refactor(blocks/smart-agent): move all local imports to top-level

- Move Future, AgentExecutorBlock, SmartDecisionMakerBlock, NodeExecutionEntry to top-level imports
- Move MockToolCall inner class to module level
- Remove duplicate SmartDecisionMakerBlock import in if/else branches
- Remove stale "avoid circular imports" comments (no circular deps exist)

autogpt_platform/backend/backend/blocks/smart_agent.py

@@ -0,0 +1,847 @@
+import logging
+import os
+import re
+from concurrent.futures import Future
+from enum import Enum
+from typing import TYPE_CHECKING, Any, Literal, NamedTuple
+
+from claude_agent_sdk import query
+from claude_agent_sdk.types import (
+    AssistantMessage,
+    ClaudeAgentOptions,
+    TextBlock,
+    ToolUseBlock,
+)
+from pydantic import BaseModel, SecretStr
+
+from backend.blocks._base import (
+    Block,
+    BlockCategory,
+    BlockOutput,
+    BlockSchemaInput,
+    BlockSchemaOutput,
+    BlockType,
+)
+from backend.blocks.agent import AgentExecutorBlock
+from backend.blocks.smart_decision_maker import SmartDecisionMakerBlock
+from backend.data.dynamic_fields import is_tool_pin
+from backend.data.execution import ExecutionContext, NodeExecutionEntry
+from backend.data.model import (
+    APIKeyCredentials,
+    CredentialsField,
+    CredentialsMetaInput,
+    SchemaField,
+)
+from backend.integrations.providers import ProviderName
+from backend.util import json
+from backend.util.clients import get_database_manager_async_client
+
+if TYPE_CHECKING:
+    from backend.data.graph import Link, Node
+    from backend.executor.manager import ExecutionProcessor
+
+logger = logging.getLogger(__name__)
+
+
+class ToolInfo(BaseModel):
+    """Processed tool call information."""
+
+    tool_call: Any  # The original tool call object from LLM response
+    tool_name: str  # The function name
+    tool_def: dict[str, Any]  # The tool definition from tool_functions
+    input_data: dict[str, Any]  # Processed input data ready for tool execution
+    field_mapping: dict[str, str]  # Field name mapping for the tool
+
+
+class ExecutionParams(BaseModel):
+    """Tool execution parameters."""
+
+    user_id: str
+    graph_id: str
+    node_id: str
+    graph_version: int
+    graph_exec_id: str
+    node_exec_id: str
+    execution_context: "ExecutionContext"
+
+
+class MockToolCall:
+    """Shim that gives ToolInfo a .id and .function attribute from raw call data."""
+
+    def __init__(self, tool_id: str, name: str, args: dict[str, Any]):
+        self.id = tool_id
+        self.function = type(
+            "Function", (), {"name": name, "arguments": json.dumps(args)}
+        )()
+
+
+def _create_tool_response(call_id: str, content: str) -> dict[str, Any]:
+    """Create a tool response in the correct format."""
+    # Anthropics format: tool IDs typically start with "toolu_"
+    if call_id.startswith("toolu_"):
+        return {
+            "role": "user",
+            "type": "message",
+            "content": [
+                {"tool_use_id": call_id, "type": "tool_result", "content": content}
+            ],
+        }
+    # OpenAI format: tool IDs typically start with "call_".
+    # Or default fallback (if the tool_id doesn't match any known prefix)
+    return {"role": "tool", "tool_call_id": call_id, "content": content}
+
+
+class ModelMetadata(NamedTuple):
+    provider: str
+    context_window: int
+    max_output_tokens: int | None
+
+
+class AgentModel(str, Enum):
+    """Available models for the Smart Agent."""
