fix(bedrock_mantle): parse converse passthrough bodies with the converse shape config for logging

litellm/llms/bedrock_mantle/passthrough/transformation.py

@@ -1,12 +1,19 @@
 from collections.abc import Mapping
-from typing import Final, Literal
+from typing import TYPE_CHECKING, Final, Literal, Optional
 
+from httpx import Response
+
+from litellm.litellm_core_utils.litellm_logging import Logging
 from litellm.llms.bedrock.passthrough.transformation import BedrockPassthroughConfig
 from litellm.llms.bedrock_mantle.common_utils import (
     MANTLE_HOST_RE,
     resolve_mantle_bearer_token,
     resolve_mantle_region,
 )
+from litellm.types.utils import LlmProviders
+
+if TYPE_CHECKING:
+    from litellm.types.utils import CostResponseTypes
 
 
 class BedrockMantlePassthroughConfig(BedrockPassthroughConfig):
@@ -42,3 +49,23 @@ class BedrockMantlePassthroughConfig(BedrockPassthroughConfig):
     def get_bedrock_bearer_token(self, litellm_params: Mapping[str, object]) -> str | None:
         api_key: Final = litellm_params.get("api_key")
         return resolve_mantle_bearer_token(api_key if isinstance(api_key, str) else None)
+
+    def logging_non_streaming_response(
+        self,
+        model: str,
+        custom_llm_provider: str,
+        httpx_response: Response,
+        request_data: dict,  # mutable-ok: mirrors the inherited BedrockPassthroughConfig signature
+        logging_obj: Logging,
+        endpoint: str,
+    ) -> Optional["CostResponseTypes"]:
+        is_converse: Final = "invoke" not in endpoint and "converse" in endpoint
+        shape_provider: Final = LlmProviders.BEDROCK.value if is_converse else custom_llm_provider
+        return super().logging_non_streaming_response(
+            model=model,
+            custom_llm_provider=shape_provider,
+            httpx_response=httpx_response,
+            request_data=request_data,
+            logging_obj=logging_obj,
+            endpoint=endpoint,
+        )

tests/test_litellm/llms/bedrock_mantle/passthrough/test_bedrock_mantle_passthrough_transformation.py

@@ -14,6 +14,7 @@ from litellm.utils import ProviderConfigManager
 
 MANTLE_API_BASE = "https://bedrock-mantle.us-east-2.api.aws"
 INVOKE_ENDPOINT = "model/us.openai.gpt-5.6-sol/invoke"
+CONVERSE_ENDPOINT = "model/us.openai.gpt-5.6-sol/converse"
 REQUEST_BODY = {"messages": [{"role": "user", "content": "say pong"}], "max_completion_tokens": 64}
 
 
@@ -150,3 +151,47 @@ def test_invoke_passthrough_route_reaches_bedrock_runtime_for_a_mantle_deploymen
     assert str(sent["url"]) == f"https://bedrock-runtime.us-east-2.amazonaws.com/{INVOKE_ENDPOINT}"
     assert sent["headers"]["Authorization"] == f"Bearer {expected_bearer}"
     assert json.loads(sent["content"]) == REQUEST_BODY
+
+
+def _logged_model_response(endpoint, body):
+    request = httpx.Request("POST", f"https://bedrock-runtime.us-east-1.amazonaws.com/{endpoint}")
+    return BedrockMantlePassthroughConfig().logging_non_streaming_response(
+        model="us.openai.gpt-5.6-sol",
+        custom_llm_provider="bedrock_mantle",
+        httpx_response=httpx.Response(200, json=body, request=request),
+        request_data={"messages": [{"role": "user", "content": [{"text": "say pong"}]}]},
+        logging_obj=MagicMock(),
+        endpoint=endpoint,
+    )
+
+
+def test_converse_logging_parses_the_converse_response_shape():
+    result = _logged_model_response(
+        CONVERSE_ENDPOINT,
+        {
+            "metrics": {"latencyMs": 800.0},
+            "output": {"message": {"content": [{"text": "pong"}], "role": "assistant"}},
+            "stopReason": "end_turn",
+            "usage": {"inputTokens": 8, "outputTokens": 5, "totalTokens": 13},
+        },
+    )
+    assert result.choices[0].message.content == "pong"
+    assert result.usage.prompt_tokens == 8
+    assert result.usage.completion_tokens == 5
+
+
+def test_invoke_logging_parses_the_openai_chat_response_shape():
+    result = _logged_model_response(
+        INVOKE_ENDPOINT,
+        {
+            "choices": [{"finish_reason": "stop", "index": 0, "message": {"content": "pong", "role": "assistant"}}],
+            "created": 1787677792,
+            "id": "chatcmpl-regression",
+            "model": "us.openai.gpt-5.6-sol",
+            "object": "chat.completion",
+            "usage": {"completion_tokens": 5, "prompt_tokens": 8, "total_tokens": 13},
+        },
+    )
+    assert result.choices[0].message.content == "pong"
+    assert result.usage.prompt_tokens == 8
+    assert result.usage.completion_tokens == 5