fix(memory): handle list type tool call arguments

Some LLM providers return tool_calls[0].arguments as a list instead of
dict or str. Add handling to extract the first dict element from the list.

Fixes /new command warning: 'unexpected arguments type list'

nanobot/agent/memory.py

@@ -128,6 +128,13 @@ class MemoryStore:
             # Some providers return arguments as a JSON string instead of dict
             if isinstance(args, str):
                 args = json.loads(args)
+            # Some providers return arguments as a list (handle edge case)
+            if isinstance(args, list):
+                if args and isinstance(args[0], dict):
+                    args = args[0]
+                else:
+                    logger.warning("Memory consolidation: unexpected arguments type list with non-dict content")
+                    return False
             if not isinstance(args, dict):
                 logger.warning("Memory consolidation: unexpected arguments type {}", type(args).__name__)
                 return False

tests/test_memory_consolidation_types.py

@@ -145,3 +145,78 @@ class TestMemoryConsolidationTypeHandling:
 
         assert result is True
         provider.chat.assert_not_called()
+
+    @pytest.mark.asyncio
+    async def test_list_arguments_extracts_first_dict(self, tmp_path: Path) -> None:
+        """Some providers return arguments as a list - extract first element if it's a dict."""
+        store = MemoryStore(tmp_path)
+        provider = AsyncMock()
+
+        # Simulate arguments being a list containing a dict
+        response = LLMResponse(
+            content=None,
+            tool_calls=[
+                ToolCallRequest(
+                    id="call_1",
+                    name="save_memory",
+                    arguments=[{
+                        "history_entry": "[2026-01-01] User discussed testing.",
+                        "memory_update": "# Memory\nUser likes testing.",
+                    }],
+                )
+            ],
+        )
+        provider.chat = AsyncMock(return_value=response)
+        session = _make_session(message_count=60)
+
+        result = await store.consolidate(session, provider, "test-model", memory_window=50)
+
+        assert result is True
+        assert "User discussed testing." in store.history_file.read_text()
+        assert "User likes testing." in store.memory_file.read_text()
+
+    @pytest.mark.asyncio
+    async def test_list_arguments_empty_list_returns_false(self, tmp_path: Path) -> None:
+        """Empty list arguments should return False."""
+        store = MemoryStore(tmp_path)
+        provider = AsyncMock()
+
+        response = LLMResponse(
+            content=None,
+            tool_calls=[
+                ToolCallRequest(
+                    id="call_1",
+                    name="save_memory",
+                    arguments=[],
+                )
+            ],
+        )
+        provider.chat = AsyncMock(return_value=response)
+        session = _make_session(message_count=60)
+
+        result = await store.consolidate(session, provider, "test-model", memory_window=50)
+
+        assert result is False
+
+    @pytest.mark.asyncio
+    async def test_list_arguments_non_dict_content_returns_false(self, tmp_path: Path) -> None:
+        """List with non-dict content should return False."""
+        store = MemoryStore(tmp_path)
+        provider = AsyncMock()
+
+        response = LLMResponse(
+            content=None,
+            tool_calls=[
+                ToolCallRequest(
+                    id="call_1",
+                    name="save_memory",
+                    arguments=["string", "content"],
+                )
+            ],
+        )
+        provider.chat = AsyncMock(return_value=response)
+        session = _make_session(message_count=60)
+
+        result = await store.consolidate(session, provider, "test-model", memory_window=50)
+
+        assert result is False