fix(vertex): propagate Vertex AI metadata in streaming success callbacks

[Sameerlite]

Summary

• Propagate vertex_ai_grounding_metadata, vertex_ai_url_context_metadata, and related Vertex fields from streaming chunks into the assembled ModelResponse used by stream_chunk_builder
• Mirror non-streaming behavior by storing the same metadata on Gemini streaming chunk _hidden_params
• Ensures standard_logging_object.response includes Vertex metadata for streaming calls in success callbacks

Fixes LIT-3494

Test plan

• pytest tests/test_litellm/litellm_core_utils/test_streaming_chunk_builder_utils.py::test_stream_chunk_builder_propagates_vertex_ai_metadata_from_chunks -v
• pytest tests/test_litellm/litellm_core_utils/test_streaming_chunk_builder_utils.py::test_stream_chunk_builder_propagates_vertex_ai_metadata_from_dict_chunks -v
• pytest tests/test_litellm/litellm_core_utils/test_litellm_logging.py::test_streaming_success_handler_includes_vertex_ai_metadata_in_standard_logging -v

---

Note

Medium Risk
Changes streaming assembly and logging/redaction paths; incorrect metadata merge could affect observability, while redaction touches sensitive grounding/URL data in logs.

Overview
Streaming Vertex/Gemini calls now merge provider metadata from chunks into the assembled ModelResponse used by stream_chunk_builder, so success callbacks and standard_logging_object.response can include the same vertex_ai_* fields as non-streaming responses.

A new provider hook apply_assembled_streaming_response_metadata runs after assembly (using the resolved response.model and optional custom_llm_provider from logging). Vertex Gemini implements it by aggregating grounding, URL context, safety, and citation metadata from chunk attributes, model_extra, and _hidden_params (including dict/model_dump chunks). Inline stream finalization reuses _set_stream_metadata_on_response for consistency with _hidden_params.

Message redaction is extended to strip those Vertex metadata fields from standard logging payloads, assembled streaming responses, deep-copied results, and litellm_params.metadata.hidden_params after success_handler merges _hidden_params.

^{Reviewed by Cursor Bugbot for commit 15507be. Bugbot is set up for automated code reviews on this repo. Configure here.}

[codecov]

Codecov Report

❌ Patch coverage is 92.24806% with 10 lines in your changes missing coverage. Please review.

Files with missing lines	Patch %	Lines
...ex_ai/gemini/vertex_and_google_ai_studio_gemini.py	88.33%	7 Missing ⚠️
litellm/llms/vertex_ai/common_utils.py	93.54%	2 Missing ⚠️
...itellm_core_utils/streaming_chunk_builder_utils.py	95.83%	1 Missing ⚠️

📢 Thoughts on this report? Let us know!

[greptile-apps]

Greptile Summary

This PR propagates Vertex AI provider metadata (vertex_ai_grounding_metadata, vertex_ai_url_context_metadata, vertex_ai_safety_ratings, vertex_ai_safety_results, vertex_ai_citation_metadata) from streaming chunks into the assembled ModelResponse used by stream_chunk_builder, making streaming success-callback payloads consistent with non-streaming ones. Redaction is extended to strip these fields from all logging surfaces including standard_logging_object.response, the deep-copied result, and litellm_params.metadata.hidden_params.

• Introduces a provider-agnostic apply_provider_assembled_streaming_metadata hook on BaseConfig called from both stream_chunk_builder return paths; VertexGeminiConfig implements it by aggregating metadata from chunk attributes, model_extra, and _hidden_params.
• Refactors inline stream chunk finalization to reuse a new _set_stream_metadata_on_response helper, eliminating duplicated setattr calls and adding conditional _hidden_params writes.
• Adds VERTEX_AI_PROVIDER_METADATA_FIELDS as a single source of truth in litellm/types/llms/vertex_ai.py and ships unit tests for all new code paths.

Confidence Score: 5/5

Safe to merge; all changes are additive, new metadata fields flow through existing logging/redaction paths, and the hook mechanism gracefully degrades on unknown providers.

The assembly and redaction changes are well-contained, with tests covering object-chunk, dict-chunk, and _hidden_params-only paths. The provider hook is properly guarded with a broad exception handler that logs at debug level, so failures in provider resolution do not affect response correctness.

litellm/litellm_core_utils/redact_messages.py imports Vertex-specific helpers directly rather than going through the same provider hook pattern used for assembly.

Important Files Changed

Filename	Overview
litellm/litellm_core_utils/redact_messages.py	Extends redaction to strip Vertex AI metadata from all response shapes; introduces a direct import of Vertex-specific helpers into a core utility, violating provider-isolation conventions.
litellm/litellm_core_utils/streaming_chunk_builder_utils.py	Adds a provider-agnostic apply_provider_assembled_streaming_metadata hook that resolves the provider config and delegates to its implementation; no Vertex-specific code remains in this file.
litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py	Extracts _set_stream_metadata_on_response to deduplicate inline-stream finalization logic; adds apply_assembled_streaming_response_metadata hook that aggregates metadata across all chunks into the final assembled response.
litellm/llms/vertex_ai/common_utils.py	Adds two redaction helpers that clear Vertex metadata fields from objects and dicts; correctly placed within the provider module.
litellm/llms/base_llm/chat/transformation.py	Adds no-op apply_assembled_streaming_response_metadata hook to BaseConfig; clean extension point for providers.
litellm/types/llms/vertex_ai.py	Introduces VERTEX_AI_PROVIDER_METADATA_FIELDS tuple as the single source of truth for all Vertex metadata field names.
litellm/main.py	Calls apply_provider_assembled_streaming_metadata at both early-return and normal-return paths of stream_chunk_builder; changes are minimal and correctly placed.

_{Reviews (9): Last reviewed commit: "refactor(vertex): move Vertex metadata r..." | Re-trigger Greptile}

[Sameerlite]

@greptileai

[veria-ai]

PR overview

All previously flagged issues have been addressed. No open security concerns remain on this pull request.

Security review

No open security issues remain on this pull request.

Fixed/addressed: 2 · PR risk: 0/10

[Sameerlite]

bugbot run

[cursor]

Cursor Bugbot has reviewed your changes using default effort and found 1 potential issue.

Autofix Details

Bugbot Autofix prepared a fix for the issue found in the latest run.

