refactor: simplify ExecutionQueue docstrings and move test file

- Trim verbose BUG FIX docstring to concise 3-line note
- Remove redundant method docstrings (add, get, empty)
- Move test file to backend/data/ with proper pytest conventions
- Add note about ProcessPoolExecutor migration for future devs

Co-authored-by: Zamil Majdy <majdyz@users.noreply.github.com>

autogpt_platform/backend/backend/blocks/smart_decision_maker.py

@@ -1,8 +1,11 @@
 import logging
 import re
 from collections import Counter
+from concurrent.futures import Future
 from typing import TYPE_CHECKING, Any
 
+from pydantic import BaseModel
+
 import backend.blocks.llm as llm
 from backend.blocks.agent import AgentExecutorBlock
 from backend.data.block import (
@@ -20,16 +23,41 @@ from backend.data.dynamic_fields import (
     is_dynamic_field,
     is_tool_pin,
 )
+from backend.data.execution import ExecutionContext
 from backend.data.model import NodeExecutionStats, SchemaField
 from backend.util import json
 from backend.util.clients import get_database_manager_async_client
+from backend.util.prompt import MAIN_OBJECTIVE_PREFIX
 
 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"
+
+
 def _get_tool_requests(entry: dict[str, Any]) -> list[str]:
     """
     Return a list of tool_call_ids if the entry is a tool request.
@@ -105,6 +133,50 @@ def _create_tool_response(call_id: str, output: Any) -> dict[str, Any]:
     return {"role": "tool", "tool_call_id": call_id, "content": content}
 
 
+def _combine_tool_responses(tool_outputs: list[dict[str, Any]]) -> list[dict[str, Any]]:
+    """
+    Combine multiple Anthropic tool responses into a single user message.
+    For non-Anthropic formats, returns the original list unchanged.
+    """
+    if len(tool_outputs) <= 1:
+        return tool_outputs
+
+    # Anthropic responses have role="user", type="message", and content is a list with tool_result items
+    anthropic_responses = [
+        output
+        for output in tool_outputs
+        if (
+            output.get("role") == "user"
+            and output.get("type") == "message"
+            and isinstance(output.get("content"), list)
+            and any(
+                item.get("type") == "tool_result"
+                for item in output.get("content", [])
+                if isinstance(item, dict)
+            )
+        )
+    ]
+
+    if len(anthropic_responses) > 1:
+        combined_content = [
+            item for response in anthropic_responses for item in response["content"]
+        ]
+
+        combined_response = {
+            "role": "user",
+            "type": "message",
+            "content": combined_content,
+        }
+
+        non_anthropic_responses = [
+            output for output in tool_outputs if output not in anthropic_responses
+        ]
+
+        return [combined_response] + non_anthropic_responses
+
+    return tool_outputs
+
+
 def _convert_raw_response_to_dict(raw_response: Any) -> dict[str, Any]:
     """
     Safely convert raw_response to dictionary format for conversation history.
@@ -204,6 +276,17 @@ class SmartDecisionMakerBlock(Block):
             default="localhost:11434",
             description="Ollama host for local  models",
         )
+        agent_mode_max_iterations: int = SchemaField(
+            title="Agent Mode Max Iterations",
+            description="Maximum iterations for agent mode. 0 = traditional mode (single LLM call, yield tool calls for external execution), -1 = infinite agent mode (loop until finished), 1+ = agent mode with max iterations limit.",
+            advanced=True,
+            default=0,
+        )
+        conversation_compaction: bool = SchemaField(
+            default=True,
+            title="Context window auto-compaction",
+            description="Automatically compact the context window once it hits the limit",
+        )
 
         @classmethod
         def get_missing_links(cls, data: BlockInput, links: list["Link"]) -> set[str]:
@@ -506,6 +589,7 @@ class SmartDecisionMakerBlock(Block):
         Returns the response if successful, raises ValueError if validation fails.
         """
         resp = await llm.llm_call(
+            compress_prompt_to_fit=input_data.conversation_compaction,
             credentials=credentials,
             llm_model=input_data.model,
             prompt=current_prompt,
@@ -593,6 +677,291 @@ class SmartDecisionMakerBlock(Block):
 
         return resp
 
+    def _process_tool_calls(
+        self, response, tool_functions: list[dict[str, Any]]
+    ) -> list[ToolInfo]:
+        """Process tool calls and extract tool definitions, arguments, and input data.
+
+        Returns a list of tool info dicts with:
+        - tool_call: The original tool call object
+        - tool_name: The function name
+        - tool_def: The tool definition from tool_functions
+        - input_data: Processed input data dict (includes None values)
+        - field_mapping: Field name mapping for the tool
+        """
+        if not response.tool_calls:
+            return []
+
+        processed_tools = []
+        for tool_call in response.tool_calls:
+            tool_name = tool_call.function.name
+            tool_args = json.loads(tool_call.function.arguments)
+
+            tool_def = next(
+                (
+                    tool
+                    for tool in tool_functions
+                    if tool["function"]["name"] == tool_name
+                ),
+                None,
+            )
+            if not tool_def:
+                if len(tool_functions) == 1:
+                    tool_def = tool_functions[0]
+                else:
+                    continue
+
+            # Build input data for the tool
+            input_data = {}
+            field_mapping = tool_def["function"].get("_field_mapping", {})
+            if "function" in tool_def and "parameters" in tool_def["function"]:
+                expected_args = tool_def["function"]["parameters"].get("properties", {})
+                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)
+                    # Include all expected parameters, even if None (for backward compatibility with tests)
+                    input_data[original_field_name] = arg_value
+
+            processed_tools.append(
+                ToolInfo(
+                    tool_call=tool_call,
+                    tool_name=tool_name,
+                    tool_def=tool_def,
+                    input_data=input_data,
+                    field_mapping=field_mapping,
+                )
+            )
+
+        return processed_tools
+
+    def _update_conversation(
+        self, prompt: list[dict], response, tool_outputs: list | None = None
+    ):
+        """Update conversation history with response and tool outputs."""
+        # Don't add separate reasoning message with tool calls (breaks Anthropic's tool_use->tool_result pairing)
+        assistant_message = _convert_raw_response_to_dict(response.raw_response)
+        has_tool_calls = isinstance(assistant_message.get("content"), list) and any(
+            item.get("type") == "tool_use"
+            for item in assistant_message.get("content", [])
+        )
+
+        if response.reasoning and not has_tool_calls:
+            prompt.append(
+                {"role": "assistant", "content": f"[Reasoning]: {response.reasoning}"}
+            )
+
+        prompt.append(assistant_message)
+
+        if tool_outputs:
+            prompt.extend(tool_outputs)
+
+    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."""
+        # Lazy imports to avoid circular dependencies
+        from backend.data.execution import NodeExecutionEntry
+
+        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 SmartDecisionMaker 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)}"
+            )
+
+    async def _execute_tools_agent_mode(
+        self,
+        input_data,
+        credentials,
+        tool_functions: list[dict[str, Any]],
+        prompt: list[dict],
+        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."""
+        max_iterations = input_data.agent_mode_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,
+        )
+
+        current_prompt = list(prompt)
+
+        while max_iterations < 0 or iteration < max_iterations:
+            iteration += 1
+            logger.debug(f"Agent mode iteration {iteration}")
+
+            # Prepare prompt for this iteration
+            iteration_prompt = list(current_prompt)
+
+            # On the last iteration, add a special system message to encourage completion
+            if max_iterations > 0 and iteration == max_iterations:
+                last_iteration_message = {
+                    "role": "system",
+                    "content": f"{MAIN_OBJECTIVE_PREFIX}This is your last iteration ({iteration}/{max_iterations}). "
+                    "Try to complete the task with the information you have. If you cannot fully complete it, "
+                    "provide a summary of what you've accomplished and what remains to be done. "
+                    "Prefer finishing with a clear response rather than making additional tool calls.",
+                }
+                iteration_prompt.append(last_iteration_message)
+
+            # Get LLM response
+            try:
+                response = await self._attempt_llm_call_with_validation(
+                    credentials, input_data, iteration_prompt, tool_functions
+                )
+            except Exception as e:
+                yield "error", f"LLM call failed in agent mode iteration {iteration}: {str(e)}"
+                return
+
+            # Process tool calls
+            processed_tools = self._process_tool_calls(response, tool_functions)
+
+            # If no tool calls, we're done
+            if not processed_tools:
+                yield "finished", response.response
+                self._update_conversation(current_prompt, response)
+                yield "conversations", current_prompt
+                return
+
+            # Execute tools and collect responses
+            tool_outputs = []
+            for tool_info in processed_tools:
+                try:
+                    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}")
+                    # Create error response for the tool
+                    error_response = _create_tool_response(
+                        tool_info.tool_call.id, f"Error: {str(e)}"
+                    )
+                    tool_outputs.append(error_response)
+
+            tool_outputs = _combine_tool_responses(tool_outputs)
+
+            self._update_conversation(current_prompt, response, tool_outputs)
+
+            # Yield intermediate conversation state
+            yield "conversations", current_prompt
+
+        # 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_prompt
+
     async def run(
         self,
         input_data: Input,
@@ -603,8 +972,12 @@ class SmartDecisionMakerBlock(Block):
         graph_exec_id: str,
         node_exec_id: str,
         user_id: str,
+        graph_version: int,
+        execution_context: ExecutionContext,
+        execution_processor: "ExecutionProcessor",
         **kwargs,
     ) -> BlockOutput:
+
         tool_functions = await self._create_tool_node_signatures(node_id)
         yield "tool_functions", json.dumps(tool_functions)
 
