 // ---------------------------------------------------------------------------
 /**
 * Resolve workspace:// URLs in markdown text to proxy download URLs.
 * Detects MIME type from the hash fragment (e.g. workspace://id#video/mp4)
 * and prefixes the alt text with "video:" so the custom img component can
 * render a <video> element instead.
 */
 function resolveWorkspaceUrls(text: string): string {
  return text.replace(
    /!\[([^\]]*)\]\(workspace:\/\/([^)#\s]+)(?:#([^)\s]*))?\)/g,
    (_match, alt: string, fileId: string, mimeHint?: string) => {
      const apiPath = getGetWorkspaceDownloadFileByIdUrl(fileId);
      const url = `/api/proxy${apiPath}`;
      if (mimeHint?.startsWith("video/")) {
        return `![video:${alt || "Video"}](${url})`;
      }
      return `![${alt || "Image"}](${url})`;
    },
  );
 }
 /**
 * Custom img component for Streamdown that renders <video> elements
 * for workspace video files (detected via "video:" alt-text prefix).
 * Falls back to <video> when an <img> fails to load for workspace files.
 */
 function WorkspaceMediaImage(props: React.JSX.IntrinsicElements["img"]) {
  const { src, alt, ...rest } = props;
  const [imgFailed, setImgFailed] = useState(false);
  const isWorkspace = src?.includes("/workspace/files/") ?? false;
  if (!src) return null;
  if (alt?.startsWith("video:") || (imgFailed && isWorkspace)) {
    return (
      <span className="my-2 inline-block">
        <video
          controls
          className="h-auto max-w-full rounded-md border border-zinc-200"
          preload="metadata"
        >
          <source src={src} />
          Your browser does not support the video tag.
        </video>
      </span>
    );
  }
  return (
    // eslint-disable-next-line @next/next/no-img-element
    <img
      src={src}
      alt={alt || "Image"}
      className="h-auto max-w-full rounded-md border border-zinc-200"
      loading="lazy"
      onError={() => {
        if (isWorkspace) setImgFailed(true);
      }}
      {...rest}
    />
  );
 }
 /** Stable components override for Streamdown (avoids re-creating on every render). */
 const STREAMDOWN_COMPONENTS = { img: WorkspaceMediaImage };
 const THINKING_PHRASES = [
  "Thinking...",
  "Considering this...",
  "Working through this...",
  "Analyzing your request...",
  "Reasoning...",
  "Looking into it...",
  "Processing your request...",
  "Mulling this over...",
  "Piecing it together...",
  "On it...",
 ];
 function getRandomPhrase() {
  return THINKING_PHRASES[Math.floor(Math.random() * THINKING_PHRASES.length)];
 }
 interface ChatMessagesContainerProps {
  messages: UIMessage<unknown, UIDataTypes, UITools>[];
  status: string;
  error: Error | undefined;
  isLoading: boolean;
 }
 export const ChatMessagesContainer = ({
  messages,
  status,
  error,
  isLoading,
 }: ChatMessagesContainerProps) => {
  const [thinkingPhrase, setThinkingPhrase] = useState(getRandomPhrase);
  useEffect(() => {
    if (status === "submitted") {
      setThinkingPhrase(getRandomPhrase());
    }
  }, [status]);
  const lastMessage = messages[messages.length - 1];
  const lastAssistantHasVisibleContent =
    lastMessage?.role === "assistant" &&
    lastMessage.parts.some(
      (p) =>
        (p.type === "text" && p.text.trim().length > 0) ||
        p.type.startsWith("tool-"),
    );
  const showThinking =
    status === "submitted" ||
    (status === "streaming" && !lastAssistantHasVisibleContent);
  return (
    <Conversation className="min-h-0 flex-1">
      <ConversationContent className="gap-6 px-3 py-6">
        {isLoading && messages.length === 0 && (
          <div className="flex flex-1 items-center justify-center">
            <LoadingSpinner size="large" className="text-neutral-400" />
          </div>
        )}
        {messages.map((message, messageIndex) => {
          const isLastAssistant =
            messageIndex === messages.length - 1 &&
            message.role === "assistant";
          const messageHasVisibleContent = message.parts.some(
            (p) =>
              (p.type === "text" && p.text.trim().length > 0) ||
              p.type.startsWith("tool-"),
          );
          return (
            <Message from={message.role} key={message.id}>
              <MessageContent
                className={
                  "text-[1rem] leading-relaxed " +
                  "group-[.is-user]:rounded-xl group-[.is-user]:bg-purple-100 group-[.is-user]:px-3 group-[.is-user]:py-2.5 group-[.is-user]:text-slate-900 group-[.is-user]:[border-bottom-right-radius:0] " +
                  "group-[.is-assistant]:bg-transparent group-[.is-assistant]:text-slate-900"
                }
              >
                {message.parts.map((part, i) => {
                  switch (part.type) {
                    case "text":
                      return (
                        <MessageResponse
                          key={`${message.id}-${i}`}
                          components={STREAMDOWN_COMPONENTS}
                        >
                          {resolveWorkspaceUrls(part.text)}
                        </MessageResponse>
                      );
                    case "tool-find_block":
                      return (
                        <FindBlocksTool
                          key={`${message.id}-${i}`}
                          part={part as ToolUIPart}
                        />
                      );
                    case "tool-find_agent":
                    case "tool-find_library_agent":
                      return (
                        <FindAgentsTool
                          key={`${message.id}-${i}`}
                          part={part as ToolUIPart}
                        />
                      );
                    case "tool-search_docs":
                    case "tool-get_doc_page":
                      return (
                        <SearchDocsTool
                          key={`${message.id}-${i}`}
                          part={part as ToolUIPart}
                        />
                      );
                    case "tool-run_block":
                      return (
                        <RunBlockTool
                          key={`${message.id}-${i}`}
                          part={part as ToolUIPart}
                        />
                      );
                    case "tool-run_agent":
                    case "tool-schedule_agent":
                      return (
                        <RunAgentTool
                          key={`${message.id}-${i}`}
                          part={part as ToolUIPart}
                        />
                      );
                    case "tool-create_agent":
                      return (
                        <CreateAgentTool
                          key={`${message.id}-${i}`}
                          part={part as ToolUIPart}
                        />
                      );
                    case "tool-edit_agent":
                      return (
                        <EditAgentTool
                          key={`${message.id}-${i}`}
                          part={part as ToolUIPart}
                        />
                      );
                    case "tool-view_agent_output":
                      return (
                        <ViewAgentOutputTool
                          key={`${message.id}-${i}`}
                          part={part as ToolUIPart}
                        />
                      );
                    default:
                      return null;
                  }
                })}
                {isLastAssistant &&
                  !messageHasVisibleContent &&
                  showThinking && (
                    <span className="inline-block animate-shimmer bg-gradient-to-r from-neutral-400 via-neutral-600 to-neutral-400 bg-[length:200%_100%] bg-clip-text text-transparent">
                      {thinkingPhrase}
                    </span>
                  )}
              </MessageContent>
            </Message>
          );
        })}
        {showThinking && lastMessage?.role !== "assistant" && (
          <Message from="assistant">
            <MessageContent className="text-[1rem] leading-relaxed">
              <span className="inline-block animate-shimmer bg-gradient-to-r from-neutral-400 via-neutral-600 to-neutral-400 bg-[length:200%_100%] bg-clip-text text-transparent">
                {thinkingPhrase}
              </span>
            </MessageContent>
          </Message>
        )}
        {error && (
          <div className="rounded-lg bg-red-50 p-3 text-red-600">
            Error: {error.message}
          </div>
        )}
      </ConversationContent>
      <ConversationScrollButton />
    </Conversation>
  );
 };
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/ChatSidebar/ChatSidebar.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/ChatSidebar/ChatSidebar.tsx
@@ -0,0 +1,188 @@
 "use client";
 import { useGetV2ListSessions } from "@/app/api/__generated__/endpoints/chat/chat";
 import { Button } from "@/components/atoms/Button/Button";
 import { LoadingSpinner } from "@/components/atoms/LoadingSpinner/LoadingSpinner";
 import { Text } from "@/components/atoms/Text/Text";
 import {
  Sidebar,
  SidebarContent,
  SidebarFooter,
  SidebarHeader,
  SidebarTrigger,
  useSidebar,
 } from "@/components/ui/sidebar";
 import { cn } from "@/lib/utils";
 import { PlusCircleIcon, PlusIcon } from "@phosphor-icons/react";
 import { motion } from "framer-motion";
 import { parseAsString, useQueryState } from "nuqs";
 export function ChatSidebar() {
  const { state } = useSidebar();
  const isCollapsed = state === "collapsed";
  const [sessionId, setSessionId] = useQueryState("sessionId", parseAsString);
  const { data: sessionsResponse, isLoading: isLoadingSessions } =
    useGetV2ListSessions({ limit: 50 });
  const sessions =
    sessionsResponse?.status === 200 ? sessionsResponse.data.sessions : [];
  function handleNewChat() {
    setSessionId(null);
  }
  function handleSelectSession(id: string) {
    setSessionId(id);
  }
  function formatDate(dateString: string) {
    const date = new Date(dateString);
    const now = new Date();
    const diffMs = now.getTime() - date.getTime();
    const diffDays = Math.floor(diffMs / (1000 * 60 * 60 * 24));
    if (diffDays === 0) return "Today";
    if (diffDays === 1) return "Yesterday";
    if (diffDays < 7) return `${diffDays} days ago`;
    const day = date.getDate();
    const ordinal =
      day % 10 === 1 && day !== 11
        ? "st"
        : day % 10 === 2 && day !== 12
          ? "nd"
          : day % 10 === 3 && day !== 13
            ? "rd"
            : "th";
    const month = date.toLocaleDateString("en-US", { month: "short" });
    const year = date.getFullYear();
    return `${day}${ordinal} ${month} ${year}`;
  }
  return (
    <Sidebar
      variant="inset"
      collapsible="icon"
      className="!top-[50px] !h-[calc(100vh-50px)] border-r border-zinc-100 px-0"
    >
      {isCollapsed && (
        <SidebarHeader
          className={cn(
            "flex",
            isCollapsed
              ? "flex-row items-center justify-between gap-y-4 md:flex-col md:items-start md:justify-start"
              : "flex-row items-center justify-between",
          )}
        >
          <motion.div
            key={isCollapsed ? "header-collapsed" : "header-expanded"}
            className="flex flex-col items-center gap-3 pt-4"
            initial={{ opacity: 0, filter: "blur(3px)" }}
            animate={{ opacity: 1, filter: "blur(0px)" }}
            transition={{ type: "spring", bounce: 0.2 }}
          >
            <div className="flex flex-col items-center gap-2">
              <SidebarTrigger />
              <Button
                variant="ghost"
                onClick={handleNewChat}
                style={{ minWidth: "auto", width: "auto" }}
              >
                <PlusCircleIcon className="!size-5" />
                <span className="sr-only">New Chat</span>
              </Button>
            </div>
          </motion.div>
        </SidebarHeader>
      )}
      <SidebarContent className="gap-4 overflow-y-auto px-4 py-4 [-ms-overflow-style:none] [scrollbar-width:none] [&::-webkit-scrollbar]:hidden">
        {!isCollapsed && (
          <motion.div
            initial={{ opacity: 0 }}
            animate={{ opacity: 1 }}
            transition={{ duration: 0.2, delay: 0.1 }}
            className="flex items-center justify-between px-3"
          >
            <Text variant="h3" size="body-medium">
              Your chats
            </Text>
            <div className="relative left-6">
              <SidebarTrigger />
            </div>
          </motion.div>
        )}
        {!isCollapsed && (
          <motion.div
            initial={{ opacity: 0 }}
            animate={{ opacity: 1 }}
            transition={{ duration: 0.2, delay: 0.15 }}
            className="mt-4 flex flex-col gap-1"
          >
            {isLoadingSessions ? (
              <div className="flex items-center justify-center py-4">
                <LoadingSpinner size="small" className="text-neutral-400" />
              </div>
            ) : sessions.length === 0 ? (
              <p className="py-4 text-center text-sm text-neutral-500">
                No conversations yet
              </p>
            ) : (
              sessions.map((session) => (
                <button
                  key={session.id}
                  onClick={() => handleSelectSession(session.id)}
                  className={cn(
                    "w-full rounded-lg px-3 py-2.5 text-left transition-colors",
                    session.id === sessionId
                      ? "bg-zinc-100"
                      : "hover:bg-zinc-50",
                  )}
                >
                  <div className="flex min-w-0 max-w-full flex-col overflow-hidden">
                    <div className="min-w-0 max-w-full">
                      <Text
                        variant="body"
                        className={cn(
                          "truncate font-normal",
                          session.id === sessionId
                            ? "text-zinc-600"
                            : "text-zinc-800",
                        )}
                      >
                        {session.title || `Untitled chat`}
                      </Text>
                    </div>
                    <Text variant="small" className="text-neutral-400">
                      {formatDate(session.updated_at)}
                    </Text>
                  </div>
                </button>
              ))
            )}
          </motion.div>
        )}
      </SidebarContent>
      {!isCollapsed && sessionId && (
        <SidebarFooter className="shrink-0 bg-zinc-50 p-3 pb-1 shadow-[0_-4px_6px_-1px_rgba(0,0,0,0.05)]">
          <motion.div
            initial={{ opacity: 0 }}
            animate={{ opacity: 1 }}
            transition={{ duration: 0.2, delay: 0.2 }}
          >
            <Button
              variant="primary"
              size="small"
              onClick={handleNewChat}
              className="w-full"
              leftIcon={<PlusIcon className="h-4 w-4" weight="bold" />}
            >
              New Chat
            </Button>
          </motion.div>
        </SidebarFooter>
      )}
    </Sidebar>
  );
 }
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotChatActionsProvider/CopilotChatActionsProvider.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotChatActionsProvider/CopilotChatActionsProvider.tsx
@@ -0,0 +1,16 @@
 "use client";
 import { CopilotChatActionsContext } from "./useCopilotChatActions";
 interface Props {
  onSend: (message: string) => void | Promise<void>;
  children: React.ReactNode;
 }
 export function CopilotChatActionsProvider({ onSend, children }: Props) {
  return (
    <CopilotChatActionsContext.Provider value={{ onSend }}>
      {children}
    </CopilotChatActionsContext.Provider>
  );
 }
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotChatActionsProvider/useCopilotChatActions.ts
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotChatActionsProvider/useCopilotChatActions.ts
@@ -0,0 +1,23 @@
 "use client";
 import { createContext, useContext } from "react";
 interface CopilotChatActions {
  onSend: (message: string) => void | Promise<void>;
 }
 const CopilotChatActionsContext = createContext<CopilotChatActions | null>(
  null,
 );
 export function useCopilotChatActions(): CopilotChatActions {
  const ctx = useContext(CopilotChatActionsContext);
  if (!ctx) {
    throw new Error(
      "useCopilotChatActions must be used within CopilotChatActionsProvider",
    );
  }
  return ctx;
 }
 export { CopilotChatActionsContext };
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/CopilotShell.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/CopilotShell.tsx
@@ -1,99 +0,0 @@
 "use client";
 import { ChatLoader } from "@/components/contextual/Chat/components/ChatLoader/ChatLoader";
 import { Text } from "@/components/atoms/Text/Text";
 import { NAVBAR_HEIGHT_PX } from "@/lib/constants";
 import type { ReactNode } from "react";
 import { DesktopSidebar } from "./components/DesktopSidebar/DesktopSidebar";
 import { MobileDrawer } from "./components/MobileDrawer/MobileDrawer";
 import { MobileHeader } from "./components/MobileHeader/MobileHeader";
 import { useCopilotShell } from "./useCopilotShell";
 interface Props {
  children: ReactNode;
 }
 export function CopilotShell({ children }: Props) {
  const {
    isMobile,
    isDrawerOpen,
    isLoading,
    isCreatingSession,
    isLoggedIn,
    hasActiveSession,
    sessions,
    currentSessionId,
    handleOpenDrawer,
    handleCloseDrawer,
    handleDrawerOpenChange,
    handleNewChatClick,
    handleSessionClick,
    hasNextPage,
    isFetchingNextPage,
    fetchNextPage,
  } = useCopilotShell();
  if (!isLoggedIn) {
    return (
      <div className="flex h-full items-center justify-center">
        <ChatLoader />
      </div>
    );
  }
  return (
    <div
      className="flex overflow-hidden bg-[#EFEFF0]"
      style={{ height: `calc(100vh - ${NAVBAR_HEIGHT_PX}px)` }}
    >
      {!isMobile && (
        <DesktopSidebar
          sessions={sessions}
          currentSessionId={currentSessionId}
          isLoading={isLoading}
          hasNextPage={hasNextPage}
          isFetchingNextPage={isFetchingNextPage}
          onSelectSession={handleSessionClick}
          onFetchNextPage={fetchNextPage}
          onNewChat={handleNewChatClick}
          hasActiveSession={Boolean(hasActiveSession)}
        />
      )}
      <div className="relative flex min-h-0 flex-1 flex-col">
        {isMobile && <MobileHeader onOpenDrawer={handleOpenDrawer} />}
        <div className="flex min-h-0 flex-1 flex-col">
          {isCreatingSession ? (
            <div className="flex h-full flex-1 flex-col items-center justify-center bg-[#f8f8f9]">
              <div className="flex flex-col items-center gap-4">
                <ChatLoader />
                <Text variant="body" className="text-zinc-500">
                  Creating your chat...
