From 9ba552d527883d7dad9778e63b73422dccbfbaf4 Mon Sep 17 00:00:00 2001
From: agustin18 <agustinsaiz02@gmail.com>
Date: Sat, 26 Sep 2026 23:51:45 -0300
Subject: [PATCH 1/9] fix(vertex_ai): consider tools when validating context
 caching min tokens (#43319)

* fix(vertex_ai): consider tools when validating context caching min tokens

Pass tools to is_prompt_caching_valid_prompt in both sync and async
check_and_create_cache before popping them into the cachedContents
request body. This allows agent-shaped requests with heavy tool schemas
and small message histories to reach the minimum token threshold and
benefit from prompt caching.

Fixes #42804

* test(vertex_ai): avoid doubles on internal code and assert tools in cache payload
---
 .../vertex_ai_context_caching.py              |   2 +
 .../test_vertex_ai_context_caching.py         | 123 ++++++++++++++++++
 2 files changed, 125 insertions(+)

diff --git a/litellm/llms/vertex_ai/context_caching/vertex_ai_context_caching.py b/litellm/llms/vertex_ai/context_caching/vertex_ai_context_caching.py
index 75d4ffbed86..2d35dd9b480 100644
--- a/litellm/llms/vertex_ai/context_caching/vertex_ai_context_caching.py
+++ b/litellm/llms/vertex_ai/context_caching/vertex_ai_context_caching.py
@@ -322,6 +322,7 @@ class ContextCachingEndpoints(VertexBase):
         if not is_prompt_caching_valid_prompt(
             model=model,
             messages=cached_messages,
+            tools=optional_params.get("tools"),
             custom_llm_provider=custom_llm_provider,
         ):
             verbose_logger.debug(
@@ -481,6 +482,7 @@ class ContextCachingEndpoints(VertexBase):
         if not is_prompt_caching_valid_prompt(
             model=model,
             messages=cached_messages,
+            tools=optional_params.get("tools"),
             custom_llm_provider=custom_llm_provider,
         ):
             verbose_logger.debug(
diff --git a/tests/unit/llms/vertex_ai/context_caching/test_vertex_ai_context_caching.py b/tests/unit/llms/vertex_ai/context_caching/test_vertex_ai_context_caching.py
index 7913700c8a7..283ed3710d0 100644
--- a/tests/unit/llms/vertex_ai/context_caching/test_vertex_ai_context_caching.py
+++ b/tests/unit/llms/vertex_ai/context_caching/test_vertex_ai_context_caching.py
@@ -1503,6 +1503,129 @@ class TestContextCachingEndpoints:
         # Restart the patcher so teardown_method can stop it cleanly
         self._token_check_patcher.start()
 
+    @pytest.mark.parametrize("is_async", [False, True])
+    @pytest.mark.parametrize(
+        "custom_llm_provider", ["gemini", "vertex_ai"]
+    )
+    @pytest.mark.asyncio
+    async def test_check_and_create_cache_considers_tools_for_min_tokens(
+        self, custom_llm_provider, is_async
+    ):
+        """Test that context caching accounts for tools when validating minimum token count.
+
+        Fixes #42804: When messages alone are below the threshold, but tools push the total
+        over the minimum token count, context caching must proceed and include tools.
+        """
+        self._token_check_patcher.stop()
+
+        short_cached_messages = [
+            {
+                "role": "system",
+                "content": "Short system instruction.",
+                "cache_control": {"type": "ephemeral"},
+            }
+        ]
+        non_cached_messages = [
+            {"role": "user", "content": "Hello world"},
+        ]
+        all_messages = short_cached_messages + non_cached_messages
+
+        large_tools = [
+            {
+                "type": "function",
+                "function": {
+                    "name": f"synthetic_tool_{i}",
+                    "description": "A very descriptive explanation of a synthetic tool designed to add tokens to the prompt cache prefix " * 8,
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            f"arg_{j}": {"type": "string", "description": "Argument description for caching verification " * 4}
+                            for j in range(10)
+                        },
+                        "required": [f"arg_{j}" for j in range(5)],
+                    },
+                },
+            }
+            for i in range(12)
+        ]
+
+        optional_params = {
+            **self.sample_optional_params,
+            "tools": large_tools,
+        }
+
+        mock_response = MagicMock()
+        mock_response.json.return_value = {
+            "name": "cachedContents/test_cache_id",
+            "model": "gemini-1.5-pro",
+        }
+        mock_response.status_code = 200
+        self.mock_client.post.return_value = mock_response
+        self.mock_async_client.post = AsyncMock(return_value=mock_response)
+
+        with patch.object(
+            self.context_caching,
+            "_get_token_and_url_context_caching",
+            return_value=("fake_token", "https://fake.url/cachedContents"),
+        ), patch.object(
+            self.context_caching,
+            "check_cache",
+            return_value=None,
+        ), patch.object(
+            self.context_caching,
+            "async_check_cache",
+            new_callable=AsyncMock,
+            return_value=None,
+        ):
+            if is_async:
+                result = await self.context_caching.async_check_and_create_cache(
+                    messages=all_messages,
+                    optional_params=optional_params,
+                    api_key="test_key",
+                    api_base=None,
+                    model="gemini-1.5-pro",
+                    client=self.mock_async_client,
+                    timeout=30.0,
+                    logging_obj=self.mock_logging,
+                    cached_content=None,
+                    custom_llm_provider=custom_llm_provider,
+                    vertex_project="test_project",
+                    vertex_location="us-central1",
+                    vertex_auth_header="test_token",
+                )
+            else:
+                result = self.context_caching.check_and_create_cache(
+                    messages=all_messages,
+                    optional_params=optional_params,
+                    api_key="test_key",
+                    api_base=None,
+                    model="gemini-1.5-pro",
+                    client=self.mock_client,
+                    timeout=30.0,
+                    logging_obj=self.mock_logging,
+                    cached_content=None,
+                    custom_llm_provider=custom_llm_provider,
+                    vertex_project="test_project",
+                    vertex_location="us-central1",
+                    vertex_auth_header="test_token",
+                )
+
+        messages, returned_params, returned_cache = result
+        assert messages == non_cached_messages
+        assert returned_cache == "cachedContents/test_cache_id"
+        assert "tools" not in returned_params
+
+        post_mock = self.mock_async_client.post if is_async else self.mock_client.post
+        post_mock.assert_called_once()
+        call_kwargs = post_mock.call_args.kwargs
+        assert call_kwargs["json"]["tools"] == large_tools
+        assert call_kwargs["json"]["contents"] == [
+            {"role": "user", "parts": [{"text": "Short system instruction."}]}
+        ]
+
+        self._token_check_patcher.start()
+
+
     def _model_turn_final_messages(self, final_cached_role):
         tool_call = {
             "id": "call_abc123",

From ba2d1c2785255f4143c26f35b18c37693e44d4eb Mon Sep 17 00:00:00 2001
From: Jeremy Schoemaker <jeremy@shoemoney.com>
Date: Sat, 26 Sep 2026 22:17:44 -0500
Subject: [PATCH 2/9] =?UTF-8?q?fix(anthropic):=20drop=20thinking=20blocks?=
 =?UTF-8?q?=20with=20empty=20thinking=20text,=20not=20just=20missing=20sig?=
 =?UTF-8?q?nature=20=F0=9F=A7=A0=F0=9F=9A=AB=20(#38049)?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

_is_unsignable_thinking_block() only checked block["signature"], so a
thinking block with a valid-looking signature but empty (or
whitespace-only) thinking text sailed through _drop_unsignable_thinking_blocks
and into anthropic_messages_pt(). Anthropic rejects that with:

  400 messages.N.content.M.thinking: each thinking block must contain thinking

This is reachable whenever a thinking_blocks history item gets replayed
through this Anthropic-shaped request path (e.g. a non-Anthropic reasoning
turn with no summary text), the same class of bug PR #36033 fixed on the
Responses adapter's own separate code path.

Now the signature check runs first (unsigned blocks are still dropped, same
as before), then an additional check drops the block if `thinking` is
missing, not a string, or strips to empty. redacted_thinking blocks are
untouched since they don't have type == "thinking".
---
 .../prompt_templates/common_utils.py          |  18 +-
 ...llm_core_utils_prompt_templates_factory.py | 176 ++++++++++++++++++
 2 files changed, 188 insertions(+), 6 deletions(-)

diff --git a/litellm/litellm_core_utils/prompt_templates/common_utils.py b/litellm/litellm_core_utils/prompt_templates/common_utils.py
index 14d47a15c6d..41563d501a9 100644
--- a/litellm/litellm_core_utils/prompt_templates/common_utils.py
+++ b/litellm/litellm_core_utils/prompt_templates/common_utils.py
@@ -1992,11 +1992,14 @@ def is_encrypted_reasoning_block(block: object) -> bool:
 def is_unsignable_thinking_block(block: object) -> bool:
     """A thinking block Anthropic cannot accept on input.
 
-    Anthropic verifies the thinking signature cryptographically, so a block whose
-    signature is null, empty, or missing (e.g. from an open-source reasoning model)
-    is rejected with a 400 and must be dropped rather than blanked or repaired, and
-    so is a block whose signature or data carries another provider's encrypted
-    reasoning. A `redacted_thinking` block Anthropic minted is always kept.
+    Anthropic verifies the signature cryptographically, so a block with a null,
+    empty, or missing signature (e.g. from an open-source reasoning model) is
+    rejected with a 400, and so is a block whose signature or data carries
+    another provider's encrypted reasoning. It also rejects a `thinking` block
+    whose text is empty or whitespace-only ("each thinking block must contain
+    thinking"), regardless of signature, e.g. when a `thinking_blocks` history
+    item from a non-Anthropic reasoning provider is replayed through this path.
+    `redacted_thinking` blocks carry no signature and are always kept.
     """
     if is_encrypted_reasoning_block(block):
         return True
@@ -2006,7 +2009,10 @@ def is_unsignable_thinking_block(block: object) -> bool:
     if mapping.get("type") != "thinking":
         return False
     signature: Final = mapping.get("signature")
-    return not (isinstance(signature, str) and len(signature) > 0)
+    if not (isinstance(signature, str) and len(signature) > 0):
+        return True
+    thinking_text: Final = mapping.get("thinking")
+    return not (isinstance(thinking_text, str) and len(thinking_text.strip()) > 0)
 
 
 def strip_encrypted_reasoning_from_messages(messages: object) -> None:
diff --git a/tests/unit/litellm_core_utils/prompt_templates/test_litellm_core_utils_prompt_templates_factory.py b/tests/unit/litellm_core_utils/prompt_templates/test_litellm_core_utils_prompt_templates_factory.py
index 26124ac24de..0c08c5dfd85 100644
--- a/tests/unit/litellm_core_utils/prompt_templates/test_litellm_core_utils_prompt_templates_factory.py
+++ b/tests/unit/litellm_core_utils/prompt_templates/test_litellm_core_utils_prompt_templates_factory.py
@@ -3895,3 +3895,179 @@ def test_anthropic_messages_pt_drops_a_system_message_with_no_text():
     result = anthropic_messages_pt(messages=messages, model="claude-opus-4-8", llm_provider="anthropic")
 
     assert [m["role"] for m in result] == ["user", "assistant"]
+
+
+def test_anthropic_messages_pt_drops_empty_but_signed_thinking_block():
+    """
+    Anthropic rejects a `thinking` block whose `thinking` text is empty, even
+    when it carries a valid-looking signature, with:
+        400 messages.N.content.M.thinking: each thinking block must contain thinking
+    This shape is reachable via cross-provider replay of a `thinking_blocks`
+    history item (see PR #36033), so `is_unsignable_thinking_block()` must
+    also check the thinking text, not just the signature.
+    """
+    from litellm.litellm_core_utils.prompt_templates.factory import (
+        anthropic_messages_pt,
+    )
+
+    messages = [
+        {"role": "user", "content": "What's 2+2?"},
+        {
+            "role": "assistant",
+            "content": "4",
+            "thinking_blocks": [
+                {
+                    "type": "thinking",
+                    "thinking": "",
+                    "signature": "sig_abc123_looks_valid",
+                }
+            ],
+        },
+    ]
+
+    result = anthropic_messages_pt(
+        messages=messages,
+        model="claude-sonnet-4-5-20250929",
+        llm_provider="anthropic",
+    )
+
+    assistant_msg = result[1]
+    assert isinstance(assistant_msg["content"], list)
+    content_types = [block.get("type") for block in assistant_msg["content"]]
+    assert "thinking" not in content_types, "empty-text thinking block must be dropped even though it has a signature"
+
+
+def test_anthropic_messages_pt_keeps_non_empty_signed_thinking_block():
+    """
+    Regression: a real, non-empty, signed thinking block must still pass
+    through unchanged.
+    """
+    from litellm.litellm_core_utils.prompt_templates.factory import (
+        anthropic_messages_pt,
+    )
+
+    messages = [
+        {"role": "user", "content": "What's 2+2?"},
+        {
+            "role": "assistant",
+            "content": "4",
+            "thinking_blocks": [
+                {
+                    "type": "thinking",
+                    "thinking": "Let me add these numbers together.",
+                    "signature": "sig_abc123_looks_valid",
+                }
+            ],
+        },
+    ]
+
+    result = anthropic_messages_pt(
+        messages=messages,
+        model="claude-sonnet-4-5-20250929",
+        llm_provider="anthropic",
+    )
+
+    assistant_msg = result[1]
+    assert isinstance(assistant_msg["content"], list)
+    thinking_block = next((b for b in assistant_msg["content"] if b.get("type") == "thinking"), None)
+    assert thinking_block is not None, "non-empty signed thinking block must be kept"
+    assert thinking_block["thinking"] == "Let me add these numbers together."
+    assert thinking_block["signature"] == "sig_abc123_looks_valid"
+
+
+def test_anthropic_messages_pt_keeps_redacted_thinking_block():
+    """
+    Regression: `redacted_thinking` blocks carry no signature and no plaintext
+    `thinking` field by design, and must always be kept regardless of the new
+    emptiness check (which only applies to `type == "thinking"` blocks).
+    """
+    from litellm.litellm_core_utils.prompt_templates.factory import (
+        anthropic_messages_pt,
+    )
+
+    messages = [
+        {"role": "user", "content": "What's 2+2?"},
+        {
+            "role": "assistant",
+            "content": "4",
+            "thinking_blocks": [
+                {
+                    "type": "redacted_thinking",
+                    "data": "encrypted_opaque_blob",
+                }
+            ],
+        },
+    ]
+
+    result = anthropic_messages_pt(
+        messages=messages,
+        model="claude-sonnet-4-5-20250929",
+        llm_provider="anthropic",
+    )
+
+    assistant_msg = result[1]
+    assert isinstance(assistant_msg["content"], list)
+    content_types = [block.get("type") for block in assistant_msg["content"]]
+    assert "redacted_thinking" in content_types, "redacted_thinking blocks must always be kept"
+
+
+def test_anthropic_messages_pt_drops_unsigned_thinking_block():
+    """
+    Regression (pre-existing behaviour): a thinking block with no signature
+    (or an empty/null one) must still be dropped, independent of whether the
+    thinking text is populated.
+    """
+    from litellm.litellm_core_utils.prompt_templates.factory import (
+        anthropic_messages_pt,
+    )
+
+    messages = [
+        {"role": "user", "content": "What's 2+2?"},
+        {
+            "role": "assistant",
+            "content": "4",
+            "thinking_blocks": [
+                {
+                    "type": "thinking",
+                    "thinking": "Let me add these numbers together.",
+                    "signature": "",
+                }
+            ],
+        },
+    ]
+
+    result = anthropic_messages_pt(
+        messages=messages,
+        model="claude-sonnet-4-5-20250929",
+        llm_provider="anthropic",
+    )
+
+    assistant_msg = result[1]
+    assert isinstance(assistant_msg["content"], list)
+    content_types = [block.get("type") for block in assistant_msg["content"]]
+    assert "thinking" not in content_types, "unsigned thinking block must still be dropped"
+
+
+def test_is_unsignable_thinking_block_treats_whitespace_only_as_empty():
+    """
+    Edge case: a `thinking` field that is present but whitespace-only (e.g.
+    a single trailing newline forwarded from another provider's empty
+    reasoning summary) is functionally empty and Anthropic's API will still
+    reject it with "each thinking block must contain thinking". We treat it
+    the same as a fully empty string and drop the block.
+
+    The check lives in the shared `is_unsignable_thinking_block` helper, which
+    `_drop_unsignable_thinking_blocks` calls standalone, so the whitespace-aware
+    test has to hold there rather than only at the factory call site.
+    """
+    from litellm.litellm_core_utils.prompt_templates.common_utils import (
+        is_unsignable_thinking_block,
+    )
+
+    whitespace_only_block = {
+        "type": "thinking",
+        "thinking": "   \n\t  ",
+        "signature": "sig_abc123_looks_valid",
+    }
+
+    assert is_unsignable_thinking_block(whitespace_only_block) is True

