feat(agent-sandbox): new tool resolver and bash execution implementation

api/core/workflow/nodes/command/node.py

@@ -4,6 +4,7 @@ import shlex
 from collections.abc import Mapping, Sequence
 from typing import Any
 
+from core.sandbox.debug import sandbox_debug
 from core.sandbox.manager import SandboxManager
 from core.virtual_environment.__base.command_future import CommandCancelledError, CommandTimeoutError
 from core.virtual_environment.__base.virtual_environment import VirtualEnvironment
@@ -83,6 +84,9 @@ class CommandNode(Node[CommandNodeData]):
 
         try:
             command = shlex.split(raw_command)
+
+            sandbox_debug("command_node", "command", command)
+
             future = sandbox.run_command(connection_handle, command, cwd=working_directory)
             result = future.result(timeout=timeout)
 

api/core/workflow/nodes/llm/node.py

@@ -13,7 +13,7 @@ from sqlalchemy import select
 
 from core.agent.entities import AgentEntity, AgentLog, AgentResult, AgentToolEntity, ExecutionContext
 from core.agent.patterns import StrategyFactory
-from core.app.entities.app_invoke_entities import ModelConfigWithCredentialsEntity
+from core.app.entities.app_invoke_entities import InvokeFrom, ModelConfigWithCredentialsEntity
 from core.file import File, FileTransferMethod, FileType, file_manager
 from core.helper.code_executor import CodeExecutor, CodeLanguage
 from core.llm_generator.output_parser.errors import OutputParserError
@@ -1581,6 +1581,35 @@ class LLMNode(Node[LLMNodeData]):
         result = yield from self._process_tool_outputs(outputs)
         return result
 
+    def _prepare_sandbox_tools(self) -> list[Tool]:
+        """Prepare sandbox tools."""
+        tool_instances = []
+
+        for tool in self._node_data.tools or []:
+            try:
+                # Get tool runtime from ToolManager
+                tool_runtime = ToolManager.get_tool_runtime(
+                    tenant_id=self.tenant_id,
+                    tool_name=tool.tool_name,
+                    provider_id=tool.provider_name,
+                    provider_type=tool.type,
+                    invoke_from=InvokeFrom.AGENT,
+                    credential_id=tool.credential_id,
+                )
+
+                # Apply custom description from extra field if available
+                if tool.extra.get("description") and tool_runtime.entity.description:
+                    tool_runtime.entity.description.llm = (
+                        tool.extra.get("description") or tool_runtime.entity.description.llm
+                    )
+
+                tool_instances.append(tool_runtime)
+            except Exception as e:
+                logger.warning("Failed to load tool %s: %s", tool, str(e))
+                continue
+
+        return tool_instances
+
     def _invoke_llm_with_sandbox(
         self,
         model_instance: ModelInstance,
@@ -1592,7 +1621,7 @@ class LLMNode(Node[LLMNodeData]):
         if not workflow_execution_id:
             raise LLMNodeError("workflow_execution_id is required for sandbox runtime mode")
 
-        configured_tools = self._prepare_tool_instances(variable_pool)
+        configured_tools = self._prepare_sandbox_tools()
 
         result: LLMGenerationData | None = None