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LiteLLM carries a Gemini thought signature inside the tool call id (call_xxx__thought__<signature>). The LiteLLM handler ran every tool call id through sanitizeOpenAiCallId, which truncates to 64 characters and appends _ + an 8-hex hash. For these ids that cut the signature and added the hash, so Gemini rejected the next request with 400 ... thought_signature ... Base64 decoding failed.
Fix
New sanitizeLiteLlmCallId (utils/tool-id.ts): ids containing __thought__ are only character-sanitized (never truncated); all other ids keep the existing OpenAI 64-character treatment. LiteLLMHandler uses it for both the standard and the R1-format message conversion.
Notes
Gemini through LiteLLM does not need the 64-character limit for these ids; the ids stay as LiteLLM generated them.
A history that mixes a long thought-signature id with a provider that enforces 64 characters is not specially handled (same as before for non-LiteLLM providers).
Tests
tool-id.spec.ts (intact long id, invalid-character sanitizing, other long ids still truncated, short ids unchanged) and a lite-llm.spec.ts test that the assistant tool_calls[].id and the tool message tool_call_id reach the request unchanged. check-types passes.
…ture
LiteLLM encodes a Gemini thought signature into the tool call id after
__thought__. sanitizeOpenAiCallId cut such ids to 64 characters and appended
a hash, so Gemini received a corrupted signature (400 Base64 decoding failed).
Only character-sanitize ids containing the marker.
FixesZoo-Code-Org#2064
Co-Authored-By: Claude Sonnet 5.5 <noreply@anthropic.com>
Improved compatibility with LiteLLM for tool calls that use long IDs containing Gemini thought signatures. These IDs are now preserved consistently between assistant messages and their corresponding tool results.
Kept existing handling for other tool-call IDs, including sanitization of invalid characters and truncation of long IDs.
LiteLLM now preserves long tool-call IDs containing a Gemini thought-signature marker in both message-conversion paths. IDs without the marker continue to use OpenAI sanitization and truncation. New tests cover both sanitizer behavior and message conversion.
Changes: LiteLLM tool-call ID handling
Layer / File(s)
Summary
Thought-signature-aware ID sanitization src/utils/tool-id.ts, src/utils/__tests__/tool-id.spec.ts
sanitizeLiteLlmCallId sanitizes characters but does not truncate IDs containing __thought__. Other IDs use existing OpenAI sanitization and truncation. Tests cover long and short IDs.
LiteLLM conversion and integration coverage src/api/providers/lite-llm.ts, src/api/providers/__tests__/lite-llm.spec.ts
Both message-conversion paths use sanitizeLiteLlmCallId. An integration test checks that a long thought-signature ID is unchanged in the assistant tool call and matching tool result.
The change stops truncating Gemini thought-signature tool-call IDs sent through LiteLLM, but it still replaces Base64 characters in the signature. Follow-up requests after a tool call can therefore still fail for some signatures. Sanitize only the ID prefix before merging.
Pre-merge checks: 7 · 1
Failed checks: 1 warning
Check name
Status
Explanation
Resolution
Regression Evidence
The standard convertToOpenAiMessages path has focused coverage for preserving a long __thought__ ID in both tool-call fields (lite-llm.spec.ts:759-798). The changed preserveReasoning path at `…
Add a focused LiteLLMHandler.createMessage test with info.preserveReasoning: true and a tool-call ID longer than 64 characters containing __thought__. Assert that the assistant tool-call ID and matching tool-message tool_call_id rem…
Passed checks: 7 passed
Check name
Status
Explanation
Title check
The title clearly and concisely identifies the LiteLLM and Gemini tool-call ID truncation fix.
Description check
The description explains the linked issue, problem, implementation, scope, limitations, and test coverage. It does not reproduce the full checklist and optional template sections, but it contains the …
Linked Issues check
Issue #2064 requires LiteLLM follow-up requests to preserve Gemini thought signatures in tool-call IDs. sanitizeLiteLlmCallId preserves IDs containing __thought__ without truncation, and `LiteLLMH…
Out of Scope Changes check
The changes stay within issue #2064. They add LiteLLM-specific ID normalization, apply it in the two LiteLLM conversion paths, and add focused unit and handler tests. No unrelated behavior or files ch…
Security Boundaries
No changed path meets the security failure conditions. sanitizeLiteLlmCallId only replaces characters or selects the existing truncation logic, and LiteLLMHandler places the result in outbound mes…
Persistence Integrity
PASS. The changed path only normalizes tool-call IDs while converting in-memory Anthropic messages for the LiteLLM request. sanitizeLiteLlmCallId is synchronous, and the diff adds no persistence, wr…
Lifecycle Resource Cleanup
No changed lifecycle resource path exists. The PR adds a synchronous string sanitizer and replaces the tool-ID normalizer in the two message-conversion branches. The changed code creates no listener, …
Full details: Description check
Explanation
The description explains the linked issue, problem, implementation, scope, limitations, and test coverage. It does not reproduce the full checklist and optional template sections, but it contains the critical review information.