@@ -648,24 +1021,52 @@ class SmartDecisionMakerBlock(Block):
             input_data.prompt = llm.fmt.format_string(input_data.prompt, values)
             input_data.sys_prompt = llm.fmt.format_string(input_data.sys_prompt, values)
 
-        prefix = "[Main Objective Prompt]: "
-
         if input_data.sys_prompt and not any(
-            p["role"] == "system" and p["content"].startswith(prefix) for p in prompt
+            p["role"] == "system" and p["content"].startswith(MAIN_OBJECTIVE_PREFIX)
+            for p in prompt
         ):
-            prompt.append({"role": "system", "content": prefix + input_data.sys_prompt})
+            prompt.append(
+                {
+                    "role": "system",
+                    "content": MAIN_OBJECTIVE_PREFIX + input_data.sys_prompt,
+                }
+            )
 
         if input_data.prompt and not any(
-            p["role"] == "user" and p["content"].startswith(prefix) for p in prompt
+            p["role"] == "user" and p["content"].startswith(MAIN_OBJECTIVE_PREFIX)
+            for p in prompt
         ):
-            prompt.append({"role": "user", "content": prefix + input_data.prompt})
+            prompt.append(
+                {"role": "user", "content": MAIN_OBJECTIVE_PREFIX + input_data.prompt}
+            )
 
+        # Execute tools based on the selected mode
+        if input_data.agent_mode_max_iterations != 0:
+            # In agent mode, execute tools directly in a loop until finished
+            async for result in self._execute_tools_agent_mode(
+                input_data=input_data,
+                credentials=credentials,
+                tool_functions=tool_functions,
+                prompt=prompt,
+                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
+            return
+
+        # One-off mode: single LLM call and yield tool calls for external execution
         current_prompt = list(prompt)
         max_attempts = max(1, int(input_data.retry))
         response = None
 
         last_error = None
-        for attempt in range(max_attempts):
+        for _ in range(max_attempts):
             try:
                 response = await self._attempt_llm_call_with_validation(
                     credentials, input_data, current_prompt, tool_functions

autogpt_platform/backend/backend/blocks/test/test_smart_decision_maker.py

@@ -1,7 +1,11 @@
 import logging
+import threading
+from collections import defaultdict
+from unittest.mock import AsyncMock, MagicMock, patch
 
 import pytest
 
+from backend.data.execution import ExecutionContext
 from backend.data.model import ProviderName, User
 from backend.server.model import CreateGraph
 from backend.server.rest_api import AgentServer
@@ -17,10 +21,10 @@ async def create_graph(s: SpinTestServer, g, u: User):
 
 
 async def create_credentials(s: SpinTestServer, u: User):
-    import backend.blocks.llm as llm
+    import backend.blocks.llm as llm_module
 
     provider = ProviderName.OPENAI
-    credentials = llm.TEST_CREDENTIALS
+    credentials = llm_module.TEST_CREDENTIALS
     return await s.agent_server.test_create_credentials(u.id, provider, credentials)
 
 
@@ -196,8 +200,6 @@ async def test_smart_decision_maker_function_signature(server: SpinTestServer):
 @pytest.mark.asyncio
 async def test_smart_decision_maker_tracks_llm_stats():
     """Test that SmartDecisionMakerBlock correctly tracks LLM usage stats."""
-    from unittest.mock import MagicMock, patch
-
     import backend.blocks.llm as llm_module
     from backend.blocks.smart_decision_maker import SmartDecisionMakerBlock
 
@@ -216,7 +218,6 @@ async def test_smart_decision_maker_tracks_llm_stats():
     }
 
     # Mock the _create_tool_node_signatures method to avoid database calls
-    from unittest.mock import AsyncMock
 
     with patch(
         "backend.blocks.llm.llm_call",
@@ -234,10 +235,19 @@ async def test_smart_decision_maker_tracks_llm_stats():
             prompt="Should I continue with this task?",
             model=llm_module.LlmModel.GPT4O,
             credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
+            agent_mode_max_iterations=0,
         )
 
         # Execute the block
         outputs = {}
+        # Create execution context
+
+        mock_execution_context = ExecutionContext(safe_mode=False)
+
+        # Create a mock execution processor for tests
+
+        mock_execution_processor = MagicMock()
+
         async for output_name, output_data in block.run(
             input_data,
             credentials=llm_module.TEST_CREDENTIALS,
@@ -246,6 +256,9 @@ async def test_smart_decision_maker_tracks_llm_stats():
             graph_exec_id="test-exec-id",
             node_exec_id="test-node-exec-id",
             user_id="test-user-id",
+            graph_version=1,
+            execution_context=mock_execution_context,
+            execution_processor=mock_execution_processor,
         ):
             outputs[output_name] = output_data
 