                </Text>
              </div>
            </div>
          ) : (
            children
          )}
        </div>
      </div>
      {isMobile && (
        <MobileDrawer
          isOpen={isDrawerOpen}
          sessions={sessions}
          currentSessionId={currentSessionId}
          isLoading={isLoading}
          hasNextPage={hasNextPage}
          isFetchingNextPage={isFetchingNextPage}
          onSelectSession={handleSessionClick}
          onFetchNextPage={fetchNextPage}
          onNewChat={handleNewChatClick}
          onClose={handleCloseDrawer}
          onOpenChange={handleDrawerOpenChange}
          hasActiveSession={Boolean(hasActiveSession)}
        />
      )}
    </div>
  );
 }
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/components/DesktopSidebar/DesktopSidebar.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/components/DesktopSidebar/DesktopSidebar.tsx
@@ -1,70 +0,0 @@
 import type { SessionSummaryResponse } from "@/app/api/__generated__/models/sessionSummaryResponse";
 import { Button } from "@/components/atoms/Button/Button";
 import { Text } from "@/components/atoms/Text/Text";
 import { scrollbarStyles } from "@/components/styles/scrollbars";
 import { cn } from "@/lib/utils";
 import { Plus } from "@phosphor-icons/react";
 import { SessionsList } from "../SessionsList/SessionsList";
 interface Props {
  sessions: SessionSummaryResponse[];
  currentSessionId: string | null;
  isLoading: boolean;
  hasNextPage: boolean;
  isFetchingNextPage: boolean;
  onSelectSession: (sessionId: string) => void;
  onFetchNextPage: () => void;
  onNewChat: () => void;
  hasActiveSession: boolean;
 }
 export function DesktopSidebar({
  sessions,
  currentSessionId,
  isLoading,
  hasNextPage,
  isFetchingNextPage,
  onSelectSession,
  onFetchNextPage,
  onNewChat,
  hasActiveSession,
 }: Props) {
  return (
    <aside className="flex h-full w-80 flex-col border-r border-zinc-100 bg-zinc-50">
      <div className="shrink-0 px-6 py-4">
        <Text variant="h3" size="body-medium">
          Your chats
        </Text>
      </div>
      <div
        className={cn(
          "flex min-h-0 flex-1 flex-col overflow-y-auto px-3 py-3",
          scrollbarStyles,
        )}
      >
        <SessionsList
          sessions={sessions}
          currentSessionId={currentSessionId}
          isLoading={isLoading}
          hasNextPage={hasNextPage}
          isFetchingNextPage={isFetchingNextPage}
          onSelectSession={onSelectSession}
          onFetchNextPage={onFetchNextPage}
        />
      </div>
      {hasActiveSession && (
        <div className="shrink-0 bg-zinc-50 p-3 shadow-[0_-4px_6px_-1px_rgba(0,0,0,0.05)]">
          <Button
            variant="primary"
            size="small"
            onClick={onNewChat}
            className="w-full"
            leftIcon={<Plus width="1rem" height="1rem" />}
          >
            New Chat
          </Button>
        </div>
      )}
    </aside>
  );
 }
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/components/MobileDrawer/MobileDrawer.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/components/MobileDrawer/MobileDrawer.tsx
@@ -1,91 +0,0 @@
 import type { SessionSummaryResponse } from "@/app/api/__generated__/models/sessionSummaryResponse";
 import { Button } from "@/components/atoms/Button/Button";
 import { scrollbarStyles } from "@/components/styles/scrollbars";
 import { cn } from "@/lib/utils";
 import { PlusIcon, X } from "@phosphor-icons/react";
 import { Drawer } from "vaul";
 import { SessionsList } from "../SessionsList/SessionsList";
 interface Props {
  isOpen: boolean;
  sessions: SessionSummaryResponse[];
  currentSessionId: string | null;
  isLoading: boolean;
  hasNextPage: boolean;
  isFetchingNextPage: boolean;
  onSelectSession: (sessionId: string) => void;
  onFetchNextPage: () => void;
  onNewChat: () => void;
  onClose: () => void;
  onOpenChange: (open: boolean) => void;
  hasActiveSession: boolean;
 }
 export function MobileDrawer({
  isOpen,
  sessions,
  currentSessionId,
  isLoading,
  hasNextPage,
  isFetchingNextPage,
  onSelectSession,
  onFetchNextPage,
  onNewChat,
  onClose,
  onOpenChange,
  hasActiveSession,
 }: Props) {
  return (
    <Drawer.Root open={isOpen} onOpenChange={onOpenChange} direction="left">
      <Drawer.Portal>
        <Drawer.Overlay className="fixed inset-0 z-[60] bg-black/10 backdrop-blur-sm" />
        <Drawer.Content className="fixed left-0 top-0 z-[70] flex h-full w-80 flex-col border-r border-zinc-200 bg-zinc-50">
          <div className="shrink-0 border-b border-zinc-200 p-4">
            <div className="flex items-center justify-between">
              <Drawer.Title className="text-lg font-semibold text-zinc-800">
                Your chats
              </Drawer.Title>
              <Button
                variant="icon"
                size="icon"
                aria-label="Close sessions"
                onClick={onClose}
              >
                <X width="1.25rem" height="1.25rem" />
              </Button>
            </div>
          </div>
          <div
            className={cn(
              "flex min-h-0 flex-1 flex-col overflow-y-auto px-3 py-3",
              scrollbarStyles,
            )}
          >
            <SessionsList
              sessions={sessions}
              currentSessionId={currentSessionId}
              isLoading={isLoading}
              hasNextPage={hasNextPage}
              isFetchingNextPage={isFetchingNextPage}
              onSelectSession={onSelectSession}
              onFetchNextPage={onFetchNextPage}
            />
          </div>
          {hasActiveSession && (
            <div className="shrink-0 bg-white p-3 shadow-[0_-4px_6px_-1px_rgba(0,0,0,0.05)]">
              <Button
                variant="primary"
                size="small"
                onClick={onNewChat}
                className="w-full"
                leftIcon={<PlusIcon width="1rem" height="1rem" />}
              >
                New Chat
              </Button>
            </div>
          )}
        </Drawer.Content>
      </Drawer.Portal>
    </Drawer.Root>
  );
 }
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/components/MobileDrawer/useMobileDrawer.ts
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/components/MobileDrawer/useMobileDrawer.ts
@@ -1,24 +0,0 @@
 import { useState } from "react";
 export function useMobileDrawer() {
  const [isDrawerOpen, setIsDrawerOpen] = useState(false);
  const handleOpenDrawer = () => {
    setIsDrawerOpen(true);
  };
  const handleCloseDrawer = () => {
    setIsDrawerOpen(false);
  };
  const handleDrawerOpenChange = (open: boolean) => {
    setIsDrawerOpen(open);
  };
  return {
    isDrawerOpen,
    handleOpenDrawer,
    handleCloseDrawer,
    handleDrawerOpenChange,
  };
 }
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/components/SessionsList/SessionsList.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/components/SessionsList/SessionsList.tsx
@@ -1,80 +0,0 @@
 import type { SessionSummaryResponse } from "@/app/api/__generated__/models/sessionSummaryResponse";
 import { Skeleton } from "@/components/__legacy__/ui/skeleton";
 import { Text } from "@/components/atoms/Text/Text";
 import { InfiniteList } from "@/components/molecules/InfiniteList/InfiniteList";
 import { cn } from "@/lib/utils";
 import { getSessionTitle } from "../../helpers";
 interface Props {
  sessions: SessionSummaryResponse[];
  currentSessionId: string | null;
  isLoading: boolean;
  hasNextPage: boolean;
  isFetchingNextPage: boolean;
  onSelectSession: (sessionId: string) => void;
  onFetchNextPage: () => void;
 }
 export function SessionsList({
  sessions,
  currentSessionId,
  isLoading,
  hasNextPage,
  isFetchingNextPage,
  onSelectSession,
  onFetchNextPage,
 }: Props) {
  if (isLoading) {
    return (
      <div className="space-y-1">
        {Array.from({ length: 5 }).map((_, i) => (
          <div key={i} className="rounded-lg px-3 py-2.5">
            <Skeleton className="h-5 w-full" />
          </div>
        ))}
      </div>
    );
  }
  if (sessions.length === 0) {
    return (
      <div className="flex h-full items-center justify-center">
        <Text variant="body" className="text-zinc-500">
          You don&apos;t have previous chats
        </Text>
      </div>
    );
  }
  return (
    <InfiniteList
      items={sessions}
      hasMore={hasNextPage}
      isFetchingMore={isFetchingNextPage}
      onEndReached={onFetchNextPage}
      className="space-y-1"
      renderItem={(session) => {
        const isActive = session.id === currentSessionId;
        return (
          <button
            onClick={() => onSelectSession(session.id)}
            className={cn(
              "w-full rounded-lg px-3 py-2.5 text-left transition-colors",
              isActive ? "bg-zinc-100" : "hover:bg-zinc-50",
            )}
          >
            <Text
              variant="body"
              className={cn(
                "font-normal",
                isActive ? "text-zinc-600" : "text-zinc-800",
              )}
            >
              {getSessionTitle(session)}
            </Text>
          </button>
        );
      }}
    />
  );
 }
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/components/SessionsList/useSessionsPagination.ts
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/components/SessionsList/useSessionsPagination.ts
@@ -1,91 +0,0 @@
 import { useGetV2ListSessions } from "@/app/api/__generated__/endpoints/chat/chat";
 import type { SessionSummaryResponse } from "@/app/api/__generated__/models/sessionSummaryResponse";
 import { okData } from "@/app/api/helpers";
 import { useEffect, useState } from "react";
 const PAGE_SIZE = 50;
 export interface UseSessionsPaginationArgs {
  enabled: boolean;
 }
 export function useSessionsPagination({ enabled }: UseSessionsPaginationArgs) {
  const [offset, setOffset] = useState(0);
  const [accumulatedSessions, setAccumulatedSessions] = useState<
    SessionSummaryResponse[]
  >([]);
  const [totalCount, setTotalCount] = useState<number | null>(null);
  const { data, isLoading, isFetching, isError } = useGetV2ListSessions(
    { limit: PAGE_SIZE, offset },
    {
      query: {
        enabled: enabled && offset >= 0,
      },
    },
  );
  useEffect(() => {
    const responseData = okData(data);
    if (responseData) {
      const newSessions = responseData.sessions;
      const total = responseData.total;
      setTotalCount(total);
      if (offset === 0) {
        setAccumulatedSessions(newSessions);
      } else {
        setAccumulatedSessions((prev) => [...prev, ...newSessions]);
      }
    } else if (!enabled) {
      setAccumulatedSessions([]);
      setTotalCount(null);
    }
  }, [data, offset, enabled]);
  const hasNextPage =
    totalCount !== null && accumulatedSessions.length < totalCount;
  const areAllSessionsLoaded =
    totalCount !== null &&
    accumulatedSessions.length >= totalCount &&
    !isFetching &&
    !isLoading;
  useEffect(() => {
    if (
      hasNextPage &&
      !isFetching &&
      !isLoading &&
      !isError &&
      totalCount !== null
    ) {
      setOffset((prev) => prev + PAGE_SIZE);
    }
  }, [hasNextPage, isFetching, isLoading, isError, totalCount]);
  const fetchNextPage = () => {
    if (hasNextPage && !isFetching) {
      setOffset((prev) => prev + PAGE_SIZE);
    }
  };
  const reset = () => {
    // Only reset the offset - keep existing sessions visible during refetch
    // The effect will replace sessions when new data arrives at offset 0
    setOffset(0);
  };
  return {
    sessions: accumulatedSessions,
    isLoading,
    isFetching,
    hasNextPage,
    areAllSessionsLoaded,
    totalCount,
    fetchNextPage,
    reset,
  };
 }
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/helpers.ts
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/helpers.ts
@@ -1,106 +0,0 @@
 import type { SessionDetailResponse } from "@/app/api/__generated__/models/sessionDetailResponse";
 import type { SessionSummaryResponse } from "@/app/api/__generated__/models/sessionSummaryResponse";
 import { format, formatDistanceToNow, isToday } from "date-fns";
 export function convertSessionDetailToSummary(session: SessionDetailResponse) {
  return {
    id: session.id,
    created_at: session.created_at,
    updated_at: session.updated_at,
    title: undefined,
  };
 }
 export function filterVisibleSessions(sessions: SessionSummaryResponse[]) {
  const fiveMinutesAgo = Date.now() - 5 * 60 * 1000;
  return sessions.filter((session) => {
    const hasBeenUpdated = session.updated_at !== session.created_at;
    if (hasBeenUpdated) return true;
    const isRecentlyCreated =
      new Date(session.created_at).getTime() > fiveMinutesAgo;
    return isRecentlyCreated;
  });
 }
 export function getSessionTitle(session: SessionSummaryResponse) {
  if (session.title) return session.title;
  const isNewSession = session.updated_at === session.created_at;
  if (isNewSession) {
    const createdDate = new Date(session.created_at);
    if (isToday(createdDate)) {
      return "Today";
    }
    return format(createdDate, "MMM d, yyyy");
  }
  return "Untitled Chat";
 }
 export function getSessionUpdatedLabel(session: SessionSummaryResponse) {
  if (!session.updated_at) return "";
  return formatDistanceToNow(new Date(session.updated_at), { addSuffix: true });
 }
 export function mergeCurrentSessionIntoList(
  accumulatedSessions: SessionSummaryResponse[],
  currentSessionId: string | null,
  currentSessionData: SessionDetailResponse | null | undefined,
  recentlyCreatedSessions?: Map<string, SessionSummaryResponse>,
 ) {
  const filteredSessions: SessionSummaryResponse[] = [];
  const addedIds = new Set<string>();
  if (accumulatedSessions.length > 0) {
    const visibleSessions = filterVisibleSessions(accumulatedSessions);
    if (currentSessionId) {
      const currentInAll = accumulatedSessions.find(
        (s) => s.id === currentSessionId,
      );
      if (currentInAll) {
        const isInVisible = visibleSessions.some(
          (s) => s.id === currentSessionId,
        );
        if (!isInVisible) {
          filteredSessions.push(currentInAll);
          addedIds.add(currentInAll.id);
        }
      }
    }
    for (const session of visibleSessions) {
      if (!addedIds.has(session.id)) {
        filteredSessions.push(session);
        addedIds.add(session.id);
      }
    }
  }
  if (currentSessionId && currentSessionData) {
    if (!addedIds.has(currentSessionId)) {
      const summarySession = convertSessionDetailToSummary(currentSessionData);
      filteredSessions.unshift(summarySession);
      addedIds.add(currentSessionId);
    }
  }
  if (recentlyCreatedSessions) {
    for (const [sessionId, sessionData] of recentlyCreatedSessions) {
      if (!addedIds.has(sessionId)) {
        filteredSessions.unshift(sessionData);
        addedIds.add(sessionId);
      }
    }
  }
  return filteredSessions;
 }
 export function getCurrentSessionId(searchParams: URLSearchParams) {
  return searchParams.get("sessionId");
 }
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/useCopilotShell.ts
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/useCopilotShell.ts
@@ -1,124 +0,0 @@
 "use client";
 import {
  getGetV2GetSessionQueryKey,
  getGetV2ListSessionsQueryKey,
  useGetV2GetSession,
 } from "@/app/api/__generated__/endpoints/chat/chat";
 import { okData } from "@/app/api/helpers";
 import { useChatStore } from "@/components/contextual/Chat/chat-store";
 import { useBreakpoint } from "@/lib/hooks/useBreakpoint";
 import { useSupabase } from "@/lib/supabase/hooks/useSupabase";
 import { useQueryClient } from "@tanstack/react-query";
 import { usePathname, useSearchParams } from "next/navigation";
 import { useCopilotStore } from "../../copilot-page-store";
 import { useCopilotSessionId } from "../../useCopilotSessionId";
 import { useMobileDrawer } from "./components/MobileDrawer/useMobileDrawer";
 import { getCurrentSessionId } from "./helpers";
 import { useShellSessionList } from "./useShellSessionList";
 export function useCopilotShell() {
  const pathname = usePathname();
  const searchParams = useSearchParams();
  const queryClient = useQueryClient();
  const breakpoint = useBreakpoint();
  const { isLoggedIn } = useSupabase();
  const isMobile =
    breakpoint === "base" || breakpoint === "sm" || breakpoint === "md";
  const { urlSessionId, setUrlSessionId } = useCopilotSessionId();
  const isOnHomepage = pathname === "/copilot";
  const paramSessionId = searchParams.get("sessionId");
  const {
    isDrawerOpen,
    handleOpenDrawer,
    handleCloseDrawer,
    handleDrawerOpenChange,
  } = useMobileDrawer();
  const paginationEnabled = !isMobile || isDrawerOpen || !!paramSessionId;
  const currentSessionId = getCurrentSessionId(searchParams);
  const { data: currentSessionData } = useGetV2GetSession(
    currentSessionId || "",
    {
      query: {
        enabled: !!currentSessionId,
        select: okData,
      },
    },
  );
  const {
    sessions,
    isLoading,
    isSessionsFetching,
    hasNextPage,
    fetchNextPage,
    resetPagination,
    recentlyCreatedSessionsRef,
  } = useShellSessionList({
    paginationEnabled,
    currentSessionId,
    currentSessionData,
    isOnHomepage,
    paramSessionId,
  });
  const stopStream = useChatStore((s) => s.stopStream);
  const isCreatingSession = useCopilotStore((s) => s.isCreatingSession);
  function handleSessionClick(sessionId: string) {
    if (sessionId === currentSessionId) return;
    // Stop current stream - SSE reconnection allows resuming later
    if (currentSessionId) {
      stopStream(currentSessionId);
    }
    if (recentlyCreatedSessionsRef.current.has(sessionId)) {
      queryClient.invalidateQueries({
        queryKey: getGetV2GetSessionQueryKey(sessionId),
      });
    }
    setUrlSessionId(sessionId, { shallow: false });
    if (isMobile) handleCloseDrawer();
  }
  function handleNewChatClick() {
    // Stop current stream - SSE reconnection allows resuming later
    if (currentSessionId) {
      stopStream(currentSessionId);
    }
    resetPagination();
    queryClient.invalidateQueries({
      queryKey: getGetV2ListSessionsQueryKey(),
    });
    setUrlSessionId(null, { shallow: false });
    if (isMobile) handleCloseDrawer();
  }
  return {
    isMobile,
    isDrawerOpen,
    isLoggedIn,
    hasActiveSession:
      Boolean(currentSessionId) && (!isOnHomepage || Boolean(paramSessionId)),
    isLoading: isLoading || isCreatingSession,
    isCreatingSession,
    sessions,
    currentSessionId: urlSessionId,
    handleOpenDrawer,
    handleCloseDrawer,
    handleDrawerOpenChange,
    handleNewChatClick,
    handleSessionClick,
    hasNextPage,
    isFetchingNextPage: isSessionsFetching,
    fetchNextPage,
  };
 }
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/useShellSessionList.ts
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/useShellSessionList.ts
@@ -1,113 +0,0 @@