Full details: Linked Issues check
Explanation
Issue #2064 requires LiteLLM follow-up requests to preserve Gemini thought signatures in tool-call IDs. sanitizeLiteLlmCallId preserves IDs containing __thought__ without truncation, and LiteLLMHandler uses it for both standard and R1 message conversion. The added tests cover long signature IDs, character sanitization, fallback truncation, and unchanged assistant and tool-message IDs.
Full details: Out of Scope Changes check
Explanation
The changes stay within issue #2064. They add LiteLLM-specific ID normalization, apply it in the two LiteLLM conversion paths, and add focused unit and handler tests. No unrelated behavior or files changed.
Full details: Security Boundaries
Explanation
No changed path meets the security failure conditions. sanitizeLiteLlmCallId only replaces characters or selects the existing truncation logic, and LiteLLMHandler places the result in outbound message IDs. The diff adds no secret or PII handling, code execution, approval bypass, or allowlist bypass.
Full details: Persistence Integrity
Explanation
PASS. The changed path only normalizes tool-call IDs while converting in-memory Anthropic messages for the LiteLLM request. sanitizeLiteLlmCallId is synchronous, and the diff adds no persistence, write, rollback, transaction, or default-propagation path. No specified persistence-integrity failure is introduced.
Full details: Lifecycle Resource Cleanup
Explanation
No changed lifecycle resource path exists. The PR adds a synchronous string sanitizer and replaces the tool-ID normalizer in the two message-conversion branches. The changed code creates no listener, watcher, timer, task, provider, or subscription, and it does not alter stream cancellation, disposal, or restart handling.
Full details: Regression Evidence
Explanation
The standard convertToOpenAiMessages path has focused coverage for preserving a long __thought__ ID in both tool-call fields (lite-llm.spec.ts:759-798). The changed preserveReasoning path at lite-llm.ts:159-163 does not. Its existing provider test uses only the short toolu_123 ID and checks message merging and reasoning, not ID normalization (lite-llm.spec.ts:1251-1312). The R1 converter test exercises only a generic lowercase callback (r1-format.spec.ts:770-779). A regression that removes or miswires sanitizeLiteLlmCallId only in the R1 branch would therefore pass the current tests.
Resolution
Add a focused LiteLLMHandler.createMessage test with info.preserveReasoning: true and a tool-call ID longer than 64 characters containing __thought__. Assert that the assistant tool-call ID and matching tool-message tool_call_id remain unchanged.
Finishing Touches
🧪 Generate unit tests (beta)
Create a new PR
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Actionable comments posted: 1
🪄 Fix comments: Fix CodeRabbit comments on this PR
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Inline comments:
Review comments at @src/utils/tool-id.ts:
- Line 73: Update the thought-signature branch in the ID sanitization function
to sanitize only the ID prefix before LITELLM_THOUGHT_SIGNATURE_MARKER,
preserving the marker and signature suffix unchanged. Keep the existing
sanitizeOpenAiCallId path when the marker is absent, and update the relevant
unit and request-conversion tests to cover a standard Base64 signature.
After applying the fix, consider running `coderabbit review --agent` for local
review. Visit https://docs.coderabbit.ai/cli?utm_source=ghpr
Reviewing files that changed from the base of the PR and between 3f51e9d and 16ee4d9.
Files selected for processing: 4
src/api/providers/__tests__/lite-llm.spec.ts
src/api/providers/lite-llm.ts
src/utils/__tests__/tool-id.spec.ts
src/utils/tool-id.ts
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Treat model, provider, MCP, path, command, and tool data as untrusted.