• ✅ Fixed: Wrong model for provider hook
  • The provider metadata hook now resolves provider config from the assembled response model and has a regression test for mismatched streaming chunk models preserving Vertex metadata.

Preview (098cca6d5d)

diff --git a/litellm/litellm_core_utils/redact_messages.py b/litellm/litellm_core_utils/redact_messages.py
--- a/litellm/litellm_core_utils/redact_messages.py
+++ b/litellm/litellm_core_utils/redact_messages.py
@@ -29,7 +29,15 @@
 else:
     LiteLLMLoggingObject = Any
 
+VERTEX_PROVIDER_METADATA_FIELDS = (
+    "vertex_ai_grounding_metadata",
+    "vertex_ai_url_context_metadata",
+    "vertex_ai_safety_ratings",
+    "vertex_ai_safety_results",
+    "vertex_ai_citation_metadata",
+)
 
+
 def redact_message_input_output_from_custom_logger(
     litellm_logging_obj: LiteLLMLoggingObject, result, custom_logger: CustomLogger
 ):
@@ -100,6 +108,26 @@
                     summary_item["text"] = redacted_str
 
 
+def _redact_vertex_provider_metadata(obj: Any) -> None:
+    if isinstance(obj, dict):
+        for field in VERTEX_PROVIDER_METADATA_FIELDS:
+            if field in obj:
+                obj[field] = []
+        hidden_params = obj.get("_hidden_params")
+        if isinstance(hidden_params, dict):
+            for field in VERTEX_PROVIDER_METADATA_FIELDS:
+                hidden_params.pop(field, None)
+        return
+
+    for field in VERTEX_PROVIDER_METADATA_FIELDS:
+        if hasattr(obj, field):
+            setattr(obj, field, [])
+    hidden_params = getattr(obj, "_hidden_params", None)
+    if isinstance(hidden_params, dict):
+        for field in VERTEX_PROVIDER_METADATA_FIELDS:
+            hidden_params.pop(field, None)
+
+
 def _redact_standard_logging_object(model_call_details: dict):
     """Redact messages and response inside standard_logging_object if present."""
     standard_logging_object = model_call_details.get("standard_logging_object")
@@ -119,10 +147,12 @@
             # ResponsesAPIResponse format - redact content in output items
             if isinstance(response.get("output"), list):
                 _redact_responses_api_output_dict(response["output"], redacted_str)
+            _redact_vertex_provider_metadata(response)
         elif isinstance(response, dict) and "choices" in response:
             # ModelResponse dict format - redact content in choices
             if isinstance(response.get("choices"), list):
                 _redact_model_response_dict_choices(response["choices"], redacted_str)
+            _redact_vertex_provider_metadata(response)
         elif isinstance(response, str):
             standard_logging_object["response"] = redacted_str
         else:
@@ -174,6 +204,7 @@
         if hasattr(_streaming_response, "choices"):
             for choice in _streaming_response.choices:
                 _redact_choice_content(choice)
+            _redact_vertex_provider_metadata(_streaming_response)
         elif hasattr(_streaming_response, "output"):
             _redact_responses_api_output(_streaming_response.output)
             # Redact reasoning field in ResponsesAPIResponse
@@ -200,12 +231,14 @@
             if hasattr(_result, "choices") and _result.choices is not None:
                 for choice in _result.choices:
                     _redact_choice_content(choice)
+            _redact_vertex_provider_metadata(_result)
         elif isinstance(_result, dict) and "choices" in _result:
             # Handle dict representation of ModelResponse (e.g., from model_dump())
             if _result.get("choices") is not None:
                 _redact_model_response_dict_choices(
                     _result["choices"], "redacted-by-litellm"
                 )
+            _redact_vertex_provider_metadata(_result)
         elif isinstance(_result, dict) and "output" in _result:
             if isinstance(_result.get("output"), list):
                 _redact_responses_api_output_dict(

diff --git a/litellm/litellm_core_utils/streaming_chunk_builder_utils.py b/litellm/litellm_core_utils/streaming_chunk_builder_utils.py
--- a/litellm/litellm_core_utils/streaming_chunk_builder_utils.py
+++ b/litellm/litellm_core_utils/streaming_chunk_builder_utils.py
@@ -80,6 +80,50 @@
         return model_response
 
     @staticmethod
+    def apply_provider_assembled_streaming_metadata(
+        response: ModelResponse,
+        chunks: List[Any],
+        logging_obj: Optional[Any] = None,
+    ) -> None:
+        if not chunks:
+            return
+
+        model = getattr(response, "model", None)
+        if not model:
+            return
+
+        custom_llm_provider = None
+        if logging_obj is not None:
+            custom_llm_provider = logging_obj.model_call_details.get(
+                "custom_llm_provider"
+            )
+
+        try:
+            from litellm.litellm_core_utils.get_llm_provider_logic import (
+                get_llm_provider,
+            )
+            from litellm.types.utils import LlmProviders
+            from litellm.utils import ProviderConfigManager
+
+            if custom_llm_provider:
+                provider = LlmProviders(custom_llm_provider)
+            else:
+                _, provider_str, _, _ = get_llm_provider(model)
+                provider = LlmProviders(provider_str)
+
+            provider_config = ProviderConfigManager.get_provider_chat_config(
+                model=model,
+                provider=provider,
+            )
+            if provider_config is not None:
+                provider_config.apply_assembled_streaming_response_metadata(
+                    response=response,
+                    chunks=chunks,
+                )
+        except Exception:
+            return
+
+    @staticmethod
     def _get_chunk_id(chunks: List[Dict[str, Any]]) -> str:
         """
         Chunks:

diff --git a/litellm/llms/base_llm/chat/transformation.py b/litellm/llms/base_llm/chat/transformation.py
--- a/litellm/llms/base_llm/chat/transformation.py
+++ b/litellm/llms/base_llm/chat/transformation.py
@@ -442,6 +442,14 @@
         """Hook for providers to post-process streaming responses. Default: pass-through."""
         return stream
 
+    def apply_assembled_streaming_response_metadata(
+        self,
+        response: "ModelResponse",
+        chunks: List[Any],
+    ) -> None:
+        """Hook for providers to merge chunk metadata into assembled streaming responses."""
+        return None
+
     def calculate_additional_costs(
         self, model: str, prompt_tokens: int, completion_tokens: int
     ) -> Optional[dict]:

diff --git a/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py b/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py
--- a/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py
+++ b/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py
@@ -2253,7 +2253,72 @@
             citation_metadata,
         )
 
