fix(anthropic-adapter): keep disabled-thinking reasoning_effort a plain string

Guard against reasoning_auto_summary wrapping "none" into a dict when
thinking is disabled — there's no reasoning trace to summarize, and
non-Claude providers (e.g. Fireworks) expect reasoning_effort as a
plain string.

litellm/llms/anthropic/experimental_pass_through/adapters/transformation.py

@@ -989,12 +989,18 @@ class LiteLLMAnthropicMessagesAdapter:
         if not reasoning_effort:
             return
 
+        thinking_type = thinking.get("type") if isinstance(thinking, dict) else None
+
         # For adaptive thinking, override with output_config.effort if available
-        if isinstance(thinking, dict) and thinking.get("type") == "adaptive":
+        if thinking_type == "adaptive":
             output_config = anthropic_message_request.get("output_config")
             if isinstance(output_config, dict) and output_config.get("effort"):
                 reasoning_effort = output_config["effort"]
 
+        if thinking_type == "disabled":
+            new_kwargs["reasoning_effort"] = reasoning_effort
+            return
+
         summary = thinking.get("summary") if isinstance(thinking, dict) else None
         auto_summary = is_reasoning_auto_summary_enabled()
         if summary:

tests/test_litellm/llms/anthropic/experimental_pass_through/adapters/test_anthropic_experimental_pass_through_adapters_transformation.py

@@ -1593,6 +1593,23 @@ def test_thinking_disabled_translated_to_reasoning_effort_none_for_non_claude_mo
     assert new_kwargs["reasoning_effort"] == "none"
 
 
+def test_thinking_disabled_stays_plain_string_when_auto_summary_enabled():
+    import litellm
+
+    adapter = LiteLLMAnthropicMessagesAdapter()
+    thinking = {"type": "disabled"}
+
+    original = litellm.reasoning_auto_summary
+    try:
+        litellm.reasoning_auto_summary = True
+        new_kwargs = {"model": CACHE_CONTROL_NON_ANTHROPIC_MODEL}
+        adapter._translate_thinking_to_openai(cast(Any, {"thinking": thinking}), cast(Any, new_kwargs))
+    finally:
+        litellm.reasoning_auto_summary = original
+
+    assert new_kwargs["reasoning_effort"] == "none"
+
+
 def test_stop_sequences_translated_to_stop_for_non_claude_model():
     from litellm.types.llms.anthropic import AnthropicMessagesRequest