From 303434d5738b5c1b0bbd226792fb40c1c21b6a16 Mon Sep 17 00:00:00 2001
From: yuneng-jiang <yuneng@berri.ai>
Date: Sat, 26 Sep 2026 20:26:23 -0700
Subject: [PATCH 3/9] test(e2e): report batch cleanup leftovers as a plain
 UserWarning (#43405)

The leftover warning used a class defined in a test-directory module. The xdist controller cannot import it, so an uncaught leftover warning crashed the whole e2e run. Same change as #43391 on rc/1.103.0
---
 tests/e2e/batches/COVERAGE.md           | 2 +-
 tests/e2e/batches/batch_cleanup.py      | 8 ++------
 tests/e2e/batches/test_batch_cleanup.py | 5 ++---
 3 files changed, 5 insertions(+), 10 deletions(-)

diff --git a/tests/e2e/batches/COVERAGE.md b/tests/e2e/batches/COVERAGE.md
index cd0fb35165e..2ba49a492ff 100644
--- a/tests/e2e/batches/COVERAGE.md
+++ b/tests/e2e/batches/COVERAGE.md
@@ -134,7 +134,7 @@ reporting failures as test errors. Already deleted files and batches that are
 terminal are safe to clean up again. Managed batch cancellation polls for up to two minutes
 before input deletion. A managed batch still `cancelling` after that is left for the provider to
 finish, and its input file is left in place because LiteLLM refuses to delete a file a non-terminal
-batch references. Both are reported as `BatchCleanupLeftover` warnings naming their ids rather than
+batch references. Both are reported as `UserWarning`s naming their ids rather than
 failing the test. Any other status or error still fails
 Accepted cancellation may still report validating or in_progress while the provider
 updates its state. Raw and model-encoded batches are polled until cancelling or
diff --git a/tests/e2e/batches/batch_cleanup.py b/tests/e2e/batches/batch_cleanup.py
index 5b3baaa624c..f1142a60782 100644
--- a/tests/e2e/batches/batch_cleanup.py
+++ b/tests/e2e/batches/batch_cleanup.py
@@ -28,10 +28,6 @@ class BatchCleanupClient(Protocol):
     def cancel_batch(self, batch_id: str, *, key: str, provider: str | None = None) -> Result[BatchObject]: ...
 
 
-class BatchCleanupLeftover(UserWarning):
-    pass
-
-
 def cleanup_result[R: BaseModel](
     action: Callable[[], Result[R]], *, wait: Callable[[float], None] = sleep
 ) -> Result[R]:
@@ -68,7 +64,7 @@ def cleanup_file(client: BatchCleanupClient, file_id: str, *, key: str, provider
     if isinstance(result, UnknownApiError) and result.status_code == 400 and FILE_IN_USE_REFUSAL in result.body:
         warnings.warn(
             f"Left file {file_id} in place: LiteLLM refused to delete it while a batch still references it",
-            BatchCleanupLeftover,
+            UserWarning,
             stacklevel=2,
         )
         return
@@ -140,7 +136,7 @@ def cleanup_batch(
             )
             warnings.warn(
                 f"Left batch {batch_id} cancelling after {BATCH_CANCEL_TIMEOUT_SECONDS}s for the provider to finish",
-                BatchCleanupLeftover,
+                UserWarning,
                 stacklevel=2,
             )
             return
diff --git a/tests/e2e/batches/test_batch_cleanup.py b/tests/e2e/batches/test_batch_cleanup.py
index 5e2ac12d300..218e79f37ac 100644
--- a/tests/e2e/batches/test_batch_cleanup.py
+++ b/tests/e2e/batches/test_batch_cleanup.py
@@ -7,7 +7,6 @@ import pytest
 from batch_cleanup import (
     BATCH_CANCEL_TIMEOUT_SECONDS,
     CLEANUP_DELAYS,
-    BatchCleanupLeftover,
     cleanup_batch,
     cleanup_file,
     cleanup_result,
@@ -141,7 +140,7 @@ class TestFileCleanup:
             calls=ExpectedCalls((f"delete None {MANAGED_FILE_ID}",)),
             files=(UnknownApiError(status_code=400, body=IN_USE_REFUSAL),),
         )
-        with pytest.warns(BatchCleanupLeftover, match=MANAGED_FILE_ID):
+        with pytest.warns(UserWarning, match=MANAGED_FILE_ID):
             cleanup_file(client, MANAGED_FILE_ID, key="test-key")
         client.calls.assert_done()
 
@@ -241,7 +240,7 @@ class TestBatchCancellation:
         key: Final = manager.key()
         manager.defer(lambda: cleanup_file(client, MANAGED_FILE_ID, key=key))
         manager.defer(lambda: cleanup_batch(client, MANAGED_BATCH_ID, key=key, clock=ticks))
-        with pytest.warns(BatchCleanupLeftover) as leftovers:
+        with pytest.warns(UserWarning, match="^Left ") as leftovers:
             manager.teardown()
         client.calls.assert_done()
         messages: Final = tuple(str(warning.message) for warning in leftovers)

From 21055e3fd840375f11a9f90d8b8e1c176284c79b Mon Sep 17 00:00:00 2001
From: Techboy bebop <142545999+kumarpriyanshu09@users.noreply.github.com>
Date: Sun, 27 Sep 2026 00:15:12 -0400
Subject: [PATCH 4/9] fix(tools): salvage concatenated JSON tool call arguments
 (#43260)

* fix(tools): salvage concatenated JSON tool call arguments

* fix(tools): harden concatenated tool-call salvage for review findings

Skip non-dict JSON during split so salvage cannot emit empty tool calls.
Collapse srvtoolu_ expansions to the first object so server results stay paired.
Allocate __concat_n ids that cannot collide with sibling tool call ids.
Propagate cache_control onto every expanded Anthropic tool_use block.
Rename the XML invoke loop variable so the key-leak gate no longer flags {args}

* test(tools): cover concat id bump and srvtoolu array keep

Only collapse srvtoolu_ when concatenated salvage expanded; a valid JSON
array argument stays one server tool input

* revert(anthropic): drop concat expansion from pass-through adapter

Co-authored-by: Techboy bebop  <kumarpriyanshu09@users.noreply.github.com>

* revert(tools): keep concat salvage out of request-side tool converters

Co-authored-by: Techboy bebop  <kumarpriyanshu09@users.noreply.github.com>

* fix(tools): expand strictly salvaged concatenated tool arguments in normalized tool calls

Co-authored-by: Techboy bebop  <kumarpriyanshu09@users.noreply.github.com>

* fix(tools): retain at most the salvage cap while validating concatenated arguments

Co-authored-by: Techboy bebop  <kumarpriyanshu09@users.noreply.github.com>

* test(tools): assert concat sibling ids unique after sanitization

A sibling id that only collides after colon-to-underscore sanitization must force the next concat suffix

Co-authored-by: Techboy bebop  <kumarpriyanshu09@users.noreply.github.com>

* refactor(tools): drop unused strict mode from split_concatenated_json_objects

Strict mode had no production caller. Rejection cases now sit on salvage, and split matches upstream main

Co-authored-by: Techboy bebop  <kumarpriyanshu09@users.noreply.github.com>

---------

Co-authored-by: Techboy bebop <kumarpriyanshu09@users.noreply.github.com>
---
 .../prompt_templates/common_utils.py          |  55 ++
 .../prompt_templates/factory.py               | 210 +++++--
 ...ore_utils_prompt_templates_common_utils.py |  40 +-
 ...llm_core_utils_prompt_templates_factory.py | 540 +++++++++---------
 4 files changed, 527 insertions(+), 318 deletions(-)

diff --git a/litellm/litellm_core_utils/prompt_templates/common_utils.py b/litellm/litellm_core_utils/prompt_templates/common_utils.py
index 41563d501a9..e555d7e8ec0 100644
--- a/litellm/litellm_core_utils/prompt_templates/common_utils.py
+++ b/litellm/litellm_core_utils/prompt_templates/common_utils.py
@@ -2503,6 +2503,61 @@ def split_concatenated_json_objects(raw: str) -> list[dict[str, object]]:
     return results
 
 
+MAX_SALVAGED_TOOL_ARGUMENT_OBJECTS: Final = 8
+
+
+def salvage_concatenated_tool_arguments(raw: str) -> tuple[dict[str, object], ...]:
+    """Return complete concatenated JSON objects that are safe to expand.
+
+    Identical objects collapse to the first one and are not capped. More than
+    ``MAX_SALVAGED_TOOL_ARGUMENT_OBJECTS`` objects that are not all identical
+    returns an empty tuple. Anything that is not a full concatenation of JSON
+    objects returns an empty tuple. Repeated copies of the first object are not
+    retained, and once the cap is passed the rest of the string is only checked.
+    """
+    stripped: Final = raw.strip()
+    if not stripped:
+        return ()
+    decoder: Final = json.JSONDecoder()
+    length: Final = len(stripped)
+    idx = 0  # rebind-ok: cursor walks the concatenated JSON string
+    count = 0  # rebind-ok: counts complete objects without retaining duplicates
+    kept = ()  # rebind-ok: holds at most one object past the salvage cap
+    exceeded = False  # rebind-ok: cap already passed, the tail is only validated
+    while idx < length:
+        while idx < length and stripped[idx] in " \t\n\r":
+            idx += 1
+        if idx >= length:
+            break
+        try:
+            obj, end_idx = decoder.raw_decode(stripped, idx)
+        except json.JSONDecodeError:
+            return ()
+        if not isinstance(obj, dict):
+            return ()
+        idx = end_idx
+        if exceeded:
+            continue
+        count += 1
+        if not kept:
+            kept = (obj,)
+            continue
+        if obj == kept[0] and len(kept) == 1:
+            continue
+        if len(kept) == 1 and count > 2 and count - 1 > MAX_SALVAGED_TOOL_ARGUMENT_OBJECTS:
+            exceeded = True
+            continue
+        if len(kept) == 1 and count > 2:
+            kept = (kept[0],) * (count - 1)
+        if len(kept) >= MAX_SALVAGED_TOOL_ARGUMENT_OBJECTS:
+            exceeded = True
+            continue
+        kept = (*kept, obj)
+    if exceeded:
+        return ()
+    return kept
+
+
 def text_completion_prompt_to_messages(prompt: object) -> tuple[AllMessageValues, ...]:
     """
     Wrap an OpenAI ``/v1/completions`` ``prompt`` into Chat Completion messages.
diff --git a/litellm/litellm_core_utils/prompt_templates/factory.py b/litellm/litellm_core_utils/prompt_templates/factory.py
index 7b12d1e939f..c4e242fd360 100644
--- a/litellm/litellm_core_utils/prompt_templates/factory.py
+++ b/litellm/litellm_core_utils/prompt_templates/factory.py
@@ -1,12 +1,14 @@
 import base64
 import copy
 import hashlib
+import itertools
 import json
 import mimetypes
 import re
 import xml.etree.ElementTree as ET
 from collections.abc import Iterator, Mapping, Sequence
 from enum import Enum
+from types import MappingProxyType
 from typing import Any, Final, TypeAlias, TypedDict, cast, overload
 
 from jinja2.sandbox import ImmutableSandboxedEnvironment
@@ -52,6 +54,7 @@ from .common_utils import (
     is_non_content_values_set,
     is_unsignable_thinking_block,
     parse_tool_call_arguments,
+    salvage_concatenated_tool_arguments,
 )
 from .image_handling import convert_url_to_base64
 
@@ -5381,80 +5384,167 @@ class NormalizedToolCall(TypedDict):
     arguments: dict[str, object]
 
 
-def _parse_tool_call_arguments(raw: object, tool_name: str | None, context: str) -> dict[str, object]:
+_ArgumentObjects: TypeAlias = tuple[dict[str, object], ...]
+_ParsedToolCall: TypeAlias = tuple[str | None, str | None, _ArgumentObjects]
+
+
+def _optional_call_id(value: object) -> str | None:
+    if isinstance(value, str) and value:
+        return value
+    return None
+
+
+def _optional_tool_name(value: object) -> str | None:
+    if isinstance(value, str):
+        return value
+    return None
+
+
+def _split_tool_call_ids(calls: Sequence[tuple[str | None, int]]) -> tuple[tuple[str | None, ...], ...]:
+    taken: Final = frozenset(_sanitize_anthropic_tool_use_id(call_id) for call_id, _ in calls if call_id)
+
+    def fresh(call_id: str) -> Iterator[str]:
+        return filter(
+            lambda candidate: _sanitize_anthropic_tool_use_id(candidate) not in taken,
+            (f"{call_id}__concat_{n}" for n in itertools.count(1)),
+        )
+
+    suffixes: Final = MappingProxyType(
+        {_sanitize_anthropic_tool_use_id(call_id): fresh(call_id) for call_id, count in calls if call_id and count > 1}
+    )
+    return tuple(
+        (
+            call_id,
+            *(next(suffixes[_sanitize_anthropic_tool_use_id(call_id)]) for _ in range(count - 1)),
+        )
+        if call_id
+        else (None,) * count
+        for call_id, count in calls
+    )
+
+
+def _parse_tool_call_arguments(raw: object, tool_name: str | None, context: str) -> _ArgumentObjects:
     # Anthropic's tool_use blocks already carry a parsed dict in "input";
     # chat completions and the Responses API carry a JSON string that may be
     # truncated by the model, so route those through the repair-aware parser.
     if isinstance(raw, dict):
-        return raw
+        return (raw,)
     if not isinstance(raw, str):
-        return {}
+        return ({},)
     normalized_raw: Final = "{}" if raw == REDACTED_BY_LITELLM else raw
-    from litellm.litellm_core_utils.prompt_templates.common_utils import (
-        parse_tool_call_arguments,
-    )
-
     try:
         parsed: Final = parse_tool_call_arguments(normalized_raw, tool_name=tool_name, context=context)
     except ValueError as e:
+        salvaged: Final = salvage_concatenated_tool_arguments(normalized_raw)
+        if salvaged:
+            verbose_logger.warning(
+                "Recovered %d tool call(s) from concatenated JSON arguments for tool '%s' (%s)",
+                len(salvaged),
+                tool_name or "<unknown>",
+                context,
+            )
+            return salvaged
         verbose_logger.warning("Failed to parse tool call arguments: %s", e)
-        return {}
-    return parsed if isinstance(parsed, dict) else {}
+        return ({},)
+    return (parsed,) if isinstance(parsed, dict) else ({},)
+
+
+def _choice_tool_calls(choice: object) -> tuple[object, ...]:
+    message: Final = get_attribute_or_key(choice, "message", None)
+    tool_calls: Final = get_attribute_or_key(message, "tool_calls", None) if message is not None else None
+    if isinstance(tool_calls, list):
+        return tuple(tool_calls)
+    return ()
+
+
+def _selected_choices(response: object, include_all_choices: bool) -> tuple[object, ...]:
+    choices: Final = get_attribute_or_key(response, "choices", None)
+    if not isinstance(choices, list) or not choices:
+        return ()
+    if include_all_choices:
+        return tuple(choices)
+    return (choices[0],)
+
+
+def _parsed_chat_tool_call(tool_call: object) -> _ParsedToolCall | None:
+    function: Final = get_attribute_or_key(tool_call, "function", None)
+    if function is None:
+        return None
+    name: Final = _optional_tool_name(get_attribute_or_key(function, "name"))
+    return (
+        _optional_call_id(get_attribute_or_key(tool_call, "id")),
+        name,
+        _parse_tool_call_arguments(
+            get_attribute_or_key(function, "arguments", "{}"),
+            tool_name=name,
+            context="chat completions",
+        ),
+    )
+
+
+def _parsed_calls_in_choice(choice: object) -> tuple[_ParsedToolCall, ...]:
+    return tuple(
+        parsed for tool_call in _choice_tool_calls(choice) if (parsed := _parsed_chat_tool_call(tool_call)) is not None
+    )
+
+
+def _parsed_chat_tool_calls(response: object, include_all_choices: bool) -> tuple[_ParsedToolCall, ...]:
+    grouped: Final = tuple(
+        _parsed_calls_in_choice(choice) for choice in _selected_choices(response, include_all_choices)
+    )
+    return tuple(itertools.chain.from_iterable(grouped))
+
+
+def _normalized_tool_calls_for_parse(
+    name: str | None,
+    call_ids: tuple[str | None, ...],
+    arguments: _ArgumentObjects,
+) -> tuple[NormalizedToolCall, ...]:
+    return tuple(
+        NormalizedToolCall(id=call_id, name=name, arguments=argument)
+        for call_id, argument in zip(call_ids, arguments, strict=True)
+    )
+
+
+def _normalized_tool_calls_from_parses(parses: Sequence[_ParsedToolCall]) -> tuple[NormalizedToolCall, ...]:
+    id_groups: Final = _split_tool_call_ids(tuple((call_id, len(arguments)) for call_id, _, arguments in parses))
+    grouped: Final = tuple(
+        _normalized_tool_calls_for_parse(name, call_ids, arguments)
+        for (_, name, arguments), call_ids in zip(parses, id_groups, strict=True)
+    )
+    return tuple(itertools.chain.from_iterable(grouped))
 
 
 def _tool_calls_from_chat_completion_response(
     response: object, include_all_choices: bool = False
-) -> list[NormalizedToolCall]:
-    choices: Final = get_attribute_or_key(response, "choices", None)
-    if not (isinstance(choices, list) and choices):
-        return []
-    tool_calls: Final[list[object]] = []
-    for choice in choices if include_all_choices else choices[:1]:
-        message = get_attribute_or_key(choice, "message", None)
-        choice_tool_calls = get_attribute_or_key(message, "tool_calls", None) if message else None
-        if isinstance(choice_tool_calls, list):
-            tool_calls.extend(choice_tool_calls)
-    result: Final[list[NormalizedToolCall]] = []
-    for tc in tool_calls:
-        fn = get_attribute_or_key(tc, "function", None)
-        if fn is None:
-            continue
-        name = get_attribute_or_key(fn, "name")
-        result.append(
-            NormalizedToolCall(
-                id=get_attribute_or_key(tc, "id"),
-                name=name,
-                arguments=_parse_tool_call_arguments(
-                    get_attribute_or_key(fn, "arguments", "{}"),
-                    tool_name=name,
-                    context="chat completions",
-                ),
-            )
-        )
-    return result
+) -> tuple[NormalizedToolCall, ...]:
+    return _normalized_tool_calls_from_parses(_parsed_chat_tool_calls(response, include_all_choices))
 