 import { getGetV2ListSessionsQueryKey } from "@/app/api/__generated__/endpoints/chat/chat";
 import type { SessionDetailResponse } from "@/app/api/__generated__/models/sessionDetailResponse";
 import type { SessionSummaryResponse } from "@/app/api/__generated__/models/sessionSummaryResponse";
 import { useChatStore } from "@/components/contextual/Chat/chat-store";
 import { useQueryClient } from "@tanstack/react-query";
 import { useEffect, useMemo, useRef } from "react";
 import { useSessionsPagination } from "./components/SessionsList/useSessionsPagination";
 import {
  convertSessionDetailToSummary,
  filterVisibleSessions,
  mergeCurrentSessionIntoList,
 } from "./helpers";
 interface UseShellSessionListArgs {
  paginationEnabled: boolean;
  currentSessionId: string | null;
  currentSessionData: SessionDetailResponse | null | undefined;
  isOnHomepage: boolean;
  paramSessionId: string | null;
 }
 export function useShellSessionList({
  paginationEnabled,
  currentSessionId,
  currentSessionData,
  isOnHomepage,
  paramSessionId,
 }: UseShellSessionListArgs) {
  const queryClient = useQueryClient();
  const onStreamComplete = useChatStore((s) => s.onStreamComplete);
  const {
    sessions: accumulatedSessions,
    isLoading: isSessionsLoading,
    isFetching: isSessionsFetching,
    hasNextPage,
    fetchNextPage,
    reset: resetPagination,
  } = useSessionsPagination({
    enabled: paginationEnabled,
  });
  const recentlyCreatedSessionsRef = useRef<
    Map<string, SessionSummaryResponse>
  >(new Map());
  useEffect(() => {
    if (isOnHomepage && !paramSessionId) {
      queryClient.invalidateQueries({
        queryKey: getGetV2ListSessionsQueryKey(),
      });
    }
  }, [isOnHomepage, paramSessionId, queryClient]);
  useEffect(() => {
    if (currentSessionId && currentSessionData) {
      const isNewSession =
        currentSessionData.updated_at === currentSessionData.created_at;
      const isNotInAccumulated = !accumulatedSessions.some(
        (s) => s.id === currentSessionId,
      );
      if (isNewSession || isNotInAccumulated) {
        const summary = convertSessionDetailToSummary(currentSessionData);
        recentlyCreatedSessionsRef.current.set(currentSessionId, summary);
      }
    }
  }, [currentSessionId, currentSessionData, accumulatedSessions]);
  useEffect(() => {
    for (const sessionId of recentlyCreatedSessionsRef.current.keys()) {
      if (accumulatedSessions.some((s) => s.id === sessionId)) {
        recentlyCreatedSessionsRef.current.delete(sessionId);
      }
    }
  }, [accumulatedSessions]);
  useEffect(() => {
    const unsubscribe = onStreamComplete(() => {
      queryClient.invalidateQueries({
        queryKey: getGetV2ListSessionsQueryKey(),
      });
    });
    return unsubscribe;
  }, [onStreamComplete, queryClient]);
  const sessions = useMemo(
    () =>
      mergeCurrentSessionIntoList(
        accumulatedSessions,
        currentSessionId,
        currentSessionData,
        recentlyCreatedSessionsRef.current,
      ),
    [accumulatedSessions, currentSessionId, currentSessionData],
  );
  const visibleSessions = useMemo(
    () => filterVisibleSessions(sessions),
    [sessions],
  );
  const isLoading = isSessionsLoading && accumulatedSessions.length === 0;
  return {
    sessions: visibleSessions,
    isLoading,
    isSessionsFetching,
    hasNextPage,
    fetchNextPage,
    resetPagination,
    recentlyCreatedSessionsRef,
  };
 }
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/EmptySession/EmptySession.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/EmptySession/EmptySession.tsx
@@ -0,0 +1,111 @@
 "use client";
 import { ChatInput } from "@/app/(platform)/copilot/components/ChatInput/ChatInput";
 import { Button } from "@/components/atoms/Button/Button";
 import { Text } from "@/components/atoms/Text/Text";
 import { useSupabase } from "@/lib/supabase/hooks/useSupabase";
 import { SpinnerGapIcon } from "@phosphor-icons/react";
 import { motion } from "framer-motion";
 import { useEffect, useState } from "react";
 import {
  getGreetingName,
  getInputPlaceholder,
  getQuickActions,
 } from "./helpers";
 interface Props {
  inputLayoutId: string;
  isCreatingSession: boolean;
  onCreateSession: () => void | Promise<string>;
  onSend: (message: string) => void | Promise<void>;
 }
 export function EmptySession({
  inputLayoutId,
  isCreatingSession,
  onSend,
 }: Props) {
  const { user } = useSupabase();
  const greetingName = getGreetingName(user);
  const quickActions = getQuickActions();
  const [loadingAction, setLoadingAction] = useState<string | null>(null);
  const [inputPlaceholder, setInputPlaceholder] = useState(
    getInputPlaceholder(),
  );
  useEffect(() => {
    setInputPlaceholder(getInputPlaceholder(window.innerWidth));
  }, [window.innerWidth]);
  async function handleQuickActionClick(action: string) {
    if (isCreatingSession || loadingAction) return;
    setLoadingAction(action);
    try {
      await onSend(action);
    } finally {
      setLoadingAction(null);
    }
  }
  return (
    <div className="flex h-full flex-1 items-center justify-center overflow-y-auto bg-[#f8f8f9] px-0 py-5 md:px-6 md:py-10">
      <motion.div
        className="w-full max-w-3xl text-center"
        initial={{ opacity: 0 }}
        animate={{ opacity: 1 }}
        transition={{ duration: 0.3 }}
      >
        <div className="mx-auto max-w-3xl">
          <Text variant="h3" className="mb-1 !text-[1.375rem] text-zinc-700">
            Hey, <span className="text-violet-600">{greetingName}</span>
          </Text>
          <Text variant="h3" className="mb-8 !font-normal">
            Tell me about your work — I&apos;ll find what to automate.
          </Text>
          <div className="mb-6">
            <motion.div
              layoutId={inputLayoutId}
              transition={{ type: "spring", bounce: 0.2, duration: 0.65 }}
              className="w-full px-2"
            >
              <ChatInput
                inputId="chat-input-empty"
                onSend={onSend}
                disabled={isCreatingSession}
                placeholder={inputPlaceholder}
                className="w-full"
              />
            </motion.div>
          </div>
        </div>
        <div className="flex flex-wrap items-center justify-center gap-3 overflow-x-auto [-ms-overflow-style:none] [scrollbar-width:none] [&::-webkit-scrollbar]:hidden">
          {quickActions.map((action) => (
            <Button
              key={action}
              type="button"
              variant="outline"
              size="small"
              onClick={() => void handleQuickActionClick(action)}
              disabled={isCreatingSession || loadingAction !== null}
              aria-busy={loadingAction === action}
              leftIcon={
                loadingAction === action ? (
                  <SpinnerGapIcon
                    className="h-4 w-4 animate-spin"
                    weight="bold"
                  />
                ) : null
              }
              className="h-auto shrink-0 border-zinc-300 px-3 py-2 text-[.9rem] text-zinc-600"
            >
              {action}
            </Button>
          ))}
        </div>
      </motion.div>
    </div>
  );
 }
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/EmptySession/helpers.ts
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/EmptySession/helpers.ts
@@ -1,6 +1,26 @@
-import type { User } from "@supabase/supabase-js";
+import { User } from "@supabase/supabase-js";
-export function getGreetingName(user?: User | null): string {
+export function getInputPlaceholder(width?: number) {
  if (!width) return "What's your role and what eats up most of your day?";
  if (width < 500) {
    return "I'm a chef and I hate...";
  }
  if (width <= 1080) {
    return "What's your role and what eats up most of your day?";
  }
  return "What's your role and what eats up most of your day? e.g. 'I'm a recruiter and I hate...'";
 }
 export function getQuickActions() {
  return [
    "I don't know where to start, just ask me stuff",
    "I do the same thing every week and it's killing me",
    "Help me find where I'm wasting my time",
  ];
 }
 export function getGreetingName(user?: User | null) {
  if (!user) return "there";
  const metadata = user.user_metadata as Record<string, unknown> | undefined;
  const fullName = metadata?.full_name;
@@ -16,30 +36,3 @@ export function getGreetingName(user?: User | null): string {
  }
  return "there";
 }
 export function buildCopilotChatUrl(prompt: string): string {
  const trimmed = prompt.trim();
  if (!trimmed) return "/copilot/chat";
  const encoded = encodeURIComponent(trimmed);
  return `/copilot/chat?prompt=${encoded}`;
 }
 export function getQuickActions(): string[] {
  return [
    "I don't know where to start, just ask me stuff",
    "I do the same thing every week and it's killing me",
    "Help me find where I'm wasting my time",
  ];
 }
 export function getInputPlaceholder(width?: number) {
  if (!width) return "What's your role and what eats up most of your day?";
  if (width < 500) {
    return "I'm a chef and I hate...";
  }
  if (width <= 1080) {
    return "What's your role and what eats up most of your day?";
  }
  return "What's your role and what eats up most of your day? e.g. 'I'm a recruiter and I hate...'";
 }
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/MobileDrawer/MobileDrawer.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/MobileDrawer/MobileDrawer.tsx
@@ -0,0 +1,140 @@
 import type { SessionSummaryResponse } from "@/app/api/__generated__/models/sessionSummaryResponse";
 import { Button } from "@/components/atoms/Button/Button";
 import { Text } from "@/components/atoms/Text/Text";
 import { scrollbarStyles } from "@/components/styles/scrollbars";
 import { cn } from "@/lib/utils";
 import { PlusIcon, SpinnerGapIcon, X } from "@phosphor-icons/react";
 import { Drawer } from "vaul";
 interface Props {
  isOpen: boolean;
  sessions: SessionSummaryResponse[];
  currentSessionId: string | null;
  isLoading: boolean;
  onSelectSession: (sessionId: string) => void;
  onNewChat: () => void;
  onClose: () => void;
  onOpenChange: (open: boolean) => void;
 }
 function formatDate(dateString: string) {
  const date = new Date(dateString);
  const now = new Date();
  const diffMs = now.getTime() - date.getTime();
  const diffDays = Math.floor(diffMs / (1000 * 60 * 60 * 24));
  if (diffDays === 0) return "Today";
  if (diffDays === 1) return "Yesterday";
  if (diffDays < 7) return `${diffDays} days ago`;
  const day = date.getDate();
  const ordinal =
    day % 10 === 1 && day !== 11
      ? "st"
      : day % 10 === 2 && day !== 12
        ? "nd"
        : day % 10 === 3 && day !== 13
          ? "rd"
          : "th";
  const month = date.toLocaleDateString("en-US", { month: "short" });
  const year = date.getFullYear();
  return `${day}${ordinal} ${month} ${year}`;
 }
 export function MobileDrawer({
  isOpen,
  sessions,
  currentSessionId,
  isLoading,
  onSelectSession,
  onNewChat,
  onClose,
  onOpenChange,
 }: Props) {
  return (
    <Drawer.Root open={isOpen} onOpenChange={onOpenChange} direction="left">
      <Drawer.Portal>
        <Drawer.Overlay className="fixed inset-0 z-[60] bg-black/10 backdrop-blur-sm" />
        <Drawer.Content className="fixed left-0 top-0 z-[70] flex h-full w-80 flex-col border-r border-zinc-200 bg-zinc-50">
          <div className="shrink-0 border-b border-zinc-200 px-4 py-2">
            <div className="flex items-center justify-between">
              <Drawer.Title className="text-lg font-semibold text-zinc-800">
                Your chats
              </Drawer.Title>
              <Button
                variant="icon"
                size="icon"
                aria-label="Close sessions"
                onClick={onClose}
              >
                <X width="1rem" height="1rem" />
              </Button>
            </div>
          </div>
          <div
            className={cn(
              "flex min-h-0 flex-1 flex-col gap-1 overflow-y-auto px-3 py-3",
              scrollbarStyles,
            )}
          >
            {isLoading ? (
              <div className="flex items-center justify-center py-4">
                <SpinnerGapIcon className="h-5 w-5 animate-spin text-neutral-400" />
              </div>
            ) : sessions.length === 0 ? (
              <p className="py-4 text-center text-sm text-neutral-500">
                No conversations yet
              </p>
            ) : (
              sessions.map((session) => (
                <button
                  key={session.id}
                  onClick={() => onSelectSession(session.id)}
                  className={cn(
                    "w-full rounded-lg px-3 py-2.5 text-left transition-colors",
                    session.id === currentSessionId
                      ? "bg-zinc-100"
                      : "hover:bg-zinc-50",
                  )}
                >
                  <div className="flex min-w-0 max-w-full flex-col overflow-hidden">
                    <div className="min-w-0 max-w-full">
                      <Text
                        variant="body"
                        className={cn(
                          "truncate font-normal",
                          session.id === currentSessionId
                            ? "text-zinc-600"
                            : "text-zinc-800",
                        )}
                      >
                        {session.title || "Untitled chat"}
                      </Text>
                    </div>
                    <Text variant="small" className="text-neutral-400">
                      {formatDate(session.updated_at)}
                    </Text>
                  </div>
                </button>
              ))
            )}
          </div>
          {currentSessionId && (
            <div className="shrink-0 bg-white p-3 shadow-[0_-4px_6px_-1px_rgba(0,0,0,0.05)]">
              <Button
                variant="primary"
                size="small"
                onClick={onNewChat}
                className="w-full"
                leftIcon={<PlusIcon width="1rem" height="1rem" />}
              >
                New Chat
              </Button>
            </div>
          )}
        </Drawer.Content>
      </Drawer.Portal>
    </Drawer.Root>
  );
 }
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/components/MobileHeader/MobileHeader.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/CopilotShell/components/MobileHeader/MobileHeader.tsx
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/MorphingTextAnimation/MorphingTextAnimation.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/MorphingTextAnimation/MorphingTextAnimation.tsx
@@ -0,0 +1,54 @@
 import { cn } from "@/lib/utils";
 import { AnimatePresence, motion } from "framer-motion";
 interface Props {
  text: string;
  className?: string;
 }
 export function MorphingTextAnimation({ text, className }: Props) {
  const letters = text.split("");
  return (
    <div className={cn(className)}>
      <AnimatePresence mode="popLayout" initial={false}>
        <motion.div key={text} className="whitespace-nowrap">
          <motion.span className="inline-flex overflow-hidden">
            {letters.map((char, index) => (
              <motion.span
                key={`${text}-${index}`}
                initial={{
                  opacity: 0,
                  y: 8,
                  rotateX: "80deg",
                  filter: "blur(6px)",
                }}
                animate={{
                  opacity: 1,
                  y: 0,
                  rotateX: "0deg",
                  filter: "blur(0px)",
                }}
                exit={{
                  opacity: 0,
                  y: -8,
                  rotateX: "-80deg",
                  filter: "blur(6px)",
                }}
                style={{ willChange: "transform" }}
                transition={{
                  delay: 0.015 * index,
                  type: "spring",
                  bounce: 0.5,
                }}
                className="inline-block"
              >
                {char === " " ? "\u00A0" : char}
              </motion.span>
            ))}
          </motion.span>
        </motion.div>
      </AnimatePresence>
    </div>
  );
 }
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/OrbitLoader/OrbitLoader.module.css
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/OrbitLoader/OrbitLoader.module.css
@@ -0,0 +1,69 @@
 .loader {
  position: relative;
  animation: rotate 1s infinite;
 }
 .loader::before,
 .loader::after {
  border-radius: 50%;
  content: "";
  display: block;
  /* 40% of container size */
  height: 40%;
  width: 40%;
 }
 .loader::before {
  animation: ball1 1s infinite;
  background-color: #a1a1aa; /* zinc-400 */
  box-shadow: calc(var(--spacing)) 0 0 #18181b; /* zinc-900 */
  margin-bottom: calc(var(--gap));
 }
 .loader::after {
  animation: ball2 1s infinite;
  background-color: #18181b; /* zinc-900 */
  box-shadow: calc(var(--spacing)) 0 0 #a1a1aa; /* zinc-400 */
 }
@keyframes rotate {
  0% {
    transform: rotate(0deg) scale(0.8);
  }
  50% {
    transform: rotate(360deg) scale(1.2);
  }
  100% {
    transform: rotate(720deg) scale(0.8);
  }
 }
@keyframes ball1 {
  0% {
    box-shadow: calc(var(--spacing)) 0 0 #18181b;
  }
  50% {
    box-shadow: 0 0 0 #18181b;
    margin-bottom: 0;
    transform: translate(calc(var(--spacing) / 2), calc(var(--spacing) / 2));
  }
  100% {
    box-shadow: calc(var(--spacing)) 0 0 #18181b;
    margin-bottom: calc(var(--gap));
  }
 }
@keyframes ball2 {
  0% {
    box-shadow: calc(var(--spacing)) 0 0 #a1a1aa;
  }
  50% {
    box-shadow: 0 0 0 #a1a1aa;
    margin-top: calc(var(--ball-size) * -1);
    transform: translate(calc(var(--spacing) / 2), calc(var(--spacing) / 2));
  }
  100% {
    box-shadow: calc(var(--spacing)) 0 0 #a1a1aa;
    margin-top: 0;
  }
 }
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/OrbitLoader/OrbitLoader.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/OrbitLoader/OrbitLoader.tsx
@@ -0,0 +1,28 @@
 import { cn } from "@/lib/utils";
 import styles from "./OrbitLoader.module.css";
 interface Props {
  size?: number;
  className?: string;
 }
 export function OrbitLoader({ size = 24, className }: Props) {
  const ballSize = Math.round(size * 0.4);
  const spacing = Math.round(size * 0.6);
  const gap = Math.round(size * 0.2);
  return (
    <div
      className={cn(styles.loader, className)}
      style={
        {
          width: size,
          height: size,
          "--ball-size": `${ballSize}px`,
          "--spacing": `${spacing}px`,
          "--gap": `${gap}px`,
        } as React.CSSProperties
      }
    />
  );
 }
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/ProgressBar/ProgressBar.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/ProgressBar/ProgressBar.tsx
@@ -0,0 +1,26 @@
 import { cn } from "@/lib/utils";
 interface Props {
  value: number;
  label?: string;
  className?: string;
 }