+    _STREAM_METADATA_FIELDS = (
+        "vertex_ai_grounding_metadata",
+        "vertex_ai_url_context_metadata",
+        "vertex_ai_safety_ratings",
+        "vertex_ai_citation_metadata",
+    )
+
     @staticmethod
+    def _get_stream_chunk_attr(chunk: Any, field_name: str) -> Any:
+        if isinstance(chunk, dict):
+            value = chunk.get(field_name)
+            if value is not None:
+                return value
+            model_extra = chunk.get("model_extra")
+            if isinstance(model_extra, dict):
+                return model_extra.get(field_name)
+            return None
+        return getattr(chunk, field_name, None)
+
+    @staticmethod
+    def _set_stream_metadata_on_response(
+        model_response: Any,
+        grounding_metadata: List[dict],
+        url_context_metadata: List[dict],
+        safety_ratings: List[dict],
+        citation_metadata: List[dict],
+    ) -> None:
+        setattr(model_response, "vertex_ai_grounding_metadata", grounding_metadata)  # type: ignore
+        if grounding_metadata:
+            model_response._hidden_params["vertex_ai_grounding_metadata"] = (
+                grounding_metadata
+            )
+        setattr(model_response, "vertex_ai_url_context_metadata", url_context_metadata)  # type: ignore
+        if url_context_metadata:
+            model_response._hidden_params["vertex_ai_url_context_metadata"] = (
+                url_context_metadata
+            )
+        setattr(model_response, "vertex_ai_safety_ratings", safety_ratings)  # type: ignore
+        if safety_ratings:
+            model_response._hidden_params["vertex_ai_safety_ratings"] = safety_ratings
+        setattr(model_response, "vertex_ai_citation_metadata", citation_metadata)  # type: ignore
+        if citation_metadata:
+            model_response._hidden_params["vertex_ai_citation_metadata"] = (
+                citation_metadata
+            )
+
+    def apply_assembled_streaming_response_metadata(
+        self,
+        response: ModelResponse,
+        chunks: List[Any],
+    ) -> None:
+        for field_name in VertexGeminiConfig._STREAM_METADATA_FIELDS:
+            merged: List[Any] = []
+            for chunk in chunks:
+                value = VertexGeminiConfig._get_stream_chunk_attr(chunk, field_name)
+                if not value:
+                    continue
+                if isinstance(value, list):
+                    merged.extend(value)
+                else:
+                    merged.append(value)
+            if merged:
+                setattr(response, field_name, merged)
+                response._hidden_params[field_name] = merged
+
+    @staticmethod
     def _convert_grounding_metadata_to_annotations(
         grounding_metadata: List[dict],
         content_text: Optional[str],
@@ -3385,10 +3450,13 @@
                 if choice.finish_reason == "stop":
                     choice.finish_reason = "tool_calls"
 
-        setattr(model_response, "vertex_ai_grounding_metadata", grounding_metadata)  # type: ignore
-        setattr(model_response, "vertex_ai_url_context_metadata", url_context_metadata)  # type: ignore
-        setattr(model_response, "vertex_ai_safety_ratings", safety_ratings)  # type: ignore
-        setattr(model_response, "vertex_ai_citation_metadata", citation_metadata)  # type: ignore
+        VertexGeminiConfig._set_stream_metadata_on_response(
+            model_response,
+            grounding_metadata,
+            url_context_metadata,
+            safety_ratings,
+            citation_metadata,
+        )
 
         return (
             grounding_metadata,

diff --git a/litellm/main.py b/litellm/main.py
--- a/litellm/main.py
+++ b/litellm/main.py
@@ -7761,6 +7761,9 @@
                     "cost",
                     logging_obj._response_cost_calculator(result=response),
                 )
+            processor.apply_provider_assembled_streaming_metadata(
+                response, chunks, logging_obj
+            )
             return response
 
         tool_call_chunks = [
@@ -7940,6 +7943,9 @@
                 usage, "cost", logging_obj._response_cost_calculator(result=response)
             )
 
+        processor.apply_provider_assembled_streaming_metadata(
+            response, chunks, logging_obj
+        )
         return response
     except Exception as e:
         verbose_logger.exception(

diff --git a/tests/test_litellm/litellm_core_utils/test_litellm_logging.py b/tests/test_litellm/litellm_core_utils/test_litellm_logging.py
--- a/tests/test_litellm/litellm_core_utils/test_litellm_logging.py
+++ b/tests/test_litellm/litellm_core_utils/test_litellm_logging.py
@@ -2165,6 +2165,41 @@
     assert assembled is result
 
 
+def test_streaming_success_handler_includes_vertex_ai_metadata_in_standard_logging():
+    """Assembled streaming responses should include Vertex AI metadata in logging payload."""
+    import datetime
+
+    from litellm.types.utils import Choices, Message
+
+    logging_obj = _make_logging_obj(stream=True)
+    grounding_metadata = [{"webSearchQueries": ["weather in SF"]}]
+    url_context_metadata = [{"urlMetadata": [{"retrievedUrl": "https://example.com"}]}]
+    result = ModelResponse(
+        id="resp-1",
+        choices=[
+            Choices(
+                index=0,
+                message=Message(role="assistant", content="hello"),
+                finish_reason="stop",
+            )
+        ],
+        model="gemini-2.5-flash",
+    )
+    setattr(result, "vertex_ai_grounding_metadata", grounding_metadata)
+    setattr(result, "vertex_ai_url_context_metadata", url_context_metadata)
+    result._hidden_params["vertex_ai_grounding_metadata"] = grounding_metadata
+    result._hidden_params["vertex_ai_url_context_metadata"] = url_context_metadata
+
+    start = datetime.datetime.now()
+    end = datetime.datetime.now()
+    logging_obj.success_handler(result=result, start_time=start, end_time=end)
+
+    payload = logging_obj.model_call_details.get("standard_logging_object")
+    assert payload is not None
+    assert payload["response"]["vertex_ai_grounding_metadata"] == grounding_metadata
+    assert payload["response"]["vertex_ai_url_context_metadata"] == url_context_metadata
+
+
 def test_get_assembled_streaming_response_returns_none_for_non_streaming_text_completion():
     """Non-streaming TextCompletionResponse should also return None."""
     import datetime