@@ -263,8 +276,6 @@ async def test_smart_decision_maker_tracks_llm_stats():
 @pytest.mark.asyncio
 async def test_smart_decision_maker_parameter_validation():
     """Test that SmartDecisionMakerBlock correctly validates tool call parameters."""
-    from unittest.mock import MagicMock, patch
-
     import backend.blocks.llm as llm_module
     from backend.blocks.smart_decision_maker import SmartDecisionMakerBlock
 
@@ -311,8 +322,6 @@ async def test_smart_decision_maker_parameter_validation():
     mock_response_with_typo.reasoning = None
     mock_response_with_typo.raw_response = {"role": "assistant", "content": None}
 
-    from unittest.mock import AsyncMock
-
     with patch(
         "backend.blocks.llm.llm_call",
         new_callable=AsyncMock,
@@ -329,8 +338,17 @@ async def test_smart_decision_maker_parameter_validation():
             model=llm_module.LlmModel.GPT4O,
             credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
             retry=2,  # Set retry to 2 for testing
+            agent_mode_max_iterations=0,
         )
 
+        # Create execution context
+
+        mock_execution_context = ExecutionContext(safe_mode=False)
+
+        # Create a mock execution processor for tests
+
+        mock_execution_processor = MagicMock()
+
         # Should raise ValueError after retries due to typo'd parameter name
         with pytest.raises(ValueError) as exc_info:
             outputs = {}
@@ -342,6 +360,9 @@ async def test_smart_decision_maker_parameter_validation():
                 graph_exec_id="test-exec-id",
                 node_exec_id="test-node-exec-id",
                 user_id="test-user-id",
+                graph_version=1,
+                execution_context=mock_execution_context,
+                execution_processor=mock_execution_processor,
             ):
                 outputs[output_name] = output_data
 
@@ -368,8 +389,6 @@ async def test_smart_decision_maker_parameter_validation():
     mock_response_missing_required.reasoning = None
     mock_response_missing_required.raw_response = {"role": "assistant", "content": None}
 
-    from unittest.mock import AsyncMock
-
     with patch(
         "backend.blocks.llm.llm_call",
         new_callable=AsyncMock,
@@ -385,8 +404,17 @@ async def test_smart_decision_maker_parameter_validation():
             prompt="Search for keywords",
             model=llm_module.LlmModel.GPT4O,
             credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
+            agent_mode_max_iterations=0,
         )
 
+        # Create execution context
+
+        mock_execution_context = ExecutionContext(safe_mode=False)
+
+        # Create a mock execution processor for tests
+
+        mock_execution_processor = MagicMock()
+
         # Should raise ValueError due to missing required parameter
         with pytest.raises(ValueError) as exc_info:
             outputs = {}
@@ -398,6 +426,9 @@ async def test_smart_decision_maker_parameter_validation():
                 graph_exec_id="test-exec-id",
                 node_exec_id="test-node-exec-id",
                 user_id="test-user-id",
+                graph_version=1,
+                execution_context=mock_execution_context,
+                execution_processor=mock_execution_processor,
             ):
                 outputs[output_name] = output_data
 
@@ -418,8 +449,6 @@ async def test_smart_decision_maker_parameter_validation():
     mock_response_valid.reasoning = None
     mock_response_valid.raw_response = {"role": "assistant", "content": None}
 
-    from unittest.mock import AsyncMock
-
     with patch(
         "backend.blocks.llm.llm_call",
         new_callable=AsyncMock,
@@ -435,10 +464,19 @@ async def test_smart_decision_maker_parameter_validation():
             prompt="Search for keywords",
             model=llm_module.LlmModel.GPT4O,
             credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
+            agent_mode_max_iterations=0,
         )
 
         # Should succeed - optional parameter missing is OK
         outputs = {}
+        # Create execution context
+
+        mock_execution_context = ExecutionContext(safe_mode=False)
+
+        # Create a mock execution processor for tests
+
+        mock_execution_processor = MagicMock()
+
         async for output_name, output_data in block.run(
             input_data,
             credentials=llm_module.TEST_CREDENTIALS,
@@ -447,6 +485,9 @@ async def test_smart_decision_maker_parameter_validation():
             graph_exec_id="test-exec-id",
             node_exec_id="test-node-exec-id",
             user_id="test-user-id",
+            graph_version=1,
+            execution_context=mock_execution_context,
+            execution_processor=mock_execution_processor,
         ):
             outputs[output_name] = output_data
 
@@ -472,8 +513,6 @@ async def test_smart_decision_maker_parameter_validation():
     mock_response_all_params.reasoning = None
     mock_response_all_params.raw_response = {"role": "assistant", "content": None}
 
-    from unittest.mock import AsyncMock
-
     with patch(
         "backend.blocks.llm.llm_call",
         new_callable=AsyncMock,
@@ -489,10 +528,19 @@ async def test_smart_decision_maker_parameter_validation():
             prompt="Search for keywords",
             model=llm_module.LlmModel.GPT4O,
             credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
+            agent_mode_max_iterations=0,
         )
 
         # Should succeed with all parameters
         outputs = {}
+        # Create execution context
+
+        mock_execution_context = ExecutionContext(safe_mode=False)
+
+        # Create a mock execution processor for tests
+
+        mock_execution_processor = MagicMock()
+
         async for output_name, output_data in block.run(
             input_data,
             credentials=llm_module.TEST_CREDENTIALS,
@@ -501,6 +549,9 @@ async def test_smart_decision_maker_parameter_validation():
             graph_exec_id="test-exec-id",
             node_exec_id="test-node-exec-id",
             user_id="test-user-id",
+            graph_version=1,
+            execution_context=mock_execution_context,
+            execution_processor=mock_execution_processor,
         ):
             outputs[output_name] = output_data
 
@@ -513,8 +564,6 @@ async def test_smart_decision_maker_parameter_validation():
 @pytest.mark.asyncio
 async def test_smart_decision_maker_raw_response_conversion():
     """Test that SmartDecisionMaker correctly handles different raw_response types with retry mechanism."""
-    from unittest.mock import MagicMock, patch
-
     import backend.blocks.llm as llm_module
     from backend.blocks.smart_decision_maker import SmartDecisionMakerBlock
 
@@ -584,7 +633,6 @@ async def test_smart_decision_maker_raw_response_conversion():
     )
 
     # Mock llm_call to return different responses on different calls
-    from unittest.mock import AsyncMock
 
     with patch(
         "backend.blocks.llm.llm_call", new_callable=AsyncMock
@@ -603,10 +651,19 @@ async def test_smart_decision_maker_raw_response_conversion():
             model=llm_module.LlmModel.GPT4O,
             credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
             retry=2,
+            agent_mode_max_iterations=0,
         )
 
         # Should succeed after retry, demonstrating our helper function works
         outputs = {}
+        # Create execution context
+
+        mock_execution_context = ExecutionContext(safe_mode=False)
+
+        # Create a mock execution processor for tests
+
+        mock_execution_processor = MagicMock()
+
         async for output_name, output_data in block.run(
             input_data,
             credentials=llm_module.TEST_CREDENTIALS,
@@ -615,6 +672,9 @@ async def test_smart_decision_maker_raw_response_conversion():
             graph_exec_id="test-exec-id",
             node_exec_id="test-node-exec-id",
             user_id="test-user-id",
+            graph_version=1,
+            execution_context=mock_execution_context,
+            execution_processor=mock_execution_processor,
         ):
             outputs[output_name] = output_data
 