 
-def _tool_calls_from_responses_api_response(response: object) -> list[NormalizedToolCall]:
+def _response_function_calls(response: object) -> tuple[object, ...]:
     output: Final = get_attribute_or_key(response, "output", None)
     if not isinstance(output, list):
-        return []
-    result: Final[list[NormalizedToolCall]] = []
-    for item in output:
-        if get_attribute_or_key(item, "type") != "function_call":
-            continue
-        name = get_attribute_or_key(item, "name")
-        result.append(
-            NormalizedToolCall(
-                id=get_attribute_or_key(item, "call_id") or get_attribute_or_key(item, "id"),
-                name=name,
-                arguments=_parse_tool_call_arguments(
-                    get_attribute_or_key(item, "arguments", "{}"),
-                    tool_name=name,
-                    context="responses API",
-                ),
-            )
-        )
-    return result
+        return ()
+    return tuple(item for item in output if get_attribute_or_key(item, "type") == "function_call")
+
+
+def _parsed_response_tool_call(item: object) -> _ParsedToolCall:
+    name: Final = _optional_tool_name(get_attribute_or_key(item, "name"))
+    raw_id: Final = get_attribute_or_key(item, "call_id") or get_attribute_or_key(item, "id")
+    return (
+        _optional_call_id(raw_id),
+        name,
+        _parse_tool_call_arguments(
+            get_attribute_or_key(item, "arguments", "{}"),
+            tool_name=name,
+            context="responses API",
+        ),
+    )
+
+
+def _tool_calls_from_responses_api_response(response: object) -> tuple[NormalizedToolCall, ...]:
+    parses: Final = tuple(_parsed_response_tool_call(item) for item in _response_function_calls(response))
+    return _normalized_tool_calls_from_parses(parses)
 
 
 def _tool_calls_from_anthropic_messages_response(response: object) -> list[NormalizedToolCall]:
@@ -5494,16 +5584,18 @@ def get_tool_calls_from_response(response: object, include_all_choices: bool = F
     Callers that only care about a specific tool should filter the result by
     ``name`` themselves -- this returns every tool call found.
     """
-    chat_tool_calls = _tool_calls_from_chat_completion_response(response, include_all_choices=include_all_choices)
+    chat_tool_calls: Final = _tool_calls_from_chat_completion_response(
+        response, include_all_choices=include_all_choices
+    )
     if chat_tool_calls:
-        return chat_tool_calls
+        return list(chat_tool_calls)
     for extractor in (
         _tool_calls_from_responses_api_response,
         _tool_calls_from_anthropic_messages_response,
     ):
         tool_calls = extractor(response)
         if tool_calls:
-            return tool_calls
+            return list(tool_calls)
     return []
 
 
diff --git a/tests/unit/litellm_core_utils/prompt_templates/test_litellm_core_utils_prompt_templates_common_utils.py b/tests/unit/litellm_core_utils/prompt_templates/test_litellm_core_utils_prompt_templates_common_utils.py
index 79c50bf2369..45fc93f04c1 100644
--- a/tests/unit/litellm_core_utils/prompt_templates/test_litellm_core_utils_prompt_templates_common_utils.py
+++ b/tests/unit/litellm_core_utils/prompt_templates/test_litellm_core_utils_prompt_templates_common_utils.py
@@ -20,7 +20,9 @@ from litellm.litellm_core_utils.prompt_templates.common_utils import (
     hoist_images_from_tool_messages,
     is_encrypted_reasoning_block,
     merge_consecutive_system_messages,
+    parse_tool_call_arguments,
     responses_reasoning_items_from_thinking_blocks,
+    salvage_concatenated_tool_arguments,
     split_concatenated_json_objects,
     strip_encrypted_reasoning_from_messages,
     system_messages_first,
@@ -269,6 +271,40 @@ def test_split_concatenated_json_salvages_prefix_before_truncated_tail():
     assert result == [{"a": 1}, {"b": 2}]
 
 
+def test_parse_tool_call_arguments_rejects_concatenated_json() -> None:
+    with pytest.raises(ValueError, match="Failed to parse tool call arguments"):
+        parse_tool_call_arguments('{"a":1}{"b":2}')
+
+
+def _distinct_json_objects(count: int) -> str:
+    return "".join(json.dumps({"n": index}, separators=(",", ":")) for index in range(count))
+
+
+@pytest.mark.parametrize(
+    ("raw", "expected"),
+    (
+        ('{"a":1}{"b":2}', ({"a": 1}, {"b": 2})),
+        ('{"a":1}{"a":1}{"a":1}', ({"a": 1},)),
+        ('{"a":1}{"a":1}{"b":2}', ({"a": 1}, {"a": 1}, {"b": 2})),
+        (_distinct_json_objects(8), tuple({"n": index} for index in range(8))),
+        (_distinct_json_objects(9), ()),
+        (_distinct_json_objects(9) + " junk", ()),
+        ('{"a":1}' * 7 + '{"b":2}', tuple({"a": 1} for _ in range(7)) + ({"b": 2},)),
+        ('{"a":1}' * 8 + '{"b":2}', ()),
+        ('{"a":1}' * 5000, ({"a": 1},)),
+        ('{"a":1}' * 20, ({"a": 1},)),
+        ('{"a":1}{"b":', ()),
+        ('0{"x":1}', ()),
+        ('{"x":1}0', ()),
+        ('[1]{"x":1}', ()),
+        ('{"a":1}{"b":2}}', ()),
+        ('{"a":1} junk', ()),
+    ),
+)
+def test_salvage_concatenated_tool_arguments(raw: str, expected: tuple[dict[str, object], ...]) -> None:
+    assert salvage_concatenated_tool_arguments(raw) == expected
+
+
 # ---------------------------------------------------------------------------
 # Regression tests for non-OpenAI file content blocks.
 #
@@ -1949,6 +1985,8 @@ class TestMergeConsecutiveSystemMessages:
         assert merged == [{"role": "system", "content": expected_content}, {"role": "user", "content": "Hello"}]
 
     def test_keeps_the_first_message_when_no_system_message_in_the_run_has_content(self):
-        merged = merge_consecutive_system_messages([{"role": "system"}, {"role": "system"}, {"role": "user", "content": "Hi"}])
+        merged = merge_consecutive_system_messages(
+            [{"role": "system"}, {"role": "system"}, {"role": "user", "content": "Hi"}]
+        )
 
         assert merged == [{"role": "system"}, {"role": "user", "content": "Hi"}]
diff --git a/tests/unit/litellm_core_utils/prompt_templates/test_litellm_core_utils_prompt_templates_factory.py b/tests/unit/litellm_core_utils/prompt_templates/test_litellm_core_utils_prompt_templates_factory.py
index 0c08c5dfd85..1e12a973cdb 100644
--- a/tests/unit/litellm_core_utils/prompt_templates/test_litellm_core_utils_prompt_templates_factory.py
+++ b/tests/unit/litellm_core_utils/prompt_templates/test_litellm_core_utils_prompt_templates_factory.py
@@ -1,4 +1,5 @@
 import base64
+import json
 import logging
 import os
 import re
@@ -16,10 +17,12 @@ from litellm.litellm_core_utils.prompt_templates.factory import (
     _bedrock_tools_pt,
     _rename_duplicate_bedrock_document_names,
     _convert_to_bedrock_tool_call_invoke,
+    _sanitize_anthropic_tool_use_id,
     _convert_to_bedrock_tool_call_result,
     anthropic_messages_pt,
     convert_to_anthropic_tool_result,
     convert_to_gemini_tool_call_result,
+    get_tool_calls_from_response,
     make_valid_bedrock_tool_name,
     ollama_pt,
     sanitize_messages_for_tool_calling,
@@ -31,9 +34,7 @@ def _get_gemini_function_response_inline_data_parts(result):
     assert isinstance(result, list), "expected Gemini parts list"
     assert len(result) == 1, "multimodal function responses should stay in one part"
     function_response_part = result[0]
-    assert (
-        "inline_data" not in function_response_part
-    ), "inline_data should be nested under function_response.parts"
+    assert "inline_data" not in function_response_part, "inline_data should be nested under function_response.parts"
     function_response = function_response_part["function_response"]
     nested_parts = function_response["parts"]
     return [part["inline_data"] for part in nested_parts if "inline_data" in part]
@@ -49,7 +50,9 @@ def test_ollama_pt_simple_messages():
 
     result = ollama_pt(model="llama2", messages=messages)
 
-    expected_prompt = "### System:\nYou are a helpful assistant\n\n### Assistant:\nHow can I help you?\n\n### User:\nHello\n\n"
+    expected_prompt = (
+        "### System:\nYou are a helpful assistant\n\n### Assistant:\nHow can I help you?\n\n### User:\nHello\n\n"
+    )
     assert isinstance(result, dict)
     assert result["prompt"] == expected_prompt
     assert result["images"] == []
@@ -104,10 +107,7 @@ async def test_anthropic_bedrock_thinking_blocks_with_none_content():
 
     # verify the result
     assert len(result) == 2
-    assert (
-        result[1]["content"][0]["reasoningContent"]["reasoningText"]["text"]
-        == "This is a test thinking block"
-    )
+    assert result[1]["content"][0]["reasoningContent"]["reasoningText"]["text"] == "This is a test thinking block"
 
 
 def test_bedrock_converse_assistant_with_empty_thinking_block_and_tool_calls():
@@ -175,11 +175,7 @@ def test_bedrock_converse_assistant_with_empty_thinking_block_and_tool_calls():
     assert len(assistant_blocks) == 1
     for block in assistant_blocks[0]["content"]:
         if "text" in block:
-            assert block[
-                "text"
-            ].strip(), (
-                f"Bedrock Converse rejects blank-text ContentBlocks; got {block!r}"
-            )
+            assert block["text"].strip(), f"Bedrock Converse rejects blank-text ContentBlocks; got {block!r}"
     # toolUse blocks must still be present
     tool_use_blocks = [b for b in assistant_blocks[0]["content"] if "toolUse" in b]
     assert len(tool_use_blocks) == 2
@@ -220,19 +216,16 @@ def test_anthropic_messages_pt_drops_unsignable_thinking_block(thinking_block):
         {"role": "user", "content": "Now what is 3+3?"},
     ]
 
-    result = anthropic_messages_pt(
-        messages=messages, model="claude-sonnet-4-6", llm_provider="anthropic"
-    )
+    result = anthropic_messages_pt(messages=messages, model="claude-sonnet-4-6", llm_provider="anthropic")
 
     assistant = next(m for m in result if m["role"] == "assistant")
     content = assistant["content"]
-    assert all(
-        block.get("type") not in ("thinking", "redacted_thinking") for block in content
-    ), f"unsignable thinking block must be dropped, got {content!r}"
-    assert any(
-        block.get("type") == "text" and block.get("text") == "2+2 equals 4."
-        for block in content
-    ), f"assistant answer text must be preserved, got {content!r}"
+    assert all(block.get("type") not in ("thinking", "redacted_thinking") for block in content), (
+        f"unsignable thinking block must be dropped, got {content!r}"
+    )
+    assert any(block.get("type") == "text" and block.get("text") == "2+2 equals 4." for block in content), (
+        f"assistant answer text must be preserved, got {content!r}"
+    )
 
 
 def test_anthropic_messages_pt_keeps_signed_thinking_block():
@@ -255,9 +248,7 @@ def test_anthropic_messages_pt_keeps_signed_thinking_block():
         {"role": "user", "content": "Now what is 3+3?"},
     ]
 
-    result = anthropic_messages_pt(
-        messages=messages, model="claude-sonnet-4-6", llm_provider="anthropic"
-    )
+    result = anthropic_messages_pt(messages=messages, model="claude-sonnet-4-6", llm_provider="anthropic")
 
     assistant = next(m for m in result if m["role"] == "assistant")
     thinking_blocks = [b for b in assistant["content"] if b.get("type") == "thinking"]
@@ -373,9 +364,7 @@ def test_bedrock_get_document_format_fallback_mimes():
     """
 
     # Test DOCX fallback
-    docx_mime = (
-        "application/vnd.openxmlformats-officedocument.wordprocessingml.document"
-    )
+    docx_mime = "application/vnd.openxmlformats-officedocument.wordprocessingml.document"
     supported_formats = ["pdf", "docx", "xlsx", "csv"]
 
     # Mock mimetypes.guess_all_extensions to return empty list (simulating Docker container scenario)
@@ -399,15 +388,11 @@ def test_bedrock_get_document_format_mimetypes_success():
     """
     Test the _get_document_format method when mimetypes.guess_all_extensions works normally.
     """
-    docx_mime = (
-        "application/vnd.openxmlformats-officedocument.wordprocessingml.document"
-    )
+    docx_mime = "application/vnd.openxmlformats-officedocument.wordprocessingml.document"
     supported_formats = ["pdf", "docx", "xlsx", "csv"]
 
     # Test normal mimetypes behavior (should not hit fallback)
-    result = BedrockImageProcessor._get_document_format(
-        mime_type=docx_mime, supported_doc_formats=supported_formats
-    )
+    result = BedrockImageProcessor._get_document_format(mime_type=docx_mime, supported_doc_formats=supported_formats)
     assert result == "docx", f"Expected 'docx', got '{result}'"
 
 
@@ -623,9 +608,7 @@ async def test_bedrock_process_image_async_factory():
 
     image_url = "data:application/pdf; qs=0.001;base64,JVBERi0xLjQKJcOkw7zDtsOfCjIgMCBvYmoKPDwvTGVuZ3RoIDMgMCBSL0ZpbHRlci9GbGF0ZURlY29kZT4"
 
-    content_block = await BedrockImageProcessor.process_image_async(
-        image_url=image_url, format=None
-    )
+    content_block = await BedrockImageProcessor.process_image_async(image_url=image_url, format=None)
     print(f"content_block: {content_block}")
 
 
@@ -668,9 +651,7 @@ def test_unpack_defs_resolves_nested_ref_inside_anyof_items():
     items_schema = schema["properties"]["vatAmounts"]["anyOf"][0]["items"]
 
