diff --git a/libs/partners/ollama/langchain_ollama/chat_models.py b/libs/partners/ollama/langchain_ollama/chat_models.py
index 24d059573c..f39c697d65 100644
--- a/libs/partners/ollama/langchain_ollama/chat_models.py
+++ b/libs/partners/ollama/langchain_ollama/chat_models.py
@@ -83,7 +83,7 @@ from langchain_core.utils.function_calling import (
     convert_to_openai_tool,
 )
 from langchain_core.utils.pydantic import TypeBaseModel, is_basemodel_subclass
-from ollama import AsyncClient, Client, Message
+from ollama import AsyncClient, Client, Message, ResponseError
 from pydantic import BaseModel, PrivateAttr, field_validator, model_validator
 from pydantic.json_schema import JsonSchemaValue
 from pydantic.v1 import BaseModel as BaseModelV1
@@ -117,6 +117,30 @@ def _get_usage_metadata_from_generation_info(
     return None
 
 
+def _reraise_if_malformed_tool_call_response(exc: ResponseError) -> None:
+    """Re-raise an Ollama tool-call parsing failure as an `OutputParserException`.
+
+    The `ollama` client parses streamed tool-call arguments internally and
+    raises a bare `ResponseError` (e.g. `"error parsing tool call: raw='...',
+    err=..."`) when the model produces malformed JSON. That error never
+    reaches `_parse_json_string`, so it otherwise propagates as an opaque,
+    non-`langchain_core` exception that crashes the entire agent run instead
+    of being recognized as a tool-call parsing issue.
+
+    Args:
+        exc: The `ResponseError` raised by the `ollama` client.
+
+    Raises:
+        OutputParserException: If `exc` is a tool-call parsing failure.
+    """
+    if "error parsing tool call" in str(exc.error):
+        msg = (
+            "Ollama returned a tool call with malformed JSON arguments that "
+            f"could not be parsed: {exc.error}"
+        )
+        raise OutputParserException(msg) from exc
+
+
 def _parse_json_string(
     json_string: str,
     *,
@@ -1109,11 +1133,15 @@ class ChatOllama(BaseChatModel):
             raise RuntimeError(msg)
         chat_params = self._chat_params(messages, stop, **kwargs)
 
-        if chat_params["stream"]:
-            async for part in await self._async_client.chat(**chat_params):
-                yield part
-        else:
-            yield await self._async_client.chat(**chat_params)
+        try:
+            if chat_params["stream"]:
+                async for part in await self._async_client.chat(**chat_params):
+                    yield part
+            else:
+                yield await self._async_client.chat(**chat_params)
+        except ResponseError as e:
+            _reraise_if_malformed_tool_call_response(e)
+            raise
 
     def _create_chat_stream(
         self,
@@ -1129,10 +1157,14 @@ class ChatOllama(BaseChatModel):
             raise RuntimeError(msg)
         chat_params = self._chat_params(messages, stop, **kwargs)
 
-        if chat_params["stream"]:
-            yield from self._client.chat(**chat_params)
-        else:
-            yield self._client.chat(**chat_params)
+        try:
+            if chat_params["stream"]:
+                yield from self._client.chat(**chat_params)
+            else:
+                yield self._client.chat(**chat_params)
+        except ResponseError as e:
+            _reraise_if_malformed_tool_call_response(e)
+            raise
 
     def _chat_stream_with_aggregation(
         self,
diff --git a/libs/partners/ollama/tests/unit_tests/test_chat_models.py b/libs/partners/ollama/tests/unit_tests/test_chat_models.py
index 7efaa8e9cb..11d759555e 100644
--- a/libs/partners/ollama/tests/unit_tests/test_chat_models.py
+++ b/libs/partners/ollama/tests/unit_tests/test_chat_models.py
@@ -10,6 +10,7 @@ import pytest
 from langchain_core.exceptions import OutputParserException
 from langchain_core.messages import AIMessage, BaseMessage, ChatMessage, HumanMessage
 from langchain_tests.unit_tests import ChatModelUnitTests
+from ollama import ResponseError
 
 from langchain_ollama.chat_models import (
     ChatOllama,
@@ -788,6 +789,55 @@ def test_invoke_raises_when_client_none() -> None:
             llm.invoke([HumanMessage("Hello")])
 
 
+def test_malformed_tool_call_response_error_is_output_parser_exception() -> None:
+    """A malformed tool-call `ResponseError` from `ollama` becomes an
+    `OutputParserException` instead of propagating as an opaque error.
+
+    See issue #34746: Ollama's client raises a bare `ResponseError` when the
+    model streams invalid JSON tool-call arguments, crashing the agent with
+    a confusing error instead of a recognizable parsing failure.
+    """
+    with patch("langchain_ollama.chat_models.Client") as mock_client_class:
+        mock_client = MagicMock()
+        mock_client_class.return_value = mock_client
+        mock_client.chat.side_effect = ResponseError(
+            "error parsing tool call: raw='{\"foo\": ' err=unexpected end of JSON input"
+        )
+
+        llm = ChatOllama(model=MODEL_NAME)
+        with pytest.raises(OutputParserException, match="error parsing tool call"):
+            llm.invoke([HumanMessage("Hello")])
+
+
+async def test_malformed_tool_call_response_error_output_parser_async() -> None:
+    """Async counterpart of the malformed tool-call `ResponseError` handling."""
+    with patch("langchain_ollama.chat_models.AsyncClient") as mock_client_class:
+        mock_client = MagicMock()
+        mock_client_class.return_value = mock_client
+        mock_client.chat = AsyncMock(
+            side_effect=ResponseError(
+                "error parsing tool call: raw='{\"foo\": ' err=unexpected end of "
+                "JSON input"
+            )
+        )
+
+        llm = ChatOllama(model=MODEL_NAME)
+        with pytest.raises(OutputParserException, match="error parsing tool call"):
+            await llm.ainvoke([HumanMessage("Hello")])
+
+
+def test_unrelated_response_error_is_not_wrapped() -> None:
+    """A `ResponseError` unrelated to tool-call parsing propagates unchanged."""
+    with patch("langchain_ollama.chat_models.Client") as mock_client_class:
+        mock_client = MagicMock()
+        mock_client_class.return_value = mock_client
+        mock_client.chat.side_effect = ResponseError("model not found", 404)
+
+        llm = ChatOllama(model=MODEL_NAME)
+        with pytest.raises(ResponseError, match="model not found"):
+            llm.invoke([HumanMessage("Hello")])
+
+
 def test_chat_ollama_ignores_strict_arg() -> None:
     """Test that ChatOllama ignores the 'strict' argument."""
     response = [