+
+    # Claude 4.x models (latest)
+    CLAUDE_4_1_OPUS = "claude-opus-4-1-20250805"
+    CLAUDE_4_OPUS = "claude-opus-4-20250514"
+    CLAUDE_4_SONNET = "claude-sonnet-4-20250514"
+    CLAUDE_4_5_OPUS = "claude-opus-4-5-20251101"
+    CLAUDE_4_5_SONNET = "claude-sonnet-4-5-20250929"
+    CLAUDE_4_5_HAIKU = "claude-haiku-4-5-20251001"
+
+    # Claude 3.x models (stable)
+    CLAUDE_3_7_SONNET = "claude-3-7-sonnet-20250219"
+    CLAUDE_3_HAIKU = "claude-3-haiku-20240307"
+
+    @property
+    def metadata(self) -> ModelMetadata:
+        return AGENT_MODEL_METADATA[self]
+
+    @property
+    def provider(self) -> str:
+        return self.metadata.provider
+
+
+# Agent model metadata mapping
+AGENT_MODEL_METADATA = {
+    # Claude 4.x models
+    AgentModel.CLAUDE_4_1_OPUS: ModelMetadata(ProviderName.ANTHROPIC, 500000, 4096),
+    AgentModel.CLAUDE_4_OPUS: ModelMetadata(ProviderName.ANTHROPIC, 500000, 4096),
+    AgentModel.CLAUDE_4_SONNET: ModelMetadata(ProviderName.ANTHROPIC, 500000, 4096),
+    AgentModel.CLAUDE_4_5_OPUS: ModelMetadata(ProviderName.ANTHROPIC, 500000, 8192),
+    AgentModel.CLAUDE_4_5_SONNET: ModelMetadata(ProviderName.ANTHROPIC, 500000, 8192),
+    AgentModel.CLAUDE_4_5_HAIKU: ModelMetadata(ProviderName.ANTHROPIC, 200000, 4096),
+    # Claude 3.x models
+    AgentModel.CLAUDE_3_7_SONNET: ModelMetadata(ProviderName.ANTHROPIC, 200000, 4096),
+    AgentModel.CLAUDE_3_HAIKU: ModelMetadata(ProviderName.ANTHROPIC, 200000, 4096),
+}
+
+# Anthropic-only credentials for Claude models
+ClaudeCredentials = CredentialsMetaInput[
+    Literal[ProviderName.ANTHROPIC], Literal["api_key"]
+]
+
+
+def ClaudeCredentialsField() -> ClaudeCredentials:
+    return CredentialsField(
+        description="Anthropic API key for Claude Agent SDK access.",
+        discriminator="model",
+        discriminator_mapping={
+            model.value: model.metadata.provider for model in AgentModel
+        },
+    )
+
+
+# Test credentials for Claude models
+TEST_CLAUDE_CREDENTIALS = APIKeyCredentials(
+    id="test-claude-creds",
+    provider=ProviderName.ANTHROPIC,
+    api_key=SecretStr("mock-anthropic-api-key"),
+    title="Mock Anthropic API key",
+    expires_at=None,
+)
+TEST_CLAUDE_CREDENTIALS_INPUT = {
+    "provider": TEST_CLAUDE_CREDENTIALS.provider,
+    "id": TEST_CLAUDE_CREDENTIALS.id,
+    "type": TEST_CLAUDE_CREDENTIALS.type,
+    "title": TEST_CLAUDE_CREDENTIALS.title,
+}
+
+
+class SmartAgentBlock(Block):
+    """
+    A smart agent block that uses Claude Agent SDK for native agent capabilities
+    while executing AutoGPT tool nodes.
+
+    This block combines Claude's native agent functionality with AutoGPT's tool ecosystem:
+    - Uses Claude Agent SDK for core agent intelligence
+    - Discovers connected AutoGPT tool nodes like SmartDecisionMaker
+    - When Claude calls tools, executes the actual AutoGPT tool nodes
+    - Provides Claude with the tool execution results
+    """
+
+    class Input(BlockSchemaInput):
+        task: str = SchemaField(
+            description="The task for the agent to complete. Be specific about your requirements.",
+            placeholder="Analyze the data file and create a summary report with key insights...",
+        )
+        model: AgentModel = SchemaField(
+            title="Model",
+            default=AgentModel.CLAUDE_4_5_SONNET,
+            description="The model to use for the agent.",
+            advanced=False,
+        )
+        credentials: ClaudeCredentials = ClaudeCredentialsField()
+        max_iterations: int = SchemaField(
+            default=15,
+            description="Maximum number of agent iterations. Use -1 for unlimited (use carefully!).",
+            advanced=False,
+        )
+        system_prompt: str = SchemaField(
+            title="System Prompt",
+            default="You are a helpful AI assistant with access to tools. Think step by step about which tools to use to complete the task efficiently. When you have completed the objective, provide a clear summary of the results.",
+            description="System prompt to guide the agent's behavior.",
+            advanced=True,
+        )
+        working_directory: str = SchemaField(
+            default="/tmp/smart_agent",
+            description="Working directory for the agent.",
+            advanced=True,
+        )
+
+    class Output(BlockSchemaOutput):
+        result: str = SchemaField(
+            description="The final result or answer from the agent."