     # Assertions: items_schema should now be the resolved object, not an empty dict
-    assert isinstance(
-        items_schema, dict
-    ), "Items schema should be a dict after unpacking"
+    assert isinstance(items_schema, dict), "Items schema should be a dict after unpacking"
     assert items_schema.get("type") == "object"
     # Ensure essential properties are present
     assert set(items_schema.get("properties", {}).keys()) == {"vatRate", "vatAmount"}
@@ -861,9 +842,7 @@ def test_convert_gemini_tool_call_result_with_multiple_anthropic_image_blocks():
         last_message_with_tool_calls=last_message_with_tool_calls,
     )
     inline_parts = _get_gemini_function_response_inline_data_parts(result)
-    assert (
-        len(inline_parts) == 2
-    ), f"expected 2 inline_data parts, got {len(inline_parts)}"
+    assert len(inline_parts) == 2, f"expected 2 inline_data parts, got {len(inline_parts)}"
     mime_types = {p["mime_type"] for p in inline_parts}
     assert mime_types == {"image/png", "image/jpeg"}
 
@@ -899,9 +878,7 @@ def test_convert_gemini_tool_call_result_with_data_url_string():
         last_message_with_tool_calls=last_message_with_tool_calls,
     )
     inline_parts = _get_gemini_function_response_inline_data_parts(result)
-    assert (
-        len(inline_parts) == 1
-    ), "data-URL image string was not converted to inline_data"
+    assert len(inline_parts) == 1, "data-URL image string was not converted to inline_data"
     assert inline_parts[0]["mime_type"] == "image/png"
     assert inline_parts[0]["data"] == tiny_png_b64
 
@@ -937,9 +914,9 @@ def test_convert_gemini_tool_call_result_with_data_url_extra_params():
     )
     inline_parts = _get_gemini_function_response_inline_data_parts(result)
     assert len(inline_parts) == 1
-    assert (
-        inline_parts[0]["mime_type"] == "image/png"
-    ), f"expected clean 'image/png', got '{inline_parts[0]['mime_type']}'"
+    assert inline_parts[0]["mime_type"] == "image/png", (
+        f"expected clean 'image/png', got '{inline_parts[0]['mime_type']}'"
+    )
 
 
 def test_bedrock_tools_unpack_defs():
@@ -1036,9 +1013,7 @@ def test_bedrock_tools_pt_strict_parameter():
             },
         }
     ]
-    result = _bedrock_tools_pt(
-        tools_with_strict, model="anthropic.claude-sonnet-4-5-20250929-v1:0"
-    )
+    result = _bedrock_tools_pt(tools_with_strict, model="anthropic.claude-sonnet-4-5-20250929-v1:0")
     assert result[0]["toolSpec"]["strict"] is True
     assert result[0]["toolSpec"]["inputSchema"]["json"]["additionalProperties"] is False
 
@@ -1060,9 +1035,7 @@ def test_bedrock_tools_pt_strict_parameter():
             },
         }
     ]
-    result = _bedrock_tools_pt(
-        tools_without_strict, model="anthropic.claude-sonnet-4-5-20250929-v1:0"
-    )
+    result = _bedrock_tools_pt(tools_without_strict, model="anthropic.claude-sonnet-4-5-20250929-v1:0")
     assert "strict" not in result[0]["toolSpec"]
     assert "additionalProperties" not in result[0]["toolSpec"]["inputSchema"]["json"]
 
@@ -1085,9 +1058,7 @@ def test_bedrock_image_processor_content_type_fallback_url_extension():
 
     # Test with .png URL
     image_url = "https://example.com/test-image.png"
-    base64_bytes, content_type = BedrockImageProcessor._post_call_image_processing(
-        mock_response, image_url
-    )
+    base64_bytes, content_type = BedrockImageProcessor._post_call_image_processing(mock_response, image_url)
 
     assert content_type == "image/png"
     assert base64_bytes == base64.b64encode(png_content).decode("utf-8")
@@ -1111,9 +1082,7 @@ def test_bedrock_image_processor_content_type_fallback_binary_detection():
 
     # Test with URL without extension
     image_url = "https://example.com/test-image-without-extension"
-    base64_bytes, content_type = BedrockImageProcessor._post_call_image_processing(
-        mock_response, image_url
-    )
+    base64_bytes, content_type = BedrockImageProcessor._post_call_image_processing(mock_response, image_url)
 
     assert content_type == "image/jpeg"
     assert base64_bytes == base64.b64encode(jpeg_content).decode("utf-8")
@@ -1136,9 +1105,7 @@ def test_bedrock_image_processor_content_type_fallback_application_octet_stream(
 
     # Test with .gif URL
     image_url = "https://s3.amazonaws.com/bucket/image.gif"
-    base64_bytes, content_type = BedrockImageProcessor._post_call_image_processing(
-        mock_response, image_url
-    )
+    base64_bytes, content_type = BedrockImageProcessor._post_call_image_processing(mock_response, image_url)
 
     assert content_type == "image/gif"
     assert base64_bytes == base64.b64encode(gif_content).decode("utf-8")
@@ -1161,9 +1128,7 @@ def test_bedrock_image_processor_content_type_with_query_params():
 
     # Test with URL containing query parameters (common in S3 signed URLs)
     image_url = "https://s3.amazonaws.com/bucket/image.webp?AWSAccessKeyId=123&Expires=456&Signature=789"
-    base64_bytes, content_type = BedrockImageProcessor._post_call_image_processing(
-        mock_response, image_url
-    )
+    base64_bytes, content_type = BedrockImageProcessor._post_call_image_processing(mock_response, image_url)
 
     assert content_type == "image/webp"
     assert base64_bytes == base64.b64encode(webp_content).decode("utf-8")
@@ -1185,9 +1150,7 @@ def test_bedrock_image_processor_content_type_normal_header():
     mock_response.content = png_content
 
     image_url = "https://example.com/test-image.png"
-    base64_bytes, content_type = BedrockImageProcessor._post_call_image_processing(
-        mock_response, image_url
-    )
+    base64_bytes, content_type = BedrockImageProcessor._post_call_image_processing(mock_response, image_url)
 
     assert content_type == "image/png"
     assert base64_bytes == base64.b64encode(png_content).decode("utf-8")
@@ -1207,7 +1170,7 @@ def test_bedrock_image_processor_content_type_fallback_failure():
     # Test with URL without recognizable extension
     image_url = "https://example.com/unknown-file"
 
-    with pytest.raises(ValueError, match='Unable to determine content type from URL: https') as excinfo:
+    with pytest.raises(ValueError, match="Unable to determine content type from URL: https") as excinfo:
         BedrockImageProcessor._post_call_image_processing(mock_response, image_url)
 
     assert "Unable to determine content type" in str(excinfo.value)
@@ -1227,16 +1190,12 @@ def test_bedrock_image_processor_content_type_jpeg_variants():
 
     # Test with .jpg extension
     image_url_jpg = "https://example.com/photo.jpg"
-    _, content_type_jpg = BedrockImageProcessor._post_call_image_processing(
-        mock_response, image_url_jpg
-    )
+    _, content_type_jpg = BedrockImageProcessor._post_call_image_processing(mock_response, image_url_jpg)
     assert content_type_jpg == "image/jpeg"
 
     # Test with .jpeg extension
     image_url_jpeg = "https://example.com/photo.jpeg"
-    _, content_type_jpeg = BedrockImageProcessor._post_call_image_processing(
-        mock_response, image_url_jpeg
-    )
+    _, content_type_jpeg = BedrockImageProcessor._post_call_image_processing(mock_response, image_url_jpeg)
     assert content_type_jpeg == "image/jpeg"
 
 
@@ -1258,9 +1217,7 @@ def test_bedrock_image_processor_content_type_pdf_document():
 
     # Test with .pdf URL
     pdf_url = "https://s3.amazonaws.com/bucket/document.pdf"
-    base64_bytes, content_type = BedrockImageProcessor._post_call_image_processing(
-        mock_response, pdf_url
-    )
+    base64_bytes, content_type = BedrockImageProcessor._post_call_image_processing(mock_response, pdf_url)
 
     assert content_type == "application/pdf"
     assert base64_bytes == base64.b64encode(pdf_content).decode("utf-8")
@@ -1293,12 +1250,8 @@ def test_bedrock_image_processor_content_type_document_formats():
     ]
 
     for url, expected_mime in test_cases:
-        _, content_type = BedrockImageProcessor._post_call_image_processing(
-            mock_response, url
-        )
-        assert (
-            content_type == expected_mime
-        ), f"Expected {expected_mime} for {url}, got {content_type}"
+        _, content_type = BedrockImageProcessor._post_call_image_processing(mock_response, url)
+        assert content_type == expected_mime, f"Expected {expected_mime} for {url}, got {content_type}"
 
 
 def test_bedrock_image_processor_content_type_s3_pdf_with_query():
@@ -1317,9 +1270,7 @@ def test_bedrock_image_processor_content_type_s3_pdf_with_query():
     # S3 signed URL with query parameters
     s3_url = "https://my-bucket.s3.us-east-1.amazonaws.com/documents/report.pdf?AWSAccessKeyId=AKIAIOSFODNN7EXAMPLE&Expires=1234567890&Signature=abcdef123456"
 
-    base64_bytes, content_type = BedrockImageProcessor._post_call_image_processing(
-        mock_response, s3_url
-    )
+    base64_bytes, content_type = BedrockImageProcessor._post_call_image_processing(mock_response, s3_url)
 
     assert content_type == "application/pdf"
     assert base64_bytes == base64.b64encode(pdf_content).decode("utf-8")
@@ -1428,12 +1379,8 @@ def test_bedrock_create_bedrock_block_normalized_base64():
     base64_content = base64.b64encode(pdf_content).decode("utf-8")
 
     # Create versions with different whitespace
-    base64_with_newlines = "\n".join(
-        [base64_content[i : i + 64] for i in range(0, len(base64_content), 64)]
-    )
-    base64_with_spaces = " ".join(
-        [base64_content[i : i + 32] for i in range(0, len(base64_content), 32)]
-    )
+    base64_with_newlines = "\n".join([base64_content[i : i + 64] for i in range(0, len(base64_content), 64)])
+    base64_with_spaces = " ".join([base64_content[i : i + 32] for i in range(0, len(base64_content), 32)])
 
     # Create blocks
     block1 = BedrockImageProcessor._create_bedrock_block(
@@ -1565,9 +1512,7 @@ def test_bedrock_create_bedrock_block_document_name_format():
 
     # Check format: DocumentPDFmessages_{16_hex_chars}_{format}
     pattern = r"^DocumentPDFmessages_[0-9a-f]{16}_pdf$"
-    assert re.match(
-        pattern, document_name
-    ), f"Document name format mismatch: {document_name}"
+    assert re.match(pattern, document_name), f"Document name format mismatch: {document_name}"
 
 
 def test_bedrock_create_bedrock_block_different_document_formats():
@@ -1620,9 +1565,7 @@ def test_bedrock_nova_web_search_options_mapping():
     assert system_tool["name"] == "nova_grounding"
 
     # Test with search_context_size (should be ignored for Nova)
-    result2 = config._map_web_search_options(
-        {"search_context_size": "high"}, "us.amazon.nova-premier-v1:0"
-    )
+    result2 = config._map_web_search_options({"search_context_size": "high"}, "us.amazon.nova-premier-v1:0")
 
     assert result2 is not None
     system_tool2 = result2.get("systemTool")
@@ -1688,9 +1631,7 @@ def test_bedrock_tools_pt_drops_unmappable_responses_builtin_tools():
         {"type": "custom", "name": "free_form"},
     ]
 
-    result = _bedrock_tools_pt(
-        tools=tools, model="anthropic.claude-sonnet-4-5-20250929-v1:0"
-    )
+    result = _bedrock_tools_pt(tools=tools, model="anthropic.claude-sonnet-4-5-20250929-v1:0")
 
     names = [block["toolSpec"]["name"] for block in result if "toolSpec" in block]
     assert names == ["noop"]
@@ -1720,9 +1661,7 @@ def test_bedrock_tools_pt_keeps_anthropic_input_schema_tools():
         },
     ]
 
-    result = _bedrock_tools_pt(
-        tools=tools, model="anthropic.claude-sonnet-4-5-20250929-v1:0"
-    )
+    result = _bedrock_tools_pt(tools=tools, model="anthropic.claude-sonnet-4-5-20250929-v1:0")
 
     names = [block["toolSpec"]["name"] for block in result if "toolSpec" in block]
     assert names == ["lookup"]
@@ -1924,9 +1863,7 @@ def test_anthropic_messages_pt_server_tool_use_passthrough():
                     "tool_use_id": "srvtoolu_01ABC123",
                     "content": {
                         "type": "tool_search_tool_search_result",
-                        "tool_references": [
-                            {"type": "tool_reference", "tool_name": "get_time"}
-                        ],
+                        "tool_references": [{"type": "tool_reference", "tool_name": "get_time"}],
                     },
                 },
                 {"type": "text", "text": "I found the time tool. How can I help you?"},
@@ -1954,20 +1891,14 @@ def test_anthropic_messages_pt_server_tool_use_passthrough():
 
     # Verify server_tool_use block is preserved
     assert "server_tool_use" in content_types
-    server_tool_use_block = next(
-        b for b in assistant_msg["content"] if b.get("type") == "server_tool_use"
-    )
+    server_tool_use_block = next(b for b in assistant_msg["content"] if b.get("type") == "server_tool_use")
     assert server_tool_use_block["id"] == "srvtoolu_01ABC123"
     assert server_tool_use_block["name"] == "tool_search_tool_regex"
     assert server_tool_use_block["input"] == {"query": ".*time.*"}
 
     # Verify tool_search_tool_result block is preserved
     assert "tool_search_tool_result" in content_types
-    tool_result_block = next(
-        b
-        for b in assistant_msg["content"]
-        if b.get("type") == "tool_search_tool_result"
-    )
+    tool_result_block = next(b for b in assistant_msg["content"] if b.get("type") == "tool_search_tool_result")
     assert tool_result_block["tool_use_id"] == "srvtoolu_01ABC123"
     assert tool_result_block["content"]["type"] == "tool_search_tool_search_result"
     assert tool_result_block["content"]["tool_references"][0]["tool_name"] == "get_time"
@@ -2019,9 +1950,7 @@ def test_bedrock_tools_unpack_defs_no_oom_with_nested_refs():
                 "anyOf": [
                     {"$ref": "#/$defs/Literal"},
                     {"$ref": "#/$defs/FieldRef"},
-                    {
-                        "$ref": "#/$defs/Expression"
-                    },  # Circular: Operand -> Expression -> Operand
+                    {"$ref": "#/$defs/Expression"},  # Circular: Operand -> Expression -> Operand
                 ],
             },
             "Literal": {
@@ -2155,9 +2084,7 @@ def test_anthropic_messages_pt_file_block_cache_control_with_explicit_provider()
 
     file_block = content_blocks[0]
     assert file_block["type"] == "document"
-    assert (
-        "cache_control" in file_block
-    ), "cache_control should be preserved on file/document content blocks"
+    assert "cache_control" in file_block, "cache_control should be preserved on file/document content blocks"
     assert file_block["cache_control"]["type"] == "ephemeral"
 
     text_block = content_blocks[1]
@@ -2365,22 +2292,16 @@ def test_bedrock_tool_call_invoke_concatenated_json():
     # First block keeps original tool id
     assert result[0]["toolUse"]["toolUseId"] == "tooluse_L7I3TewYAUhoheJZQEuwVN"
     assert result[0]["toolUse"]["name"] == "shell"
-    assert result[0]["toolUse"]["input"] == {
-        "command": ["curl", "-i", "http://localhost:9009", "-m", "10"]
-    }
+    assert result[0]["toolUse"]["input"] == {"command": ["curl", "-i", "http://localhost:9009", "-m", "10"]}
 
     # Subsequent blocks get suffixed ids
     assert result[1]["toolUse"]["toolUseId"] == "tooluse_L7I3TewYAUhoheJZQEuwVN_1"
     assert result[1]["toolUse"]["name"] == "shell"
-    assert result[1]["toolUse"]["input"] == {
-        "command": ["curl", "-i", "http://localhost:9009/robots.txt", "-m", "5"]
-    }
+    assert result[1]["toolUse"]["input"] == {"command": ["curl", "-i", "http://localhost:9009/robots.txt", "-m", "5"]}
 
     assert result[2]["toolUse"]["toolUseId"] == "tooluse_L7I3TewYAUhoheJZQEuwVN_2"
     assert result[2]["toolUse"]["name"] == "shell"
-    assert result[2]["toolUse"]["input"] == {
-        "command": ["curl", "-i", "http://localhost:9009/sitemap.xml", "-m", "5"]
-    }
+    assert result[2]["toolUse"]["input"] == {"command": ["curl", "-i", "http://localhost:9009/sitemap.xml", "-m", "5"]}
 
 
 def test_bedrock_tool_call_invoke_concatenated_json_with_cache_control():
@@ -2535,9 +2456,7 @@ def test_bedrock_tool_call_invoke_unconvertible_raises_non_retryable_bad_request
 def test_make_valid_bedrock_tool_name_preserves_hyphens():
     assert make_valid_bedrock_tool_name("my-tool") == "my-tool"
     assert (
-        make_valid_bedrock_tool_name(
-            "CreateCaseKnowledgeArticle_foTWsqR6yDt-OnSsvR5e6Q"
-        )
+        make_valid_bedrock_tool_name("CreateCaseKnowledgeArticle_foTWsqR6yDt-OnSsvR5e6Q")
         == "CreateCaseKnowledgeArticle_foTWsqR6yDt-OnSsvR5e6Q"
     )
 