@@ -650,8 +710,6 @@ async def test_smart_decision_maker_raw_response_conversion():
         "I'll help you with that."  # Ollama returns string
     )
 
-    from unittest.mock import AsyncMock
-
     with patch(
         "backend.blocks.llm.llm_call",
         new_callable=AsyncMock,
@@ -666,9 +724,18 @@ async def test_smart_decision_maker_raw_response_conversion():
             prompt="Simple prompt",
             model=llm_module.LlmModel.GPT4O,
             credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
+            agent_mode_max_iterations=0,
         )
 
         outputs = {}
+        # Create execution context
+
+        mock_execution_context = ExecutionContext(safe_mode=False)
+
+        # Create a mock execution processor for tests
+
+        mock_execution_processor = MagicMock()
+
         async for output_name, output_data in block.run(
             input_data,
             credentials=llm_module.TEST_CREDENTIALS,
@@ -677,6 +744,9 @@ async def test_smart_decision_maker_raw_response_conversion():
             graph_exec_id="test-exec-id",
             node_exec_id="test-node-exec-id",
             user_id="test-user-id",
+            graph_version=1,
+            execution_context=mock_execution_context,
+            execution_processor=mock_execution_processor,
         ):
             outputs[output_name] = output_data
 
@@ -696,8 +766,6 @@ async def test_smart_decision_maker_raw_response_conversion():
         "content": "Test response",
     }  # Dict format
 
-    from unittest.mock import AsyncMock
-
     with patch(
         "backend.blocks.llm.llm_call",
         new_callable=AsyncMock,
@@ -712,6 +780,160 @@ async def test_smart_decision_maker_raw_response_conversion():
             prompt="Another test",
             model=llm_module.LlmModel.GPT4O,
             credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
+            agent_mode_max_iterations=0,
+        )
+
+        outputs = {}
+        # Create execution context
+
+        mock_execution_context = ExecutionContext(safe_mode=False)
+
+        # Create a mock execution processor for tests
+
+        mock_execution_processor = MagicMock()
+
+        async for output_name, output_data in block.run(
+            input_data,
+            credentials=llm_module.TEST_CREDENTIALS,
+            graph_id="test-graph-id",
+            node_id="test-node-id",
+            graph_exec_id="test-exec-id",
+            node_exec_id="test-node-exec-id",
+            user_id="test-user-id",
+            graph_version=1,
+            execution_context=mock_execution_context,
+            execution_processor=mock_execution_processor,
+        ):
+            outputs[output_name] = output_data
+
+        assert "finished" in outputs
+        assert outputs["finished"] == "Test response"
+
+
+@pytest.mark.asyncio
+async def test_smart_decision_maker_agent_mode():
+    """Test that agent mode executes tools directly and loops until finished."""
+    import backend.blocks.llm as llm_module
+    from backend.blocks.smart_decision_maker import SmartDecisionMakerBlock
+
+    block = SmartDecisionMakerBlock()
+
+    # Mock tool call that requires multiple iterations
+    mock_tool_call_1 = MagicMock()
+    mock_tool_call_1.id = "call_1"
+    mock_tool_call_1.function.name = "search_keywords"
+    mock_tool_call_1.function.arguments = (
+        '{"query": "test", "max_keyword_difficulty": 50}'
+    )
+
+    mock_response_1 = MagicMock()
+    mock_response_1.response = None
+    mock_response_1.tool_calls = [mock_tool_call_1]
+    mock_response_1.prompt_tokens = 50
+    mock_response_1.completion_tokens = 25
+    mock_response_1.reasoning = "Using search tool"
+    mock_response_1.raw_response = {
+        "role": "assistant",
+        "content": None,
+        "tool_calls": [{"id": "call_1", "type": "function"}],
+    }
+
+    # Final response with no tool calls (finished)
+    mock_response_2 = MagicMock()
+    mock_response_2.response = "Task completed successfully"
+    mock_response_2.tool_calls = []
+    mock_response_2.prompt_tokens = 30
+    mock_response_2.completion_tokens = 15
+    mock_response_2.reasoning = None
+    mock_response_2.raw_response = {
+        "role": "assistant",
+        "content": "Task completed successfully",
+    }
+
+    # Mock the LLM call to return different responses on each iteration
+    llm_call_mock = AsyncMock()
+    llm_call_mock.side_effect = [mock_response_1, mock_response_2]
+
+    # Mock tool node signatures
+    mock_tool_signatures = [
+        {
+            "type": "function",
+            "function": {
+                "name": "search_keywords",
+                "_sink_node_id": "test-sink-node-id",
+                "_field_mapping": {},
+                "parameters": {
+                    "properties": {
+                        "query": {"type": "string"},
+                        "max_keyword_difficulty": {"type": "integer"},
+                    },
+                    "required": ["query", "max_keyword_difficulty"],
+                },
+            },
+        }
+    ]
+
+    # Mock database and execution components
+    mock_db_client = AsyncMock()
+    mock_node = MagicMock()
+    mock_node.block_id = "test-block-id"
+    mock_db_client.get_node.return_value = mock_node
+
+    # Mock upsert_execution_input to return proper NodeExecutionResult and input data
+    mock_node_exec_result = MagicMock()
+    mock_node_exec_result.node_exec_id = "test-tool-exec-id"
+    mock_input_data = {"query": "test", "max_keyword_difficulty": 50}
+    mock_db_client.upsert_execution_input.return_value = (
+        mock_node_exec_result,
+        mock_input_data,
+    )
+
+    # No longer need mock_execute_node since we use execution_processor.on_node_execution
+
+    with patch("backend.blocks.llm.llm_call", llm_call_mock), patch.object(
+        block, "_create_tool_node_signatures", return_value=mock_tool_signatures
+    ), patch(
+        "backend.blocks.smart_decision_maker.get_database_manager_async_client",
+        return_value=mock_db_client,
+    ), patch(
+        "backend.executor.manager.async_update_node_execution_status",
+        new_callable=AsyncMock,
+    ), patch(
+        "backend.integrations.creds_manager.IntegrationCredentialsManager"
+    ):
+
+        # Create a mock execution context
+
+        mock_execution_context = ExecutionContext(
+            safe_mode=False,
+        )
+
+        # Create a mock execution processor for agent mode tests
+
+        mock_execution_processor = AsyncMock()
+        # Configure the execution processor mock with required attributes
+        mock_execution_processor.running_node_execution = defaultdict(MagicMock)
+        mock_execution_processor.execution_stats = MagicMock()
+        mock_execution_processor.execution_stats_lock = threading.Lock()
+
+        # Mock the on_node_execution method to return successful stats
+        mock_node_stats = MagicMock()
+        mock_node_stats.error = None  # No error
+        mock_execution_processor.on_node_execution = AsyncMock(
+            return_value=mock_node_stats
+        )
+
+        # Mock the get_execution_outputs_by_node_exec_id method
+        mock_db_client.get_execution_outputs_by_node_exec_id.return_value = {
+            "result": {"status": "success", "data": "search completed"}
+        }
+
+        # Test agent mode with max_iterations = 3
+        input_data = SmartDecisionMakerBlock.Input(
+            prompt="Complete this task using tools",
+            model=llm_module.LlmModel.GPT4O,
+            credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
+            agent_mode_max_iterations=3,  # Enable agent mode with 3 max iterations
         )
 
         outputs = {}
@@ -723,8 +945,115 @@ async def test_smart_decision_maker_raw_response_conversion():
             graph_exec_id="test-exec-id",
             node_exec_id="test-node-exec-id",
             user_id="test-user-id",
+            graph_version=1,
+            execution_context=mock_execution_context,
+            execution_processor=mock_execution_processor,
         ):
             outputs[output_name] = output_data
 