+        )
+        iterations_used: int = SchemaField(
+            description="Number of iterations used to complete the task."
+        )
+        tools_used: list[str] = SchemaField(
+            description="List of AutoGPT tools used during execution.",
+            default_factory=list,
+        )
+        success: bool = SchemaField(
+            description="Whether the task was completed successfully."
+        )
+        error: str = SchemaField(
+            default="", description="Error message if the task failed."
+        )
+        # Tool output pins for connecting to other blocks (like SmartDecisionMakerBlock)
+        tools: Any = SchemaField(
+            description="Tool calls output for connecting to other AutoGPT blocks."
+        )
+        conversations: list[Any] = SchemaField(
+            description="Conversation history with Claude Agent SDK.",
+            default_factory=list,
+        )
+
+    def __init__(self):
+        super().__init__(
+            id="dbe509c8-5078-49c5-ba1a-41a9ef9b459a",
+            description=(
+                "An AI agent powered by Claude Agent SDK that executes connected AutoGPT tool nodes. "
+                "Combines Claude's native agent capabilities with AutoGPT's tool ecosystem."
+            ),
+            categories={BlockCategory.AI},
+            block_type=BlockType.AI,
+            input_schema=SmartAgentBlock.Input,
+            output_schema=SmartAgentBlock.Output,
+            test_input={
+                "task": "What tools are available?",
+                "credentials": TEST_CLAUDE_CREDENTIALS_INPUT,
+                "model": AgentModel.CLAUDE_4_5_SONNET,
+            },
+            test_output=[],
+            test_credentials=TEST_CLAUDE_CREDENTIALS,
+        )
+
+    @staticmethod
+    def cleanup(s: str):
+        """Clean up block names for use as tool function names."""
+        return re.sub(r"[^a-zA-Z0-9_-]", "_", s).lower()
+
+    async def _create_tool_node_signatures(
+        self,
+        node_id: str,
+    ) -> list[dict[str, Any]]:
+        """
+        Creates function signatures for connected tools.
+        Args:
+            node_id: The node_id for which to create function signatures.
+        Returns:
+            List of function signatures for tools
+        """
+        db_client = get_database_manager_async_client()
+        tools = [
+            (link, node)
+            for link, node in await db_client.get_connected_output_nodes(node_id)
+            if is_tool_pin(link.source_name) and link.source_id == node_id
+        ]
+        if not tools:
+            raise ValueError("There is no next node to execute.")
+        return_tool_functions: list[dict[str, Any]] = []
+        grouped_tool_links: dict[str, tuple["Node", list["Link"]]] = {}
+        for link, node in tools:
+            if link.sink_id not in grouped_tool_links:
+                grouped_tool_links[link.sink_id] = (node, [link])
+            else:
+                grouped_tool_links[link.sink_id][1].append(link)
+        for sink_node, links in grouped_tool_links.values():
+            if not sink_node:
+                raise ValueError(f"Sink node not found: {links[0].sink_id}")
+            if sink_node.block_id == AgentExecutorBlock().id:
+                tool_func = (
+                    await SmartDecisionMakerBlock._create_agent_function_signature(
+                        sink_node, links
+                    )
+                )
+            else:
+                tool_func = (
+                    await SmartDecisionMakerBlock._create_block_function_signature(
+                        sink_node, links
+                    )
+                )
+            return_tool_functions.append(tool_func)
+        return return_tool_functions
+
+    async def _execute_single_tool_with_manager(
+        self,
+        tool_info: ToolInfo,
+        execution_params: ExecutionParams,
+        execution_processor: "ExecutionProcessor",
+    ) -> dict:
+        """Execute a single tool using the execution manager for proper integration."""