@@ -2564,9 +2483,7 @@ def test_bedrock_tool_name_sanitized_consistently_in_tools_and_tool_use():
             "function": {"name": raw_name, "arguments": "{}"},
         }
     ]
-    tool_use_name = _convert_to_bedrock_tool_call_invoke(tool_calls)[0]["toolUse"][
-        "name"
-    ]
+    tool_use_name = _convert_to_bedrock_tool_call_invoke(tool_calls)[0]["toolUse"]["name"]
 
     assert tool_spec_name == "foo_bar"
     assert tool_use_name == tool_spec_name
@@ -2589,15 +2506,8 @@ def test_bedrock_converse_messages_pt_tool_use_matches_tool_spec_hyphen_name():
             ],
         },
     ]
-    translated = _bedrock_converse_messages_pt(
-        messages=messages, model="", llm_provider=""
-    )
-    tool_use_blocks = [
-        block
-        for msg in translated
-        for block in msg.get("content", [])
-        if "toolUse" in block
-    ]
+    translated = _bedrock_converse_messages_pt(messages=messages, model="", llm_provider="")
+    tool_use_blocks = [block for msg in translated for block in msg.get("content", []) if "toolUse" in block]
     assert len(tool_use_blocks) == 1
     assert tool_use_blocks[0]["toolUse"]["name"] == tool_name
 
@@ -2694,11 +2604,7 @@ def test_sanitize_messages_deduplicates_tool_results():
         result = sanitize_messages_for_tool_calling(messages)
 
         # Count tool messages with this ID — should be exactly 1
-        tool_results = [
-            m
-            for m in result
-            if m.get("role") == "tool" and m.get("tool_call_id") == "call_abc123"
-        ]
+        tool_results = [m for m in result if m.get("role") == "tool" and m.get("tool_call_id") == "call_abc123"]
         assert len(tool_results) == 1
         # Should keep the LAST occurrence (most complete)
         assert tool_results[0]["content"] == '{"temperature": 72, "condition": "sunny"}'
@@ -2833,11 +2739,7 @@ def test_sanitize_messages_dedup_scoped_per_turn_preserves_cross_turn():
         result = sanitize_messages_for_tool_calling(messages)
 
         # Both tool results must survive — one per turn
-        tool_results = [
-            m
-            for m in result
-            if m.get("role") == "tool" and m.get("tool_call_id") == "call_X"
-        ]
+        tool_results = [m for m in result if m.get("role") == "tool" and m.get("tool_call_id") == "call_X"]
         assert len(tool_results) == 2, (
             f"Expected 2 tool results (one per turn), got {len(tool_results)}. "
             "Dedup may be global instead of per-turn scoped."
@@ -2891,32 +2793,26 @@ def test_sanitize_messages_combined_case_a_and_case_d():
         tool_results = [m for m in result if m.get("role") in ("tool", "function")]
 
         # Case A: call_missing should have a dummy result injected
-        missing_results = [
-            m for m in tool_results if m.get("tool_call_id") == "call_missing"
-        ]
-        assert (
-            len(missing_results) == 1
-        ), f"Expected 1 dummy result for call_missing (Case A), got {len(missing_results)}"
+        missing_results = [m for m in tool_results if m.get("tool_call_id") == "call_missing"]
+        assert len(missing_results) == 1, (
+            f"Expected 1 dummy result for call_missing (Case A), got {len(missing_results)}"
+        )
 
         # Case D: call_duped should have exactly 1 result (the fresh one)
-        duped_results = [
-            m for m in tool_results if m.get("tool_call_id") == "call_duped"
-        ]
-        assert (
-            len(duped_results) == 1
-        ), f"Expected 1 result for call_duped after dedup (Case D), got {len(duped_results)}"
-        assert (
-            duped_results[0]["content"] == "fresh_result"
-        ), f"Expected last-wins 'fresh_result', got '{duped_results[0]['content']}'"
+        duped_results = [m for m in tool_results if m.get("tool_call_id") == "call_duped"]
+        assert len(duped_results) == 1, (
+            f"Expected 1 result for call_duped after dedup (Case D), got {len(duped_results)}"
+        )
+        assert duped_results[0]["content"] == "fresh_result", (
+            f"Expected last-wins 'fresh_result', got '{duped_results[0]['content']}'"
+        )
 
         # Verify tool results immediately follow the assistant message
         asst_idx = next(i for i, m in enumerate(result) if m.get("role") == "assistant")
-        tool_msgs_after_asst = [
-            m for m in result[asst_idx + 1 :] if m.get("role") in ("tool", "function")
-        ]
-        assert (
-            len(tool_msgs_after_asst) == 2
-        ), f"Expected 2 tool results after assistant, got {len(tool_msgs_after_asst)}"
+        tool_msgs_after_asst = [m for m in result[asst_idx + 1 :] if m.get("role") in ("tool", "function")]
+        assert len(tool_msgs_after_asst) == 2, (
+            f"Expected 2 tool results after assistant, got {len(tool_msgs_after_asst)}"
+        )
         # Both tool_call_ids should be present (order may vary)
         tool_ids = {m["tool_call_id"] for m in tool_msgs_after_asst}
         assert tool_ids == {
@@ -2958,9 +2854,7 @@ def test_anthropic_messages_pt_file_block_preserves_cache_control():
         }
     ]
 
-    result = anthropic_messages_pt(
-        messages, model="claude-sonnet-4-20250514", llm_provider="anthropic"
-    )
+    result = anthropic_messages_pt(messages, model="claude-sonnet-4-20250514", llm_provider="anthropic")
 
     content_blocks = result[0]["content"]
     assert len(content_blocks) == 2
@@ -2968,9 +2862,7 @@ def test_anthropic_messages_pt_file_block_preserves_cache_control():
     # Document block (from file) should preserve cache_control
     doc_block = content_blocks[0]
     assert doc_block["type"] == "document"
-    assert (
-        "cache_control" in doc_block
-    ), "cache_control was dropped from file/document block"
+    assert "cache_control" in doc_block, "cache_control was dropped from file/document block"
     assert doc_block["cache_control"]["type"] == "ephemeral"
 
     # Text block should also preserve cache_control
@@ -3013,9 +2905,7 @@ def test_add_cache_point_tool_block_passes_ttl_for_claude_4_5(monkeypatch):
         }
 
         # Claude 4.5 model: ttl should be preserved
-        result = add_cache_point_tool_block(
-            tool_with_1h, model="jp.anthropic.claude-opus-4-7"
-        )
+        result = add_cache_point_tool_block(tool_with_1h, model="jp.anthropic.claude-opus-4-7")
         assert result is not None
         assert result["cachePoint"]["type"] == "default"
         assert result["cachePoint"]["ttl"] == "1h"
@@ -3024,16 +2914,12 @@ def test_add_cache_point_tool_block_passes_ttl_for_claude_4_5(monkeypatch):
         tool_with_5m = {
             "cache_control": {"type": "ephemeral", "ttl": "5m"},
         }
-        result_5m = add_cache_point_tool_block(
-            tool_with_5m, model="jp.anthropic.claude-opus-4-7"
-        )
+        result_5m = add_cache_point_tool_block(tool_with_5m, model="jp.anthropic.claude-opus-4-7")
         assert result_5m is not None
         assert result_5m["cachePoint"]["ttl"] == "5m"
 
         # Older model: ttl should be stripped
-        result_old = add_cache_point_tool_block(
-            tool_with_1h, model="anthropic.claude-3-5-sonnet-20241022-v2:0"
-        )
+        result_old = add_cache_point_tool_block(tool_with_1h, model="anthropic.claude-3-5-sonnet-20241022-v2:0")
         assert result_old is not None
         assert result_old["cachePoint"]["type"] == "default"
         assert "ttl" not in result_old["cachePoint"]
@@ -3052,9 +2938,7 @@ def test_add_cache_point_tool_block_passes_ttl_for_claude_4_5(monkeypatch):
 
         # cache_control without ttl: returns default cachePoint (unchanged behavior)
         tool_no_ttl = {"cache_control": {"type": "ephemeral"}}
-        result_no_ttl = add_cache_point_tool_block(
-            tool_no_ttl, model="us.anthropic.claude-sonnet-4-5-20250929-v1:0"
-        )
+        result_no_ttl = add_cache_point_tool_block(tool_no_ttl, model="us.anthropic.claude-sonnet-4-5-20250929-v1:0")
         assert result_no_ttl is not None
         assert result_no_ttl["cachePoint"]["type"] == "default"
         assert "ttl" not in result_no_ttl["cachePoint"]
@@ -3127,9 +3011,7 @@ def test_bedrock_tools_pt_passes_ttl_for_claude_4_5(monkeypatch):
         assert cache_blocks[0]["cachePoint"]["ttl"] == "1h"
 
         # Older model: cachePoint should not have ttl
-        result_old = _bedrock_tools_pt(
-            tools, model="anthropic.claude-3-5-sonnet-20241022-v2:0"
-        )
+        result_old = _bedrock_tools_pt(tools, model="anthropic.claude-3-5-sonnet-20241022-v2:0")
         cache_blocks_old = [b for b in result_old if "cachePoint" in b]
         assert len(cache_blocks_old) == 1
         assert "ttl" not in cache_blocks_old[0]["cachePoint"]
@@ -3204,9 +3086,7 @@ def test_bedrock_converse_messages_pt_document_various_formats():
             }
         ]
 
-        result = _bedrock_converse_messages_pt(
-            messages, "anthropic.claude-sonnet-4-6", "bedrock"
-        )
+        result = _bedrock_converse_messages_pt(messages, "anthropic.claude-sonnet-4-6", "bedrock")
 
         doc_block = result[0]["content"][0]
         assert doc_block["document"]["format"] == expected_format, (
@@ -3233,12 +3113,8 @@ def test_bedrock_converse_messages_pt_document_deterministic_name():
         }
     ]
 
-    result1 = _bedrock_converse_messages_pt(
-        messages, "anthropic.claude-sonnet-4-6", "bedrock"
-    )
-    result2 = _bedrock_converse_messages_pt(
-        messages, "anthropic.claude-sonnet-4-6", "bedrock"
-    )
+    result1 = _bedrock_converse_messages_pt(messages, "anthropic.claude-sonnet-4-6", "bedrock")
+    result2 = _bedrock_converse_messages_pt(messages, "anthropic.claude-sonnet-4-6", "bedrock")
 
     name1 = result1[0]["content"][0]["document"]["name"]
     name2 = result2[0]["content"][0]["document"]["name"]
@@ -3272,34 +3148,18 @@ def test_bedrock_converse_messages_pt_renames_duplicate_document_names():
         },
     ]
 
-    result1 = _bedrock_converse_messages_pt(
-        messages, "anthropic.claude-sonnet-4-6", "bedrock"
-    )
-    result2 = _bedrock_converse_messages_pt(
-        messages, "anthropic.claude-sonnet-4-6", "bedrock"
-    )
+    result1 = _bedrock_converse_messages_pt(messages, "anthropic.claude-sonnet-4-6", "bedrock")
+    result2 = _bedrock_converse_messages_pt(messages, "anthropic.claude-sonnet-4-6", "bedrock")
 
-    names1 = [
-        block["document"]["name"]
-        for message in result1
-        for block in message["content"]
-        if "document" in block
-    ]
-    names2 = [
-        block["document"]["name"]
-        for message in result2
-        for block in message["content"]
-        if "document" in block
-    ]
+    names1 = [block["document"]["name"] for message in result1 for block in message["content"] if "document" in block]
+    names2 = [block["document"]["name"] for message in result2 for block in message["content"] if "document" in block]
 
     assert len(names1) == 2
     assert len(set(names1)) == 2
     assert names1[1] == f"{names1[0]}_2"
     assert names1 == names2
 
-    single_turn = _bedrock_converse_messages_pt(
-        [messages[0]], "anthropic.claude-sonnet-4-6", "bedrock"
-    )
+    single_turn = _bedrock_converse_messages_pt([messages[0]], "anthropic.claude-sonnet-4-6", "bedrock")
     assert names1[0] == single_turn[0]["content"][0]["document"]["name"]
 
 
@@ -3321,14 +3181,10 @@ def test_rename_duplicate_bedrock_document_names_skips_organic_suffixes():
     def _names(contents):
         return [block["document"]["name"] for block in contents[0]["content"]]
 
-    organic_first = _rename_duplicate_bedrock_document_names(
-        _contents(["report", "report_2", "report"])
-    )
+    organic_first = _rename_duplicate_bedrock_document_names(_contents(["report", "report_2", "report"]))
     assert _names(organic_first) == ["report", "report_2", "report_3"]
 
-    organic_last = _rename_duplicate_bedrock_document_names(
-        _contents(["report", "report", "report_2"])
-    )
+    organic_last = _rename_duplicate_bedrock_document_names(_contents(["report", "report", "report_2"]))
     assert _names(organic_last) == ["report", "report_3", "report_2"]
 
 
@@ -3350,18 +3206,11 @@ def test_bedrock_converse_messages_pt_document_rejects_url_source():
     ]
 
     with pytest.raises(ValueError, match="only supports base64-encoded"):
-        _bedrock_converse_messages_pt(
-            messages, "anthropic.claude-sonnet-4-6", "bedrock"
-        )
+        _bedrock_converse_messages_pt(messages, "anthropic.claude-sonnet-4-6", "bedrock")
 
 
 def _collect_cache_points(blocks):
-    return [
-        block["cachePoint"]
-        for message in blocks
-        for block in message["content"]
-        if "cachePoint" in block
-    ]
+    return [block["cachePoint"] for message in blocks for block in message["content"] if "cachePoint" in block]
 
 
 @pytest.mark.parametrize(
@@ -3527,6 +3376,189 @@ def test_get_tool_calls_from_response_warns_for_malformed_arguments(caplog):
     assert "Failed to parse tool call arguments" in caplog.text
 
