fix(autogen-ext): skip LangChain callback-manager (run_manager) when inferring tool args schema

python/packages/autogen-ext/src/autogen_ext/tools/langchain/_langchain_adapter.py

@@ -2,7 +2,8 @@
 
 import asyncio
 import inspect
-from typing import TYPE_CHECKING, Any, Callable, Dict, Type, cast
+import types
+from typing import TYPE_CHECKING, Any, Callable, Dict, Type, Union, cast, get_args, get_origin, get_type_hints
 
 from autogen_core import CancellationToken
 from autogen_core.tools import BaseTool
@@ -12,6 +13,32 @@
     from langchain_core.tools import BaseTool as LangChainTool
 
 
+def _is_callback_manager_annotation(annotation: Any) -> bool:
+    """Whether *annotation* is a LangChain callback-manager type.
+
+    LangChain injects a ``run_manager`` (``CallbackManagerForToolRun`` or
+    ``AsyncCallbackManagerForToolRun``) into a tool's ``_run``/``_arun``. It is
+    not a real tool input and pydantic cannot generate a schema for it. Unwrap
+    ``Optional[...]`` / ``Union[...]`` so both the bare type and
+    ``Optional[CallbackManagerForToolRun]`` are detected.
+    """
+    if annotation is None or annotation is inspect.Parameter.empty:
+        return False
+    origin = get_origin(annotation)
+    if origin is Union or (hasattr(types, "UnionType") and origin is types.UnionType):
+        return any(_is_callback_manager_annotation(arg) for arg in get_args(annotation))
+    try:
+        from langchain_core.callbacks.manager import (
+            AsyncCallbackManagerForToolRun,
+            CallbackManagerForToolRun,
+        )
+    except ImportError:
+        return False
+    return isinstance(annotation, type) and issubclass(
+        annotation, (CallbackManagerForToolRun, AsyncCallbackManagerForToolRun)
+    )
+
+
 class LangChainToolAdapter(BaseTool[BaseModel, Any]):
     """Allows you to wrap a LangChain tool and make it available to AutoGen.
 
@@ -165,12 +192,33 @@ def __init__(self, langchain_tool: LangChainTool):
             args_type = self._langchain_tool.args_schema  # pyright: ignore
         else:
             # Infer args_type from the callable's signature
-            sig = inspect.signature(cast(Callable[..., Any], self._callable))  # type: ignore
-            fields = {
-                k: (v.annotation, Field(...))
-                for k, v in sig.parameters.items()
-                if k != "self" and v.kind not in (inspect.Parameter.VAR_POSITIONAL, inspect.Parameter.VAR_KEYWORD)
-            }
+            callable_ = cast(Callable[..., Any], self._callable)  # type: ignore
+            sig = inspect.signature(callable_)
+            # Resolve annotations (they may be strings under
+            # ``from __future__ import annotations``) so we can recognize
+            # callback-manager-typed parameters.
+            try:
+                type_hints = get_type_hints(callable_)
+            except Exception:
+                type_hints = {}
+            fields = {}
+            for k, v in sig.parameters.items():
+                if k == "self" or v.kind in (
+                    inspect.Parameter.VAR_POSITIONAL,
+                    inspect.Parameter.VAR_KEYWORD,
+                ):
+                    continue
+                # LangChain injects a ``run_manager``
+                # (CallbackManagerForToolRun / its async variant) into a tool's
+                # ``_run``/``_arun`` at run time. It is not a user-facing
+                # argument and pydantic cannot build a schema for it, so skip
+                # it. We detect it by annotation when resolvable and, as a
+                # fallback for unresolvable (string) annotations, by the
+                # conventional parameter name.
+                annotation = type_hints.get(k, v.annotation)
+                if _is_callback_manager_annotation(annotation) or k == "run_manager":
+                    continue
+                fields[k] = (annotation, Field(...))
             args_type = create_model(f"{name}Args", **fields)  # type: ignore
             # Note: type ignore is used due to a LangChain typing limitation
 

python/packages/autogen-ext/tests/tools/test_langchain_tools.py

@@ -100,3 +100,38 @@ async def test_langchain_tool_adapter(caplog: pytest.LogCaptureFixture) -> None:
     # Test run method for CustomCalculatorTool
     custom_result = await custom_adapter.run_json({"a": 3, "b": 4}, CancellationToken())
     assert custom_result == 12
+
+
+class NoSchemaTool(LangChainTool):
+    name: str = "NoSchema"
+    description: str = "a tool without an explicit args schema"
+
+    def _run(self, a: int, b: int, run_manager: Optional[CallbackManagerForToolRun] = None) -> int:
+        return a + b
+
+    async def _arun(
+        self,
+        a: int,
+        b: int,
+        run_manager: Optional[AsyncCallbackManagerForToolRun] = None,
+    ) -> int:
+        return a + b
+
+
+@pytest.mark.asyncio
+async def test_langchain_tool_adapter_skips_run_manager() -> None:
+    # Tools without an explicit args_schema get their args inferred from the
+    # callable's signature. LangChain injects a ``run_manager`` into ``_run``
+    # which is not a user-facing input and cannot be turned into a pydantic
+    # schema; the adapter must skip it (see #6385).
+    tool = NoSchemaTool()
+    adapter = LangChainToolAdapter(tool)  # type: ignore
+
+    schema = adapter.schema
+    assert schema["name"] == "NoSchema"
+    props = schema["parameters"]["properties"]
+    assert set(props.keys()) == {"a", "b"}
+    assert set(schema["parameters"]["required"]) == {"a", "b"}
+
+    result = await adapter.run_json({"a": 2, "b": 3}, CancellationToken())
+    assert result == 5