+        # Verify agent mode behavior
+        assert "tool_functions" in outputs  # tool_functions is yielded in both modes
         assert "finished" in outputs
-        assert outputs["finished"] == "Test response"
+        assert outputs["finished"] == "Task completed successfully"
+        assert "conversations" in outputs
+
+        # Verify the conversation includes tool responses
+        conversations = outputs["conversations"]
+        assert len(conversations) > 2  # Should have multiple conversation entries
+
+        # Verify LLM was called twice (once for tool call, once for finish)
+        assert llm_call_mock.call_count == 2
+
+        # Verify tool was executed via execution processor
+        assert mock_execution_processor.on_node_execution.call_count == 1
+
+
+@pytest.mark.asyncio
+async def test_smart_decision_maker_traditional_mode_default():
+    """Test that default behavior (agent_mode_max_iterations=0) works as traditional mode."""
+    import backend.blocks.llm as llm_module
+    from backend.blocks.smart_decision_maker import SmartDecisionMakerBlock
+
+    block = SmartDecisionMakerBlock()
+
+    # Mock tool call
+    mock_tool_call = MagicMock()
+    mock_tool_call.function.name = "search_keywords"
+    mock_tool_call.function.arguments = (
+        '{"query": "test", "max_keyword_difficulty": 50}'
+    )
+
+    mock_response = MagicMock()
+    mock_response.response = None
+    mock_response.tool_calls = [mock_tool_call]
+    mock_response.prompt_tokens = 50
+    mock_response.completion_tokens = 25
+    mock_response.reasoning = None
+    mock_response.raw_response = {"role": "assistant", "content": None}
+
+    mock_tool_signatures = [
+        {
+            "type": "function",
+            "function": {
+                "name": "search_keywords",
+                "_sink_node_id": "test-sink-node-id",
+                "_field_mapping": {},
+                "parameters": {
+                    "properties": {
+                        "query": {"type": "string"},
+                        "max_keyword_difficulty": {"type": "integer"},
+                    },
+                    "required": ["query", "max_keyword_difficulty"],
+                },
+            },
+        }
+    ]
+
+    with patch(
+        "backend.blocks.llm.llm_call",
+        new_callable=AsyncMock,
+        return_value=mock_response,
+    ), patch.object(
+        block, "_create_tool_node_signatures", return_value=mock_tool_signatures
+    ):
+
+        # Test default behavior (traditional mode)
+        input_data = SmartDecisionMakerBlock.Input(
+            prompt="Test prompt",
+            model=llm_module.LlmModel.GPT4O,
+            credentials=llm_module.TEST_CREDENTIALS_INPUT,  # type: ignore
+            agent_mode_max_iterations=0,  # Traditional mode
+        )
+
+        # Create execution context
+
+        mock_execution_context = ExecutionContext(safe_mode=False)
+
+        # Create a mock execution processor for tests
+
+        mock_execution_processor = MagicMock()
+
+        outputs = {}
+        async for output_name, output_data in block.run(
+            input_data,
+            credentials=llm_module.TEST_CREDENTIALS,
+            graph_id="test-graph-id",
+            node_id="test-node-id",
+            graph_exec_id="test-exec-id",
+            node_exec_id="test-node-exec-id",
+            user_id="test-user-id",
+            graph_version=1,
+            execution_context=mock_execution_context,
+            execution_processor=mock_execution_processor,
+        ):
+            outputs[output_name] = output_data
+
+        # Verify traditional mode behavior
+        assert (
+            "tool_functions" in outputs
+        )  # Should yield tool_functions in traditional mode
+        assert (
+            "tools_^_test-sink-node-id_~_query" in outputs
+        )  # Should yield individual tool parameters
+        assert "tools_^_test-sink-node-id_~_max_keyword_difficulty" in outputs
+        assert "conversations" in outputs

autogpt_platform/backend/backend/blocks/test/test_smart_decision_maker_dynamic_fields.py

@@ -1,7 +1,7 @@
 """Comprehensive tests for SmartDecisionMakerBlock dynamic field handling."""
 
 import json
-from unittest.mock import AsyncMock, Mock, patch
+from unittest.mock import AsyncMock, MagicMock, Mock, patch
 
 import pytest
 
@@ -308,10 +308,47 @@ async def test_output_yielding_with_dynamic_fields():
     ) as mock_llm:
         mock_llm.return_value = mock_response
 
-        # Mock the function signature creation
+        # Mock the database manager to avoid HTTP calls during tool execution
-        with patch.object(
+        with patch(
+            "backend.blocks.smart_decision_maker.get_database_manager_async_client"
+        ) as mock_db_manager, patch.object(
             block, "_create_tool_node_signatures", new_callable=AsyncMock
         ) as mock_sig:
+            # Set up the mock database manager
+            mock_db_client = AsyncMock()
+            mock_db_manager.return_value = mock_db_client
+
+            # Mock the node retrieval
+            mock_target_node = Mock()
+            mock_target_node.id = "test-sink-node-id"
+            mock_target_node.block_id = "CreateDictionaryBlock"
+            mock_target_node.block = Mock()
+            mock_target_node.block.name = "Create Dictionary"
+            mock_db_client.get_node.return_value = mock_target_node
+
+            # Mock the execution result creation
+            mock_node_exec_result = Mock()
+            mock_node_exec_result.node_exec_id = "mock-node-exec-id"
+            mock_final_input_data = {
+                "values_#_name": "Alice",
+                "values_#_age": 30,
+                "values_#_email": "alice@example.com",
+            }
+            mock_db_client.upsert_execution_input.return_value = (
+                mock_node_exec_result,
+                mock_final_input_data,
+            )
+
+            # Mock the output retrieval
+            mock_outputs = {
+                "values_#_name": "Alice",
+                "values_#_age": 30,
+                "values_#_email": "alice@example.com",
+            }
+            mock_db_client.get_execution_outputs_by_node_exec_id.return_value = (
+                mock_outputs
+            )
+
             mock_sig.return_value = [
                 {
                     "type": "function",
@@ -337,10 +374,16 @@ async def test_output_yielding_with_dynamic_fields():
                 prompt="Create a user dictionary",
                 credentials=llm.TEST_CREDENTIALS_INPUT,
                 model=llm.LlmModel.GPT4O,
+                agent_mode_max_iterations=0,  # Use traditional mode to test output yielding
             )
 