+        tool_call = tool_info.tool_call
+        tool_def = tool_info.tool_def
+        raw_input_data = tool_info.input_data
+
+        # Get sink node and field mapping
+        sink_node_id = tool_def["function"]["_sink_node_id"]
+
+        # Use proper database operations for tool execution
+        db_client = get_database_manager_async_client()
+
+        # Get target node
+        target_node = await db_client.get_node(sink_node_id)
+        if not target_node:
+            raise ValueError(f"Target node {sink_node_id} not found")
+
+        # Create proper node execution using upsert_execution_input
+        node_exec_result = None
+        final_input_data = None
+
+        # Add all inputs to the execution
+        if not raw_input_data:
+            raise ValueError(f"Tool call has no input data: {tool_call}")
+
+        for input_name, input_value in raw_input_data.items():
+            node_exec_result, final_input_data = await db_client.upsert_execution_input(
+                node_id=sink_node_id,
+                graph_exec_id=execution_params.graph_exec_id,
+                input_name=input_name,
+                input_data=input_value,
+            )
+
+        assert node_exec_result is not None, "node_exec_result should not be None"
+
+        # Create NodeExecutionEntry for execution manager
+        node_exec_entry = NodeExecutionEntry(
+            user_id=execution_params.user_id,
+            graph_exec_id=execution_params.graph_exec_id,
+            graph_id=execution_params.graph_id,
+            graph_version=execution_params.graph_version,
+            node_exec_id=node_exec_result.node_exec_id,
+            node_id=sink_node_id,
+            block_id=target_node.block_id,
+            inputs=final_input_data or {},
+            execution_context=execution_params.execution_context,
+        )
+
+        # Use the execution manager to execute the tool node
+        try:
+            # Get NodeExecutionProgress from the execution manager's running nodes
+            node_exec_progress = execution_processor.running_node_execution[
+                sink_node_id
+            ]
+
+            # Use the execution manager's own graph stats
+            graph_stats_pair = (
+                execution_processor.execution_stats,
+                execution_processor.execution_stats_lock,
+            )
+
+            # Create a completed future for the task tracking system
+            node_exec_future = Future()
+            node_exec_progress.add_task(
+                node_exec_id=node_exec_result.node_exec_id,
+                task=node_exec_future,
+            )
+
+            # Execute the node directly since we're in the SmartAgent context
+            node_exec_future.set_result(
+                await execution_processor.on_node_execution(
+                    node_exec=node_exec_entry,
+                    node_exec_progress=node_exec_progress,
+                    nodes_input_masks=None,
+                    graph_stats_pair=graph_stats_pair,
+                )
+            )
+
+            # Get outputs from database after execution completes using database manager client
+            node_outputs = await db_client.get_execution_outputs_by_node_exec_id(
+                node_exec_result.node_exec_id
+            )
+
+            # Create tool response
+            tool_response_content = (
+                json.dumps(node_outputs)
+                if node_outputs
+                else "Tool executed successfully"
+            )
+            return _create_tool_response(tool_call.id, tool_response_content)
+        except Exception as e:
+            logger.error(f"Tool execution with manager failed: {e}")
+            # Return error response
+            return _create_tool_response(
+                tool_call.id, f"Tool execution failed: {str(e)}"
+            )
+
+    def _setup_environment(
+        self, credentials: APIKeyCredentials, working_dir: str
+    ) -> dict[str, str]:
+        """Setup environment for Claude Agent SDK."""
+        os.makedirs(working_dir, exist_ok=True)
+        env = os.environ.copy()
+        env["ANTHROPIC_API_KEY"] = credentials.api_key.get_secret_value()
+        return env
+
+    def _build_tool_descriptions_for_claude(
+        self, tool_functions: list[dict[str, Any]]
+    ) -> str:
+        """Build description of available AutoGPT tools for Claude."""
+        if not tool_functions:
+            return "No tools are currently connected to this agent."
+
+        tool_descriptions = ["Available AutoGPT tools:"]
+        for tool_def in tool_functions:
+            func_def = tool_def.get("function", {})
+            name = func_def.get("name", "unknown")
+            description = func_def.get("description", "No description")
+            tool_descriptions.append(f"- {name}: {description}")
+
+        tool_descriptions.append(
+            "\nWhen you need to use a tool, call it with function calling syntax."
+        )
+        return "\n".join(tool_descriptions)
+
+    def _extract_tool_calls_from_claude_message(
+        self, message: AssistantMessage
+    ) -> list[dict[str, Any]]:
+        """Extract tool calls from Claude Agent SDK message."""