diff --git a/tests/test_litellm/litellm_core_utils/test_redact_messages.py b/tests/test_litellm/litellm_core_utils/test_redact_messages.py
--- a/tests/test_litellm/litellm_core_utils/test_redact_messages.py
+++ b/tests/test_litellm/litellm_core_utils/test_redact_messages.py
@@ -349,3 +349,66 @@
 
         assert redacted.output[0].content[0].text == "redacted-by-litellm"
         assert response.output[0].content[0].text == "sensitive output"
+
+    def test_redacts_vertex_provider_metadata_in_standard_logging_response(self):
+        details = {
+            "standard_logging_object": {
+                "messages": [{"role": "user", "content": "sensitive prompt"}],
+                "response": {
+                    "choices": [
+                        {
+                            "message": {
+                                "content": "sensitive answer",
+                                "role": "assistant",
+                            }
+                        }
+                    ],
+                    "vertex_ai_grounding_metadata": [
+                        {"webSearchQueries": ["sensitive search term"]}
+                    ],
+                    "vertex_ai_url_context_metadata": [
+                        {"urlMetadata": [{"retrievedUrl": "https://example.com"}]}
+                    ],
+                },
+            }
+        }
+
+        perform_redaction(details, None)
+
+        response = details["standard_logging_object"]["response"]
+        assert response["choices"][0]["message"]["content"] == "redacted-by-litellm"
+        assert response["vertex_ai_grounding_metadata"] == []
+        assert response["vertex_ai_url_context_metadata"] == []
+
+    def test_redacts_vertex_provider_metadata_on_streaming_model_response(self):
+        response = litellm.ModelResponse(
+            id="resp-1",
+            choices=[
+                litellm.Choices(
+                    message=litellm.Message(
+                        content="sensitive answer",
+                        role="assistant",
+                    )
+                )
+            ],
+            model="gemini-2.5-flash",
+        )
+        setattr(
+            response,
+            "vertex_ai_grounding_metadata",
+            [{"webSearchQueries": ["sensitive search term"]}],
+        )
+        response._hidden_params["vertex_ai_grounding_metadata"] = [
+            {"webSearchQueries": ["sensitive search term"]}
+        ]
+
+        details = {
+            "stream": True,
+            "complete_streaming_response": response,
+        }
+
+        perform_redaction(details, response)
+
+        assert response.choices[0].message.content == "redacted-by-litellm"
+        assert getattr(response, "vertex_ai_grounding_metadata") == []
+        assert "vertex_ai_grounding_metadata" not in response._hidden_params

diff --git a/tests/test_litellm/litellm_core_utils/test_streaming_chunk_builder_utils.py b/tests/test_litellm/litellm_core_utils/test_streaming_chunk_builder_utils.py
--- a/tests/test_litellm/litellm_core_utils/test_streaming_chunk_builder_utils.py
+++ b/tests/test_litellm/litellm_core_utils/test_streaming_chunk_builder_utils.py
@@ -613,3 +613,123 @@
     assert (
         response._hidden_params["provider_specific_fields"]["traffic_type"] == "default"
     )
+
+
+def test_stream_chunk_builder_propagates_vertex_ai_metadata_from_chunks():
+    """Vertex AI metadata on streaming chunks must appear on assembled response."""
+    grounding_metadata = [{"webSearchQueries": ["weather in SF"]}]
+    url_context_metadata = [{"urlMetadata": [{"retrievedUrl": "https://example.com"}]}]
+
+    chunk1 = ModelResponseStream(
+        id="chatcmpl-vertex-1",
+        created=1,
+        model="gemini-2.5-flash",
+        object="chat.completion.chunk",
+        choices=[
+            StreamingChoices(
+                finish_reason=None,
+                index=0,
+                delta=Delta(content="The weather", role="assistant"),
+            )
+        ],
+    )
+    setattr(chunk1, "vertex_ai_grounding_metadata", grounding_metadata)
+    chunk1._hidden_params["vertex_ai_grounding_metadata"] = grounding_metadata
+
+    chunk2 = ModelResponseStream(
+        id="chatcmpl-vertex-1",
+        created=1,
+        model="gemini-2.5-flash",
+        object="chat.completion.chunk",
+        choices=[
+            StreamingChoices(
+                finish_reason="stop",
+                index=0,
+                delta=Delta(content=" is sunny.", role="assistant"),
+            )
+        ],
+    )
+    setattr(chunk2, "vertex_ai_url_context_metadata", url_context_metadata)
+    chunk2._hidden_params["vertex_ai_url_context_metadata"] = url_context_metadata
+
+    response = stream_chunk_builder(chunks=[chunk1, chunk2])
+    assert response is not None
+    assert getattr(response, "vertex_ai_grounding_metadata") == grounding_metadata
+    assert getattr(response, "vertex_ai_url_context_metadata") == url_context_metadata
+    assert response._hidden_params["vertex_ai_grounding_metadata"] == grounding_metadata
+    assert (
+        response._hidden_params["vertex_ai_url_context_metadata"]
+        == url_context_metadata
+    )
+
+    dumped = response.model_dump()
+    assert dumped["vertex_ai_grounding_metadata"] == grounding_metadata
+    assert dumped["vertex_ai_url_context_metadata"] == url_context_metadata
+
+
+def test_stream_chunk_builder_uses_assembled_model_for_provider_metadata():
+    grounding_metadata = [{"webSearchQueries": ["weather in SF"]}]
+
+    chunk1 = ModelResponseStream(
+        id="chatcmpl-vertex-router",
+        created=1,
+        model="gpt-4o",
+        object="chat.completion.chunk",
+        choices=[
+            StreamingChoices(
+                finish_reason=None,
+                index=0,
+                delta=Delta(content="The weather", role="assistant"),
+            )
+        ],
+    )
+    chunk2 = ModelResponseStream(
+        id="chatcmpl-vertex-router",
+        created=1,
+        model="gemini-2.5-flash",
+        object="chat.completion.chunk",
+        choices=[
+            StreamingChoices(
+                finish_reason="stop",
+                index=0,
+                delta=Delta(content=" is sunny.", role=None),
+            )
+        ],
+    )
+    setattr(chunk2, "vertex_ai_grounding_metadata", grounding_metadata)
+    chunk2._hidden_params["vertex_ai_grounding_metadata"] = grounding_metadata
+
+    response = stream_chunk_builder(chunks=[chunk1, chunk2])
+    assert response is not None
+    assert response.model == "gemini-2.5-flash"
+    assert getattr(response, "vertex_ai_grounding_metadata") == grounding_metadata
+
+
+def test_stream_chunk_builder_propagates_vertex_ai_metadata_from_dict_chunks():
+    """Dict snapshot chunks (model_dump) should also propagate Vertex AI metadata."""
+    chunk_dict = ModelResponseStream(
+        id="chatcmpl-vertex-2",
+        created=1,
+        model="gemini-2.5-flash",
+        object="chat.completion.chunk",
+        choices=[
+            StreamingChoices(
+                finish_reason="stop",
+                index=0,
+                delta=Delta(content="hello", role="assistant"),
+            )
+        ],
+    ).model_dump()
+    chunk_dict["vertex_ai_grounding_metadata"] = [{"webSearchQueries": ["test query"]}]
+    chunk_dict["_hidden_params"] = {
+        "vertex_ai_grounding_metadata": [{"webSearchQueries": ["test query"]}]
+    }
+
+    response = stream_chunk_builder(chunks=[chunk_dict])
+    assert response is not None
+    assert getattr(response, "vertex_ai_grounding_metadata") == [
+        {"webSearchQueries": ["test query"]}
+    ]
+    assert response.model_dump()["vertex_ai_grounding_metadata"] == [
+        {"webSearchQueries": ["test query"]}
+    ]