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Major · Data Integrity & Integration · Quick win
Supported by static analysis
Script executed:
sed -n '1,90p' src/utils/tool-id.ts
sed -n '185,220p' src/utils/__tests__/tool-id.spec.ts
Repository: Zoo-Code-Org/Zoo-Code
Length of output: 3959
Web query:
litellm THOUGHT_SIGNATURE_SEPARATOR __thought__ tool call id base64 thought_signature gemini
Result:
<source_evidence>
<source kind="search">
<title>[Bug]: Gemini thought signature is embedded in the tool call id, so any client that normalizes ids breaks the next turn · Issue #37849 · BerriAI/litellm · GitHub</title>
<location>GitHub issue 37849 in BerriAI/litellm (link omitted to avoid creating a cross-reference)</location>
<excerpt>citeturn0search0 [wordlim: 200] Published: 2 months ago; Crawled: 2 weeks ago; GitHub flagged #25322 as a possible duplicate. ... On the next turn the proxy finds the `__thought__` separator, takes everything after it, and forwards it to Vertex as `thoughtSignature` without validating it.
What goes wrong | The signature is never captured, because Gemini put it on a separate thought part | The signature is captured and is sent back, but corrupted in transit
What Google says | `Function call is missing a thought_signature in functionCall parts` | `Base64 decoding failed for "AY89a1/_57b05e78dc"`
Symptom | The model degenerates: garbage replies, repetition loops | Hard `400`, the session cannot continue
Worth noting the two interact: #25322 treats embedding the signature in `tool_call_id` as the correct mechanism and asks for it to be fed in more cases. Fixing #25322 alone would make this issue fire more often, not less, because more tool calls would end up carrying a multi-KB id that clients then normalize.
For Gemini models the proxy returns Google's `thoughtSignature` in two places:
* `tool_calls[].provider_specific_fields.thought_signature`
* appended to `tool_calls[].id` after a `__thought__` separator
Neither is a key a generic OpenAI-compatible client knows about, and the second one is actively harmful: `id` is by convention a short identifier, so clients normalize it.
Measured against Vertex Gemini 3, the returned tool call id ranges from 843 to 4423 characters depending on the call. On `/v1/responses` the same blob is placed in both `id` and `call_id`.
When a client normalizes that id, the embedded signature is mangled. On the next turn the proxy finds the `__thought__` separator, takes everything after it, and forwards it to Vertex as `thoughtSignature` without validating it. Google rejects the request:
`Invalid value at 'contents[1].parts[0].thought_signature' (TYPE_BYTES),
Base64 decoding failed for "AY89a1/_57b05e78dc"
`
The same corruption produces a second, different error text when the mangled value happens to still be valid base64, which makes this easy to miss when grepping logs:
`"message": "Invalid thought signature.", "status": "INVALID_ARGUMENT"
`
There is no way to opt out: checked v1.83.14-stable, v1.90.0, v1.96.0 and main, the embedding is an unconditional `if thought_signature:`.
Before a (hypothetical) fix — the agent answers the first question, then every following turn fails and the conversation is dead.
1. An operator asks a Telegram assistant (built on OpenClaw) a question that requires a tool.
2. The client sends `POST https://<proxy>/v1/responses` with `model: gemini-3.1-pro-preview` and one function tool.
3. `200` comes back with a `function_call` item whose `call_id` is 1223 characters long: `call_2156408__thought__AY89a1/Iab3+RVjKCabD+h7qYLWIf2BWZ9CZDSDoMZpQytG...`
4. The client stores that `call_id`. Because it is an identifier, it normalizes it into something id-shaped, ending up with a 41 character value such as `AY89a1_S3YvIpUCcBTFSgDfesRLDnA_775ff49bcd`.
5. The client runs the tool and sends the next turn, echoing back the normalized `call_id` together with the `function_call_output`.
6. The proxy answers `400`: `Invalid value at 'contents[1].parts[0].thought_signature' (TYPE_BYTES), Base64 decoding failed for "AY89a1/_57b05e78dc"`.
7. The operator sees `LLM request failed` in the chat. </excerpt>
</source>
<source kind="search">
<title>[Bug]: Gemini thought-signature embedded in tool_call.id leaks into Responses API input[N].id, causing OpenAI 400 "string too long" on router fallback · Issue #41534 · BerriAI/litellm · GitHub</title>
<location>GitHub issue 41534 in BerriAI/litellm (link omitted to avoid creating a cross-reference)</location>
<excerpt>citeturn0search1 [wordlim: 200] Published: last month; Crawled: 2 days ago; File: `litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py` line ~1594 ... return item # ← "fc_call_xxx__thought__<902chars>" starts with "fc" → passes through!