 export function ProgressBar({ value, label, className }: Props) {
  const clamped = Math.min(100, Math.max(0, value));
  return (
    <div className={cn("flex flex-col gap-1.5", className)}>
      <div className="flex items-center justify-between text-xs text-neutral-500">
        <span>{label ?? "Working on it..."}</span>
        <span>{Math.round(clamped)}%</span>
      </div>
      <div className="h-2 w-full overflow-hidden rounded-full bg-neutral-200">
        <div
          className="h-full rounded-full bg-neutral-900 transition-[width] duration-300 ease-out"
          style={{ width: `${clamped}%` }}
        />
      </div>
    </div>
  );
 }
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/PulseLoader/PulseLoader.module.css
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/PulseLoader/PulseLoader.module.css
@@ -0,0 +1,34 @@
 .loader {
  position: relative;
  display: inline-block;
  flex-shrink: 0;
 }
 .loader::before,
 .loader::after {
  content: "";
  box-sizing: border-box;
  width: 100%;
  height: 100%;
  border-radius: 50%;
  background: currentColor;
  position: absolute;
  left: 0;
  top: 0;
  animation: ripple 2s linear infinite;
 }
 .loader::after {
  animation-delay: 1s;
 }
@keyframes ripple {
  0% {
    transform: scale(0);
    opacity: 1;
  }
  100% {
    transform: scale(1);
    opacity: 0;
  }
 }
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/PulseLoader/PulseLoader.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/PulseLoader/PulseLoader.tsx
@@ -0,0 +1,16 @@
 import { cn } from "@/lib/utils";
 import styles from "./PulseLoader.module.css";
 interface Props {
  size?: number;
  className?: string;
 }
 export function PulseLoader({ size = 24, className }: Props) {
  return (
    <div
      className={cn(styles.loader, className)}
      style={{ width: size, height: size }}
    />
  );
 }
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/SpinnerLoader/SpinnerLoader.module.css
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/SpinnerLoader/SpinnerLoader.module.css
@@ -0,0 +1,57 @@
 .loader {
  position: relative;
  display: inline-block;
  flex-shrink: 0;
  transform: rotateZ(45deg);
  perspective: 1000px;
  border-radius: 50%;
  color: currentColor;
 }
 .loader::before,
 .loader::after {
  content: "";
  display: block;
  position: absolute;
  top: 0;
  left: 0;
  width: inherit;
  height: inherit;
  border-radius: 50%;
  transform: rotateX(70deg);
  animation: spin 1s linear infinite;
 }
 .loader::after {
  color: var(--spinner-accent, #a855f7);
  transform: rotateY(70deg);
  animation-delay: 0.4s;
 }
@keyframes spin {
  0%,
  100% {
    box-shadow: 0.2em 0 0 0 currentColor;
  }
  12% {
    box-shadow: 0.2em 0.2em 0 0 currentColor;
  }
  25% {
    box-shadow: 0 0.2em 0 0 currentColor;
  }
  37% {
    box-shadow: -0.2em 0.2em 0 0 currentColor;
  }
  50% {
    box-shadow: -0.2em 0 0 0 currentColor;
  }
  62% {
    box-shadow: -0.2em -0.2em 0 0 currentColor;
  }
  75% {
    box-shadow: 0 -0.2em 0 0 currentColor;
  }
  87% {
    box-shadow: 0.2em -0.2em 0 0 currentColor;
  }
 }
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/SpinnerLoader/SpinnerLoader.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/SpinnerLoader/SpinnerLoader.tsx
@@ -0,0 +1,16 @@
 import { cn } from "@/lib/utils";
 import styles from "./SpinnerLoader.module.css";
 interface Props {
  size?: number;
  className?: string;
 }
 export function SpinnerLoader({ size = 24, className }: Props) {
  return (
    <div
      className={cn(styles.loader, className)}
      style={{ width: size, height: size }}
    />
  );
 }
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/ToolAccordion/AccordionContent.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/ToolAccordion/AccordionContent.tsx
@@ -0,0 +1,235 @@
 import { Link } from "@/components/atoms/Link/Link";
 import { Text } from "@/components/atoms/Text/Text";
 import { cn } from "@/lib/utils";
 /* ------------------------------------------------------------------ */
 /*  Layout                                                            */
 /* ------------------------------------------------------------------ */
 export function ContentGrid({
  children,
  className,
 }: {
  children: React.ReactNode;
  className?: string;
 }) {
  return <div className={cn("grid gap-2", className)}>{children}</div>;
 }
 /* ------------------------------------------------------------------ */
 /*  Card                                                              */
 /* ------------------------------------------------------------------ */
 export function ContentCard({
  children,
  className,
 }: {
  children: React.ReactNode;
  className?: string;
 }) {
  return (
    <div
      className={cn(
        "rounded-lg bg-gradient-to-r from-purple-500/30 to-blue-500/30 p-[1px]",
        className,
      )}
    >
      <div className="rounded-lg bg-neutral-100 p-3">{children}</div>
    </div>
  );
 }
 /** Flex row with a left content area (`children`) and an optional right‑side `action`. */
 export function ContentCardHeader({
  children,
  action,
  className,
 }: {
  children: React.ReactNode;
  action?: React.ReactNode;
  className?: string;
 }) {
  return (
    <div className={cn("flex items-start justify-between gap-2", className)}>
      <div className="min-w-0">{children}</div>
      {action}
    </div>
  );
 }
 export function ContentCardTitle({
  children,
  className,
 }: {
  children: React.ReactNode;
  className?: string;
 }) {
  return (
    <Text
      variant="body-medium"
      className={cn("truncate text-zinc-800", className)}
    >
      {children}
    </Text>
  );
 }
 export function ContentCardSubtitle({
  children,
  className,
 }: {
  children: React.ReactNode;
  className?: string;
 }) {
  return (
    <Text
      variant="small"
      className={cn("mt-0.5 truncate font-mono text-zinc-800", className)}
    >
      {children}
    </Text>
  );
 }
 export function ContentCardDescription({
  children,
  className,
 }: {
  children: React.ReactNode;
  className?: string;
 }) {
  return (
    <Text variant="body" className={cn("mt-2 text-zinc-800", className)}>
      {children}
    </Text>
  );
 }
 /* ------------------------------------------------------------------ */
 /*  Text                                                              */
 /* ------------------------------------------------------------------ */
 export function ContentMessage({
  children,
  className,
 }: {
  children: React.ReactNode;
  className?: string;
 }) {
  return (
    <Text variant="body" className={cn("text-zinc-800", className)}>
      {children}
    </Text>
  );
 }
 export function ContentHint({
  children,
  className,
 }: {
  children: React.ReactNode;
  className?: string;
 }) {
  return (
    <Text variant="small" className={cn("text-neutral-500", className)}>
      {children}
    </Text>
  );
 }
 /* ------------------------------------------------------------------ */
 /*  Code / data                                                       */
 /* ------------------------------------------------------------------ */
 export function ContentCodeBlock({
  children,
  className,
 }: {
  children: React.ReactNode;
  className?: string;
 }) {
  return (
    <pre
      className={cn(
        "whitespace-pre-wrap rounded-lg border bg-black p-3 text-xs text-neutral-200",
        className,
      )}
    >
      {children}
    </pre>
  );
 }
 /* ------------------------------------------------------------------ */
 /*  Inline elements                                                   */
 /* ------------------------------------------------------------------ */
 export function ContentBadge({
  children,
  className,
 }: {
  children: React.ReactNode;
  className?: string;
 }) {
  return (
    <Text
      variant="small"
      as="span"
      className={cn(
        "shrink-0 rounded-full border bg-muted px-2 py-0.5 text-[11px] text-zinc-800",
        className,
      )}
    >
      {children}
    </Text>
  );
 }
 export function ContentLink({
  href,
  children,
  className,
  ...rest
 }: Omit<React.ComponentProps<typeof Link>, "className"> & {
  className?: string;
 }) {
  return (
    <Link
      variant="primary"
      isExternal
      href={href}
      className={cn("shrink-0 text-xs text-purple-500", className)}
      {...rest}
    >
      {children}
    </Link>
  );
 }
 /* ------------------------------------------------------------------ */
 /*  Lists                                                             */
 /* ------------------------------------------------------------------ */
 export function ContentSuggestionsList({
  items,
  max = 5,
  className,
 }: {
  items: string[];
  max?: number;
  className?: string;
 }) {
  if (items.length === 0) return null;
  return (
    <ul
      className={cn(
        "mt-2 list-disc space-y-1 pl-5 font-sans text-[0.75rem] leading-[1.125rem] text-zinc-800",
        className,
      )}
    >
      {items.slice(0, max).map((s) => (
        <li key={s}>{s}</li>
      ))}
    </ul>
  );
 }
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/ToolAccordion/ToolAccordion.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/ToolAccordion/ToolAccordion.tsx
@@ -0,0 +1,102 @@
 "use client";
 import { cn } from "@/lib/utils";
 import { CaretDownIcon } from "@phosphor-icons/react";
 import { AnimatePresence, motion, useReducedMotion } from "framer-motion";
 import { useId } from "react";
 import { useToolAccordion } from "./useToolAccordion";
 interface Props {
  icon: React.ReactNode;
  title: React.ReactNode;
  titleClassName?: string;
  description?: React.ReactNode;
  children: React.ReactNode;
  className?: string;
  defaultExpanded?: boolean;
  expanded?: boolean;
  onExpandedChange?: (expanded: boolean) => void;
 }
 export function ToolAccordion({
  icon,
  title,
  titleClassName,
  description,
  children,
  className,
  defaultExpanded,
  expanded,
  onExpandedChange,
 }: Props) {
  const shouldReduceMotion = useReducedMotion();
  const contentId = useId();
  const { isExpanded, toggle } = useToolAccordion({
    expanded,
    defaultExpanded,
    onExpandedChange,
  });
  return (
    <div
      className={cn(
        "mt-2 w-full rounded-lg border border-slate-200 bg-slate-100 px-3 py-2",
        className,
      )}
    >
      <button
        type="button"
        aria-expanded={isExpanded}
        aria-controls={contentId}
        onClick={toggle}
        className="flex w-full items-center justify-between gap-3 py-1 text-left"
      >
        <div className="flex min-w-0 items-center gap-3">
          <span className="flex shrink-0 items-center text-gray-800">
            {icon}
          </span>
          <div className="min-w-0">
            <p
              className={cn(
                "truncate text-sm font-medium text-gray-800",
                titleClassName,
              )}
            >
              {title}
            </p>
            {description && (
              <p className="truncate text-xs text-slate-800">{description}</p>
            )}
          </div>
        </div>
        <CaretDownIcon
          className={cn(
            "h-4 w-4 shrink-0 text-slate-500 transition-transform",
            isExpanded && "rotate-180",
          )}
          weight="bold"
        />
      </button>
      <AnimatePresence initial={false}>
        {isExpanded && (
          <motion.div
            id={contentId}
            initial={{ height: 0, opacity: 0, filter: "blur(10px)" }}
            animate={{ height: "auto", opacity: 1, filter: "blur(0px)" }}
            exit={{ height: 0, opacity: 0, filter: "blur(10px)" }}
            transition={
              shouldReduceMotion
                ? { duration: 0 }
                : { type: "spring", bounce: 0.35, duration: 0.55 }
            }
            className="overflow-hidden"
            style={{ willChange: "height, opacity, filter" }}
          >
            <div className="pb-2 pt-3">{children}</div>
          </motion.div>
        )}
      </AnimatePresence>
    </div>
  );
 }
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/components/ToolAccordion/useToolAccordion.ts
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/components/ToolAccordion/useToolAccordion.ts
@@ -0,0 +1,32 @@
 import { useState } from "react";
 interface UseToolAccordionOptions {
  expanded?: boolean;
  defaultExpanded?: boolean;
  onExpandedChange?: (expanded: boolean) => void;
 }
 interface UseToolAccordionResult {
  isExpanded: boolean;
  toggle: () => void;
 }
 export function useToolAccordion({
  expanded,
  defaultExpanded = false,
  onExpandedChange,
 }: UseToolAccordionOptions): UseToolAccordionResult {
  const [uncontrolledExpanded, setUncontrolledExpanded] =
    useState(defaultExpanded);
  const isControlled = typeof expanded === "boolean";
  const isExpanded = isControlled ? expanded : uncontrolledExpanded;
  function toggle() {
    const next = !isExpanded;
    if (!isControlled) setUncontrolledExpanded(next);
    onExpandedChange?.(next);
  }
  return { isExpanded, toggle };
 }
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/copilot-page-store.ts
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/copilot-page-store.ts
@@ -1,56 +0,0 @@
 "use client";
 import { create } from "zustand";
 interface CopilotStoreState {
  isStreaming: boolean;
  isSwitchingSession: boolean;
  isCreatingSession: boolean;
  isInterruptModalOpen: boolean;
  pendingAction: (() => void) | null;
 }
 interface CopilotStoreActions {
  setIsStreaming: (isStreaming: boolean) => void;
  setIsSwitchingSession: (isSwitchingSession: boolean) => void;
  setIsCreatingSession: (isCreating: boolean) => void;
  openInterruptModal: (onConfirm: () => void) => void;
  confirmInterrupt: () => void;
  cancelInterrupt: () => void;
 }
 type CopilotStore = CopilotStoreState & CopilotStoreActions;
 export const useCopilotStore = create<CopilotStore>((set, get) => ({
  isStreaming: false,
  isSwitchingSession: false,
  isCreatingSession: false,
  isInterruptModalOpen: false,
  pendingAction: null,
  setIsStreaming(isStreaming) {
    set({ isStreaming });
  },
  setIsSwitchingSession(isSwitchingSession) {
    set({ isSwitchingSession });
  },
  setIsCreatingSession(isCreatingSession) {
    set({ isCreatingSession });
  },
  openInterruptModal(onConfirm) {
    set({ isInterruptModalOpen: true, pendingAction: onConfirm });
  },
  confirmInterrupt() {
    const { pendingAction } = get();
    set({ isInterruptModalOpen: false, pendingAction: null });
    if (pendingAction) pendingAction();
  },
  cancelInterrupt() {
    set({ isInterruptModalOpen: false, pendingAction: null });
  },
 }));
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/helpers/convertChatSessionToUiMessages.ts
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/helpers/convertChatSessionToUiMessages.ts
@@ -0,0 +1,128 @@
 import type { UIMessage, UIDataTypes, UITools } from "ai";
 interface SessionChatMessage {
  role: string;
  content: string | null;
  tool_call_id: string | null;
  tool_calls: unknown[] | null;
 }
 function coerceSessionChatMessages(
  rawMessages: unknown[],
 ): SessionChatMessage[] {
  return rawMessages
    .map((m) => {
      if (!m || typeof m !== "object") return null;
      const msg = m as Record<string, unknown>;
      const role = typeof msg.role === "string" ? msg.role : null;
      if (!role) return null;
      return {
        role,
        content:
          typeof msg.content === "string"
            ? msg.content
            : msg.content == null
              ? null
              : String(msg.content),
        tool_call_id:
          typeof msg.tool_call_id === "string"
            ? msg.tool_call_id
            : msg.tool_call_id == null
              ? null
              : String(msg.tool_call_id),
        tool_calls: Array.isArray(msg.tool_calls) ? msg.tool_calls : null,
      };
    })
    .filter((m): m is SessionChatMessage => m !== null);
 }
 function safeJsonParse(value: string): unknown {
  try {
    return JSON.parse(value) as unknown;
  } catch {
    return value;
  }
 }
 function toToolInput(rawArguments: unknown): unknown {
  if (typeof rawArguments === "string") {
    const trimmed = rawArguments.trim();
    return trimmed ? safeJsonParse(trimmed) : {};
  }
  if (rawArguments && typeof rawArguments === "object") return rawArguments;
  return {};
 }
 export function convertChatSessionMessagesToUiMessages(
  sessionId: string,
  rawMessages: unknown[],
 ): UIMessage<unknown, UIDataTypes, UITools>[] {
  const messages = coerceSessionChatMessages(rawMessages);
  const toolOutputsByCallId = new Map<string, unknown>();
  for (const msg of messages) {
    if (msg.role !== "tool") continue;
    if (!msg.tool_call_id) continue;
    if (msg.content == null) continue;
    toolOutputsByCallId.set(msg.tool_call_id, msg.content);
  }
  const uiMessages: UIMessage<unknown, UIDataTypes, UITools>[] = [];
  messages.forEach((msg, index) => {
    if (msg.role === "tool") return;
    if (msg.role !== "user" && msg.role !== "assistant") return;
    const parts: UIMessage<unknown, UIDataTypes, UITools>["parts"] = [];
    if (typeof msg.content === "string" && msg.content.trim()) {
      parts.push({ type: "text", text: msg.content, state: "done" });
    }
    if (msg.role === "assistant" && Array.isArray(msg.tool_calls)) {
      for (const rawToolCall of msg.tool_calls) {
        if (!rawToolCall || typeof rawToolCall !== "object") continue;
        const toolCall = rawToolCall as {
          id?: unknown;
          function?: { name?: unknown; arguments?: unknown };
        };
        const toolCallId = String(toolCall.id ?? "").trim();
        const toolName = String(toolCall.function?.name ?? "").trim();
        if (!toolCallId || !toolName) continue;
        const input = toToolInput(toolCall.function?.arguments);
        const output = toolOutputsByCallId.get(toolCallId);
        if (output !== undefined) {
          parts.push({
            type: `tool-${toolName}`,
            toolCallId,
            state: "output-available",
            input,
            output: typeof output === "string" ? safeJsonParse(output) : output,
          });
        } else {
          parts.push({
            type: `tool-${toolName}`,
            toolCallId,
            state: "input-available",
            input,
          });
        }
      }
    }
    if (parts.length === 0) return;
    uiMessages.push({
      id: `${sessionId}-${index}`,
      role: msg.role,
      parts,
    });
  });
  return uiMessages;
 }
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/useCopilotSessionId.ts
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/useCopilotSessionId.ts
--- a/autogpt_platform/frontend/src/components/contextual/Chat/components/ToolCallMessage/useAsymptoticProgress.ts
+++ b/autogpt_platform/frontend/src/components/contextual/Chat/components/ToolCallMessage/useAsymptoticProgress.ts
@@ -5,17 +5,16 @@ import { useEffect, useRef, useState } from "react";
 * asymptotically approaching but never reaching the max value.