+        tool_calls = []
+        for content_block in message.content:
+            if isinstance(content_block, ToolUseBlock):
+                tool_calls.append(
+                    {
+                        "id": content_block.id,
+                        "function": {
+                            "name": content_block.name,
+                            "arguments": content_block.input,
+                        },
+                    }
+                )
+        return tool_calls
+
+    def _extract_text_content_from_claude_message(
+        self, message: AssistantMessage
+    ) -> str:
+        """Extract text content from Claude Agent SDK message."""
+        text_parts = []
+        for content_block in message.content:
+            if isinstance(content_block, TextBlock):
+                text_parts.append(content_block.text)
+        return "".join(text_parts)
+
+    def _format_conversation_for_claude(self, conversation: list[dict]) -> str:
+        """Format conversation history for Claude Agent SDK."""
+        formatted = []
+        for msg in conversation:
+            role = msg.get("role", "user")
+            content = msg.get("content", "")
+
+            if role == "tool":
+                # Format tool response
+                tool_id = msg.get("tool_call_id", "unknown")
+                formatted.append(f"Tool result ({tool_id}): {content}")
+            else:
+                # Simple format for user/assistant messages
+                formatted.append(f"{role.title()}: {content}")
+
+        return "\n\n".join(formatted)
+
+    def _normalize_tool_args(self, tool_args: Any) -> dict:
+        """Normalize tool arguments to dict format."""
+        if isinstance(tool_args, str):
+            return json.loads(tool_args)
+        elif isinstance(tool_args, dict):
+            return tool_args
+        else:
+            return dict(tool_args) if tool_args else {}
+
+    def _create_tool_info_from_claude_call(
+        self, tool_call: dict[str, Any], tool_functions: list[dict[str, Any]]
+    ) -> ToolInfo:
+        """Convert Claude tool call to AutoGPT ToolInfo format."""
+        tool_name = tool_call["function"]["name"]
+        tool_args = self._normalize_tool_args(tool_call["function"]["arguments"])
+        tool_id = tool_call["id"]
+
+        # Find the AutoGPT tool definition
+        tool_def = next(
+            (
+                tf
+                for tf in tool_functions
+                if tf.get("function", {}).get("name") == tool_name
+            ),
+            None,
+        )
+
+        if not tool_def:
+            raise ValueError(f"AutoGPT tool '{tool_name}' not found")
+
+        # Build input data from arguments
+        field_mapping = tool_def["function"].get("_field_mapping", {})
+        expected_args = tool_def["function"]["parameters"].get("properties", {})
+
+        input_data = {
+            field_mapping.get(clean_arg_name, clean_arg_name): tool_args.get(
+                clean_arg_name
+            )
+            for clean_arg_name in expected_args
+        }
+
+        return ToolInfo(
+            tool_call=MockToolCall(tool_id, tool_name, tool_args),
+            tool_name=tool_name,
+            tool_def=tool_def,
+            input_data=input_data,
+            field_mapping=field_mapping,
+        )
+
+    async def _attempt_claude_call_with_validation(
+        self,
+        prompt: str,
+        options: ClaudeAgentOptions,
+    ) -> AssistantMessage:
+        """Claude SDK call - let generator cleanup happen naturally to avoid cancel scope issues."""
+        try:
+            # Simple approach: don't try to manually manage the generator lifecycle
+            async for message in query(prompt=prompt, options=options):
+                if isinstance(message, AssistantMessage):
+                    return message
+
+            raise ValueError("No AssistantMessage received from Claude SDK")
+
+        except Exception as e:
+            logger.error(f"Claude SDK call failed: {e}")
+            raise
+        # Note: No finally block - let the generator be cleaned up naturally by garbage collection
+
+    async def _execute_tools_agent_mode(
+        self,
+        input_data: Input,
+        credentials,
+        tool_functions: list[dict[str, Any]],
+        graph_exec_id: str,
+        node_id: str,
+        node_exec_id: str,
+        user_id: str,
+        graph_id: str,
+        graph_version: int,
+        execution_context: ExecutionContext,
+        execution_processor: "ExecutionProcessor",
+    ):
+        """Execute tools in agent mode with a loop until finished, following SmartDecisionMakerBlock pattern."""