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bugbot run

[cursor]

Cursor Bugbot has reviewed your changes using default effort and found 1 potential issue.

Autofix Details

Bugbot Autofix prepared a fix for the issue found in the latest run.

• ✅ Fixed: Dict chunks ignore hidden params
  • Dict stream chunks now fall back to _hidden_params when Vertex metadata is absent from top-level fields and model_extra, with a regression test covering hidden-param-only chunks.

Preview (15507be90a)

diff --git a/litellm/litellm_core_utils/redact_messages.py b/litellm/litellm_core_utils/redact_messages.py
--- a/litellm/litellm_core_utils/redact_messages.py
+++ b/litellm/litellm_core_utils/redact_messages.py
@@ -29,7 +29,15 @@
 else:
     LiteLLMLoggingObject = Any
 
+VERTEX_PROVIDER_METADATA_FIELDS = (
+    "vertex_ai_grounding_metadata",
+    "vertex_ai_url_context_metadata",
+    "vertex_ai_safety_ratings",
+    "vertex_ai_safety_results",
+    "vertex_ai_citation_metadata",
+)
 
+
 def redact_message_input_output_from_custom_logger(
     litellm_logging_obj: LiteLLMLoggingObject, result, custom_logger: CustomLogger
 ):
@@ -100,6 +108,50 @@
                     summary_item["text"] = redacted_str
 
 
+def _redact_vertex_provider_metadata(obj: Any) -> None:
+    if isinstance(obj, dict):
+        for field in VERTEX_PROVIDER_METADATA_FIELDS:
+            if field in obj:
+                obj[field] = []
+        hidden_params = obj.get("_hidden_params")
+        if isinstance(hidden_params, dict):
+            for field in VERTEX_PROVIDER_METADATA_FIELDS:
+                hidden_params.pop(field, None)
+        return
+
+    for field in VERTEX_PROVIDER_METADATA_FIELDS:
+        if hasattr(obj, field):
+            setattr(obj, field, [])
+    hidden_params = getattr(obj, "_hidden_params", None)
+    if isinstance(hidden_params, dict):
+        for field in VERTEX_PROVIDER_METADATA_FIELDS:
+            hidden_params.pop(field, None)
+
+
+def _redact_vertex_provider_metadata_from_litellm_params(
+    model_call_details: dict,
+) -> None:
+    """
+    Scrub Vertex provider metadata copied into litellm_params metadata.
+
+    success_handler() merges response._hidden_params into
+    litellm_params.metadata['hidden_params'] before perform_redaction() runs.
+    """
+    litellm_params = model_call_details.get("litellm_params")
+    if not isinstance(litellm_params, dict):
+        return
+
+    for metadata_key in ("metadata", "litellm_metadata"):
+        metadata = litellm_params.get(metadata_key)
+        if not isinstance(metadata, dict):
+            continue
+        hidden_params = metadata.get("hidden_params")
+        if not isinstance(hidden_params, dict):
+            continue
+        for field in VERTEX_PROVIDER_METADATA_FIELDS:
+            hidden_params.pop(field, None)
+
+
 def _redact_standard_logging_object(model_call_details: dict):
     """Redact messages and response inside standard_logging_object if present."""
     standard_logging_object = model_call_details.get("standard_logging_object")
@@ -119,10 +171,12 @@
             # ResponsesAPIResponse format - redact content in output items
             if isinstance(response.get("output"), list):
                 _redact_responses_api_output_dict(response["output"], redacted_str)
+            _redact_vertex_provider_metadata(response)
         elif isinstance(response, dict) and "choices" in response:
             # ModelResponse dict format - redact content in choices
             if isinstance(response.get("choices"), list):
                 _redact_model_response_dict_choices(response["choices"], redacted_str)
+            _redact_vertex_provider_metadata(response)
         elif isinstance(response, str):
             standard_logging_object["response"] = redacted_str
         else:
@@ -164,6 +218,7 @@
     model_call_details["prompt"] = ""
     model_call_details["input"] = ""
     _redact_standard_logging_object(model_call_details)
+    _redact_vertex_provider_metadata_from_litellm_params(model_call_details)
 