 *
 * Uses a half-life formula: progress = max * (1 - 0.5^(time/halfLife))
- * This creates the "game loading bar" effect where:
+ * This creates a "loading bar" effect where:
 * - 50% is reached at halfLifeSeconds
 * - 75% is reached at 2 * halfLifeSeconds
 * - 87.5% is reached at 3 * halfLifeSeconds
 * - and so on...
 *
 * @param isActive - Whether the progress should be animating
 * @param halfLifeSeconds - Time in seconds to reach 50% progress (default: 30)
 * @param maxProgress - Maximum progress value to approach (default: 100)
 * @param intervalMs - Update interval in milliseconds (default: 100)
- * @returns Current progress value (0-maxProgress)
+ * @returns Current progress value (0–maxProgress)
 */
 export function useAsymptoticProgress(
  isActive: boolean,
@@ -35,8 +34,6 @@ export function useAsymptoticProgress(
    const interval = setInterval(() => {
      elapsedTimeRef.current += intervalMs / 1000;
      // Half-life approach: progress = max * (1 - 0.5^(time/halfLife))
      // At t=halfLife: 50%, at t=2*halfLife: 75%, at t=3*halfLife: 87.5%, etc.
      const newProgress =
        maxProgress *
        (1 - Math.pow(0.5, elapsedTimeRef.current / halfLifeSeconds));
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/layout.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/layout.tsx
@@ -1,13 +0,0 @@
 "use client";
 import { FeatureFlagPage } from "@/services/feature-flags/FeatureFlagPage";
 import { Flag } from "@/services/feature-flags/use-get-flag";
 import { type ReactNode } from "react";
 import { CopilotShell } from "./components/CopilotShell/CopilotShell";
 export default function CopilotLayout({ children }: { children: ReactNode }) {
  return (
    <FeatureFlagPage flag={Flag.CHAT} whenDisabled="/library">
      <CopilotShell>{children}</CopilotShell>
    </FeatureFlagPage>
  );
 }
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/page.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/page.tsx
@@ -1,149 +1,13 @@
 "use client";
-import { Button } from "@/components/atoms/Button/Button";
+import { FeatureFlagPage } from "@/services/feature-flags/FeatureFlagPage";
-import { Skeleton } from "@/components/atoms/Skeleton/Skeleton";
+import { Flag } from "@/services/feature-flags/use-get-flag";
-import { Text } from "@/components/atoms/Text/Text";
+import { CopilotPage } from "./CopilotPage";
 import { Chat } from "@/components/contextual/Chat/Chat";
 import { ChatInput } from "@/components/contextual/Chat/components/ChatInput/ChatInput";
 import { Dialog } from "@/components/molecules/Dialog/Dialog";
 import { useEffect, useState } from "react";
 import { useCopilotStore } from "./copilot-page-store";
 import { getInputPlaceholder } from "./helpers";
 import { useCopilotPage } from "./useCopilotPage";
-export default function CopilotPage() {
+export default function Page() {
  const { state, handlers } = useCopilotPage();
  const isInterruptModalOpen = useCopilotStore((s) => s.isInterruptModalOpen);
  const confirmInterrupt = useCopilotStore((s) => s.confirmInterrupt);
  const cancelInterrupt = useCopilotStore((s) => s.cancelInterrupt);
  const [inputPlaceholder, setInputPlaceholder] = useState(
    getInputPlaceholder(),
  );
  useEffect(() => {
    const handleResize = () => {
      setInputPlaceholder(getInputPlaceholder(window.innerWidth));
    };
    handleResize();
    window.addEventListener("resize", handleResize);
    return () => window.removeEventListener("resize", handleResize);
  }, []);
  const { greetingName, quickActions, isLoading, hasSession, initialPrompt } =
    state;
  const {
    handleQuickAction,
    startChatWithPrompt,
    handleSessionNotFound,
    handleStreamingChange,
  } = handlers;
  if (hasSession) {
  return (
-      <div className="flex h-full flex-col">
+    <FeatureFlagPage flag={Flag.CHAT} whenDisabled="/library">
-        <Chat
+      <CopilotPage />
-          className="flex-1"
+    </FeatureFlagPage>
          initialPrompt={initialPrompt}
          onSessionNotFound={handleSessionNotFound}
          onStreamingChange={handleStreamingChange}
        />
        <Dialog
          title="Interrupt current chat?"
          styling={{ maxWidth: 300, width: "100%" }}
          controlled={{
            isOpen: isInterruptModalOpen,
            set: (open) => {
              if (!open) cancelInterrupt();
            },
          }}
          onClose={cancelInterrupt}
        >
          <Dialog.Content>
            <div className="flex flex-col gap-4">
              <Text variant="body">
                The current chat response will be interrupted. Are you sure you
                want to continue?
              </Text>
              <Dialog.Footer>
                <Button
                  type="button"
                  variant="outline"
                  onClick={cancelInterrupt}
                >
                  Cancel
                </Button>
                <Button
                  type="button"
                  variant="primary"
                  onClick={confirmInterrupt}
                >
                  Continue
                </Button>
              </Dialog.Footer>
            </div>
          </Dialog.Content>
        </Dialog>
      </div>
    );
  }
  return (
    <div className="flex h-full flex-1 items-center justify-center overflow-y-auto bg-[#f8f8f9] px-3 py-5 md:px-6 md:py-10">
      <div className="w-full text-center">
        {isLoading ? (
          <div className="mx-auto max-w-2xl">
            <Skeleton className="mx-auto mb-3 h-8 w-64" />
            <Skeleton className="mx-auto mb-8 h-6 w-80" />
            <div className="mb-8">
              <Skeleton className="mx-auto h-14 w-full rounded-lg" />
            </div>
            <div className="flex flex-wrap items-center justify-center gap-3">
              {Array.from({ length: 4 }).map((_, i) => (
                <Skeleton key={i} className="h-9 w-48 rounded-md" />
              ))}
            </div>
          </div>
        ) : (
          <>
            <div className="mx-auto max-w-3xl">
              <Text
                variant="h3"
                className="mb-1 !text-[1.375rem] text-zinc-700"
              >
                Hey, <span className="text-violet-600">{greetingName}</span>
              </Text>
              <Text variant="h3" className="mb-8 !font-normal">
                Tell me about your work — I&apos;ll find what to automate.
              </Text>
              <div className="mb-6">
                <ChatInput
                  onSend={startChatWithPrompt}
                  placeholder={inputPlaceholder}
                />
              </div>
            </div>
            <div className="flex flex-wrap items-center justify-center gap-3 overflow-x-auto [-ms-overflow-style:none] [scrollbar-width:none] [&::-webkit-scrollbar]:hidden">
              {quickActions.map((action) => (
                <Button
                  key={action}
                  type="button"
                  variant="outline"
                  size="small"
                  onClick={() => handleQuickAction(action)}
                  className="h-auto shrink-0 border-zinc-300 px-3 py-2 text-[.9rem] text-zinc-600"
                >
                  {action}
                </Button>
              ))}
            </div>
          </>
        )}
      </div>
    </div>
  );
 }
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/styleguide/page.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/styleguide/page.tsx
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/tools/CreateAgent/CreateAgent.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/tools/CreateAgent/CreateAgent.tsx
@@ -0,0 +1,237 @@
 "use client";
 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,
  ContentCodeBlock,
  ContentGrid,
  ContentHint,
  ContentLink,
  ContentMessage,
 } from "../../components/ToolAccordion/AccordionContent";
 import { ToolAccordion } from "../../components/ToolAccordion/ToolAccordion";
 import { useAsymptoticProgress } from "../../hooks/useAsymptoticProgress";
 import {
  ClarificationQuestionsCard,
  ClarifyingQuestion,
 } from "./components/ClarificationQuestionsCard";
 import {
  AccordionIcon,
  formatMaybeJson,
  getAnimationText,
  getCreateAgentToolOutput,
  isAgentPreviewOutput,
  isAgentSavedOutput,
  isClarificationNeededOutput,
  isErrorOutput,
  isOperationInProgressOutput,
  isOperationPendingOutput,
  isOperationStartedOutput,
  ToolIcon,
  truncateText,
  type CreateAgentToolOutput,
 } from "./helpers";
 export interface CreateAgentToolPart {
  type: string;
  toolCallId: string;
  state: ToolUIPart["state"];
  input?: unknown;
  output?: unknown;
 }
 interface Props {
  part: CreateAgentToolPart;
 }
 function getAccordionMeta(output: CreateAgentToolOutput): {
  icon: React.ReactNode;
  title: React.ReactNode;
  titleClassName?: string;
  description?: string;
 } {
  const icon = <AccordionIcon />;
  if (isAgentSavedOutput(output)) {
    return { icon, title: output.agent_name };
  }
  if (isAgentPreviewOutput(output)) {
    return {
      icon,
      title: output.agent_name,
      description: `${output.node_count} block${output.node_count === 1 ? "" : "s"}`,
    };
  }
  if (isClarificationNeededOutput(output)) {
    const questions = output.questions ?? [];
    return {
      icon,
      title: "Needs clarification",
      description: `${questions.length} question${questions.length === 1 ? "" : "s"}`,
    };
  }
  if (
    isOperationStartedOutput(output) ||
    isOperationPendingOutput(output) ||
    isOperationInProgressOutput(output)
  ) {
    return {
      icon: <OrbitLoader size={32} />,
      title: "Creating agent, this may take a few minutes. Sit back and relax.",
    };
  }
  return {
    icon: (
      <WarningDiamondIcon size={32} weight="light" className="text-red-500" />
    ),
    title: "Error",
    titleClassName: "text-red-500",
  };
 }
 export function CreateAgentTool({ part }: Props) {
  const text = getAnimationText(part);
  const { onSend } = useCopilotChatActions();
  const isStreaming =
    part.state === "input-streaming" || part.state === "input-available";
  const output = getCreateAgentToolOutput(part);
  const isError =
    part.state === "output-error" || (!!output && isErrorOutput(output));
  const isOperating =
    !!output &&
    (isOperationStartedOutput(output) ||
      isOperationPendingOutput(output) ||
      isOperationInProgressOutput(output));
  const progress = useAsymptoticProgress(isOperating);
  const hasExpandableContent =
    part.state === "output-available" &&
    !!output &&
    (isOperationStartedOutput(output) ||
      isOperationPendingOutput(output) ||
      isOperationInProgressOutput(output) ||
      isAgentPreviewOutput(output) ||
      isAgentSavedOutput(output) ||
      isClarificationNeededOutput(output) ||
      isErrorOutput(output));
  function handleClarificationAnswers(answers: Record<string, string>) {
    const questions =
      output && isClarificationNeededOutput(output)
        ? (output.questions ?? [])
        : [];
    const contextMessage = questions
      .map((q) => {
        const answer = answers[q.keyword] || "";
        return `> ${q.question}\n\n${answer}`;
      })
      .join("\n\n");
    onSend(
      `**Here are my answers:**\n\n${contextMessage}\n\nPlease proceed with creating the agent.`,
    );
  }
  return (
    <div className="py-2">
      <div className="flex items-center gap-2 text-sm text-muted-foreground">
        <ToolIcon isStreaming={isStreaming} isError={isError} />
        <MorphingTextAnimation
          text={text}
          className={isError ? "text-red-500" : undefined}
        />
      </div>
      {hasExpandableContent && output && (
        <ToolAccordion
          {...getAccordionMeta(output)}
          defaultExpanded={isOperating || isClarificationNeededOutput(output)}
        >
          {isOperating && (
            <ContentGrid>
              <ProgressBar value={progress} className="max-w-[280px]" />
              <ContentHint>
                This could take a few minutes, grab a coffee ☕
              </ContentHint>
            </ContentGrid>
          )}
          {isAgentSavedOutput(output) && (
            <ContentGrid>
              <ContentMessage>{output.message}</ContentMessage>
              <div className="flex flex-wrap gap-2">
                <ContentLink href={output.library_agent_link}>
                  Open in library
                </ContentLink>
                <ContentLink href={output.agent_page_link}>
                  Open in builder
                </ContentLink>
              </div>
              <ContentCodeBlock>
                {truncateText(
                  formatMaybeJson({ agent_id: output.agent_id }),
                  800,
                )}
              </ContentCodeBlock>
            </ContentGrid>
          )}
          {isAgentPreviewOutput(output) && (
            <ContentGrid>
              <ContentMessage>{output.message}</ContentMessage>
              {output.description?.trim() && (
                <ContentCardDescription>
                  {output.description}
                </ContentCardDescription>
              )}
              <ContentCodeBlock>
                {truncateText(formatMaybeJson(output.agent_json), 1600)}
              </ContentCodeBlock>
            </ContentGrid>
          )}
          {isClarificationNeededOutput(output) && (
            <ClarificationQuestionsCard
              questions={(output.questions ?? []).map((q) => {
                const item: ClarifyingQuestion = {
                  question: q.question,
                  keyword: q.keyword,
                };
                const example =
                  typeof q.example === "string" && q.example.trim()
                    ? q.example.trim()
                    : null;
                if (example) item.example = example;
                return item;
              })}
              message={output.message}
              onSubmitAnswers={handleClarificationAnswers}
            />
          )}
          {isErrorOutput(output) && (
            <ContentGrid>
              <ContentMessage>{output.message}</ContentMessage>
              {output.error && (
                <ContentCodeBlock>
                  {formatMaybeJson(output.error)}
                </ContentCodeBlock>
              )}
              {output.details && (
                <ContentCodeBlock>
                  {formatMaybeJson(output.details)}
                </ContentCodeBlock>
              )}
            </ContentGrid>
          )}
        </ToolAccordion>
      )}
    </div>
  );
 }
--- a/autogpt_platform/frontend/src/components/contextual/Chat/components/ClarificationQuestionsWidget/ClarificationQuestionsWidget.tsx
+++ b/autogpt_platform/frontend/src/components/contextual/Chat/components/ClarificationQuestionsWidget/ClarificationQuestionsWidget.tsx
@@ -6,7 +6,7 @@ import { Input } from "@/components/atoms/Input/Input";
 import { Text } from "@/components/atoms/Text/Text";
 import { cn } from "@/lib/utils";
 import { CheckCircleIcon, QuestionIcon } from "@phosphor-icons/react";
-import { useState, useEffect, useRef } from "react";
+import { useEffect, useRef, useState } from "react";
 export interface ClarifyingQuestion {
  question: string;
@@ -24,12 +24,7 @@ interface Props {
  className?: string;
 }
-function getStorageKey(sessionId?: string): string | null {
+export function ClarificationQuestionsCard({
  if (!sessionId) return null;
  return `clarification_answers_${sessionId}`;
 }
 export function ClarificationQuestionsWidget({
  questions,
  message,
  sessionId,
@@ -241,3 +236,8 @@ export function ClarificationQuestionsWidget({
    </div>
  );
 }
 function getStorageKey(sessionId?: string): string | null {
  if (!sessionId) return null;
  return `clarification_answers_${sessionId}`;
 }
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/tools/CreateAgent/helpers.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/tools/CreateAgent/helpers.tsx
@@ -0,0 +1,186 @@
 import type { AgentPreviewResponse } from "@/app/api/__generated__/models/agentPreviewResponse";
 import type { AgentSavedResponse } from "@/app/api/__generated__/models/agentSavedResponse";
 import type { ClarificationNeededResponse } from "@/app/api/__generated__/models/clarificationNeededResponse";
 import type { ErrorResponse } from "@/app/api/__generated__/models/errorResponse";
 import type { OperationInProgressResponse } from "@/app/api/__generated__/models/operationInProgressResponse";
 import type { OperationPendingResponse } from "@/app/api/__generated__/models/operationPendingResponse";
 import type { OperationStartedResponse } from "@/app/api/__generated__/models/operationStartedResponse";
 import { ResponseType } from "@/app/api/__generated__/models/responseType";
 import {
  PlusCircleIcon,
  PlusIcon,
  WarningDiamondIcon,
 } from "@phosphor-icons/react";
 import type { ToolUIPart } from "ai";
 import { OrbitLoader } from "../../components/OrbitLoader/OrbitLoader";
 export type CreateAgentToolOutput =
  | OperationStartedResponse
  | OperationPendingResponse
  | OperationInProgressResponse
  | AgentPreviewResponse
  | AgentSavedResponse
  | ClarificationNeededResponse
  | ErrorResponse;
 function parseOutput(output: unknown): CreateAgentToolOutput | null {
  if (!output) return null;
  if (typeof output === "string") {
    const trimmed = output.trim();
    if (!trimmed) return null;
    try {
      return parseOutput(JSON.parse(trimmed) as unknown);
    } catch {
      return null;
    }
  }
  if (typeof output === "object") {
    const type = (output as { type?: unknown }).type;
    if (
      type === ResponseType.operation_started ||
      type === ResponseType.operation_pending ||
      type === ResponseType.operation_in_progress ||
      type === ResponseType.agent_preview ||
      type === ResponseType.agent_saved ||
      type === ResponseType.clarification_needed ||
      type === ResponseType.error
    ) {
      return output as CreateAgentToolOutput;
    }
    if ("operation_id" in output && "tool_name" in output)
      return output as OperationStartedResponse | OperationPendingResponse;
    if ("tool_call_id" in output) return output as OperationInProgressResponse;
    if ("agent_json" in output && "agent_name" in output)
      return output as AgentPreviewResponse;
    if ("agent_id" in output && "library_agent_id" in output)
      return output as AgentSavedResponse;