+        max_iterations = input_data.max_iterations
+        iteration = 0
+
+        # Execution parameters for tool execution
+        execution_params = ExecutionParams(
+            user_id=user_id,
+            graph_id=graph_id,
+            node_id=node_id,
+            graph_version=graph_version,
+            graph_exec_id=graph_exec_id,
+            node_exec_id=node_exec_id,
+            execution_context=execution_context,
+        )
+
+        # Build enhanced task prompt with tool descriptions
+        tool_descriptions = self._build_tool_descriptions_for_claude(tool_functions)
+        enhanced_task = f"""{input_data.task}
+
+{tool_descriptions}
+
+Complete the task step by step using the available tools as needed."""
+
+        # Start conversation with enhanced task
+        current_conversation = [{"role": "user", "content": enhanced_task}]
+
+        while max_iterations < 0 or iteration < max_iterations:
+            iteration += 1
+            logger.debug(f"Claude agent mode iteration {iteration}")
+
+            # Prepare conversation for this iteration
+            iteration_conversation = list(current_conversation)
+
+            # On the last iteration, add encouragement to finish
+            if max_iterations > 0 and iteration == max_iterations:
+                last_iteration_message = {
+                    "role": "system",
+                    "content": f"This is your last iteration ({iteration}/{max_iterations}). "
+                    "Try to complete the task with the information you have. "
+                    "Prefer finishing with a clear response rather than making additional tool calls.",
+                }
+                iteration_conversation.append(last_iteration_message)
+
+            # Format conversation for Claude SDK
+            conversation_text = self._format_conversation_for_claude(
+                iteration_conversation
+            )
+
+            # Setup Claude options for this iteration
+            claude_options = ClaudeAgentOptions(
+                system_prompt=input_data.system_prompt,
+                model=input_data.model.value,
+                max_turns=1,  # Single turn per iteration
+                cwd=input_data.working_directory,
+                env=self._setup_environment(credentials, input_data.working_directory),
+                permission_mode="bypassPermissions",
+            )
+
+            # Get Claude response
+            logger.debug(f"Claude agent iteration {iteration}: Making Claude SDK call")
+            try:
+                claude_response = await self._attempt_claude_call_with_validation(
+                    conversation_text, claude_options
+                )
+                logger.debug(f"Claude agent iteration {iteration}: Received response")
+            except Exception as e:
+                logger.error(
+                    f"Claude agent iteration {iteration}: Call failed with {type(e).__name__}: {str(e)}"
+                )
+                yield (
+                    "error",
+                    f"Claude call failed in agent mode iteration {iteration}: {str(e)}",
+                )
+                return
+
+            # Process tool calls
+            tool_calls = self._extract_tool_calls_from_claude_message(claude_response)
+            text_content = self._extract_text_content_from_claude_message(
+                claude_response
+            )
+
+            # Add Claude's response to conversation
+            assistant_message = {
+                "role": "assistant",
+                "content": text_content,
+                "tool_calls": tool_calls if tool_calls else [],
+            }
+            current_conversation.append(assistant_message)
+
+            # If no tool calls, we're done
+            if not tool_calls:
+                yield "finished", text_content
+                yield "conversations", current_conversation
+                return
+
+            # Execute tools and collect responses
+            tool_outputs = []
+            for tool_call in tool_calls:
+                # Convert tool call to ToolInfo format for AutoGPT execution
+                tool_info = self._create_tool_info_from_claude_call(
+                    tool_call, tool_functions
+                )
+
+                try:
+                    # Execute via AutoGPT's execution manager
+                    tool_response = await self._execute_single_tool_with_manager(
+                        tool_info, execution_params, execution_processor
+                    )
+                    tool_outputs.append(tool_response)
+                except Exception as e:
+                    logger.error(f"Tool execution failed: {e}")
+                    error_response = _create_tool_response(
+                        tool_call["id"], f"Error: {str(e)}"
+                    )
+                    tool_outputs.append(error_response)
+
+            # Add tool results to conversation
+            current_conversation.extend(tool_outputs)
+
+            # Yield intermediate conversation state
+            yield "conversations", current_conversation
+
+        # If we reach max iterations, yield the current state
+        if max_iterations < 0:
+            yield "finished", f"Agent mode completed after {iteration} iterations"
+        else:
+            yield (
+                "finished",
+                f"Agent mode completed after {max_iterations} iterations (limit reached)",
+            )
+        yield "conversations", current_conversation
+
+    async def _execute_single_call_mode(
+        self,
+        input_data: Input,  # Used for configuration and consistency with agent mode
+        tool_functions: list[dict[str, Any]],
+        enhanced_task: str,
+        claude_options: ClaudeAgentOptions | None,
+    ):
+        """Execute single call mode and yield tool outputs for external execution."""