 
+def _concatenated_json(*payloads: dict[str, object]) -> str:
+    return "".join(json.dumps(payload, separators=(",", ":")) for payload in payloads)
+
+
+def _function_tool_call(call_id: str | None, name: str, arguments: str) -> dict[str, object]:
+    return {"id": call_id, "function": {"name": name, "arguments": arguments}}
+
+
+def _chat_tool_response(*tool_calls: dict[str, object]) -> dict[str, object]:
+    return {"choices": [{"message": {"tool_calls": list(tool_calls)}}]}
+
+
+def test_get_tool_calls_from_response_expands_distinct_concatenated_arguments(caplog):
+    raw = '{"flag":true}{"box":"A","limit":50}'
+    response: Final = _chat_tool_response(_function_tool_call("call_move", "move", raw))
+
+    with caplog.at_level(logging.WARNING, logger="LiteLLM"):
+        tool_calls: Final = get_tool_calls_from_response(response)
+
+    assert tool_calls == [
+        {"id": "call_move", "name": "move", "arguments": {"flag": True}},
+        {"id": "call_move__concat_1", "name": "move", "arguments": {"box": "A", "limit": 50}},
+    ]
+    assert "Recovered 2 tool call(s)" in caplog.text
+    assert "move" in caplog.text
+    assert "flag" not in caplog.text
+
+
+def test_get_tool_calls_from_response_expands_responses_api_concatenated_arguments():
+    response: Final = {
+        "output": [
+            {
+                "type": "function_call",
+                "call_id": "call_move",
+                "name": "move",
+                "arguments": '{"flag":true}{"box":"A","limit":50}',
+            }
+        ]
+    }
+
+    assert get_tool_calls_from_response(response) == [
+        {"id": "call_move", "name": "move", "arguments": {"flag": True}},
+        {"id": "call_move__concat_1", "name": "move", "arguments": {"box": "A", "limit": 50}},
+    ]
+
+
+def test_get_tool_calls_from_response_collapses_identical_concatenated_arguments():
+    response: Final = _chat_tool_response(_function_tool_call("call_move", "move", '{"flag":true}' * 3))
+
+    assert get_tool_calls_from_response(response) == [
+        {"id": "call_move", "name": "move", "arguments": {"flag": True}},
+    ]
+
+
+def test_get_tool_calls_from_response_does_not_expand_a_valid_json_array():
+    response: Final = _chat_tool_response(_function_tool_call("call_batch", "batch", '[{"a":1},{"b":2}]'))
+
+    assert get_tool_calls_from_response(response) == [
+        {"id": "call_batch", "name": "batch", "arguments": {}},
+    ]
+
+
+@pytest.mark.parametrize("arguments", ('{"a":1}{"b":', '0{"x":1}'))
+def test_get_tool_calls_from_response_drops_partial_concatenated_arguments(arguments: str, caplog):
+    response: Final = _chat_tool_response(_function_tool_call("call_move", "move", arguments))
+
+    with caplog.at_level(logging.WARNING, logger="LiteLLM"):
+        tool_calls: Final = get_tool_calls_from_response(response)
+
+    assert tool_calls == [{"id": "call_move", "name": "move", "arguments": {}}]
+    assert "Failed to parse tool call arguments" in caplog.text
+
+
+@pytest.mark.parametrize(("count", "expands"), ((8, True), (9, False)))
+def test_get_tool_calls_from_response_caps_distinct_concatenated_arguments(count: int, expands: bool):
+    raw = _concatenated_json(*({"n": index} for index in range(count)))
+    response: Final = _chat_tool_response(_function_tool_call("call", "move", raw))
+
+    tool_calls: Final = get_tool_calls_from_response(response)
+
+    if expands:
+        assert [call["id"] for call in tool_calls] == ["call", *(f"call__concat_{index}" for index in range(1, count))]
+        assert [call["arguments"] for call in tool_calls] == [{"n": index} for index in range(count)]
+        return
+    assert tool_calls == [{"id": "call", "name": "move", "arguments": {}}]
+
+
+def test_get_tool_calls_from_response_skips_concat_ids_taken_by_a_sibling():
+    raw = _concatenated_json({"a": 1}, {"b": 2})
+    response: Final = _chat_tool_response(
+        _function_tool_call("call", "move", raw),
+        _function_tool_call("call__concat_1", "look", '{"x":1}'),
+    )
+
+    assert [call["id"] for call in get_tool_calls_from_response(response)] == [
+        "call",
+        "call__concat_2",
+        "call__concat_1",
+    ]
+
+
+def test_get_tool_calls_from_response_keeps_sanitized_concat_ids_distinct():
+    raw = _concatenated_json({"a": 1}, {"b": 2})
+    response: Final = _chat_tool_response(
+        _function_tool_call("a:b", "move", raw),
+        _function_tool_call("a_b__concat_1", "look", '{"x":1}'),
+    )
+
+    ids: Final = [call["id"] for call in get_tool_calls_from_response(response)]
+    sanitized: Final = [_sanitize_anthropic_tool_use_id(call_id) for call_id in ids if isinstance(call_id, str)]
+
+    assert len(sanitized) == len(set(sanitized))
+    assert ids == ["a:b", "a:b__concat_2", "a_b__concat_1"]
+
+
+def test_get_tool_calls_from_response_bumps_suffix_when_sibling_sanitizes_onto_it():
+    raw = _concatenated_json({"a": 1}, {"b": 2})
+    response: Final = _chat_tool_response(
+        _function_tool_call("a_b", "move", raw),
+        _function_tool_call("a:b__concat_1", "look", '{"x":1}'),
+    )
+
+    ids: Final = [call["id"] for call in get_tool_calls_from_response(response)]
+    sanitized: Final = [_sanitize_anthropic_tool_use_id(call_id) for call_id in ids if isinstance(call_id, str)]
+
+    assert len(ids) == len(sanitized)
+    assert len(sanitized) == len(set(sanitized))
+    assert ids == ["a_b", "a_b__concat_2", "a:b__concat_1"]
+
+
+def test_get_tool_calls_from_response_continues_concat_suffixes_per_sanitized_base():
+    raw = _concatenated_json({"a": 1}, {"b": 2})
+    response: Final = _chat_tool_response(
+        _function_tool_call("x", "move", raw),
+        _function_tool_call("x", "move", raw),
+    )
+
+    assert [call["id"] for call in get_tool_calls_from_response(response)] == [
+        "x",
+        "x__concat_1",
+        "x",
+        "x__concat_2",
+    ]
+
+
+def test_get_tool_calls_from_response_skips_a_run_of_reserved_concat_ids():
+    raw = _concatenated_json({"a": 1}, {"b": 2})
+    siblings: Final = tuple(_function_tool_call(f"call__concat_{index}", "look", '{"x":1}') for index in range(1, 51))
+    response: Final = _chat_tool_response(_function_tool_call("call", "move", raw), *siblings)
+
+    ids: Final = [call["id"] for call in get_tool_calls_from_response(response)]
+
+    assert ids[0] == "call"
+    assert ids[1] == "call__concat_51"
+
+
+def test_get_tool_calls_from_response_reserves_concat_ids_across_choices():
+    raw = _concatenated_json({"a": 1}, {"b": 2})
+    response: Final = {
+        "choices": [
+            {"message": {"tool_calls": [_function_tool_call("call", "move", raw)]}},
+            {"message": {"tool_calls": [_function_tool_call("call__concat_1", "look", '{"x":1}')]}},
+        ]
+    }
+
+    assert [call["id"] for call in get_tool_calls_from_response(response, include_all_choices=True)] == [
+        "call",
+        "call__concat_2",
+        "call__concat_1",
+    ]
+
+
+def test_get_tool_calls_from_response_does_not_invent_ids_for_a_missing_call_id():
+    raw = _concatenated_json({"a": 1}, {"b": 2})
+    response: Final = _chat_tool_response(_function_tool_call(None, "move", raw))
+
+    tool_calls: Final = get_tool_calls_from_response(response)
+
+    assert len(tool_calls) == 2
+    assert all(call["id"] is None for call in tool_calls)
+    assert [call["arguments"] for call in tool_calls] == [{"a": 1}, {"b": 2}]
+
+
 def test_group_tool_exchanges_pairs_assistant_with_its_tool_rows():
     from litellm.litellm_core_utils.prompt_templates.factory import group_tool_exchanges
 
@@ -3625,9 +3657,7 @@ def test_bedrock_converse_pdf_only_user_message_gets_text_block():
         }
     ]
 
-    result = _bedrock_converse_messages_pt(
-        messages, "anthropic.claude-haiku-4-5", "bedrock"
-    )
+    result = _bedrock_converse_messages_pt(messages, "anthropic.claude-haiku-4-5", "bedrock")
 
     assert len(result) == 1
     assert any("document" in block for block in result[0]["content"])
@@ -3645,9 +3675,7 @@ def test_bedrock_converse_document_with_text_gets_no_extra_text_block():
         }
     ]
 
-    result = _bedrock_converse_messages_pt(
-        messages, "anthropic.claude-haiku-4-5", "bedrock"
-    )
+    result = _bedrock_converse_messages_pt(messages, "anthropic.claude-haiku-4-5", "bedrock")
 
     assert _text_blocks(result[0]) == ["summarize this"]
 
@@ -3660,9 +3688,7 @@ def test_bedrock_converse_image_only_user_message_gets_no_text_block():
         }
     ]
 
-    result = _bedrock_converse_messages_pt(
-        messages, "anthropic.claude-haiku-4-5", "bedrock"
-    )
+    result = _bedrock_converse_messages_pt(messages, "anthropic.claude-haiku-4-5", "bedrock")
 
     assert any("image" in block for block in result[0]["content"])
     assert _text_blocks(result[0]) == []
@@ -3705,9 +3731,7 @@ def test_bedrock_converse_tool_round_trip_document_injects_text_before_cache_poi
         },
     ]
 
-    result = _bedrock_converse_messages_pt(
-        messages, "anthropic.claude-haiku-4-5", "bedrock"
-    )
+    result = _bedrock_converse_messages_pt(messages, "anthropic.claude-haiku-4-5", "bedrock")
 
     assert _text_blocks(result[0]) == ["read the pdf"]
     document_message = result[-1]

From 829cba1bf18c22593ddf65737e30d1b905651259 Mon Sep 17 00:00:00 2001
From: Thippaluri Yaseen Basha <yasenroycey6@gmail.com>
Date: Sun, 27 Sep 2026 09:48:33 +0530
Subject: [PATCH 5/9] fix(gemini): forward seed to the Gemini API instead of
 rejecting it (#43197)

* fix(gemini): forward seed to the Gemini API instead of rejecting it

The gemini/ provider left seed out of its supported params, so requests with seed
failed with UnsupportedParamsError, or lost the seed silently when drop_params was on.
The Gemini API accepts generationConfig.seed and the inherited mapping already
translates it, so adding it to the allowlist is enough

* test(gemini): assert the forwarded seed without mutating shared state
---
 litellm/llms/gemini/chat/transformation.py    |  1 +
 ...test_vertex_and_google_ai_studio_gemini.py | 28 +++++++++++++++++++
 2 files changed, 29 insertions(+)

diff --git a/litellm/llms/gemini/chat/transformation.py b/litellm/llms/gemini/chat/transformation.py
index 285350aecba..cae0ba49c3f 100644
--- a/litellm/llms/gemini/chat/transformation.py
+++ b/litellm/llms/gemini/chat/transformation.py
@@ -96,6 +96,7 @@ class GoogleAIStudioGeminiConfig(VertexGeminiConfig):
             "logprobs",
             "frequency_penalty",
             "presence_penalty",
+            "seed",
             "modalities",
             "parallel_tool_calls",
             "web_search_options",
diff --git a/tests/unit/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py b/tests/unit/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py
index 7548f3c2daa..fd735afb16e 100644
--- a/tests/unit/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py
+++ b/tests/unit/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py
@@ -3413,6 +3413,34 @@ def test_google_ai_studio_presence_penalty_supported():
     assert "presence_penalty" in supported_params
 
 
+@pytest.mark.asyncio
+@pytest.mark.parametrize("drop_params", [False, True])
+async def test_google_ai_studio_forwards_seed_to_generation_config(drop_params: bool):
+    def echo_seed_sent_upstream(request: httpx.Request) -> httpx.Response:
+        seed_sent: Final = json.loads(request.content).get("generationConfig", {}).get("seed")
+        return httpx.Response(
+            200,
+            json={
+                "candidates": [
+                    {"content": {"parts": [{"text": f"seed={seed_sent}"}], "role": "model"}, "finishReason": "STOP"}
+                ],
+                "usageMetadata": {"promptTokenCount": 1, "candidatesTokenCount": 1, "totalTokenCount": 2},
+            },
+            request=request,
+        )
+
+    response: Final = await litellm.acompletion(
+        model="gemini/gemini-3.8-flash",
+        messages=[{"role": "user", "content": "hi"}],
+        seed=42,
+        drop_params=drop_params,
+        api_key="fake-gemini-key",
+        client=AsyncHTTPHandler(transport=httpx.MockTransport(echo_seed_sent_upstream)),
+    )
+
+    assert response.choices[0].message.content == "seed=42"
+
+
 # ==================== Tool Type Separation Tests ====================
 # These tests verify that each Tool object contains exactly one type per Vertex AI API spec
 # Ref: https://cloud.google.com/vertex-ai/generative-ai/docs/reference/rest/v1beta1/Tool

From 2101c860c25546250fa55c5a67653093a9d28873 Mon Sep 17 00:00:00 2001
From: Stewart Park <388348+stewartpark@users.noreply.github.com>
Date: Sat, 26 Sep 2026 21:26:38 -0700
Subject: [PATCH 6/9] fix(vertex_ai): make Gemma fake streams work with traced
 Responses (#43147)

* test(vertex_ai): reproduce traced Gemma Responses stream failure

* fix(vertex_ai): wrap Gemma fake streams for Responses tracing

* test(vertex_ai): cover Gemma traced streams and usage options

* test(vertex_ai): inject gemma test deps and assert hidden usage accounting

Replace class-level patches in the Vertex AI shard test with the
provider's documented dependency-injection seams (httpx.MockTransport
client + credential cache), and pin the default/omit-usage trace
behavior: LiteLLM still accounts all tokens; ddtrace's metric is
absent by design, asserted rather than silent.

Mutation-checked: commenting out CustomStreamWrapper chunk accumulation
turns the new assertions red; restoring them turns green.

* test(vertex_ai): drop explanatory comment from usage-option assertions
---
 .../vertex_gemma_models/transformation.py     |  30 ++++-
 .../test_vertex_gemma_transformation.py       |  93 ++++++++++++++
 .../test_vertex_gemma_transformation.py       | 117 +++++++++++++++++-
 3 files changed, 229 insertions(+), 11 deletions(-)
 create mode 100644 tests/test_litellm/llms/vertex_ai/vertex_gemma_models/test_vertex_gemma_transformation.py

diff --git a/litellm/llms/vertex_ai/vertex_gemma_models/transformation.py b/litellm/llms/vertex_ai/vertex_gemma_models/transformation.py
index ea97f0a0a9a..33922e38674 100644
--- a/litellm/llms/vertex_ai/vertex_gemma_models/transformation.py
+++ b/litellm/llms/vertex_ai/vertex_gemma_models/transformation.py
@@ -28,8 +28,8 @@ from litellm.types.utils import ModelResponse
 
 if TYPE_CHECKING:
     from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
+    from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper
     from litellm.litellm_core_utils.tokenizer import Encoding as Tokenizer
-    from litellm.llms.base_llm.base_model_iterator import MockResponseIterator
 
 
 def parse_vertex_gemma_container_error(predictions: object) -> VertexGemmaContainerError | None:
@@ -73,7 +73,9 @@ class VertexGemmaConfig(OpenAIGPTConfig):
         self,
         model_response: ModelResponse,
         stream: bool,
-    ) -> "ModelResponse | MockResponseIterator":
+        model: str,
+        logging_obj: "LiteLLMLoggingObj",
+    ) -> "ModelResponse | CustomStreamWrapper":
         """
         Helper method to return fake stream iterator if streaming is requested.
 
@@ -82,12 +84,18 @@ class VertexGemmaConfig(OpenAIGPTConfig):
             stream: Whether streaming was requested
 
         Returns:
-            MockResponseIterator if stream=True, otherwise the model_response
+            CustomStreamWrapper if stream=True, otherwise the model_response
         """
         if stream:
+            from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper
             from litellm.llms.base_llm.base_model_iterator import MockResponseIterator
 
-            return MockResponseIterator(model_response=model_response)
+            return CustomStreamWrapper(
+                completion_stream=MockResponseIterator(model_response=model_response),
+                model=model,
+                custom_llm_provider="vertex_ai",
+                logging_obj=logging_obj,
+            )
         return model_response
 
     def transform_request(
@@ -373,7 +381,12 @@ class VertexGemmaConfig(OpenAIGPTConfig):
         )
 
         # Return fake stream iterator if streaming was requested
-        return self._handle_fake_stream_response(model_response=model_response, stream=stream)
+        return self._handle_fake_stream_response(
+            model_response=model_response,
+            stream=stream,
+            model=model,
+            logging_obj=logging_obj,
+        )
 
     async def _async_completion(
         self,
@@ -463,4 +476,9 @@ class VertexGemmaConfig(OpenAIGPTConfig):
         )
 