    if ("questions" in output) return output as ClarificationNeededResponse;
    if ("error" in output || "details" in output)
      return output as ErrorResponse;
  }
  return null;
 }
 export function getCreateAgentToolOutput(
  part: unknown,
 ): CreateAgentToolOutput | null {
  if (!part || typeof part !== "object") return null;
  return parseOutput((part as { output?: unknown }).output);
 }
 export function isOperationStartedOutput(
  output: CreateAgentToolOutput,
 ): output is OperationStartedResponse {
  return (
    output.type === ResponseType.operation_started ||
    ("operation_id" in output && "tool_name" in output)
  );
 }
 export function isOperationPendingOutput(
  output: CreateAgentToolOutput,
 ): output is OperationPendingResponse {
  return output.type === ResponseType.operation_pending;
 }
 export function isOperationInProgressOutput(
  output: CreateAgentToolOutput,
 ): output is OperationInProgressResponse {
  return (
    output.type === ResponseType.operation_in_progress ||
    "tool_call_id" in output
  );
 }
 export function isAgentPreviewOutput(
  output: CreateAgentToolOutput,
 ): output is AgentPreviewResponse {
  return output.type === ResponseType.agent_preview || "agent_json" in output;
 }
 export function isAgentSavedOutput(
  output: CreateAgentToolOutput,
 ): output is AgentSavedResponse {
  return (
    output.type === ResponseType.agent_saved || "agent_page_link" in output
  );
 }
 export function isClarificationNeededOutput(
  output: CreateAgentToolOutput,
 ): output is ClarificationNeededResponse {
  return (
    output.type === ResponseType.clarification_needed || "questions" in output
  );
 }
 export function isErrorOutput(
  output: CreateAgentToolOutput,
 ): output is ErrorResponse {
  return output.type === ResponseType.error || "error" in output;
 }
 export function getAnimationText(part: {
  state: ToolUIPart["state"];
  input?: unknown;
  output?: unknown;
 }): string {
  switch (part.state) {
    case "input-streaming":
    case "input-available":
      return "Creating a new agent";
    case "output-available": {
      const output = parseOutput(part.output);
      if (!output) return "Creating a new agent";
      if (isOperationStartedOutput(output)) return "Agent creation started";
      if (isOperationPendingOutput(output)) return "Agent creation in progress";
      if (isOperationInProgressOutput(output))
        return "Agent creation already in progress";
      if (isAgentSavedOutput(output)) return `Saved "${output.agent_name}"`;
      if (isAgentPreviewOutput(output)) return `Preview "${output.agent_name}"`;
      if (isClarificationNeededOutput(output)) return "Needs clarification";
      return "Error creating agent";
    }
    case "output-error":
      return "Error creating agent";
    default:
      return "Creating a new agent";
  }
 }
 export function ToolIcon({
  isStreaming,
  isError,
 }: {
  isStreaming?: boolean;
  isError?: boolean;
 }) {
  if (isError) {
    return (
      <WarningDiamondIcon size={14} weight="regular" className="text-red-500" />
    );
  }
  if (isStreaming) {
    return <OrbitLoader size={24} />;
  }
  return <PlusIcon size={14} weight="regular" className="text-neutral-400" />;
 }
 export function AccordionIcon() {
  return <PlusCircleIcon size={32} weight="light" />;
 }
 export function formatMaybeJson(value: unknown): string {
  if (typeof value === "string") return value;
  try {
    return JSON.stringify(value, null, 2);
  } catch {
    return String(value);
  }
 }
 export function truncateText(text: string, maxChars: number): string {
  const trimmed = text.trim();
  if (trimmed.length <= maxChars) return trimmed;
  return `${trimmed.slice(0, maxChars).trimEnd()}…`;
 }
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/tools/EditAgent/EditAgent.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/tools/EditAgent/EditAgent.tsx
@@ -0,0 +1,234 @@
 "use client";
 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,
  ContentCodeBlock,
  ContentGrid,
  ContentHint,
  ContentLink,
  ContentMessage,
 } from "../../components/ToolAccordion/AccordionContent";
 import { ToolAccordion } from "../../components/ToolAccordion/ToolAccordion";
 import { useAsymptoticProgress } from "../../hooks/useAsymptoticProgress";
 import {
  ClarificationQuestionsCard,
  ClarifyingQuestion,
 } from "../CreateAgent/components/ClarificationQuestionsCard";
 import {
  AccordionIcon,
  formatMaybeJson,
  getAnimationText,
  getEditAgentToolOutput,
  isAgentPreviewOutput,
  isAgentSavedOutput,
  isClarificationNeededOutput,
  isErrorOutput,
  isOperationInProgressOutput,
  isOperationPendingOutput,
  isOperationStartedOutput,
  ToolIcon,
  truncateText,
  type EditAgentToolOutput,
 } from "./helpers";
 export interface EditAgentToolPart {
  type: string;
  toolCallId: string;
  state: ToolUIPart["state"];
  input?: unknown;
  output?: unknown;
 }
 interface Props {
  part: EditAgentToolPart;
 }
 function getAccordionMeta(output: EditAgentToolOutput): {
  icon: React.ReactNode;
  title: string;
  titleClassName?: string;
  description?: string;
 } {
  const icon = <AccordionIcon />;
  if (isAgentSavedOutput(output)) {
    return { icon, title: output.agent_name };
  }
  if (isAgentPreviewOutput(output)) {
    return {
      icon,
      title: output.agent_name,
      description: `${output.node_count} block${output.node_count === 1 ? "" : "s"}`,
    };
  }
  if (isClarificationNeededOutput(output)) {
    const questions = output.questions ?? [];
    return {
      icon,
      title: "Needs clarification",
      description: `${questions.length} question${questions.length === 1 ? "" : "s"}`,
    };
  }
  if (
    isOperationStartedOutput(output) ||
    isOperationPendingOutput(output) ||
    isOperationInProgressOutput(output)
  ) {
    return { icon: <OrbitLoader size={32} />, title: "Editing agent" };
  }
  return {
    icon: (
      <WarningDiamondIcon size={32} weight="light" className="text-red-500" />
    ),
    title: "Error",
    titleClassName: "text-red-500",
  };
 }
 export function EditAgentTool({ part }: Props) {
  const text = getAnimationText(part);
  const { onSend } = useCopilotChatActions();
  const isStreaming =
    part.state === "input-streaming" || part.state === "input-available";
  const output = getEditAgentToolOutput(part);
  const isError =
    part.state === "output-error" || (!!output && isErrorOutput(output));
  const isOperating =
    !!output &&
    (isOperationStartedOutput(output) ||
      isOperationPendingOutput(output) ||
      isOperationInProgressOutput(output));
  const progress = useAsymptoticProgress(isOperating);
  const hasExpandableContent =
    part.state === "output-available" &&
    !!output &&
    (isOperationStartedOutput(output) ||
      isOperationPendingOutput(output) ||
      isOperationInProgressOutput(output) ||
      isAgentPreviewOutput(output) ||
      isAgentSavedOutput(output) ||
      isClarificationNeededOutput(output) ||
      isErrorOutput(output));
  function handleClarificationAnswers(answers: Record<string, string>) {
    const questions =
      output && isClarificationNeededOutput(output)
        ? (output.questions ?? [])
        : [];
    const contextMessage = questions
      .map((q) => {
        const answer = answers[q.keyword] || "";
        return `> ${q.question}\n\n${answer}`;
      })
      .join("\n\n");
    onSend(
      `**Here are my answers:**\n\n${contextMessage}\n\nPlease proceed with editing the agent.`,
    );
  }
  return (
    <div className="py-2">
      <div className="flex items-center gap-2 text-sm text-muted-foreground">
        <ToolIcon isStreaming={isStreaming} isError={isError} />
        <MorphingTextAnimation
          text={text}
          className={isError ? "text-red-500" : undefined}
        />
      </div>
      {hasExpandableContent && output && (
        <ToolAccordion
          {...getAccordionMeta(output)}
          defaultExpanded={isOperating || isClarificationNeededOutput(output)}
        >
          {isOperating && (
            <ContentGrid>
              <ProgressBar value={progress} className="max-w-[280px]" />
              <ContentHint>
                This could take a few minutes, grab a coffee ☕
              </ContentHint>
            </ContentGrid>
          )}
          {isAgentSavedOutput(output) && (
            <ContentGrid>
              <ContentMessage>{output.message}</ContentMessage>
              <div className="flex flex-wrap gap-2">
                <ContentLink href={output.library_agent_link}>
                  Open in library
                </ContentLink>
                <ContentLink href={output.agent_page_link}>
                  Open in builder
                </ContentLink>
              </div>
              <ContentCodeBlock>
                {truncateText(
                  formatMaybeJson({ agent_id: output.agent_id }),
                  800,
                )}
              </ContentCodeBlock>
            </ContentGrid>
          )}
          {isAgentPreviewOutput(output) && (
            <ContentGrid>
              <ContentMessage>{output.message}</ContentMessage>
              {output.description?.trim() && (
                <ContentCardDescription>
                  {output.description}
                </ContentCardDescription>
              )}
              <ContentCodeBlock>
                {truncateText(formatMaybeJson(output.agent_json), 1600)}
              </ContentCodeBlock>
            </ContentGrid>
          )}
          {isClarificationNeededOutput(output) && (
            <ClarificationQuestionsCard
              questions={(output.questions ?? []).map((q) => {
                const item: ClarifyingQuestion = {
                  question: q.question,
                  keyword: q.keyword,
                };
                const example =
                  typeof q.example === "string" && q.example.trim()
                    ? q.example.trim()
                    : null;
                if (example) item.example = example;
                return item;
              })}
              message={output.message}
              onSubmitAnswers={handleClarificationAnswers}
            />
          )}
          {isErrorOutput(output) && (
            <ContentGrid>
              <ContentMessage>{output.message}</ContentMessage>
              {output.error && (
                <ContentCodeBlock>
                  {formatMaybeJson(output.error)}
                </ContentCodeBlock>
              )}
              {output.details && (
                <ContentCodeBlock>
                  {formatMaybeJson(output.details)}
                </ContentCodeBlock>
              )}
            </ContentGrid>
          )}
        </ToolAccordion>
      )}
    </div>
  );
 }
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/tools/EditAgent/helpers.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/tools/EditAgent/helpers.tsx
@@ -0,0 +1,188 @@
 import type { AgentPreviewResponse } from "@/app/api/__generated__/models/agentPreviewResponse";
 import type { AgentSavedResponse } from "@/app/api/__generated__/models/agentSavedResponse";
 import type { ClarificationNeededResponse } from "@/app/api/__generated__/models/clarificationNeededResponse";
 import type { ErrorResponse } from "@/app/api/__generated__/models/errorResponse";
 import type { OperationInProgressResponse } from "@/app/api/__generated__/models/operationInProgressResponse";
 import type { OperationPendingResponse } from "@/app/api/__generated__/models/operationPendingResponse";
 import type { OperationStartedResponse } from "@/app/api/__generated__/models/operationStartedResponse";
 import { ResponseType } from "@/app/api/__generated__/models/responseType";
 import {
  NotePencilIcon,
  PencilLineIcon,
  WarningDiamondIcon,
 } from "@phosphor-icons/react";
 import type { ToolUIPart } from "ai";
 import { OrbitLoader } from "../../components/OrbitLoader/OrbitLoader";
 export type EditAgentToolOutput =
  | OperationStartedResponse
  | OperationPendingResponse
  | OperationInProgressResponse
  | AgentPreviewResponse
  | AgentSavedResponse
  | ClarificationNeededResponse
  | ErrorResponse;
 function parseOutput(output: unknown): EditAgentToolOutput | null {
  if (!output) return null;
  if (typeof output === "string") {
    const trimmed = output.trim();
    if (!trimmed) return null;
    try {
      return parseOutput(JSON.parse(trimmed) as unknown);
    } catch {
      return null;
    }
  }
  if (typeof output === "object") {
    const type = (output as { type?: unknown }).type;
    if (
      type === ResponseType.operation_started ||
      type === ResponseType.operation_pending ||
      type === ResponseType.operation_in_progress ||
      type === ResponseType.agent_preview ||
      type === ResponseType.agent_saved ||
      type === ResponseType.clarification_needed ||
      type === ResponseType.error
    ) {
      return output as EditAgentToolOutput;
    }
    if ("operation_id" in output && "tool_name" in output)
      return output as OperationStartedResponse | OperationPendingResponse;
    if ("tool_call_id" in output) return output as OperationInProgressResponse;
    if ("agent_json" in output && "agent_name" in output)
      return output as AgentPreviewResponse;
    if ("agent_id" in output && "library_agent_id" in output)
      return output as AgentSavedResponse;
    if ("questions" in output) return output as ClarificationNeededResponse;
    if ("error" in output || "details" in output)
      return output as ErrorResponse;
  }
  return null;
 }
 export function getEditAgentToolOutput(
  part: unknown,
 ): EditAgentToolOutput | null {
  if (!part || typeof part !== "object") return null;
  return parseOutput((part as { output?: unknown }).output);
 }
 export function isOperationStartedOutput(
  output: EditAgentToolOutput,
 ): output is OperationStartedResponse {
  return (
    output.type === ResponseType.operation_started ||
    ("operation_id" in output && "tool_name" in output)
  );
 }
 export function isOperationPendingOutput(
  output: EditAgentToolOutput,
 ): output is OperationPendingResponse {
  return output.type === ResponseType.operation_pending;
 }
 export function isOperationInProgressOutput(
  output: EditAgentToolOutput,
 ): output is OperationInProgressResponse {
  return (
    output.type === ResponseType.operation_in_progress ||
    "tool_call_id" in output
  );
 }
 export function isAgentPreviewOutput(
  output: EditAgentToolOutput,
 ): output is AgentPreviewResponse {
  return output.type === ResponseType.agent_preview || "agent_json" in output;
 }
 export function isAgentSavedOutput(
  output: EditAgentToolOutput,
 ): output is AgentSavedResponse {
  return (
    output.type === ResponseType.agent_saved || "agent_page_link" in output
  );
 }
 export function isClarificationNeededOutput(
  output: EditAgentToolOutput,
 ): output is ClarificationNeededResponse {
  return (
    output.type === ResponseType.clarification_needed || "questions" in output
  );
 }
 export function isErrorOutput(
  output: EditAgentToolOutput,
 ): output is ErrorResponse {
  return output.type === ResponseType.error || "error" in output;
 }
 export function getAnimationText(part: {
  state: ToolUIPart["state"];
  input?: unknown;
  output?: unknown;
 }): string {
  switch (part.state) {
    case "input-streaming":
    case "input-available":
      return "Editing the agent";
    case "output-available": {
      const output = parseOutput(part.output);
      if (!output) return "Editing the agent";
      if (isOperationStartedOutput(output)) return "Agent update started";
      if (isOperationPendingOutput(output)) return "Agent update in progress";
      if (isOperationInProgressOutput(output))
        return "Agent update already in progress";
      if (isAgentSavedOutput(output)) return `Saved "${output.agent_name}"`;
      if (isAgentPreviewOutput(output)) return `Preview "${output.agent_name}"`;
      if (isClarificationNeededOutput(output)) return "Needs clarification";
      return "Error editing agent";
    }
    case "output-error":
      return "Error editing agent";
    default:
      return "Editing the agent";
  }
 }
 export function ToolIcon({
  isStreaming,
  isError,
 }: {
  isStreaming?: boolean;
  isError?: boolean;
 }) {
  if (isError) {
    return (
      <WarningDiamondIcon size={14} weight="regular" className="text-red-500" />
    );
  }
  if (isStreaming) {
    return <OrbitLoader size={24} />;
  }
  return (
    <PencilLineIcon size={14} weight="regular" className="text-neutral-400" />
  );
 }
 export function AccordionIcon() {
  return <NotePencilIcon size={32} weight="light" />;
 }
 export function formatMaybeJson(value: unknown): string {
  if (typeof value === "string") return value;
  try {
    return JSON.stringify(value, null, 2);
  } catch {
    return String(value);
  }
 }
 export function truncateText(text: string, maxChars: number): string {
  const trimmed = text.trim();
  if (trimmed.length <= maxChars) return trimmed;
  return `${trimmed.slice(0, maxChars).trimEnd()}…`;
 }
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/tools/FindAgents/FindAgents.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/tools/FindAgents/FindAgents.tsx
@@ -0,0 +1,127 @@
 "use client";
 import { ToolUIPart } from "ai";
 import { MorphingTextAnimation } from "../../components/MorphingTextAnimation/MorphingTextAnimation";
 import {
  ContentBadge,
  ContentCard,
  ContentCardDescription,
  ContentCardHeader,
  ContentCardTitle,
  ContentGrid,
  ContentLink,
 } from "../../components/ToolAccordion/AccordionContent";
 import { ToolAccordion } from "../../components/ToolAccordion/ToolAccordion";
 import {
  AccordionIcon,
  getAgentHref,
  getAnimationText,
  getFindAgentsOutput,
  getSourceLabelFromToolType,
  isAgentsFoundOutput,
  isErrorOutput,
  ToolIcon,
 } from "./helpers";
 export interface FindAgentsToolPart {
  type: string;
  toolCallId: string;
  state: ToolUIPart["state"];
  input?: unknown;
  output?: unknown;
 }
 interface Props {
  part: FindAgentsToolPart;
 }
 export function FindAgentsTool({ part }: Props) {
  const text = getAnimationText(part);
  const output = getFindAgentsOutput(part);
  const isStreaming =
    part.state === "input-streaming" || part.state === "input-available";
  const isError =
    part.state === "output-error" || (!!output && isErrorOutput(output));
  const query =
    typeof part.input === "object" && part.input !== null