+        # Create Claude options for single call if not provided
+        if claude_options is None:
+            claude_options = ClaudeAgentOptions(
+                system_prompt=input_data.system_prompt,
+                model=input_data.model.value,
+                max_turns=1,  # Single call mode
+                cwd=input_data.working_directory,
+                permission_mode="bypassPermissions",
+            )
+        else:
+            # Override max_turns to 1 for single call
+            claude_options.max_turns = 1
+
+        try:
+            claude_response = await self._attempt_claude_call_with_validation(
+                enhanced_task, claude_options
+            )
+        except Exception as e:
+            yield "error", f"Claude SDK error: {str(e)}"
+            yield "success", False
+            return
+
+        if claude_response:
+            text_content = self._extract_text_content_from_claude_message(
+                claude_response
+            )
+            tool_calls = self._extract_tool_calls_from_claude_message(claude_response)
+
+            if not tool_calls:
+                # No tool calls - just return the result
+                yield "result", text_content
+                yield "success", True
+                yield "tools", []  # No tools used
+                return
+
+            # Process and yield tool calls for external execution
+            for tool_call in tool_calls:
+                tool_name = tool_call["function"]["name"]
+                tool_args = self._normalize_tool_args(
+                    tool_call["function"]["arguments"]
+                )
+
+                # Find the tool definition (fallback to first if only one available)
+                tool_def = next(
+                    (
+                        tool
+                        for tool in tool_functions
+                        if tool["function"]["name"] == tool_name
+                    ),
+                    tool_functions[0] if len(tool_functions) == 1 else None,
+                )
+                if not tool_def:
+                    continue
+
+                # Get field mapping and sink node ID
+                field_mapping = tool_def["function"].get("_field_mapping", {})
+                sink_node_id = tool_def["function"]["_sink_node_id"]
+                expected_args = tool_def["function"]["parameters"].get(
+                    "properties", tool_args.keys()
+                )
+
+                # Yield tool outputs like SmartDecisionMakerBlock
+                for clean_arg_name in expected_args:
+                    original_field_name = field_mapping.get(
+                        clean_arg_name, clean_arg_name
+                    )
+                    arg_value = tool_args.get(clean_arg_name)
+
+                    # Create the same emit key format as SmartDecisionMakerBlock
+                    sanitized_arg_name = self.cleanup(original_field_name)
+                    emit_key = f"tools_^_{sink_node_id}_~_{sanitized_arg_name}"
+
+                    logger.debug(f"Yielding tool output: {emit_key}")
+                    yield emit_key, arg_value
+
+            # Yield conversation and tool results
+            yield (
+                "conversations",
+                [
+                    {
+                        "role": "assistant",
+                        "content": text_content,
+                        "tool_calls": tool_calls,
+                    }
+                ],
+            )
+            yield "tools", tool_calls
+            yield "success", True
+            return
+
+        # If no messages received
+        yield "error", "No response from Claude Agent SDK"
+        yield "success", False
+
+    async def run(
+        self,
+        input_data: Input,
+        *,
+        credentials: APIKeyCredentials,
+        graph_id: str,
+        node_id: str,
+        graph_exec_id: str,
+        node_exec_id: str,
+        user_id: str,
+        graph_version: int,
+        execution_context: ExecutionContext,
+        execution_processor: "ExecutionProcessor",
+        **kwargs,  # Additional execution context parameters
+    ) -> BlockOutput:
+        _ = kwargs  # Suppress unused parameter warning
+        # Validate credentials
+        if credentials.provider != ProviderName.ANTHROPIC:
+            error_msg = f"SmartAgentBlock requires Anthropic/Claude credentials, but received {credentials.provider} credentials. Please configure Anthropic API key credentials."