     # Redact streaming response
     if (
@@ -174,6 +229,7 @@
         if hasattr(_streaming_response, "choices"):
             for choice in _streaming_response.choices:
                 _redact_choice_content(choice)
+            _redact_vertex_provider_metadata(_streaming_response)
         elif hasattr(_streaming_response, "output"):
             _redact_responses_api_output(_streaming_response.output)
             # Redact reasoning field in ResponsesAPIResponse
@@ -200,12 +256,14 @@
             if hasattr(_result, "choices") and _result.choices is not None:
                 for choice in _result.choices:
                     _redact_choice_content(choice)
+            _redact_vertex_provider_metadata(_result)
         elif isinstance(_result, dict) and "choices" in _result:
             # Handle dict representation of ModelResponse (e.g., from model_dump())
             if _result.get("choices") is not None:
                 _redact_model_response_dict_choices(
                     _result["choices"], "redacted-by-litellm"
                 )
+            _redact_vertex_provider_metadata(_result)
         elif isinstance(_result, dict) and "output" in _result:
             if isinstance(_result.get("output"), list):
                 _redact_responses_api_output_dict(

diff --git a/litellm/litellm_core_utils/streaming_chunk_builder_utils.py b/litellm/litellm_core_utils/streaming_chunk_builder_utils.py
--- a/litellm/litellm_core_utils/streaming_chunk_builder_utils.py
+++ b/litellm/litellm_core_utils/streaming_chunk_builder_utils.py
@@ -80,6 +80,50 @@
         return model_response
 
     @staticmethod
+    def apply_provider_assembled_streaming_metadata(
+        response: ModelResponse,
+        chunks: List[Any],
+        logging_obj: Optional[Any] = None,
+    ) -> None:
+        if not chunks:
+            return
+
+        model = getattr(response, "model", None)
+        if not model:
+            return
+
+        custom_llm_provider = None
+        if logging_obj is not None:
+            custom_llm_provider = logging_obj.model_call_details.get(
+                "custom_llm_provider"
+            )
+
+        try:
+            from litellm.litellm_core_utils.get_llm_provider_logic import (
+                get_llm_provider,
+            )
+            from litellm.types.utils import LlmProviders
+            from litellm.utils import ProviderConfigManager
+
+            if custom_llm_provider:
+                provider = LlmProviders(custom_llm_provider)
+            else:
+                _, provider_str, _, _ = get_llm_provider(model)
+                provider = LlmProviders(provider_str)
+
+            provider_config = ProviderConfigManager.get_provider_chat_config(
+                model=model,
+                provider=provider,
+            )
+            if provider_config is not None:
+                provider_config.apply_assembled_streaming_response_metadata(
+                    response=response,
+                    chunks=chunks,
+                )
+        except Exception:
+            return
+
+    @staticmethod
     def _get_chunk_id(chunks: List[Dict[str, Any]]) -> str:
         """
         Chunks:

diff --git a/litellm/llms/base_llm/chat/transformation.py b/litellm/llms/base_llm/chat/transformation.py
--- a/litellm/llms/base_llm/chat/transformation.py
+++ b/litellm/llms/base_llm/chat/transformation.py
@@ -442,6 +442,14 @@
         """Hook for providers to post-process streaming responses. Default: pass-through."""
         return stream
 
+    def apply_assembled_streaming_response_metadata(
+        self,
+        response: "ModelResponse",
+        chunks: List[Any],
+    ) -> None:
+        """Hook for providers to merge chunk metadata into assembled streaming responses."""
+        return None
+
     def calculate_additional_costs(
         self, model: str, prompt_tokens: int, completion_tokens: int
     ) -> Optional[dict]:

diff --git a/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py b/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py
--- a/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py
+++ b/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py
@@ -2253,7 +2253,77 @@
             citation_metadata,
         )
 
+    _STREAM_METADATA_FIELDS = (
+        "vertex_ai_grounding_metadata",
+        "vertex_ai_url_context_metadata",
+        "vertex_ai_safety_ratings",
+        "vertex_ai_citation_metadata",
+    )
+
     @staticmethod
+    def _get_stream_chunk_attr(chunk: Any, field_name: str) -> Any:
+        if isinstance(chunk, dict):
+            value = chunk.get(field_name)
+            if value is not None:
+                return value
+            model_extra = chunk.get("model_extra")
+            if isinstance(model_extra, dict):
+                value = model_extra.get(field_name)
+                if value is not None:
+                    return value
+            hidden_params = chunk.get("_hidden_params")
+            if isinstance(hidden_params, dict):
+                return hidden_params.get(field_name)
+            return None
+        return getattr(chunk, field_name, None)
+
+    @staticmethod
+    def _set_stream_metadata_on_response(
+        model_response: Any,
+        grounding_metadata: List[dict],
+        url_context_metadata: List[dict],
+        safety_ratings: List[dict],
+        citation_metadata: List[dict],
+    ) -> None:
+        setattr(model_response, "vertex_ai_grounding_metadata", grounding_metadata)  # type: ignore
+        if grounding_metadata:
+            model_response._hidden_params["vertex_ai_grounding_metadata"] = (
+                grounding_metadata
+            )
+        setattr(model_response, "vertex_ai_url_context_metadata", url_context_metadata)  # type: ignore
+        if url_context_metadata:
+            model_response._hidden_params["vertex_ai_url_context_metadata"] = (
+                url_context_metadata
+            )
+        setattr(model_response, "vertex_ai_safety_ratings", safety_ratings)  # type: ignore
+        if safety_ratings:
+            model_response._hidden_params["vertex_ai_safety_ratings"] = safety_ratings
+        setattr(model_response, "vertex_ai_citation_metadata", citation_metadata)  # type: ignore
+        if citation_metadata:
+            model_response._hidden_params["vertex_ai_citation_metadata"] = (
+                citation_metadata
+            )
+
+    def apply_assembled_streaming_response_metadata(
+        self,
+        response: ModelResponse,
+        chunks: List[Any],
+    ) -> None:
+        for field_name in VertexGeminiConfig._STREAM_METADATA_FIELDS:
+            merged: List[Any] = []
+            for chunk in chunks:
+                value = VertexGeminiConfig._get_stream_chunk_attr(chunk, field_name)
+                if not value:
+                    continue
+                if isinstance(value, list):
+                    merged.extend(value)
+                else:
+                    merged.append(value)
+            if merged:
+                setattr(response, field_name, merged)
+                response._hidden_params[field_name] = merged
+
+    @staticmethod
     def _convert_grounding_metadata_to_annotations(
         grounding_metadata: List[dict],
         content_text: Optional[str],
@@ -3385,10 +3455,13 @@
                 if choice.finish_reason == "stop":
                     choice.finish_reason = "tool_calls"
 