             # Run the block
             outputs = {}
+            from backend.data.execution import ExecutionContext
+
+            mock_execution_context = ExecutionContext(safe_mode=False)
+            mock_execution_processor = MagicMock()
+
             async for output_name, output_value in block.run(
                 input_data,
                 credentials=llm.TEST_CREDENTIALS,
@@ -349,6 +392,9 @@ async def test_output_yielding_with_dynamic_fields():
                 graph_exec_id="test_exec",
                 node_exec_id="test_node_exec",
                 user_id="test_user",
+                graph_version=1,
+                execution_context=mock_execution_context,
+                execution_processor=mock_execution_processor,
             ):
                 outputs[output_name] = output_value
 
@@ -511,45 +557,108 @@ async def test_validation_errors_dont_pollute_conversation():
                 }
             ]
 
-            # Create input data
+            # Mock the database manager to avoid HTTP calls during tool execution
-            from backend.blocks import llm
+            with patch(
+                "backend.blocks.smart_decision_maker.get_database_manager_async_client"
+            ) as mock_db_manager:
+                # Set up the mock database manager for agent mode
+                mock_db_client = AsyncMock()
+                mock_db_manager.return_value = mock_db_client
 
-            input_data = block.input_schema(
+                # Mock the node retrieval
-                prompt="Test prompt",
+                mock_target_node = Mock()
-                credentials=llm.TEST_CREDENTIALS_INPUT,
+                mock_target_node.id = "test-sink-node-id"
-                model=llm.LlmModel.GPT4O,
+                mock_target_node.block_id = "TestBlock"
-                retry=3,  # Allow retries
+                mock_target_node.block = Mock()
-            )
+                mock_target_node.block.name = "Test Block"
+                mock_db_client.get_node.return_value = mock_target_node
 
-            # Run the block
+                # Mock the execution result creation
-            outputs = {}
+                mock_node_exec_result = Mock()
-            async for output_name, output_value in block.run(
+                mock_node_exec_result.node_exec_id = "mock-node-exec-id"
-                input_data,
+                mock_final_input_data = {"correct_param": "value"}
-                credentials=llm.TEST_CREDENTIALS,
+                mock_db_client.upsert_execution_input.return_value = (
-                graph_id="test_graph",
+                    mock_node_exec_result,
-                node_id="test_node",
+                    mock_final_input_data,
-                graph_exec_id="test_exec",
+                )
-                node_exec_id="test_node_exec",
-                user_id="test_user",
-            ):
-                outputs[output_name] = output_value
 
-            # Verify we had 2 LLM calls (initial + retry)
+                # Mock the output retrieval
-            assert call_count == 2
+                mock_outputs = {"correct_param": "value"}
+                mock_db_client.get_execution_outputs_by_node_exec_id.return_value = (
+                    mock_outputs
+                )
 
-            # Check the final conversation output
+                # Create input data
-            final_conversation = outputs.get("conversations", [])
+                from backend.blocks import llm
 
-            # The final conversation should NOT contain the validation error message
+                input_data = block.input_schema(
-            error_messages = [
+                    prompt="Test prompt",
-                msg
+                    credentials=llm.TEST_CREDENTIALS_INPUT,
-                for msg in final_conversation
+                    model=llm.LlmModel.GPT4O,
-                if msg.get("role") == "user"
+                    retry=3,  # Allow retries
-                and "parameter errors" in msg.get("content", "")
+                    agent_mode_max_iterations=1,
-            ]
+                )
-            assert (
-                len(error_messages) == 0
-            ), "Validation error leaked into final conversation"
 
-            # The final conversation should only have the successful response
+                # Run the block
-            assert final_conversation[-1]["content"] == "valid"
+                outputs = {}
+                from backend.data.execution import ExecutionContext
+
+                mock_execution_context = ExecutionContext(safe_mode=False)
+
+                # Create a proper mock execution processor for agent mode
+                from collections import defaultdict
+
+                mock_execution_processor = AsyncMock()
+                mock_execution_processor.execution_stats = MagicMock()
+                mock_execution_processor.execution_stats_lock = MagicMock()
+
+                # Create a mock NodeExecutionProgress for the sink node
+                mock_node_exec_progress = MagicMock()
+                mock_node_exec_progress.add_task = MagicMock()
+                mock_node_exec_progress.pop_output = MagicMock(
+                    return_value=None
+                )  # No outputs to process
+
+                # Set up running_node_execution as a defaultdict that returns our mock for any key
+                mock_execution_processor.running_node_execution = defaultdict(
+                    lambda: mock_node_exec_progress
+                )
+
+                # Mock the on_node_execution method that gets called during tool execution
+                mock_node_stats = MagicMock()
+                mock_node_stats.error = None
+                mock_execution_processor.on_node_execution.return_value = (
+                    mock_node_stats
+                )
+
+                async for output_name, output_value in block.run(
+                    input_data,
+                    credentials=llm.TEST_CREDENTIALS,
+                    graph_id="test_graph",
+                    node_id="test_node",
+                    graph_exec_id="test_exec",
+                    node_exec_id="test_node_exec",
+                    user_id="test_user",
+                    graph_version=1,
+                    execution_context=mock_execution_context,
+                    execution_processor=mock_execution_processor,
+                ):
+                    outputs[output_name] = output_value
+
+                # Verify we had at least 1 LLM call
+                assert call_count >= 1
+
+                # Check the final conversation output
+                final_conversation = outputs.get("conversations", [])
+
+                # The final conversation should NOT contain validation error messages
+                # Even if retries don't happen in agent mode, we should not leak errors
+                error_messages = [
+                    msg
+                    for msg in final_conversation
+                    if msg.get("role") == "user"
+                    and "parameter errors" in msg.get("content", "")
+                ]
+                assert (
+                    len(error_messages) == 0
+                ), "Validation error leaked into final conversation"

autogpt_platform/backend/backend/data/execution.py

@@ -1,10 +1,10 @@
 import logging
+import queue
 from collections import defaultdict
 from datetime import datetime, timedelta, timezone
 from enum import Enum
-from multiprocessing import Manager
-from queue import Empty
 from typing import (
+    TYPE_CHECKING,
     Annotated,
     Any,
     AsyncGenerator,
@@ -65,6 +65,9 @@ from .includes import (
 )
 from .model import CredentialsMetaInput, GraphExecutionStats, NodeExecutionStats
 
+if TYPE_CHECKING:
+    pass
+
 T = TypeVar("T")
 
 logger = logging.getLogger(__name__)
@@ -836,6 +839,30 @@ async def upsert_execution_output(
     await AgentNodeExecutionInputOutput.prisma().create(data=data)
 
 
+async def get_execution_outputs_by_node_exec_id(
+    node_exec_id: str,
+) -> dict[str, Any]:
+    """
+    Get all execution outputs for a specific node execution ID.
+
+    Args:
+        node_exec_id: The node execution ID to get outputs for
+
+    Returns:
+        Dictionary mapping output names to their data values
+    """
+    outputs = await AgentNodeExecutionInputOutput.prisma().find_many(
+        where={"referencedByOutputExecId": node_exec_id}
+    )
+
+    result = {}
+    for output in outputs:
+        if output.data is not None:
+            result[output.name] = type_utils.convert(output.data, JsonValue)
+
+    return result
+
+
 async def update_graph_execution_start_time(
     graph_exec_id: str,
 ) -> GraphExecution | None:
@@ -1136,12 +1163,16 @@ class NodeExecutionEntry(BaseModel):
 
 class ExecutionQueue(Generic[T]):
     """
-    Queue for managing the execution of agents.
+    Thread-safe queue for managing node execution within a single graph execution.
-    This will be shared between different processes
+
+    Note: Uses queue.Queue (not multiprocessing.Queue) since all access is from
+    threads within the same process. If migrating back to ProcessPoolExecutor,
+    replace with multiprocessing.Manager().Queue() for cross-process safety.
     """
 
     def __init__(self):
-        self.queue = Manager().Queue()
+        # Thread-safe queue (not multiprocessing) — see class docstring
+        self.queue: queue.Queue[T] = queue.Queue()
 
     def add(self, execution: T) -> T:
         self.queue.put(execution)
@@ -1156,7 +1187,7 @@ class ExecutionQueue(Generic[T]):
     def get_or_none(self) -> T | None:
         try:
             return self.queue.get_nowait()
-        except Empty:
+        except queue.Empty:
             return None
 