      ? String((part.input as { query?: unknown }).query ?? "").trim()
      : "";
  const agentsFoundOutput =
    part.state === "output-available" && output && isAgentsFoundOutput(output)
      ? output
      : null;
  const hasAgents =
    !!agentsFoundOutput &&
    agentsFoundOutput.agents.length > 0 &&
    (typeof agentsFoundOutput.count !== "number" ||
      agentsFoundOutput.count > 0);
  const totalCount = agentsFoundOutput ? agentsFoundOutput.count : 0;
  const { source } = getSourceLabelFromToolType(part.type);
  const scopeText =
    source === "library"
      ? "in your library"
      : source === "marketplace"
        ? "in marketplace"
        : "";
  const accordionDescription = `Found ${totalCount}${scopeText ? ` ${scopeText}` : ""}${
    query ? ` for "${query}"` : ""
  }`;
  return (
    <div className="py-2">
      <div className="flex items-center gap-2 text-sm text-muted-foreground">
        <ToolIcon
          toolType={part.type}
          isStreaming={isStreaming}
          isError={isError}
        />
        <MorphingTextAnimation
          text={text}
          className={isError ? "text-red-500" : undefined}
        />
      </div>
      {hasAgents && agentsFoundOutput && (
        <ToolAccordion
          icon={<AccordionIcon toolType={part.type} />}
          title="Agent results"
          description={accordionDescription}
        >
          <ContentGrid className="sm:grid-cols-2">
            {agentsFoundOutput.agents.map((agent) => {
              const href = getAgentHref(agent);
              const agentSource =
                agent.source === "library"
                  ? "Library"
                  : agent.source === "marketplace"
                    ? "Marketplace"
                    : null;
              return (
                <ContentCard key={agent.id}>
                  <ContentCardHeader
                    action={
                      href ? <ContentLink href={href}>Open</ContentLink> : null
                    }
                  >
                    <div className="flex items-center gap-2">
                      <ContentCardTitle>{agent.name}</ContentCardTitle>
                      {agentSource && (
                        <ContentBadge>{agentSource}</ContentBadge>
                      )}
                    </div>
                    <ContentCardDescription className="mt-1 line-clamp-2">
                      {agent.description}
                    </ContentCardDescription>
                  </ContentCardHeader>
                </ContentCard>
              );
            })}
          </ContentGrid>
        </ToolAccordion>
      )}
    </div>
  );
 }
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/tools/FindAgents/helpers.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/tools/FindAgents/helpers.tsx
@@ -0,0 +1,187 @@
 import type { AgentInfo } from "@/app/api/__generated__/models/agentInfo";
 import type { AgentsFoundResponse } from "@/app/api/__generated__/models/agentsFoundResponse";
 import type { ErrorResponse } from "@/app/api/__generated__/models/errorResponse";
 import type { NoResultsResponse } from "@/app/api/__generated__/models/noResultsResponse";
 import { ResponseType } from "@/app/api/__generated__/models/responseType";
 import {
  FolderOpenIcon,
  MagnifyingGlassIcon,
  SquaresFourIcon,
  StorefrontIcon,
 } from "@phosphor-icons/react";
 import { ToolUIPart } from "ai";
 export interface FindAgentInput {
  query: string;
 }
 export type FindAgentsOutput =
  | AgentsFoundResponse
  | NoResultsResponse
  | ErrorResponse;
 export type FindAgentsToolType =
  | "tool-find_agent"
  | "tool-find_library_agent"
  | (string & {});
 function parseOutput(output: unknown): FindAgentsOutput | null {
  if (!output) return null;
  if (typeof output === "string") {
    const trimmed = output.trim();
    if (!trimmed) return null;
    try {
      return parseOutput(JSON.parse(trimmed) as unknown);
    } catch {
      return null;
    }
  }
  if (typeof output === "object") {
    const type = (output as { type?: unknown }).type;
    if (
      type === ResponseType.agents_found ||
      type === ResponseType.no_results ||
      type === ResponseType.error
    ) {
      return output as FindAgentsOutput;
    }
    if ("agents" in output && "count" in output)
      return output as AgentsFoundResponse;
    if ("suggestions" in output && !("error" in output))
      return output as NoResultsResponse;
    if ("error" in output || "details" in output)
      return output as ErrorResponse;
  }
  return null;
 }
 export function getFindAgentsOutput(part: unknown): FindAgentsOutput | null {
  if (!part || typeof part !== "object") return null;
  return parseOutput((part as { output?: unknown }).output);
 }
 export function isAgentsFoundOutput(
  output: FindAgentsOutput,
 ): output is AgentsFoundResponse {
  return output.type === ResponseType.agents_found || "agents" in output;
 }
 export function isNoResultsOutput(
  output: FindAgentsOutput,
 ): output is NoResultsResponse {
  return (
    output.type === ResponseType.no_results ||
    ("suggestions" in output && !("error" in output))
  );
 }
 export function isErrorOutput(
  output: FindAgentsOutput,
 ): output is ErrorResponse {
  return output.type === ResponseType.error || "error" in output;
 }
 export function getSourceLabelFromToolType(toolType?: FindAgentsToolType): {
  source: "marketplace" | "library" | "unknown";
  label: string;
 } {
  if (toolType === "tool-find_library_agent") {
    return { source: "library", label: "Library" };
  }
  if (toolType === "tool-find_agent") {
    return { source: "marketplace", label: "Marketplace" };
  }
  return { source: "unknown", label: "Agents" };
 }
 export function getAnimationText(part: {
  type?: FindAgentsToolType;
  state: ToolUIPart["state"];
  input?: unknown;
  output?: unknown;
 }): string {
  const { source } = getSourceLabelFromToolType(part.type);
  const query = (part.input as FindAgentInput | undefined)?.query?.trim();
  // Action phrase matching legacy ToolCallMessage
  const actionPhrase =
    source === "library"
      ? "Looking for library agents"
      : "Looking for agents in the marketplace";
  const queryText = query ? ` matching "${query}"` : "";
  switch (part.state) {
    case "input-streaming":
    case "input-available":
      return `${actionPhrase}${queryText}`;
    case "output-available": {
      const output = parseOutput(part.output);
      if (!output) {
        return `${actionPhrase}${queryText}`;
      }
      if (isNoResultsOutput(output)) {
        return `No agents found${queryText}`;
      }
      if (isAgentsFoundOutput(output)) {
        const count = output.count ?? output.agents?.length ?? 0;
        return `Found ${count} agent${count === 1 ? "" : "s"}${queryText}`;
      }
      if (isErrorOutput(output)) {
        return `Error finding agents${queryText}`;
      }
      return `${actionPhrase}${queryText}`;
    }
    case "output-error":
      return `Error finding agents${queryText}`;
    default:
      return actionPhrase;
  }
 }
 export function getAgentHref(agent: AgentInfo): string | null {
  if (agent.source === "library") {
    return `/library/agents/${encodeURIComponent(agent.id)}`;
  }
  const [creator, slug, ...rest] = agent.id.split("/");
  if (!creator || !slug || rest.length > 0) return null;
  return `/marketplace/agent/${encodeURIComponent(creator)}/${encodeURIComponent(slug)}`;
 }
 export function ToolIcon({
  toolType,
  isStreaming,
  isError,
 }: {
  toolType?: FindAgentsToolType;
  isStreaming?: boolean;
  isError?: boolean;
 }) {
  const { source } = getSourceLabelFromToolType(toolType);
  const IconComponent =
    source === "library" ? MagnifyingGlassIcon : SquaresFourIcon;
  return (
    <IconComponent
      size={14}
      weight="regular"
      className={
        isError
          ? "text-red-500"
          : isStreaming
            ? "text-neutral-500"
            : "text-neutral-400"
      }
    />
  );
 }
 export function AccordionIcon({ toolType }: { toolType?: FindAgentsToolType }) {
  const { source } = getSourceLabelFromToolType(toolType);
  const IconComponent = source === "library" ? FolderOpenIcon : StorefrontIcon;
  return <IconComponent size={32} weight="light" />;
 }
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/tools/FindBlocks/FindBlocks.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/tools/FindBlocks/FindBlocks.tsx
@@ -0,0 +1,92 @@
 "use client";
 import { MorphingTextAnimation } from "../../components/MorphingTextAnimation/MorphingTextAnimation";
 import { ToolAccordion } from "../../components/ToolAccordion/ToolAccordion";
 import {
  ContentCard,
  ContentCardDescription,
  ContentCardTitle,
 } from "../../components/ToolAccordion/AccordionContent";
 import type { BlockListResponse } from "@/app/api/__generated__/models/blockListResponse";
 import type { BlockInfoSummary } from "@/app/api/__generated__/models/blockInfoSummary";
 import { ToolUIPart } from "ai";
 import { HorizontalScroll } from "@/app/(platform)/build/components/NewControlPanel/NewBlockMenu/HorizontalScroll";
 import {
  AccordionIcon,
  getAnimationText,
  parseOutput,
  ToolIcon,
 } from "./helpers";
 export interface FindBlockInput {
  query: string;
 }
 export type FindBlockOutput = BlockListResponse;
 export interface FindBlockToolPart {
  type: string;
  toolName?: string;
  toolCallId: string;
  state: ToolUIPart["state"];
  input?: FindBlockInput | unknown;
  output?: string | FindBlockOutput | unknown;
  title?: string;
 }
 interface Props {
  part: FindBlockToolPart;
 }
 function BlockCard({ block }: { block: BlockInfoSummary }) {
  return (
    <ContentCard className="w-48 shrink-0">
      <ContentCardTitle>{block.name}</ContentCardTitle>
      <ContentCardDescription className="mt-1 line-clamp-2">
        {block.description}
      </ContentCardDescription>
    </ContentCard>
  );
 }
 export function FindBlocksTool({ part }: Props) {
  const text = getAnimationText(part);
  const isStreaming =
    part.state === "input-streaming" || part.state === "input-available";
  const isError = part.state === "output-error";
  const parsed =
    part.state === "output-available" ? parseOutput(part.output) : null;
  const hasBlocks = !!parsed && parsed.blocks.length > 0;
  const query = (part.input as FindBlockInput | undefined)?.query?.trim();
  const accordionDescription = parsed
    ? `Found ${parsed.count} block${parsed.count === 1 ? "" : "s"}${query ? ` for "${query}"` : ""}`
    : undefined;
  return (
    <div className="py-2">
      <div className="flex items-center gap-2 text-sm text-muted-foreground">
        <ToolIcon isStreaming={isStreaming} isError={isError} />
        <MorphingTextAnimation
          text={text}
          className={isError ? "text-red-500" : undefined}
        />
      </div>
      {hasBlocks && parsed && (
        <ToolAccordion
          icon={<AccordionIcon />}
          title="Block results"
          description={accordionDescription}
        >
          <HorizontalScroll dependencyList={[parsed.blocks.length]}>
            {parsed.blocks.map((block) => (
              <BlockCard key={block.id} block={block} />
            ))}
          </HorizontalScroll>
        </ToolAccordion>
      )}
    </div>
  );
 }
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/tools/FindBlocks/helpers.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/tools/FindBlocks/helpers.tsx
@@ -0,0 +1,75 @@
 import type { BlockListResponse } from "@/app/api/__generated__/models/blockListResponse";
 import { ResponseType } from "@/app/api/__generated__/models/responseType";
 import { CubeIcon, PackageIcon } from "@phosphor-icons/react";
 import { FindBlockInput, FindBlockToolPart } from "./FindBlocks";
 export function parseOutput(output: unknown): BlockListResponse | null {
  if (!output) return null;
  if (typeof output === "string") {
    const trimmed = output.trim();
    if (!trimmed) return null;
    try {
      return parseOutput(JSON.parse(trimmed) as unknown);
    } catch {
      return null;
    }
  }
  if (typeof output === "object") {
    const type = (output as { type?: unknown }).type;
    if (type === ResponseType.block_list || "blocks" in output) {
      return output as BlockListResponse;
    }
  }
  return null;
 }
 export function getAnimationText(part: FindBlockToolPart): string {
  const query = (part.input as FindBlockInput | undefined)?.query?.trim();
  const queryText = query ? ` matching "${query}"` : "";
  switch (part.state) {
    case "input-streaming":
    case "input-available":
      return `Searching for blocks${queryText}`;
    case "output-available": {
      const parsed = parseOutput(part.output);
      if (parsed) {
        return `Found ${parsed.count} block${parsed.count === 1 ? "" : "s"}${queryText}`;
      }
      return `Searching for blocks${queryText}`;
    }
    case "output-error":
      return `Error finding blocks${queryText}`;
    default:
      return "Searching for blocks";
  }
 }
 export function ToolIcon({
  isStreaming,
  isError,
 }: {
  isStreaming?: boolean;
  isError?: boolean;
 }) {
  return (
    <PackageIcon
      size={14}
      weight="regular"
      className={
        isError
          ? "text-red-500"
          : isStreaming
            ? "text-neutral-500"
            : "text-neutral-400"
      }
    />
  );
 }
 export function AccordionIcon() {
  return <CubeIcon size={32} weight="light" />;
 }
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/tools/RunAgent/RunAgent.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/tools/RunAgent/RunAgent.tsx
@@ -0,0 +1,93 @@
 "use client";
 import type { ToolUIPart } from "ai";
 import { MorphingTextAnimation } from "../../components/MorphingTextAnimation/MorphingTextAnimation";
 import { ToolAccordion } from "../../components/ToolAccordion/ToolAccordion";
 import { ContentMessage } from "../../components/ToolAccordion/AccordionContent";
 import {
  getAccordionMeta,
  getAnimationText,
  getRunAgentToolOutput,
  isRunAgentAgentDetailsOutput,
  isRunAgentErrorOutput,
  isRunAgentExecutionStartedOutput,
  isRunAgentNeedLoginOutput,
  isRunAgentSetupRequirementsOutput,
  ToolIcon,
 } from "./helpers";
 import { ExecutionStartedCard } from "./components/ExecutionStartedCard/ExecutionStartedCard";
 import { AgentDetailsCard } from "./components/AgentDetailsCard/AgentDetailsCard";
 import { SetupRequirementsCard } from "./components/SetupRequirementsCard/SetupRequirementsCard";
 import { ErrorCard } from "./components/ErrorCard/ErrorCard";
 export interface RunAgentToolPart {
  type: string;
  toolCallId: string;
  state: ToolUIPart["state"];
  input?: unknown;
  output?: unknown;
 }
 interface Props {
  part: RunAgentToolPart;
 }
 export function RunAgentTool({ part }: Props) {
  const text = getAnimationText(part);
  const isStreaming =
    part.state === "input-streaming" || part.state === "input-available";
  const output = getRunAgentToolOutput(part);
  const isError =
    part.state === "output-error" ||
    (!!output && isRunAgentErrorOutput(output));
  const hasExpandableContent =
    part.state === "output-available" &&
    !!output &&
    (isRunAgentExecutionStartedOutput(output) ||
      isRunAgentAgentDetailsOutput(output) ||
      isRunAgentSetupRequirementsOutput(output) ||
      isRunAgentNeedLoginOutput(output) ||
      isRunAgentErrorOutput(output));
  return (
    <div className="py-2">
      <div className="flex items-center gap-2 text-sm text-muted-foreground">
        <ToolIcon isStreaming={isStreaming} isError={isError} />
        <MorphingTextAnimation
          text={text}
          className={isError ? "text-red-500" : undefined}
        />
      </div>
      {hasExpandableContent && output && (
        <ToolAccordion
          {...getAccordionMeta(output)}
          defaultExpanded={
            isRunAgentExecutionStartedOutput(output) ||
            isRunAgentSetupRequirementsOutput(output) ||
            isRunAgentAgentDetailsOutput(output)
          }
        >
          {isRunAgentExecutionStartedOutput(output) && (
            <ExecutionStartedCard output={output} />
          )}
          {isRunAgentAgentDetailsOutput(output) && (
            <AgentDetailsCard output={output} />
          )}
          {isRunAgentSetupRequirementsOutput(output) && (
            <SetupRequirementsCard output={output} />
          )}
          {isRunAgentNeedLoginOutput(output) && (
            <ContentMessage>{output.message}</ContentMessage>
          )}
          {isRunAgentErrorOutput(output) && <ErrorCard output={output} />}
        </ToolAccordion>
      )}
    </div>
  );
 }
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/tools/RunAgent/components/AgentDetailsCard/AgentDetailsCard.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/tools/RunAgent/components/AgentDetailsCard/AgentDetailsCard.tsx
@@ -0,0 +1,116 @@
 "use client";
 import type { AgentDetailsResponse } from "@/app/api/__generated__/models/agentDetailsResponse";
 import { Button } from "@/components/atoms/Button/Button";
 import { Text } from "@/components/atoms/Text/Text";
 import { FormRenderer } from "@/components/renderers/InputRenderer/FormRenderer";
 import { AnimatePresence, motion } from "framer-motion";
 import { useState } from "react";
 import { useCopilotChatActions } from "../../../../components/CopilotChatActionsProvider/useCopilotChatActions";
 import { ContentMessage } from "../../../../components/ToolAccordion/AccordionContent";
 import { buildInputSchema } from "./helpers";
 interface Props {
  output: AgentDetailsResponse;
 }
 export function AgentDetailsCard({ output }: Props) {
  const { onSend } = useCopilotChatActions();
  const [showInputForm, setShowInputForm] = useState(false);
  const [inputValues, setInputValues] = useState<Record<string, unknown>>({});
  function handleRunWithExamples() {
    onSend(
      `Run the agent "${output.agent.name}" with placeholder/example values so I can test it.`,
    );
  }
  function handleRunWithInputs() {
    const nonEmpty = Object.fromEntries(
      Object.entries(inputValues).filter(
        ([, v]) => v !== undefined && v !== null && v !== "",
      ),
    );
    onSend(
      `Run the agent "${output.agent.name}" with these inputs: ${JSON.stringify(nonEmpty, null, 2)}`,
    );
    setShowInputForm(false);
    setInputValues({});
  }
  return (
    <div className="grid gap-2">
      <ContentMessage>
        Run this agent with example values or your own inputs.
