diff --git a/nanobot/agent/memory.py b/nanobot/agent/memory.py
index 1301d47..e7eac88 100644
--- a/nanobot/agent/memory.py
+++ b/nanobot/agent/memory.py
@@ -57,6 +57,20 @@ def _normalize_save_memory_args(args: Any) -> dict[str, Any] | None:
         return args[0] if args and isinstance(args[0], dict) else None
     return args if isinstance(args, dict) else None
 
+_TOOL_CHOICE_ERROR_MARKERS = (
+    "tool_choice",
+    "toolchoice",
+    "does not support",
+    'should be ["none", "auto"]',
+)
+
+
+def _is_tool_choice_unsupported(content: str | None) -> bool:
+    """Detect provider errors caused by forced tool_choice being unsupported."""
+    text = (content or "").lower()
+    return any(m in text for m in _TOOL_CHOICE_ERROR_MARKERS)
+
+
 class MemoryStore:
     """Two-layer memory: MEMORY.md (long-term facts) + HISTORY.md (grep-searchable log)."""
 
@@ -118,15 +132,33 @@ class MemoryStore:
         ]
 
         try:
+            forced = {"type": "function", "function": {"name": "save_memory"}}
             response = await provider.chat_with_retry(
                 messages=chat_messages,
                 tools=_SAVE_MEMORY_TOOL,
                 model=model,
-                tool_choice={"type": "function", "function": {"name": "save_memory"}},
+                tool_choice=forced,
             )
 
+            if response.finish_reason == "error" and _is_tool_choice_unsupported(
+                response.content
+            ):
+                logger.warning("Forced tool_choice unsupported, retrying with auto")
+                response = await provider.chat_with_retry(
+                    messages=chat_messages,
+                    tools=_SAVE_MEMORY_TOOL,
+                    model=model,
+                    tool_choice="auto",
+                )
+
             if not response.has_tool_calls:
-                logger.warning("Memory consolidation: LLM did not call save_memory, skipping")
+                logger.warning(
+                    "Memory consolidation: LLM did not call save_memory "
+                    "(finish_reason={}, content_len={}, content_preview={})",
+                    response.finish_reason,
+                    len(response.content or ""),
+                    (response.content or "")[:200],
+                )
                 return False
 
             args = _normalize_save_memory_args(response.tool_calls[0].arguments)
diff --git a/tests/test_memory_consolidation_types.py b/tests/test_memory_consolidation_types.py
index 69be858..f1280fc 100644
--- a/tests/test_memory_consolidation_types.py
+++ b/tests/test_memory_consolidation_types.py
@@ -288,3 +288,60 @@ class TestMemoryConsolidationTypeHandling:
         assert "temperature" not in kwargs
         assert "max_tokens" not in kwargs
         assert "reasoning_effort" not in kwargs
+
+    @pytest.mark.asyncio
+    async def test_tool_choice_fallback_on_unsupported_error(self, tmp_path: Path) -> None:
+        """Forced tool_choice rejected by provider -> retry with auto and succeed."""
+        store = MemoryStore(tmp_path)
+        error_resp = LLMResponse(
+            content="Error calling LLM: litellm.BadRequestError: "
+            "The tool_choice parameter does not support being set to required or object",
+            finish_reason="error",
+            tool_calls=[],
+        )
+        ok_resp = _make_tool_response(
+            history_entry="[2026-01-01] Fallback worked.",
+            memory_update="# Memory\nFallback OK.",
+        )
+
+        call_log: list[dict] = []
+
+        async def _tracking_chat(**kwargs):
+            call_log.append(kwargs)
+            return error_resp if len(call_log) == 1 else ok_resp
+
+        provider = AsyncMock()
+        provider.chat_with_retry = AsyncMock(side_effect=_tracking_chat)
+        messages = _make_messages(message_count=60)
+
+        result = await store.consolidate(messages, provider, "test-model")
+
+        assert result is True
+        assert len(call_log) == 2
+        assert isinstance(call_log[0]["tool_choice"], dict)
+        assert call_log[1]["tool_choice"] == "auto"
+        assert "Fallback worked." in store.history_file.read_text()
+
+    @pytest.mark.asyncio
+    async def test_tool_choice_fallback_auto_no_tool_call(self, tmp_path: Path) -> None:
+        """Forced rejected, auto retry also produces no tool call -> return False."""
+        store = MemoryStore(tmp_path)
+        error_resp = LLMResponse(
+            content="Error: tool_choice must be none or auto",
+            finish_reason="error",
+            tool_calls=[],
+        )
+        no_tool_resp = LLMResponse(
+            content="Here is a summary.",
+            finish_reason="stop",
+            tool_calls=[],
+        )
+
+        provider = AsyncMock()
+        provider.chat_with_retry = AsyncMock(side_effect=[error_resp, no_tool_resp])
+        messages = _make_messages(message_count=60)
+
+        result = await store.consolidate(messages, provider, "test-model")
+
+        assert result is False
+        assert not store.history_file.exists()