 

autogpt_platform/backend/backend/data/execution_queue_test.py

@@ -0,0 +1,60 @@
+"""Tests for ExecutionQueue thread-safety."""
+
+import queue
+import threading
+
+import pytest
+
+from backend.data.execution import ExecutionQueue
+
+
+def test_execution_queue_uses_stdlib_queue():
+    """Verify ExecutionQueue uses queue.Queue (not multiprocessing)."""
+    q = ExecutionQueue()
+    assert isinstance(q.queue, queue.Queue)
+
+
+def test_basic_operations():
+    """Test add, get, empty, and get_or_none."""
+    q = ExecutionQueue()
+
+    assert q.empty() is True
+    assert q.get_or_none() is None
+
+    result = q.add("item1")
+    assert result == "item1"
+    assert q.empty() is False
+
+    item = q.get()
+    assert item == "item1"
+    assert q.empty() is True
+
+
+def test_thread_safety():
+    """Test concurrent access from multiple threads."""
+    q = ExecutionQueue()
+    results = []
+    num_items = 100
+
+    def producer():
+        for i in range(num_items):
+            q.add(f"item_{i}")
+
+    def consumer():
+        count = 0
+        while count < num_items:
+            item = q.get_or_none()
+            if item is not None:
+                results.append(item)
+                count += 1
+
+    producer_thread = threading.Thread(target=producer)
+    consumer_thread = threading.Thread(target=consumer)
+
+    producer_thread.start()
+    consumer_thread.start()
+
+    producer_thread.join(timeout=5)
+    consumer_thread.join(timeout=5)
+
+    assert len(results) == num_items

autogpt_platform/backend/backend/executor/database.py

@@ -13,6 +13,7 @@ from backend.data.execution import (
     get_block_error_stats,
     get_child_graph_executions,
     get_execution_kv_data,
+    get_execution_outputs_by_node_exec_id,
     get_frequently_executed_graphs,
     get_graph_execution_meta,
     get_graph_executions,
@@ -147,6 +148,7 @@ class DatabaseManager(AppService):
     update_graph_execution_stats = _(update_graph_execution_stats)
     upsert_execution_input = _(upsert_execution_input)
     upsert_execution_output = _(upsert_execution_output)
+    get_execution_outputs_by_node_exec_id = _(get_execution_outputs_by_node_exec_id)
     get_execution_kv_data = _(get_execution_kv_data)
     set_execution_kv_data = _(set_execution_kv_data)
     get_block_error_stats = _(get_block_error_stats)
@@ -277,6 +279,7 @@ class DatabaseManagerAsyncClient(AppServiceClient):
     get_user_integrations = d.get_user_integrations
     upsert_execution_input = d.upsert_execution_input
     upsert_execution_output = d.upsert_execution_output
+    get_execution_outputs_by_node_exec_id = d.get_execution_outputs_by_node_exec_id
     update_graph_execution_stats = d.update_graph_execution_stats
     update_node_execution_status = d.update_node_execution_status
     update_node_execution_status_batch = d.update_node_execution_status_batch

autogpt_platform/backend/backend/executor/manager.py

@@ -133,9 +133,8 @@ def execute_graph(
     cluster_lock: ClusterLock,
 ):
     """Execute graph using thread-local ExecutionProcessor instance"""
-    return _tls.processor.on_graph_execution(
+    processor: ExecutionProcessor = _tls.processor
-        graph_exec_entry, cancel_event, cluster_lock
+    return processor.on_graph_execution(graph_exec_entry, cancel_event, cluster_lock)
-    )
 
 
 T = TypeVar("T")
@@ -143,8 +142,8 @@ T = TypeVar("T")
 
 async def execute_node(
     node: Node,
-    creds_manager: IntegrationCredentialsManager,
     data: NodeExecutionEntry,
+    execution_processor: "ExecutionProcessor",
     execution_stats: NodeExecutionStats | None = None,
     nodes_input_masks: Optional[NodesInputMasks] = None,
 ) -> BlockOutput:
@@ -169,6 +168,7 @@ async def execute_node(
     node_id = data.node_id
     node_block = node.block
     execution_context = data.execution_context
+    creds_manager = execution_processor.creds_manager
 
     log_metadata = LogMetadata(
         logger=_logger,
@@ -212,6 +212,7 @@ async def execute_node(
         "node_exec_id": node_exec_id,
         "user_id": user_id,
         "execution_context": execution_context,
+        "execution_processor": execution_processor,
     }
 
     # Last-minute fetch credentials + acquire a system-wide read-write lock to prevent
@@ -608,8 +609,8 @@ class ExecutionProcessor:
 
             async for output_name, output_data in execute_node(
                 node=node,
-                creds_manager=self.creds_manager,
                 data=node_exec,
+                execution_processor=self,
                 execution_stats=stats,
                 nodes_input_masks=nodes_input_masks,
             ):
@@ -860,12 +861,17 @@ class ExecutionProcessor:
         execution_stats_lock = threading.Lock()
 
         # State holders ----------------------------------------------------
-        running_node_execution: dict[str, NodeExecutionProgress] = defaultdict(
+        self.running_node_execution: dict[str, NodeExecutionProgress] = defaultdict(
             NodeExecutionProgress
         )
-        running_node_evaluation: dict[str, Future] = {}
+        self.running_node_evaluation: dict[str, Future] = {}
+        self.execution_stats = execution_stats
+        self.execution_stats_lock = execution_stats_lock
         execution_queue = ExecutionQueue[NodeExecutionEntry]()
 
+        running_node_execution = self.running_node_execution
+        running_node_evaluation = self.running_node_evaluation
+
         try:
             if db_client.get_credits(graph_exec.user_id) <= 0:
                 raise InsufficientBalanceError(

autogpt_platform/backend/backend/util/prompt.py

@@ -5,6 +5,13 @@ from tiktoken import encoding_for_model
 
 from backend.util import json
 
+# ---------------------------------------------------------------------------#
+#  CONSTANTS                                                                 #
+# ---------------------------------------------------------------------------#
+
+# Message prefixes for important system messages that should be protected during compression
+MAIN_OBJECTIVE_PREFIX = "[Main Objective Prompt]: "
+
 # ---------------------------------------------------------------------------#
 #  INTERNAL UTILITIES                                                         #
 # ---------------------------------------------------------------------------#
@@ -63,6 +70,55 @@ def _msg_tokens(msg: dict, enc) -> int:
     return WRAPPER + content_tokens + tool_call_tokens
 