-        setattr(model_response, "vertex_ai_grounding_metadata", grounding_metadata)  # type: ignore
-        setattr(model_response, "vertex_ai_url_context_metadata", url_context_metadata)  # type: ignore
-        setattr(model_response, "vertex_ai_safety_ratings", safety_ratings)  # type: ignore
-        setattr(model_response, "vertex_ai_citation_metadata", citation_metadata)  # type: ignore
+        VertexGeminiConfig._set_stream_metadata_on_response(
+            model_response,
+            grounding_metadata,
+            url_context_metadata,
+            safety_ratings,
+            citation_metadata,
+        )
 
         return (
             grounding_metadata,

diff --git a/litellm/main.py b/litellm/main.py
--- a/litellm/main.py
+++ b/litellm/main.py
@@ -7761,6 +7761,9 @@
                     "cost",
                     logging_obj._response_cost_calculator(result=response),
                 )
+            processor.apply_provider_assembled_streaming_metadata(
+                response, chunks, logging_obj
+            )
             return response
 
         tool_call_chunks = [
@@ -7940,6 +7943,9 @@
                 usage, "cost", logging_obj._response_cost_calculator(result=response)
             )
 
+        processor.apply_provider_assembled_streaming_metadata(
+            response, chunks, logging_obj
+        )
         return response
     except Exception as e:
         verbose_logger.exception(

diff --git a/tests/test_litellm/litellm_core_utils/test_litellm_logging.py b/tests/test_litellm/litellm_core_utils/test_litellm_logging.py
--- a/tests/test_litellm/litellm_core_utils/test_litellm_logging.py
+++ b/tests/test_litellm/litellm_core_utils/test_litellm_logging.py
@@ -2165,6 +2165,41 @@
     assert assembled is result
 
 
+def test_streaming_success_handler_includes_vertex_ai_metadata_in_standard_logging():
+    """Assembled streaming responses should include Vertex AI metadata in logging payload."""
+    import datetime
+
+    from litellm.types.utils import Choices, Message
+
+    logging_obj = _make_logging_obj(stream=True)
+    grounding_metadata = [{"webSearchQueries": ["weather in SF"]}]
+    url_context_metadata = [{"urlMetadata": [{"retrievedUrl": "https://example.com"}]}]
+    result = ModelResponse(
+        id="resp-1",
+        choices=[
+            Choices(
+                index=0,
+                message=Message(role="assistant", content="hello"),
+                finish_reason="stop",
+            )
+        ],
+        model="gemini-2.5-flash",
+    )
+    setattr(result, "vertex_ai_grounding_metadata", grounding_metadata)
+    setattr(result, "vertex_ai_url_context_metadata", url_context_metadata)
+    result._hidden_params["vertex_ai_grounding_metadata"] = grounding_metadata
+    result._hidden_params["vertex_ai_url_context_metadata"] = url_context_metadata
+
+    start = datetime.datetime.now()
+    end = datetime.datetime.now()
+    logging_obj.success_handler(result=result, start_time=start, end_time=end)
+
+    payload = logging_obj.model_call_details.get("standard_logging_object")
+    assert payload is not None
+    assert payload["response"]["vertex_ai_grounding_metadata"] == grounding_metadata
+    assert payload["response"]["vertex_ai_url_context_metadata"] == url_context_metadata
+
+
 def test_get_assembled_streaming_response_returns_none_for_non_streaming_text_completion():
     """Non-streaming TextCompletionResponse should also return None."""
     import datetime

diff --git a/tests/test_litellm/litellm_core_utils/test_redact_messages.py b/tests/test_litellm/litellm_core_utils/test_redact_messages.py
--- a/tests/test_litellm/litellm_core_utils/test_redact_messages.py
+++ b/tests/test_litellm/litellm_core_utils/test_redact_messages.py
@@ -349,3 +349,96 @@
 
         assert redacted.output[0].content[0].text == "redacted-by-litellm"
         assert response.output[0].content[0].text == "sensitive output"
+
+    def test_redacts_vertex_provider_metadata_in_standard_logging_response(self):
+        details = {
+            "standard_logging_object": {
+                "messages": [{"role": "user", "content": "sensitive prompt"}],
+                "response": {
+                    "choices": [
+                        {
+                            "message": {
+                                "content": "sensitive answer",
+                                "role": "assistant",
+                            }
+                        }
+                    ],
+                    "vertex_ai_grounding_metadata": [
+                        {"webSearchQueries": ["sensitive search term"]}
+                    ],
+                    "vertex_ai_url_context_metadata": [
+                        {"urlMetadata": [{"retrievedUrl": "https://example.com"}]}
+                    ],
+                },
+            }
+        }
+
+        perform_redaction(details, None)
+
+        response = details["standard_logging_object"]["response"]
+        assert response["choices"][0]["message"]["content"] == "redacted-by-litellm"
+        assert response["vertex_ai_grounding_metadata"] == []
+        assert response["vertex_ai_url_context_metadata"] == []
+
+    def test_redacts_vertex_provider_metadata_on_streaming_model_response(self):
+        response = litellm.ModelResponse(
+            id="resp-1",
+            choices=[
+                litellm.Choices(
+                    message=litellm.Message(
+                        content="sensitive answer",
+                        role="assistant",
+                    )
+                )
+            ],
+            model="gemini-2.5-flash",
+        )
+        setattr(
+            response,
+            "vertex_ai_grounding_metadata",
+            [{"webSearchQueries": ["sensitive search term"]}],
+        )
+        response._hidden_params["vertex_ai_grounding_metadata"] = [
+            {"webSearchQueries": ["sensitive search term"]}
+        ]
+
+        details = {
+            "stream": True,
+            "complete_streaming_response": response,
+        }
+
+        perform_redaction(details, response)
+
+        assert response.choices[0].message.content == "redacted-by-litellm"
+        assert getattr(response, "vertex_ai_grounding_metadata") == []
+        assert "vertex_ai_grounding_metadata" not in response._hidden_params
+
+    def test_redacts_vertex_provider_metadata_from_metadata_hidden_params(self):
+        """Streaming success_handler copies _hidden_params into metadata before redaction."""
+        details = {
+            "stream": True,
+            "litellm_params": {
+                "metadata": {
+                    "hidden_params": {
+                        "response_cost": 0.01,
+                        "vertex_ai_grounding_metadata": [
+                            {"webSearchQueries": ["sensitive search term"]}
+                        ],
+                        "vertex_ai_url_context_metadata": [
+                            {"urlMetadata": [{"retrievedUrl": "https://example.com"}]}
+                        ],
+                        "vertex_ai_safety_ratings": [{"category": "HARM"}],
+                        "vertex_ai_citation_metadata": [{"citations": ["source"]}],
+                    }
+                }
+            },
+        }
+
+        perform_redaction(details, None)
+
+        hidden_params = details["litellm_params"]["metadata"]["hidden_params"]
+        assert hidden_params["response_cost"] == 0.01
+        assert "vertex_ai_grounding_metadata" not in hidden_params
+        assert "vertex_ai_url_context_metadata" not in hidden_params
+        assert "vertex_ai_safety_ratings" not in hidden_params
+        assert "vertex_ai_citation_metadata" not in hidden_params

