small refactor of the structure

src/tools_adapter.py

@@ -0,0 +1,51 @@
+"""
+Tools adapter — bridges the existing skill/tool system with LangGraph's ToolNode.
+
+LangGraph's ToolNode expects callable tools (typically @tool-decorated functions).
+This module wraps our skill-based tool definitions and async executors so
+ToolNode can invoke them without any changes to the skills/ layer.
+"""
+
+from __future__ import annotations
+
+import json
+from typing import Any
+
+from langchain_core.tools import tool
+
+from agents.skills import get_all_tools, execute_tool
+
+
+def build_langgraph_tools(skill_names: list[str]) -> list:
+    """
+    Convert the registered skill tool definitions into LangChain-compatible
+    @tool-decorated functions that ToolNode can call.
+
+    Each tool wraps the existing `execute_tool()` pipeline, so the skill
+    system's ToolResult + httpx session handling is fully preserved.
+    """
+    tool_defs = get_all_tools(skill_names)
+    wrapped: list = []
+
+    for td in tool_defs:
+        fn_def = td.get("function", {})
+        fn_name = fn_def.get("name", "")
+        fn_desc = fn_def.get("description", "")
+
+        # Create a unique factory so each closure captures the right fn_name
+        def _make_tool(name: str, desc: str, skills: list[str]):
+            @tool(name, description=desc)
+            async def _wrapped(**kwargs: Any) -> str:
+                """Execute the tool via the skill system and return its content."""
+                result = await execute_tool(skills, name, kwargs)
+                if result is None:
+                    return f"Tool '{name}' is not available."
+                return result.content
+
+            # Stash the original OpenAI schema so LangGraph can use it
+            _wrapped.metadata = fn_def
+            return _wrapped
+
+        wrapped.append(_make_tool(fn_name, fn_desc, skill_names))
+
+    return wrapped