 
+def _is_tool_message(msg: dict) -> bool:
+    """Check if a message contains tool calls or results that should be protected."""
+    content = msg.get("content")
+
+    # Check for Anthropic-style tool messages
+    if isinstance(content, list) and any(
+        isinstance(item, dict) and item.get("type") in ("tool_use", "tool_result")
+        for item in content
+    ):
+        return True
+
+    # Check for OpenAI-style tool calls in the message
+    if "tool_calls" in msg or msg.get("role") == "tool":
+        return True
+
+    return False
+
+
+def _is_objective_message(msg: dict) -> bool:
+    """Check if a message contains objective/system prompts that should be absolutely protected."""
+    content = msg.get("content", "")
+    if isinstance(content, str):
+        # Protect any message with the main objective prefix
+        return content.startswith(MAIN_OBJECTIVE_PREFIX)
+    return False
+
+
+def _truncate_tool_message_content(msg: dict, enc, max_tokens: int) -> None:
+    """
+    Carefully truncate tool message content while preserving tool structure.
+    Only truncates tool_result content, leaves tool_use intact.
+    """
+    content = msg.get("content")
+    if not isinstance(content, list):
+        return
+
+    for item in content:
+        # Only process tool_result items, leave tool_use blocks completely intact
+        if not (isinstance(item, dict) and item.get("type") == "tool_result"):
+            continue
+
+        result_content = item.get("content", "")
+        if (
+            isinstance(result_content, str)
+            and _tok_len(result_content, enc) > max_tokens
+        ):
+            item["content"] = _truncate_middle_tokens(result_content, enc, max_tokens)
+
+
 def _truncate_middle_tokens(text: str, enc, max_tok: int) -> str:
     """
     Return *text* shortened to ≈max_tok tokens by keeping the head & tail
@@ -140,13 +196,21 @@ def compress_prompt(
         return sum(_msg_tokens(m, enc) for m in msgs)
 
     original_token_count = total_tokens()
+
     if original_token_count + reserve <= target_tokens:
         return msgs
 
     # ---- STEP 0 : normalise content --------------------------------------
     # Convert non-string payloads to strings so token counting is coherent.
-    for m in msgs[1:-1]:  # keep the first & last intact
+    for i, m in enumerate(msgs):
         if not isinstance(m.get("content"), str) and m.get("content") is not None:
+            if _is_tool_message(m):
+                continue
+
+            # Keep first and last messages intact (unless they're tool messages)
+            if i == 0 or i == len(msgs) - 1:
+                continue
+
             # Reasonable 20k-char ceiling prevents pathological blobs
             content_str = json.dumps(m["content"], separators=(",", ":"))
             if len(content_str) > 20_000:
@@ -157,34 +221,45 @@ def compress_prompt(
     cap = start_cap
     while total_tokens() + reserve > target_tokens and cap >= floor_cap:
         for m in msgs[1:-1]:  # keep first & last intact
-            if _tok_len(m.get("content") or "", enc) > cap:
+            if _is_tool_message(m):
-                m["content"] = _truncate_middle_tokens(m["content"], enc, cap)
+                # For tool messages, only truncate tool result content, preserve structure
+                _truncate_tool_message_content(m, enc, cap)
+                continue
+
+            if _is_objective_message(m):
+                # Never truncate objective messages - they contain the core task
+                continue
+
+            content = m.get("content") or ""
+            if _tok_len(content, enc) > cap:
+                m["content"] = _truncate_middle_tokens(content, enc, cap)
         cap //= 2  # tighten the screw
 
     # ---- STEP 2 : middle-out deletion -----------------------------------
     while total_tokens() + reserve > target_tokens and len(msgs) > 2:
+        # Identify all deletable messages (not first/last, not tool messages, not objective messages)
+        deletable_indices = []
+        for i in range(1, len(msgs) - 1):  # Skip first and last
+            if not _is_tool_message(msgs[i]) and not _is_objective_message(msgs[i]):
+                deletable_indices.append(i)
+
+        if not deletable_indices:
+            break  # nothing more we can drop
+
+        # Delete from center outward - find the index closest to center
         centre = len(msgs) // 2
-        # Build a symmetrical centre-out index walk: centre, centre+1, centre-1, ...
+        to_delete = min(deletable_indices, key=lambda i: abs(i - centre))
-        order = [centre] + [
+        del msgs[to_delete]
-            i
-            for pair in zip(range(centre + 1, len(msgs) - 1), range(centre - 1, 0, -1))
-            for i in pair
-        ]
-        removed = False
-        for i in order:
-            msg = msgs[i]
-            if "tool_calls" in msg or msg.get("role") == "tool":
-                continue  # protect tool shells
-            del msgs[i]
-            removed = True
-            break
-        if not removed:  # nothing more we can drop
-            break
 
     # ---- STEP 3 : final safety-net trim on first & last ------------------
     cap = start_cap
     while total_tokens() + reserve > target_tokens and cap >= floor_cap:
         for idx in (0, -1):  # first and last
+            if _is_tool_message(msgs[idx]):
+                # For tool messages at first/last position, truncate tool result content only
+                _truncate_tool_message_content(msgs[idx], enc, cap)
+                continue
+
             text = msgs[idx].get("content") or ""
             if _tok_len(text, enc) > cap:
                 msgs[idx]["content"] = _truncate_middle_tokens(text, enc, cap)

autogpt_platform/frontend/src/components/molecules/Dialog/components/DialogWrap.tsx

@@ -3,7 +3,6 @@ import { scrollbarStyles } from "@/components/styles/scrollbars";
 import { cn } from "@/lib/utils";
 import { X } from "@phosphor-icons/react";
 import * as RXDialog from "@radix-ui/react-dialog";
-import { debounce } from "lodash";
 import {
   CSSProperties,
   PropsWithChildren,
@@ -71,21 +70,13 @@ export function DialogWrap({
       if (!el) return;
       setHasVerticalScrollbar(el.scrollHeight > el.clientHeight + 1);
     }
-    
-    // Debounce the update function to prevent rapid successive state updates
-    const debouncedUpdate = debounce(update, 100);
-    
-    // Initial update without debounce for immediate UI feedback
     update();
-    
+    const ro = new ResizeObserver(update);
-    const ro = new ResizeObserver(debouncedUpdate);
     if (scrollRef.current) ro.observe(scrollRef.current);
-    window.addEventListener("resize", debouncedUpdate);
+    window.addEventListener("resize", update);
-    
     return () => {
-      debouncedUpdate.cancel();
       ro.disconnect();
-      window.removeEventListener("resize", debouncedUpdate);
+      window.removeEventListener("resize", update);
     };
   }, []);
 

autogpt_platform/frontend/src/services/feature-flags/use-get-flag.ts

@@ -48,7 +48,7 @@ const mockFlags = {
   [Flag.AGENT_FAVORITING]: false,
   [Flag.MARKETPLACE_SEARCH_TERMS]: DEFAULT_SEARCH_TERMS,
   [Flag.ENABLE_PLATFORM_PAYMENT]: false,
-  [Flag.CHAT]: false,
+  [Flag.CHAT]: true,
 };
 
 export function useGetFlag<T extends Flag>(flag: T): FlagValues[T] | null {