         # Return fake stream iterator if streaming was requested
-        return self._handle_fake_stream_response(model_response=model_response, stream=stream)
+        return self._handle_fake_stream_response(
+            model_response=model_response,
+            stream=stream,
+            model=model,
+            logging_obj=logging_obj,
+        )
diff --git a/tests/test_litellm/llms/vertex_ai/vertex_gemma_models/test_vertex_gemma_transformation.py b/tests/test_litellm/llms/vertex_ai/vertex_gemma_models/test_vertex_gemma_transformation.py
new file mode 100644
index 00000000000..294d26b2e58
--- /dev/null
+++ b/tests/test_litellm/llms/vertex_ai/vertex_gemma_models/test_vertex_gemma_transformation.py
@@ -0,0 +1,93 @@
+import json
+from collections.abc import AsyncIterator
+from types import SimpleNamespace
+from typing import Any, cast
+
+import httpx
+import pytest
+
+import litellm
+from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper
+from litellm.main import vertex_gemma_chat_completion
+from litellm.types.llms.openai import OutputTextDeltaEvent, ResponseCompletedEvent, ResponsesAPIStreamingResponse
+
+_VERTEX_URL = "https://example.invalid/v1/projects/test/locations/us-central1/endpoints/test:predict"
+_MESSAGES = [{"role": "user", "content": "Reply exactly READY"}]
+_FAKE_CREDENTIALS = "gemma-test-credentials"
+
+
+def _vertex_response():
+    return {
+        "predictions": {
+            "id": "chatcmpl-stream-test",
+            "created": 1759863903,
+            "model": "google/gemma-3-12b-it",
+            "object": "chat.completion",
+            "choices": [{"index": 0, "finish_reason": "stop", "message": {"role": "assistant", "content": "READY"}}],
+            "usage": {"prompt_tokens": 14, "completion_tokens": 1, "total_tokens": 15},
+        }
+    }
+
+
+@pytest.fixture(autouse=True)
+def _cached_access_token():
+    """Serve a fake token from the handler's credential cache so no auth round-trip runs."""
+    cache = vertex_gemma_chat_completion._credentials_project_mapping
+    key = (_FAKE_CREDENTIALS, "test")
+    cache[key] = (SimpleNamespace(token="fake-token", expired=False), "test")
+    yield
+    cache.pop(key, None)
+
+
+def test_sync_gemma_stream():
+    captured: dict[str, Any] = {}
+
+    def handle(request: httpx.Request) -> httpx.Response:
+        captured["body"] = json.loads(request.content)
+        return httpx.Response(200, json=_vertex_response())
+
+    stream = litellm.completion(
+        model="vertex_ai/gemma/test-model",
+        messages=_MESSAGES,
+        stream=True,
+        api_base=_VERTEX_URL,
+        vertex_project="test",
+        vertex_location="us-central1",
+        vertex_credentials=_FAKE_CREDENTIALS,
+        client=httpx.Client(transport=httpx.MockTransport(handle)),
+    )
+
+    assert isinstance(stream, CustomStreamWrapper)
+    chunks = list(stream)
+
+    assert "stream" not in captured["body"]["instances"][0]
+    assert len(chunks) == 2
+    assert chunks[0].choices[0].delta.content == "READY"
+    assert chunks[1].choices[0].finish_reason == "stop"
+
+
+@pytest.mark.asyncio
+async def test_async_gemma_responses_stream():
+    captured: dict[str, Any] = {}
+
+    def handle(request: httpx.Request) -> httpx.Response:
+        captured["body"] = json.loads(request.content)
+        return httpx.Response(200, json=_vertex_response())
+
+    response = await litellm.aresponses(
+        model="vertex_ai/gemma/test-model",
+        input="Reply exactly READY",
+        stream=True,
+        api_base=_VERTEX_URL,
+        vertex_project="test",
+        vertex_location="us-central1",
+        vertex_credentials=_FAKE_CREDENTIALS,
+        client=httpx.AsyncClient(transport=httpx.MockTransport(handle)),
+    )
+    events = [event async for event in cast(AsyncIterator[ResponsesAPIStreamingResponse], response)]
+
+    assert "stream" not in captured["body"]["instances"][0]
+    assert "READY" in "".join(event.delta for event in events if isinstance(event, OutputTextDeltaEvent))
+    assert isinstance(events[-1], ResponseCompletedEvent)
+    assert events[-1].response.usage is not None
+    assert events[-1].response.usage.total_tokens == 15
diff --git a/tests/unit/llms/vertex_ai/vertex_gemma_models/test_vertex_gemma_transformation.py b/tests/unit/llms/vertex_ai/vertex_gemma_models/test_vertex_gemma_transformation.py
index e5ca31833ce..97f4f290958 100644
--- a/tests/unit/llms/vertex_ai/vertex_gemma_models/test_vertex_gemma_transformation.py
+++ b/tests/unit/llms/vertex_ai/vertex_gemma_models/test_vertex_gemma_transformation.py
@@ -5,11 +5,18 @@ Maps to: litellm/llms/vertex_ai/vertex_gemma_models/transformation.py
 """
 
 import json
+from collections.abc import AsyncIterator
+from typing import cast
 from unittest.mock import AsyncMock, Mock, patch
 
 import pytest
 
 import litellm
+from litellm.types.llms.openai import (
+    OutputTextDeltaEvent,
+    ResponseCompletedEvent,
+    ResponsesAPIStreamingResponse,
+)
 
 
 @pytest.fixture(autouse=True)
@@ -439,8 +446,9 @@ class TestVertexGemmaCompletion:
 
         Verifies:
         1. Request body does NOT include 'stream' parameter (model doesn't support it)
-        2. Response returns a MockResponseIterator that yields chunks
+        2. Response wraps a MockResponseIterator and yields chunks
         """
+        from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper
         from litellm.llms.base_llm.base_model_iterator import MockResponseIterator
 
         # Mock Vertex response
@@ -502,8 +510,8 @@ class TestVertexGemmaCompletion:
                 vertex_location="us-central1",
             )
 
-            # Verify the response is a MockResponseIterator
-            assert isinstance(response, MockResponseIterator), f"Expected MockResponseIterator, got {type(response)}"
+            assert isinstance(response, CustomStreamWrapper)
+            assert isinstance(response.completion_stream, MockResponseIterator)
 
             # Verify the request sent to Vertex does NOT include 'stream'
             call_args = mock_client.post.call_args
@@ -520,8 +528,9 @@ class TestVertexGemmaCompletion:
             async for chunk in response:
                 chunks.append(chunk)
 
-            # Should get exactly one chunk (fake streaming)
-            assert len(chunks) == 1, f"Expected 1 chunk from fake stream, got {len(chunks)}"
+            assert len(chunks) == 2
+            assert chunks[1].choices[0].finish_reason == "stop"
+            assert all(getattr(chunk, "usage", None) is None for chunk in chunks)
 
             # Verify the chunk has the expected content
             chunk = chunks[0]
@@ -529,6 +538,104 @@ class TestVertexGemmaCompletion:
             assert len(chunk.choices) > 0
             assert chunk.choices[0].delta.content == "Streaming test response"
 
+    @pytest.mark.asyncio
+    async def test_aresponses_streams_vertex_gemma_with_llm_tracing(self):
+        pytest.importorskip("ddtrace")
+        from ddtrace.contrib.internal.litellm.patch import patch as patch_litellm
+        from ddtrace.contrib.internal.litellm.patch import unpatch as unpatch_litellm
+        from ddtrace.llmobs._integrations.base_stream_handler import TracedAsyncStream
+
+        from litellm.responses.litellm_completion_transformation.streaming_iterator import (
+            LiteLLMCompletionStreamingIterator,
+        )
+
+        reply = Mock(status_code=200)
+        reply.json.return_value = _make_gemma_vertex_response(content="READY")
+        client = Mock()
+        client.post = AsyncMock(return_value=reply)
+
+        with (
+            patch("litellm.llms.custom_httpx.http_handler.get_async_httpx_client", return_value=client),
+            patch(
+                "litellm.llms.vertex_ai.vertex_gemma_models.main.VertexAIGemmaModels._ensure_access_token",
+                return_value=("fake-access-token", "test-project"),
+            ),
+        ):
+            patch_litellm()
+            try:
+                response = await litellm.aresponses(
+                    model="vertex_ai/gemma/test-model",
+                    input="Reply exactly READY",
+                    stream=True,
+                    api_base="https://example.invalid/v1/projects/test-project/locations/us-central1/endpoints/test:predict",
+                    vertex_project="test-project",
+                    vertex_location="us-central1",
+                )
+                bridge = cast(LiteLLMCompletionStreamingIterator, response)
+                traced_stream = bridge.litellm_custom_stream_wrapper
+                assert isinstance(traced_stream, TracedAsyncStream)
+                events = [event async for event in cast(AsyncIterator[ResponsesAPIStreamingResponse], response)]
+                span = traced_stream.handler.primary_span
+                assert span.finished
+                assert span.get_tag("_dd.llmobs.span_kind") == "llm"
+                assert span.get_metric("_dd.llmobs.total_tokens") == 114
+            finally:
+                unpatch_litellm()
+
+        assert "stream" not in client.post.call_args.kwargs["json"]["instances"][0]
+        assert "READY" in "".join(event.delta for event in events if isinstance(event, OutputTextDeltaEvent))
+        assert isinstance(events[-1], ResponseCompletedEvent)
+        assert events[-1].response.usage.total_tokens == 114
+
+    @pytest.mark.asyncio
+    @pytest.mark.parametrize("stream_options", [None, {"include_usage": False}, {"include_usage": True}])
+    async def test_acompletion_stream_respects_usage_option_with_llm_tracing(self, stream_options):
+        pytest.importorskip("ddtrace")
+        from ddtrace.contrib.internal.litellm.patch import patch as patch_litellm
+        from ddtrace.contrib.internal.litellm.patch import unpatch as unpatch_litellm
+
+        reply = Mock(status_code=200)
+        reply.json.return_value = _make_gemma_vertex_response(content="READY")
+        client = Mock(post=AsyncMock(return_value=reply))
+        with (
+            patch("litellm.llms.custom_httpx.http_handler.get_async_httpx_client", return_value=client),
+            patch(
+                "litellm.llms.vertex_ai.vertex_gemma_models.main.VertexAIGemmaModels._ensure_access_token",
+                return_value=("fake-access-token", "test-project"),
+            ),
+        ):
+            patch_litellm()
+            try:
+                stream = await litellm.acompletion(
+                    model="vertex_ai/gemma/test-model",
+                    messages=[{"role": "user", "content": "Reply exactly READY"}],
+                    stream=True,
+                    **({"stream_options": stream_options} if stream_options is not None else {}),
+                    api_base="https://example.invalid/v1/projects/test-project/locations/us-central1/endpoints/test:predict",
+                    vertex_project="test-project",
+                    vertex_location="us-central1",
+                )
+                chunks = [chunk async for chunk in stream]
+                span = stream.handler.primary_span
+                assert span.finished
+                assert span.get_tag("_dd.llmobs.span_kind") == "llm"
+            finally:
+                unpatch_litellm()
+
+        assert len(chunks) == (3 if stream_options and stream_options["include_usage"] else 2)
+        assert chunks[0].choices[0].delta.content == "READY"
+        assert chunks[1].choices[0].finish_reason == "stop"
+        if stream_options and stream_options["include_usage"]:
+            assert chunks[-1].choices[0].delta.content is None
+            assert chunks[-1].usage.total_tokens == 114
+            assert span.get_metric("_dd.llmobs.total_tokens") == 114
+        else:
+            from litellm.litellm_core_utils.streaming_handler import calculate_total_usage
+
+            assert all(getattr(chunk, "usage", None) is None for chunk in chunks)
+            assert calculate_total_usage(chunks=stream.chunks).total_tokens == 114
+            assert span.get_metric("_dd.llmobs.total_tokens") is None
+
     @pytest.mark.asyncio
     async def test_acompletion_filters_stream_and_stream_options(self):
         """

From 491d454826342aa8b53aa69edd0242ba3b6f8b4d Mon Sep 17 00:00:00 2001
From: "devin-ai-integration[bot]"
 <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Date: Sun, 27 Sep 2026 04:51:56 +0000
Subject: [PATCH 7/9] fix(responses): emit the reasoning item on streaming
 /v1/responses for signature-only thinking (#43414)

* fix(responses): emit the reasoning item on streaming /v1/responses for signature-only thinking

Anthropic models return thinking blocks with empty text and the reasoning carried in the
signature: Claude Fable 5.1 and Claude Opus 5.5 by default, and Bedrock adaptive thinking
with or without an effort. On streaming /v1/responses the chat->Responses bridge opened a
reasoning output item only on reasoning_content text
(LiteLLMCompletionStreamingIterator._ensure_output_item_for_chunk), and
ChunkProcessor.get_combined_thinking_content kept an assembled thinking block only when it
had thinking text. Such a response emitted no reasoning item mid-stream and none in
response.completed, so a streaming Responses client could not replay the reasoning even
though the reasoning tokens were billed. Non-streaming /v1/responses was unaffected.

Open the reasoning item when the delta carries a signed or redacted thinking block, and
keep a signed block through stream assembly even when its thinking text is empty.
Unsigned text-only fragments are still dropped. The reasoning-text path is unchanged.

(cherry picked from commit bc9b6f8a5c3ac9a2b46e3f9f01f7c2c5f9b688e7)

* test(vertex_ai): move orphaned gemma streaming tests into the llm-vertex-ai shard

PR #43147 left a copy of the Gemma streaming tests under
tests/test_litellm/llms, a tree no CI shard claims, which broke
assert-ci-coverage and assert-shard-coverage on main. Fold the two
streaming tests into the existing tests/unit/llms/vertex_ai file so the
llm-vertex-ai shard runs them

Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>

---------

Co-authored-by: Chloe Lu <chloe.lxd@gmail.com>
Co-authored-by: Krrish Dholakia <krrishdholakia@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---
 .../streaming_chunk_builder_utils.py          |  2 +-
 .../streaming_iterator.py                     |  7 +-
 .../test_vertex_gemma_transformation.py       | 93 -------------------
 .../test_streaming_chunk_builder_utils.py     | 25 +++++
 .../test_vertex_gemma_transformation.py       | 78 ++++++++++++++++
 .../test_streaming_iterator_transformation.py | 40 ++++++++
 6 files changed, 150 insertions(+), 95 deletions(-)
 delete mode 100644 tests/test_litellm/llms/vertex_ai/vertex_gemma_models/test_vertex_gemma_transformation.py

diff --git a/litellm/litellm_core_utils/streaming_chunk_builder_utils.py b/litellm/litellm_core_utils/streaming_chunk_builder_utils.py
index d975c3551f3..67684a230e3 100644
--- a/litellm/litellm_core_utils/streaming_chunk_builder_utils.py
+++ b/litellm/litellm_core_utils/streaming_chunk_builder_utils.py
@@ -685,7 +685,7 @@ class ChunkProcessor:
 
         def _flush_thinking_block() -> None:
             nonlocal current_thinking_text_parts, current_signature
-            if len(current_thinking_text_parts) > 0 and current_signature:
+            if current_signature:
                 thinking_blocks.append(
                     ChatCompletionThinkingBlock(
                         type="thinking",
diff --git a/litellm/responses/litellm_completion_transformation/streaming_iterator.py b/litellm/responses/litellm_completion_transformation/streaming_iterator.py
index 5173cd04a89..21a33c17ab8 100644
--- a/litellm/responses/litellm_completion_transformation/streaming_iterator.py
+++ b/litellm/responses/litellm_completion_transformation/streaming_iterator.py
@@ -73,6 +73,11 @@ def _output_items_with_id(items: tuple[Any, ...], item_type: str, item_id: str |
     )
 
 
+def _delta_has_signed_thinking_block(delta: object) -> bool:
+    blocks: Final = getattr(delta, "thinking_blocks", None) or ()
+    return any(isinstance(b, dict) and (b.get("signature") or b.get("data")) for b in blocks)
+
+
 class LiteLLMCompletionStreamingIterator(ResponsesAPIStreamingIterator):
     """
     Async iterator for processing streaming responses from the Responses API.
@@ -936,7 +941,7 @@ class LiteLLMCompletionStreamingIterator(ResponsesAPIStreamingIterator):
         self.sent_output_item_added_event = True
 