diff --git a/tests/test_litellm/litellm_core_utils/test_streaming_chunk_builder_utils.py b/tests/test_litellm/litellm_core_utils/test_streaming_chunk_builder_utils.py
--- a/tests/test_litellm/litellm_core_utils/test_streaming_chunk_builder_utils.py
+++ b/tests/test_litellm/litellm_core_utils/test_streaming_chunk_builder_utils.py
@@ -613,3 +613,122 @@
     assert (
         response._hidden_params["provider_specific_fields"]["traffic_type"] == "default"
     )
+
+
+def test_stream_chunk_builder_propagates_vertex_ai_metadata_from_chunks():
+    """Vertex AI metadata on streaming chunks must appear on assembled response."""
+    grounding_metadata = [{"webSearchQueries": ["weather in SF"]}]
+    url_context_metadata = [{"urlMetadata": [{"retrievedUrl": "https://example.com"}]}]
+
+    chunk1 = ModelResponseStream(
+        id="chatcmpl-vertex-1",
+        created=1,
+        model="gemini-2.5-flash",
+        object="chat.completion.chunk",
+        choices=[
+            StreamingChoices(
+                finish_reason=None,
+                index=0,
+                delta=Delta(content="The weather", role="assistant"),
+            )
+        ],
+    )
+    setattr(chunk1, "vertex_ai_grounding_metadata", grounding_metadata)
+    chunk1._hidden_params["vertex_ai_grounding_metadata"] = grounding_metadata
+
+    chunk2 = ModelResponseStream(
+        id="chatcmpl-vertex-1",
+        created=1,
+        model="gemini-2.5-flash",
+        object="chat.completion.chunk",
+        choices=[
+            StreamingChoices(
+                finish_reason="stop",
+                index=0,
+                delta=Delta(content=" is sunny.", role="assistant"),
+            )
+        ],
+    )
+    setattr(chunk2, "vertex_ai_url_context_metadata", url_context_metadata)
+    chunk2._hidden_params["vertex_ai_url_context_metadata"] = url_context_metadata
+
+    response = stream_chunk_builder(chunks=[chunk1, chunk2])
+    assert response is not None
+    assert getattr(response, "vertex_ai_grounding_metadata") == grounding_metadata
+    assert getattr(response, "vertex_ai_url_context_metadata") == url_context_metadata
+    assert response._hidden_params["vertex_ai_grounding_metadata"] == grounding_metadata
+    assert (
+        response._hidden_params["vertex_ai_url_context_metadata"]
+        == url_context_metadata
+    )
+
+    dumped = response.model_dump()
+    assert dumped["vertex_ai_grounding_metadata"] == grounding_metadata
+    assert dumped["vertex_ai_url_context_metadata"] == url_context_metadata
+
+
+def test_stream_chunk_builder_uses_assembled_model_for_provider_metadata():
+    grounding_metadata = [{"webSearchQueries": ["weather in SF"]}]
+
+    chunk1 = ModelResponseStream(
+        id="chatcmpl-vertex-router",
+        created=1,
+        model="gpt-4o",
+        object="chat.completion.chunk",
+        choices=[
+            StreamingChoices(
+                finish_reason=None,
+                index=0,
+                delta=Delta(content="The weather", role="assistant"),
+            )
+        ],
+    )
+    chunk2 = ModelResponseStream(
+        id="chatcmpl-vertex-router",
+        created=1,
+        model="gemini-2.5-flash",
+        object="chat.completion.chunk",
+        choices=[
+            StreamingChoices(
+                finish_reason="stop",
+                index=0,
+                delta=Delta(content=" is sunny.", role=None),
+            )
+        ],
+    )
+    setattr(chunk2, "vertex_ai_grounding_metadata", grounding_metadata)
+    chunk2._hidden_params["vertex_ai_grounding_metadata"] = grounding_metadata
+
+    response = stream_chunk_builder(chunks=[chunk1, chunk2])
+    assert response is not None
+    assert response.model == "gemini-2.5-flash"
+    assert getattr(response, "vertex_ai_grounding_metadata") == grounding_metadata
+
+
+def test_stream_chunk_builder_propagates_vertex_ai_metadata_from_dict_chunks():
+    """Dict snapshot chunks (model_dump) should also propagate Vertex AI metadata."""
+    chunk_dict = ModelResponseStream(
+        id="chatcmpl-vertex-2",
+        created=1,
+        model="gemini-2.5-flash",
+        object="chat.completion.chunk",
+        choices=[
+            StreamingChoices(
+                finish_reason="stop",
+                index=0,
+                delta=Delta(content="hello", role="assistant"),
+            )
+        ],
+    ).model_dump()
+    chunk_dict["_hidden_params"] = {
+        "vertex_ai_grounding_metadata": [{"webSearchQueries": ["test query"]}]
+    }
+
+    response = stream_chunk_builder(chunks=[chunk_dict])
+    assert response is not None
+    assert getattr(response, "vertex_ai_grounding_metadata") == [
+        {"webSearchQueries": ["test query"]}
+    ]
+    assert response.model_dump()["vertex_ai_grounding_metadata"] == [
+        {"webSearchQueries": ["test query"]}
+    ]

_{You can send follow-ups to the cloud agent here.}

[Sameerlite]

bugbot run

[cursor]

✅ Bugbot reviewed your changes and found no new issues!

Comment @cursor review or bugbot run to trigger another review on this PR

^{Reviewed by Cursor Bugbot for commit 15507be. Configure here.}

[mateo-berri]

@greptileai

[mateo-berri]

@greptileai

[mateo-berri]

@greptileai

[mateo-berri]

@greptileai

[mateo-berri]

@greptileai

[mateo-berri]

@greptileai

[mateo-berri]

LGTM; thanks!