      </ContentMessage>
      <div className="flex gap-2 pt-4">
        <Button size="small" className="w-fit" onClick={handleRunWithExamples}>
          Run with example values
        </Button>
        <Button
          variant="outline"
          size="small"
          className="w-fit"
          onClick={() => setShowInputForm((prev) => !prev)}
        >
          Run with my inputs
        </Button>
      </div>
      <AnimatePresence initial={false}>
        {showInputForm && buildInputSchema(output.agent.inputs) && (
          <motion.div
            initial={{ height: 0, opacity: 0, filter: "blur(6px)" }}
            animate={{ height: "auto", opacity: 1, filter: "blur(0px)" }}
            exit={{ height: 0, opacity: 0, filter: "blur(6px)" }}
            transition={{
              height: { type: "spring", bounce: 0.15, duration: 0.5 },
              opacity: { duration: 0.25 },
              filter: { duration: 0.2 },
            }}
            className="overflow-hidden"
            style={{ willChange: "height, opacity, filter" }}
          >
            <div className="mt-4 rounded-2xl border bg-background p-3 pt-4">
              <Text variant="body-medium">Enter your inputs</Text>
              <FormRenderer
                jsonSchema={buildInputSchema(output.agent.inputs)!}
                handleChange={(v) => setInputValues(v.formData ?? {})}
                uiSchema={{
                  "ui:submitButtonOptions": { norender: true },
                }}
                initialValues={inputValues}
                formContext={{
                  showHandles: false,
                  size: "small",
                }}
              />
              <div className="-mt-8 flex gap-2">
                <Button
                  variant="primary"
                  size="small"
                  className="w-fit"
                  onClick={handleRunWithInputs}
                >
                  Run
                </Button>
                <Button
                  variant="secondary"
                  size="small"
                  className="w-fit"
                  onClick={() => {
                    setShowInputForm(false);
                    setInputValues({});
                  }}
                >
                  Cancel
                </Button>
              </div>
            </div>
          </motion.div>
        )}
      </AnimatePresence>
    </div>
  );
 }
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/tools/RunAgent/components/AgentDetailsCard/helpers.ts
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/tools/RunAgent/components/AgentDetailsCard/helpers.ts
@@ -0,0 +1,8 @@
 import type { RJSFSchema } from "@rjsf/utils";
 export function buildInputSchema(inputs: unknown): RJSFSchema | null {
  if (!inputs || typeof inputs !== "object") return null;
  const properties = inputs as RJSFSchema["properties"];
  if (!properties || Object.keys(properties).length === 0) return null;
  return inputs as RJSFSchema;
 }
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/tools/RunAgent/components/ErrorCard/ErrorCard.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/tools/RunAgent/components/ErrorCard/ErrorCard.tsx
@@ -0,0 +1,27 @@
 "use client";
 import type { ErrorResponse } from "@/app/api/__generated__/models/errorResponse";
 import {
  ContentCodeBlock,
  ContentGrid,
  ContentMessage,
 } from "../../../../components/ToolAccordion/AccordionContent";
 import { formatMaybeJson } from "../../helpers";
 interface Props {
  output: ErrorResponse;
 }
 export function ErrorCard({ output }: Props) {
  return (
    <ContentGrid>
      <ContentMessage>{output.message}</ContentMessage>
      {output.error && (
        <ContentCodeBlock>{formatMaybeJson(output.error)}</ContentCodeBlock>
      )}
      {output.details && (
        <ContentCodeBlock>{formatMaybeJson(output.details)}</ContentCodeBlock>
      )}
    </ContentGrid>
  );
 }
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/tools/RunAgent/components/ExecutionStartedCard/ExecutionStartedCard.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/tools/RunAgent/components/ExecutionStartedCard/ExecutionStartedCard.tsx
@@ -0,0 +1,39 @@
 "use client";
 import type { ExecutionStartedResponse } from "@/app/api/__generated__/models/executionStartedResponse";
 import { Button } from "@/components/atoms/Button/Button";
 import { useRouter } from "next/navigation";
 import {
  ContentCard,
  ContentCardDescription,
  ContentCardSubtitle,
  ContentCardTitle,
  ContentGrid,
 } from "../../../../components/ToolAccordion/AccordionContent";
 interface Props {
  output: ExecutionStartedResponse;
 }
 export function ExecutionStartedCard({ output }: Props) {
  const router = useRouter();
  return (
    <ContentGrid>
      <ContentCard>
        <ContentCardTitle>Execution started</ContentCardTitle>
        <ContentCardSubtitle>{output.execution_id}</ContentCardSubtitle>
        <ContentCardDescription>{output.message}</ContentCardDescription>
        {output.library_agent_link && (
          <Button
            size="small"
            className="mt-3"
            onClick={() => router.push(output.library_agent_link!)}
          >
            View Execution
          </Button>
        )}
      </ContentCard>
    </ContentGrid>
  );
 }
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/tools/RunAgent/components/SetupRequirementsCard/SetupRequirementsCard.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/tools/RunAgent/components/SetupRequirementsCard/SetupRequirementsCard.tsx
@@ -0,0 +1,105 @@
 "use client";
 import { useState } from "react";
 import { CredentialsGroupedView } from "@/components/contextual/CredentialsInput/components/CredentialsGroupedView/CredentialsGroupedView";
 import { Button } from "@/components/atoms/Button/Button";
 import type { CredentialsMetaInput } from "@/lib/autogpt-server-api/types";
 import type { SetupRequirementsResponse } from "@/app/api/__generated__/models/setupRequirementsResponse";
 import { useCopilotChatActions } from "../../../../components/CopilotChatActionsProvider/useCopilotChatActions";
 import {
  ContentBadge,
  ContentCardDescription,
  ContentCardTitle,
  ContentMessage,
 } from "../../../../components/ToolAccordion/AccordionContent";
 import { coerceCredentialFields, coerceExpectedInputs } from "./helpers";
 interface Props {
  output: SetupRequirementsResponse;
 }
 export function SetupRequirementsCard({ output }: Props) {
  const { onSend } = useCopilotChatActions();
  const [inputCredentials, setInputCredentials] = useState<
    Record<string, CredentialsMetaInput | undefined>
  >({});
  const [hasSent, setHasSent] = useState(false);
  const { credentialFields, requiredCredentials } = coerceCredentialFields(
    output.setup_info.user_readiness?.missing_credentials,
  );
  const expectedInputs = coerceExpectedInputs(
    (output.setup_info.requirements as Record<string, unknown>)?.inputs,
  );
  function handleCredentialChange(key: string, value?: CredentialsMetaInput) {
    setInputCredentials((prev) => ({ ...prev, [key]: value }));
  }
  const isAllComplete =
    credentialFields.length > 0 &&
    [...requiredCredentials].every((key) => !!inputCredentials[key]);
  function handleProceed() {
    setHasSent(true);
    onSend(
      "I've configured the required credentials. Please check if everything is ready and proceed with running the agent.",
    );
  }
  return (
    <div className="grid gap-2">
      <ContentMessage>{output.message}</ContentMessage>
      {credentialFields.length > 0 && (
        <div className="rounded-2xl border bg-background p-3">
          <CredentialsGroupedView
            credentialFields={credentialFields}
            requiredCredentials={requiredCredentials}
            inputCredentials={inputCredentials}
            inputValues={{}}
            onCredentialChange={handleCredentialChange}
          />
          {isAllComplete && !hasSent && (
            <Button
              variant="primary"
              size="small"
              className="mt-3 w-full"
              onClick={handleProceed}
            >
              Proceed
            </Button>
          )}
        </div>
      )}
      {expectedInputs.length > 0 && (
        <div className="rounded-2xl border bg-background p-3">
          <ContentCardTitle className="text-xs">
            Expected inputs
          </ContentCardTitle>
          <div className="mt-2 grid gap-2">
            {expectedInputs.map((input) => (
              <div key={input.name} className="rounded-xl border p-2">
                <div className="flex items-center justify-between gap-2">
                  <ContentCardTitle className="text-xs">
                    {input.title}
                  </ContentCardTitle>
                  <ContentBadge>
                    {input.required ? "Required" : "Optional"}
                  </ContentBadge>
                </div>
                <ContentCardDescription className="mt-1">
                  {input.name} &bull; {input.type}
                  {input.description ? ` \u2022 ${input.description}` : ""}
                </ContentCardDescription>
              </div>
            ))}
          </div>
        </div>
      )}
    </div>
  );
 }
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/tools/RunAgent/components/SetupRequirementsCard/helpers.ts
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/tools/RunAgent/components/SetupRequirementsCard/helpers.ts
@@ -0,0 +1,116 @@
 import type { CredentialField } from "@/components/contextual/CredentialsInput/components/CredentialsGroupedView/helpers";
 const VALID_CREDENTIAL_TYPES = new Set([
  "api_key",
  "oauth2",
  "user_password",
  "host_scoped",
 ]);
 /**
 * Transforms raw missing_credentials from SetupRequirementsResponse
 * into CredentialField[] tuples compatible with CredentialsGroupedView.
 *
 * Each CredentialField is [key, schema] where schema matches
 * BlockIOCredentialsSubSchema shape.
 */
 export function coerceCredentialFields(rawMissingCredentials: unknown): {
  credentialFields: CredentialField[];
  requiredCredentials: Set<string>;
 } {
  const missing =
    rawMissingCredentials && typeof rawMissingCredentials === "object"
      ? (rawMissingCredentials as Record<string, unknown>)
      : {};
  const credentialFields: CredentialField[] = [];
  const requiredCredentials = new Set<string>();
  Object.entries(missing).forEach(([key, value]) => {
    if (!value || typeof value !== "object") return;
    const cred = value as Record<string, unknown>;
    const provider =
      typeof cred.provider === "string" ? cred.provider.trim() : "";
    if (!provider) return;
    const types =
      Array.isArray(cred.types) && cred.types.length > 0
        ? cred.types
        : typeof cred.type === "string"
          ? [cred.type]
          : [];
    const credentialTypes = types
      .map((t) => (typeof t === "string" ? t.trim() : ""))
      .filter((t) => VALID_CREDENTIAL_TYPES.has(t));
    if (credentialTypes.length === 0) return;
    const scopes = Array.isArray(cred.scopes)
      ? cred.scopes.filter((s): s is string => typeof s === "string")
      : undefined;
    const schema = {
      type: "object" as const,
      properties: {},
      credentials_provider: [provider],
      credentials_types: credentialTypes,
      credentials_scopes: scopes,
    };
    credentialFields.push([key, schema]);
    requiredCredentials.add(key);
  });
  return { credentialFields, requiredCredentials };
 }
 export function coerceExpectedInputs(rawInputs: unknown): Array<{
  name: string;
  title: string;
  type: string;
  description?: string;
  required: boolean;
 }> {
  if (!Array.isArray(rawInputs)) return [];
  const results: Array<{
    name: string;
    title: string;
    type: string;
    description?: string;
    required: boolean;
  }> = [];
  rawInputs.forEach((value, index) => {
    if (!value || typeof value !== "object") return;
    const input = value as Record<string, unknown>;
    const name =
      typeof input.name === "string" && input.name.trim()
        ? input.name.trim()
        : `input-${index}`;
    const title =
      typeof input.title === "string" && input.title.trim()
        ? input.title.trim()
        : name;
    const type = typeof input.type === "string" ? input.type : "unknown";
    const description =
      typeof input.description === "string" && input.description.trim()
        ? input.description.trim()
        : undefined;
    const required = Boolean(input.required);
    const item: {
      name: string;
      title: string;
      type: string;
      description?: string;
      required: boolean;
    } = { name, title, type, required };
    if (description) item.description = description;
    results.push(item);
  });
  return results;
 }
--- a/autogpt_platform/frontend/src/app/(platform)/copilot/tools/RunAgent/helpers.tsx
+++ b/autogpt_platform/frontend/src/app/(platform)/copilot/tools/RunAgent/helpers.tsx
@@ -0,0 +1,248 @@
 import type { AgentDetailsResponse } from "@/app/api/__generated__/models/agentDetailsResponse";
 import type { ErrorResponse } from "@/app/api/__generated__/models/errorResponse";
 import type { ExecutionStartedResponse } from "@/app/api/__generated__/models/executionStartedResponse";
 import type { NeedLoginResponse } from "@/app/api/__generated__/models/needLoginResponse";
 import { ResponseType } from "@/app/api/__generated__/models/responseType";
 import type { SetupRequirementsResponse } from "@/app/api/__generated__/models/setupRequirementsResponse";
 import {
  PlayIcon,
  RocketLaunchIcon,
  WarningDiamondIcon,
 } from "@phosphor-icons/react";
 import type { ToolUIPart } from "ai";
 import { SpinnerLoader } from "../../components/SpinnerLoader/SpinnerLoader";
 export interface RunAgentInput {
  username_agent_slug?: string;
  library_agent_id?: string;
  inputs?: Record<string, unknown>;
  use_defaults?: boolean;
  schedule_name?: string;
  cron?: string;
  timezone?: string;
 }
 export type RunAgentToolOutput =
  | SetupRequirementsResponse
  | ExecutionStartedResponse
  | AgentDetailsResponse
  | NeedLoginResponse
  | ErrorResponse;
 const RUN_AGENT_OUTPUT_TYPES = new Set<string>([
  ResponseType.setup_requirements,
  ResponseType.execution_started,
  ResponseType.agent_details,
  ResponseType.need_login,
  ResponseType.error,
 ]);
 export function isRunAgentSetupRequirementsOutput(
  output: RunAgentToolOutput,
 ): output is SetupRequirementsResponse {
  return (
    output.type === ResponseType.setup_requirements ||
    ("setup_info" in output && typeof output.setup_info === "object")
  );
 }
 export function isRunAgentExecutionStartedOutput(
  output: RunAgentToolOutput,
 ): output is ExecutionStartedResponse {
  return (
    output.type === ResponseType.execution_started || "execution_id" in output
  );
 }
 export function isRunAgentAgentDetailsOutput(
  output: RunAgentToolOutput,
 ): output is AgentDetailsResponse {
  return output.type === ResponseType.agent_details || "agent" in output;
 }
 export function isRunAgentNeedLoginOutput(
  output: RunAgentToolOutput,
 ): output is NeedLoginResponse {
  return output.type === ResponseType.need_login;
 }
 export function isRunAgentErrorOutput(
  output: RunAgentToolOutput,
 ): output is ErrorResponse {
  return output.type === ResponseType.error || "error" in output;
 }
 function parseOutput(output: unknown): RunAgentToolOutput | null {
  if (!output) return null;
  if (typeof output === "string") {
    const trimmed = output.trim();
    if (!trimmed) return null;
    try {
      return parseOutput(JSON.parse(trimmed) as unknown);
    } catch {
      return null;
    }
  }
  if (typeof output === "object") {
    const type = (output as { type?: unknown }).type;
    if (typeof type === "string" && RUN_AGENT_OUTPUT_TYPES.has(type)) {
      return output as RunAgentToolOutput;
    }
    if ("execution_id" in output) return output as ExecutionStartedResponse;
    if ("setup_info" in output) return output as SetupRequirementsResponse;
    if ("agent" in output) return output as AgentDetailsResponse;
    if ("error" in output || "details" in output)
      return output as ErrorResponse;
    if (type === ResponseType.need_login) return output as NeedLoginResponse;
  }
  return null;
 }
 export function getRunAgentToolOutput(
  part: unknown,
 ): RunAgentToolOutput | null {
  if (!part || typeof part !== "object") return null;
  return parseOutput((part as { output?: unknown }).output);
 }
 function getAgentIdentifierText(
  input: RunAgentInput | undefined,
 ): string | null {
  if (!input) return null;
  const slug = input.username_agent_slug?.trim();
  if (slug) return slug;
  const libraryId = input.library_agent_id?.trim();
  if (libraryId) return `Library agent ${libraryId}`;
  return null;
 }
 export function getAnimationText(part: {
  state: ToolUIPart["state"];
  input?: unknown;
  output?: unknown;
 }): string {
  const input = part.input as RunAgentInput | undefined;
  const agentIdentifier = getAgentIdentifierText(input);
  const isSchedule = Boolean(
    input?.schedule_name?.trim() || input?.cron?.trim(),
  );
  const actionPhrase = isSchedule
    ? "Scheduling the agent to run"
    : "Running the agent";
  const identifierText = agentIdentifier ? ` "${agentIdentifier}"` : "";
  switch (part.state) {
    case "input-streaming":
    case "input-available":
      return `${actionPhrase}${identifierText}`;
    case "output-available": {
      const output = parseOutput(part.output);
      if (!output) return `${actionPhrase}${identifierText}`;
      if (isRunAgentExecutionStartedOutput(output)) {
        return `Started "${output.graph_name}"`;
      }
      if (isRunAgentAgentDetailsOutput(output)) {
        return `Agent inputs needed for "${output.agent.name}"`;
      }
      if (isRunAgentSetupRequirementsOutput(output)) {
        return `Setup needed for "${output.setup_info.agent_name}"`;
      }
      if (isRunAgentNeedLoginOutput(output))
        return "Sign in required to run agent";
      return "Error running agent";
    }
    case "output-error":
      return "Error running agent";
    default:
      return actionPhrase;
  }
 }
 export function ToolIcon({
  isStreaming,
  isError,
 }: {
  isStreaming?: boolean;
  isError?: boolean;
 }) {
  if (isError) {
    return (
      <WarningDiamondIcon size={14} weight="regular" className="text-red-500" />
    );
  }
  if (isStreaming) {
    return <SpinnerLoader size={40} className="text-neutral-700" />;
  }
  return <PlayIcon size={14} weight="regular" className="text-neutral-400" />;
 }
 export function AccordionIcon() {
  return <RocketLaunchIcon size={28} weight="light" />;
 }
 export function formatMaybeJson(value: unknown): string {
  if (typeof value === "string") return value;
  try {
    return JSON.stringify(value, null, 2);
  } catch {
    return String(value);
  }
 }
 export function getAccordionMeta(output: RunAgentToolOutput): {
  icon: React.ReactNode;
  title: string;
  titleClassName?: string;
  description?: string;
 } {
  const icon = <AccordionIcon />;
  if (isRunAgentExecutionStartedOutput(output)) {
    const statusText =
      typeof output.status === "string" && output.status.trim()
        ? output.status.trim()
        : "started";
    return {
      icon: <SpinnerLoader size={28} className="text-neutral-700" />,
      title: output.graph_name,
      description: `Status: ${statusText}`,
    };
  }
  if (isRunAgentAgentDetailsOutput(output)) {
    return {
      icon,
      title: output.agent.name,
      description: "Inputs required",
    };
  }
  if (isRunAgentSetupRequirementsOutput(output)) {
    const missingCredsCount = Object.keys(
      (output.setup_info.user_readiness?.missing_credentials ?? {}) as Record<
        string,
        unknown
      >,
    ).length;
    return {
      icon,
      title: output.setup_info.agent_name,
      description:
        missingCredsCount > 0
          ? `Missing ${missingCredsCount} credential${missingCredsCount === 1 ? "" : "s"}`
          : output.message,
    };
  }
  if (isRunAgentNeedLoginOutput(output)) {
    return { icon, title: "Sign in required" };
  }
  return {
    icon: (
      <WarningDiamondIcon size={28} weight="light" className="text-red-500" />
    ),
    title: "Error",
    titleClassName: "text-red-500",
  };
 }
--- a/Show More
+++ b/Show More