         # Reasoning-first
-        if hasattr(delta, "reasoning_content") and delta.reasoning_content:
+        if (hasattr(delta, "reasoning_content") and delta.reasoning_content) or _delta_has_signed_thinking_block(delta):
             self._reasoning_active = True
             if self._cached_reasoning_item_id is None:
                 self._cached_reasoning_item_id = f"rs_{uuid.uuid4()}"
diff --git a/tests/test_litellm/llms/vertex_ai/vertex_gemma_models/test_vertex_gemma_transformation.py b/tests/test_litellm/llms/vertex_ai/vertex_gemma_models/test_vertex_gemma_transformation.py
deleted file mode 100644
index 294d26b2e58..00000000000
--- a/tests/test_litellm/llms/vertex_ai/vertex_gemma_models/test_vertex_gemma_transformation.py
+++ /dev/null
@@ -1,93 +0,0 @@
-import json
-from collections.abc import AsyncIterator
-from types import SimpleNamespace
-from typing import Any, cast
-
-import httpx
-import pytest
-
-import litellm
-from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper
-from litellm.main import vertex_gemma_chat_completion
-from litellm.types.llms.openai import OutputTextDeltaEvent, ResponseCompletedEvent, ResponsesAPIStreamingResponse
-
-_VERTEX_URL = "https://example.invalid/v1/projects/test/locations/us-central1/endpoints/test:predict"
-_MESSAGES = [{"role": "user", "content": "Reply exactly READY"}]
-_FAKE_CREDENTIALS = "gemma-test-credentials"
-
-
-def _vertex_response():
-    return {
-        "predictions": {
-            "id": "chatcmpl-stream-test",
-            "created": 1759863903,
-            "model": "google/gemma-3-12b-it",
-            "object": "chat.completion",
-            "choices": [{"index": 0, "finish_reason": "stop", "message": {"role": "assistant", "content": "READY"}}],
-            "usage": {"prompt_tokens": 14, "completion_tokens": 1, "total_tokens": 15},
-        }
-    }
-
-
-@pytest.fixture(autouse=True)
-def _cached_access_token():
-    """Serve a fake token from the handler's credential cache so no auth round-trip runs."""
-    cache = vertex_gemma_chat_completion._credentials_project_mapping
-    key = (_FAKE_CREDENTIALS, "test")
-    cache[key] = (SimpleNamespace(token="fake-token", expired=False), "test")
-    yield
-    cache.pop(key, None)
-
-
-def test_sync_gemma_stream():
-    captured: dict[str, Any] = {}
-
-    def handle(request: httpx.Request) -> httpx.Response:
-        captured["body"] = json.loads(request.content)
-        return httpx.Response(200, json=_vertex_response())
-
-    stream = litellm.completion(
-        model="vertex_ai/gemma/test-model",
-        messages=_MESSAGES,
-        stream=True,
-        api_base=_VERTEX_URL,
-        vertex_project="test",
-        vertex_location="us-central1",
-        vertex_credentials=_FAKE_CREDENTIALS,
-        client=httpx.Client(transport=httpx.MockTransport(handle)),
-    )
-
-    assert isinstance(stream, CustomStreamWrapper)
-    chunks = list(stream)
-
-    assert "stream" not in captured["body"]["instances"][0]
-    assert len(chunks) == 2
-    assert chunks[0].choices[0].delta.content == "READY"
-    assert chunks[1].choices[0].finish_reason == "stop"
-
-
-@pytest.mark.asyncio
-async def test_async_gemma_responses_stream():
-    captured: dict[str, Any] = {}
-
-    def handle(request: httpx.Request) -> httpx.Response:
-        captured["body"] = json.loads(request.content)
-        return httpx.Response(200, json=_vertex_response())
-
-    response = await litellm.aresponses(
-        model="vertex_ai/gemma/test-model",
-        input="Reply exactly READY",
-        stream=True,
-        api_base=_VERTEX_URL,
-        vertex_project="test",
-        vertex_location="us-central1",
-        vertex_credentials=_FAKE_CREDENTIALS,
-        client=httpx.AsyncClient(transport=httpx.MockTransport(handle)),
-    )
-    events = [event async for event in cast(AsyncIterator[ResponsesAPIStreamingResponse], response)]
-
-    assert "stream" not in captured["body"]["instances"][0]
-    assert "READY" in "".join(event.delta for event in events if isinstance(event, OutputTextDeltaEvent))
-    assert isinstance(events[-1], ResponseCompletedEvent)
-    assert events[-1].response.usage is not None
-    assert events[-1].response.usage.total_tokens == 15
diff --git a/tests/unit/litellm_core_utils/test_streaming_chunk_builder_utils.py b/tests/unit/litellm_core_utils/test_streaming_chunk_builder_utils.py
index af763da2d87..aaf877df364 100644
--- a/tests/unit/litellm_core_utils/test_streaming_chunk_builder_utils.py
+++ b/tests/unit/litellm_core_utils/test_streaming_chunk_builder_utils.py
@@ -236,6 +236,31 @@ def test_get_combined_thinking_content_preserves_interleaved_blocks():
     assert result[2]["signature"] == "sig_block2"
 
 
+def test_get_combined_thinking_content_keeps_signed_block_without_thinking_text():
+    chunks: Final = [
+        ModelResponseStream(
+            id="chatcmpl-123",
+            object="chat.completion.chunk",
+            created=1234567890,
+            model="claude-sonnet-4-20250514",
+            choices=[
+                StreamingChoices(
+                    index=0,
+                    delta=Delta(thinking_blocks=[{"type": "thinking", "thinking": "", "signature": "sig_only"}]),
+                    finish_reason=None,
+                )
+            ],
+        )
+    ]
+
+    result: Final = ChunkProcessor(chunks=chunks).get_combined_thinking_content(chunks)
+
+    assert result is not None
+    assert [(block["type"], block["thinking"], block["signature"]) for block in result] == [
+        ("thinking", "", "sig_only")
+    ]
+
+
 def test_cache_read_input_tokens_retained():
     chunk1 = ModelResponseStream(
         id="chatcmpl-95aabb85-c39f-443d-ae96-0370c404d70c",
diff --git a/tests/unit/llms/vertex_ai/vertex_gemma_models/test_vertex_gemma_transformation.py b/tests/unit/llms/vertex_ai/vertex_gemma_models/test_vertex_gemma_transformation.py
index 97f4f290958..92e684e42c8 100644
--- a/tests/unit/llms/vertex_ai/vertex_gemma_models/test_vertex_gemma_transformation.py
+++ b/tests/unit/llms/vertex_ai/vertex_gemma_models/test_vertex_gemma_transformation.py
@@ -1303,3 +1303,81 @@ class TestVertexGemmaCompletion:
         mock_async_post.assert_awaited_once()
         assert mock_async_post.call_args.kwargs["client"] is None
         assert response.choices[0].message.content == "default async handler fallback"
+
+
+_GEMMA_VERTEX_URL = "https://example.invalid/v1/projects/test/locations/us-central1/endpoints/test:predict"
+_FAKE_GEMMA_CREDENTIALS = "gemma-test-credentials"
+
+
+@pytest.fixture
+def _gemma_cached_access_token():
+    """Serve a fake token from the handler's credential cache so no auth round-trip runs."""
+    from types import SimpleNamespace
+
+    from litellm.main import vertex_gemma_chat_completion
+
+    cache = vertex_gemma_chat_completion._credentials_project_mapping
+    key = (_FAKE_GEMMA_CREDENTIALS, "test")
+    cache[key] = (SimpleNamespace(token="fake-token", expired=False), "test")
+    yield
+    cache.pop(key, None)
+
+
+def test_sync_gemma_stream(_gemma_cached_access_token):
+    import httpx
+
+    from litellm.litellm_core_utils.streaming_handler import CustomStreamWrapper
+
+    captured = {}
+
+    def handle(request):
+        captured["body"] = json.loads(request.content)
+        return httpx.Response(200, json=_make_gemma_vertex_response(content="READY", total_tokens=15))
+
+    stream = litellm.completion(
+        model="vertex_ai/gemma/test-model",
+        messages=[{"role": "user", "content": "Reply exactly READY"}],
+        stream=True,
+        api_base=_GEMMA_VERTEX_URL,
+        vertex_project="test",
+        vertex_location="us-central1",
+        vertex_credentials=_FAKE_GEMMA_CREDENTIALS,
+        client=httpx.Client(transport=httpx.MockTransport(handle)),
+    )
+
+    assert isinstance(stream, CustomStreamWrapper)
+    chunks = list(stream)
+
+    assert "stream" not in captured["body"]["instances"][0]
+    assert len(chunks) == 2
+    assert chunks[0].choices[0].delta.content == "READY"
+    assert chunks[1].choices[0].finish_reason == "stop"
+
+
+@pytest.mark.asyncio
+async def test_async_gemma_responses_stream(_gemma_cached_access_token):
+    import httpx
+
+    captured = {}
+
+    def handle(request):
+        captured["body"] = json.loads(request.content)
+        return httpx.Response(200, json=_make_gemma_vertex_response(content="READY", total_tokens=15))
+
+    response = await litellm.aresponses(
+        model="vertex_ai/gemma/test-model",
+        input="Reply exactly READY",
+        stream=True,
+        api_base=_GEMMA_VERTEX_URL,
+        vertex_project="test",
+        vertex_location="us-central1",
+        vertex_credentials=_FAKE_GEMMA_CREDENTIALS,
+        client=httpx.AsyncClient(transport=httpx.MockTransport(handle)),
+    )
+    events = [event async for event in cast(AsyncIterator[ResponsesAPIStreamingResponse], response)]
+
+    assert "stream" not in captured["body"]["instances"][0]
+    assert "READY" in "".join(event.delta for event in events if isinstance(event, OutputTextDeltaEvent))
+    assert isinstance(events[-1], ResponseCompletedEvent)
+    assert events[-1].response.usage is not None
+    assert events[-1].response.usage.total_tokens == 15
diff --git a/tests/unit/responses/litellm_completion_transformation/test_streaming_iterator_transformation.py b/tests/unit/responses/litellm_completion_transformation/test_streaming_iterator_transformation.py
index 8fbba0dbf87..041bcf1b6d7 100644
--- a/tests/unit/responses/litellm_completion_transformation/test_streaming_iterator_transformation.py
+++ b/tests/unit/responses/litellm_completion_transformation/test_streaming_iterator_transformation.py
@@ -978,6 +978,25 @@ def _reasoning_chunk(reasoning: str, finish_reason: str | None = None) -> ModelR
     )
 
 
+def _signature_only_thinking_chunk(signature: str) -> ModelResponseStream:
+    return ModelResponseStream(
+        id=CHAT_COMPLETION_ID,
+        created=1748575031,
+        model="claude-haiku-4-5",
+        object="chat.completion.chunk",
+        choices=[
+            StreamingChoices(
+                index=0,
+                delta=Delta(
+                    role="assistant",
+                    thinking_blocks=[{"type": "thinking", "thinking": "", "signature": signature}],
+                ),
+                finish_reason=None,
+            )
+        ],
+    )
+
+
 async def _collect_events(
     iterator: LiteLLMCompletionStreamingIterator, sync_mode: bool
 ) -> list[BaseLiteLLMOpenAIResponseObject]:
@@ -1015,6 +1034,27 @@ async def test_tool_only_stream_emits_no_message_item_events(sync_mode: bool):
     assert any(getattr(event, "type", None) == ResponsesAPIStreamEvents.RESPONSE_COMPLETED for event in events)
 
 
+@pytest.mark.parametrize("sync_mode", [True, False])
+@pytest.mark.asyncio
+async def test_signature_only_thinking_streams_a_replayable_reasoning_item(sync_mode: bool):
+    iterator: Final = _build_iterator([_signature_only_thinking_chunk("sig_only"), _chunk("4", finish_reason="stop")])
+
+    events: Final = await _collect_events(iterator, sync_mode)
+
+    added_item_types: Final = [
+        event.item.type
+        for event in events
+        if getattr(event, "type", None) == ResponsesAPIStreamEvents.OUTPUT_ITEM_ADDED
+    ]
+    completed: Final = next(
+        event for event in events if getattr(event, "type", None) == ResponsesAPIStreamEvents.RESPONSE_COMPLETED
+    )
+    reasoning_items: Final = [item for item in completed.response.output if getattr(item, "type", None) == "reasoning"]
+    assert added_item_types[0] == "reasoning"
+    assert len(reasoning_items) == 1
+    assert json.loads(reasoning_items[0].encrypted_content)[0]["signature"] == "sig_only"
+
+
 @pytest.mark.parametrize("sync_mode", [True, False])
 @pytest.mark.asyncio
 async def test_reasoning_then_text_announces_message_item_before_text_events(sync_mode: bool):

From 9a0ff249d5935ca73216d19597603083e6a0845c Mon Sep 17 00:00:00 2001
From: "devin-ai-integration[bot]"
 <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Date: Sun, 27 Sep 2026 05:02:35 +0000
Subject: [PATCH 8/9] fix(anthropic): forward the per-turn-control beta to
 Azure AI Foundry (#43415)

Co-authored-by: Krrish Dholakia <krrishdholakia@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---
 litellm/anthropic_beta_headers_config.json                | 2 +-
 .../messages/test_anthropic_messages_per_turn_control.py  | 8 +++++++-
 2 files changed, 8 insertions(+), 2 deletions(-)

diff --git a/litellm/anthropic_beta_headers_config.json b/litellm/anthropic_beta_headers_config.json
index 3a28d65e47c..71e7081b440 100644
--- a/litellm/anthropic_beta_headers_config.json
+++ b/litellm/anthropic_beta_headers_config.json
@@ -57,7 +57,7 @@
     "mcp-servers-2025-12-04": null,
     "output-128k-2025-02-19": null,
     "structured-output-2024-03-01": null,
-    "per-turn-control-2026-07-01": null,
+    "per-turn-control-2026-07-01": "per-turn-control-2026-07-01",
     "prompt-caching-scope-2026-01-05": "prompt-caching-scope-2026-01-05",
     "skills-2025-10-02": "skills-2025-10-02",
     "structured-outputs-2025-11-13": "structured-outputs-2025-11-13",
diff --git a/tests/unit/llms/anthropic/pass_through/messages/test_anthropic_messages_per_turn_control.py b/tests/unit/llms/anthropic/pass_through/messages/test_anthropic_messages_per_turn_control.py
index 557305a945c..4197192e4af 100644
--- a/tests/unit/llms/anthropic/pass_through/messages/test_anthropic_messages_per_turn_control.py
+++ b/tests/unit/llms/anthropic/pass_through/messages/test_anthropic_messages_per_turn_control.py
@@ -95,13 +95,19 @@ def test_added_per_turn_control_beta_survives_the_anthropic_allowlist():
     assert PER_TURN_CONTROL in _betas(filtered)
 
 
-@pytest.mark.parametrize("provider", ["bedrock", "bedrock_converse", "vertex_ai", "azure_ai", "databricks"])
+@pytest.mark.parametrize("provider", ["bedrock", "bedrock_converse", "vertex_ai", "databricks"])
 def test_per_turn_control_beta_is_dropped_for_providers_without_it(provider):
     filtered = update_headers_with_filtered_beta(headers={"anthropic-beta": PER_TURN_CONTROL}, provider=provider)
 
     assert "anthropic-beta" not in filtered
 
 
+def test_per_turn_control_beta_is_forwarded_for_azure_ai():
+    filtered = update_headers_with_filtered_beta(headers={"anthropic-beta": PER_TURN_CONTROL}, provider="azure_ai")
+
+    assert _betas(filtered) == {PER_TURN_CONTROL}
+
+
 def test_json_provider_passthrough_adds_per_turn_control_beta():
     config = JSONProviderAnthropicMessagesConfig(
         SimpleProviderConfig(

From c1f761eba50bb344259bb7a3ff2ef538ff94600f Mon Sep 17 00:00:00 2001
From: "devin-ai-integration[bot]"
 <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Date: Sun, 27 Sep 2026 05:02:44 +0000
Subject: [PATCH 9/9] test(vertex_ai): move stray Gemma streaming tests to
 tests/unit so CI coverage passes (#43422)

Co-authored-by: Krrish Dholakia <krrishdholakia@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---
 .../vertex_gemma_models/test_vertex_gemma_transformation.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/tests/unit/llms/vertex_ai/vertex_gemma_models/test_vertex_gemma_transformation.py b/tests/unit/llms/vertex_ai/vertex_gemma_models/test_vertex_gemma_transformation.py
index 92e684e42c8..efe97ce33a8 100644
--- a/tests/unit/llms/vertex_ai/vertex_gemma_models/test_vertex_gemma_transformation.py
+++ b/tests/unit/llms/vertex_ai/vertex_gemma_models/test_vertex_gemma_transformation.py
@@ -1332,7 +1332,7 @@ def test_sync_gemma_stream(_gemma_cached_access_token):
 
     def handle(request):
         captured["body"] = json.loads(request.content)
-        return httpx.Response(200, json=_make_gemma_vertex_response(content="READY", total_tokens=15))
+        return httpx.Response(200, json=_make_gemma_vertex_response(content="READY"))
 
     stream = litellm.completion(
         model="vertex_ai/gemma/test-model",
@@ -1362,7 +1362,7 @@ async def test_async_gemma_responses_stream(_gemma_cached_access_token):
 
     def handle(request):
         captured["body"] = json.loads(request.content)
-        return httpx.Response(200, json=_make_gemma_vertex_response(content="READY", total_tokens=15))
+        return httpx.Response(200, json=_make_gemma_vertex_response(content="READY"))
 
     response = await litellm.aresponses(
         model="vertex_ai/gemma/test-model",
@@ -1380,4 +1380,4 @@ async def test_async_gemma_responses_stream(_gemma_cached_access_token):
     assert "READY" in "".join(event.delta for event in events if isinstance(event, OutputTextDeltaEvent))
     assert isinstance(events[-1], ResponseCompletedEvent)
     assert events[-1].response.usage is not None
-    assert events[-1].response.usage.total_tokens == 15
+    assert events[-1].response.usage.total_tokens == 114