+            logger.error(error_msg)
+            yield "error", error_msg
+            yield "success", False
+            return
+        # Discover connected AutoGPT tool nodes
+        try:
+            tool_functions = await self._create_tool_node_signatures(node_id)
+        except ValueError as e:
+            if "no next node" in str(e).lower():
+                # Agent can work without tools - just provide Claude with reasoning capability
+                tool_functions = []
+                logger.info("No tools connected - running as pure Claude Agent")
+            else:
+                raise
+
+        yield "tool_functions", json.dumps(tool_functions)
+
+        # Always run Claude Agent SDK in agent mode (iterative execution)
+        async for result in self._execute_tools_agent_mode(
+            input_data=input_data,
+            credentials=credentials,
+            tool_functions=tool_functions,
+            graph_exec_id=graph_exec_id,
+            node_id=node_id,
+            node_exec_id=node_exec_id,
+            user_id=user_id,
+            graph_id=graph_id,
+            graph_version=graph_version,
+            execution_context=execution_context,
+            execution_processor=execution_processor,
+        ):
+            yield result

docs/integrations/README.md

@@ -233,6 +233,7 @@ Below is a comprehensive list of all available blocks, categorized by their prim
 | [Perplexity](block-integrations/llm.md#perplexity) | Query Perplexity's sonar models with real-time web search capabilities and receive annotated responses with source citations |
 | [Replicate Flux Advanced Model](block-integrations/replicate/flux_advanced.md#replicate-flux-advanced-model) | This block runs Flux models on Replicate with advanced settings |
 | [Replicate Model](block-integrations/replicate/replicate_block.md#replicate-model) | Run Replicate models synchronously |
+| [Smart Agent](block-integrations/llm.md#smart-agent) | An AI agent powered by Claude Agent SDK that executes connected AutoGPT tool nodes |
 | [Smart Decision Maker](block-integrations/llm.md#smart-decision-maker) | Uses AI to intelligently decide what tool to use |
 | [Stagehand Act](block-integrations/stagehand/blocks.md#stagehand-act) | Interact with a web page by performing actions on a web page |
 | [Stagehand Extract](block-integrations/stagehand/blocks.md#stagehand-extract) | Extract structured data from a webpage |

docs/integrations/block-integrations/llm.md

@@ -746,6 +746,51 @@ Choose from different sonar model variants including deep-research for comprehen
 
 ---
 
+## Smart Agent
+
+### What it is
+An AI agent powered by Claude Agent SDK that executes connected AutoGPT tool nodes. Combines Claude's native agent capabilities with AutoGPT's tool ecosystem.
+
+### How it works
+<!-- MANUAL: how_it_works -->
+This block enables agentic behavior by letting an LLM decide which tools to use based on the prompt. Connect tool outputs to feed back results, creating autonomous reasoning loops.
+
+Configure agent_mode_max_iterations to control loop behavior: 0 for single decisions, -1 for infinite looping, or a positive number for max iterations. The block outputs tool calls or a finished message.
+<!-- END MANUAL -->
+
+### Inputs
+
+| Input | Description | Type | Required |
+|-------|-------------|------|----------|
+| task | The task for the agent to complete. Be specific about your requirements. | str | Yes |
+| model | The model to use for the agent. | "claude-opus-4-1-20250805" \| "claude-opus-4-20250514" \| "claude-sonnet-4-20250514" \| "claude-opus-4-5-20251101" \| "claude-sonnet-4-5-20250929" \| "claude-haiku-4-5-20251001" \| "claude-3-7-sonnet-20250219" \| "claude-3-haiku-20240307" | No |
+| max_iterations | Maximum number of agent iterations. Use -1 for unlimited (use carefully!). | int | No |
+| system_prompt | System prompt to guide the agent's behavior. | str | No |
+| working_directory | Working directory for the agent. | str | No |
+
+### Outputs
+
+| Output | Description | Type |
+|--------|-------------|------|
+| error | Error message if the task failed. | str |
+| result | The final result or answer from the agent. | str |
+| iterations_used | Number of iterations used to complete the task. | int |
+| tools_used | List of AutoGPT tools used during execution. | List[str] |
+| success | Whether the task was completed successfully. | bool |
+| tools | Tool calls output for connecting to other AutoGPT blocks. | Tools |
+| conversations | Conversation history with Claude Agent SDK. | List[Any] |
+
+### Possible use case
+<!-- MANUAL: use_case -->
+**Autonomous Agents**: Build agents that can independently decide which tools to use for tasks.
+
+**Dynamic Workflows**: Create workflows that adapt their execution path based on AI decisions.
+
+**Multi-Tool Orchestration**: Let AI coordinate multiple tools to accomplish complex goals.
+<!-- END MANUAL -->
+
+---
+
 ## Smart Decision Maker
 
 ### What it is