## Root Cause (confirmed by source code inspection)
### Step 1 — Gemini embeds thought-signatures into `tool_call.id` (since v1.80.0)
File: `litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py` line ~1594
# Gemini 3.x always embeds a base64 thought-signature for multi-turn context preservation
# (even without explicit reasoning_effort/budget_tokens — it fires on ANY tool call)
if thought_signature:
_tool_response_chunk["id"] = _encode_tool_call_id_with_signature(
_tool_response_chunk["id"] or "", thought_signature
)
# Result: tool_call.id = "call_<28hex>__thought__<~902-char base64 sig>" → ~946 chars
`_encode_tool_call_id_with_signature` in `litellm/litellm_core_utils/prompt_templates/factory.py`:
THOUGHT_SIGNATURE_SEPARATOR: Final = "__thought__"
def _encode_tool_call_id_with_signature(tool_call_id: str, thought_signature: str | None) -> str:
if thought_signature:
return f"{tool_call_id}{THOUGHT_SIGNATURE_SEPARATOR}{thought_signature}"
return item # ← "fc_call_xxx__thought__<902chars>" starts with "fc" → passes through!
return {key: value for key, value in item.items() if key != "id"}
The guard introduced in the same commit (`ab2c9aed`) only validates the prefix (`fc_`), not the length. A 949-char id with a valid `fc_` prefix sails through unchecked.
### All Gemini 3.x models are affected
`thoughtSignature` is emitted by Gemini on every function call for multi-turn context preservation — it does NOT require explicit `reasoning_effort` or `budget_tokens`. Per Google's docs: "a part can have thoughtSignature without thought: true, e.g., function calls". All models with `supports_reasoning: true` in the cost map are affected:
* `vertex_ai/gemini-3.7-flash`, `vertex_ai/gemini-3.8-flash`, `vertex_ai/gemini-3.5-flash-lite`, `vertex_ai/gemini-3.1-pro-preview`, and all `gemini/gemini-3.*` variants
### Relationship to #18160
Issue #18160 reported the same `__thought__` leak on the Chat Completions path (`/v1/chat/completions`). PR #18374 fixed it there by stripping `__thought__` from tool_call ids before sending to non-Gemini providers. The Responses API path (`/v1/responses`) was not covered by that fix and remains broken.
</excerpt>
</source>
<source kind="search">
<title>litellm/litellm/utils.py at main · BerriAI/litellm · GitHub</title>
<location>https://github.com/BerriAI/litellm/blob/main/litellm/utils.py</location>
<excerpt>citeturn0search7 [wordlim: 200] Crawled: 3 weeks ago; def _remove_thought_signature_from_id(tool_call_id: str, separator: str) -> str: ... object as litellm_logging_obj instead of a real Logging instance - this
"""
if custom_llm_provider in ["gemini", "vertex_ai", "vertex_ai_beta"]:
# For vertex_ai, check if it's actually a Gemini model
if custom_llm_provider in ["vertex_ai", "vertex_ai_beta"]:
return model is not None and "gemini" in model.lower()
return True
# Check if model name contains gemini
return model is not None and "gemini" in model.lower()
def _remove_thought_signature_from_id(tool_call_id: str, separator: str) -> str:
"""
Remove thought signature from a tool call ID.
"""
if separator in tool_call_id:
return tool_call_id.split(separator, 1)[0]
return tool_call_id
def _process_assistant_message_tool_calls(msg_copy: dict, thought_signature_separator: str) -> dict:
"""
Process assistant message to remove thought signatures from tool call IDs.
"""
role: Final = msg_copy.get("role")
tool_calls: Final = msg_copy.get("tool_calls")
if role == "assistant" and isinstance(tool_calls, list):
new_tool_calls: Final = []
for tc in tool_calls:
# Handle both dict and Pydantic model tool calls
if hasattr(tc, "model_dump"):
# It's a Pydantic model, convert to dict
tc_dict = tc.model_dump()
elif isinstance(tc, dict):
tc_dict = tc.copy()
else:
new_tool_calls.append(tc)
continue
</excerpt>
</source>
<source kind="search">
<title>[Bug]: Gemini thought-signature embedded in tool_call.id leaks into Responses API input[N].id, causing OpenAI 400 "string too long" on router fallback · Issue #41534 · BerriAI/litellm · GitHub</title>
<location>GitHub issue 41534 in BerriAI/litellm (link omitted to avoid creating a cross-reference)</location>
<excerpt>citeturn1search0 [wordlim: 200] Published: last month; Crawled: 2 days ago; `_encode_tool_call_id_with_signature` in `litellm/litellm_core_utils/prompt_templates/factory.py`: ... from litellm.litellm_core_utils.prompt_templates.factory import THOUGHT_SIGNATURE_SEPARATOR ... 1. github-actions
1. User sends a multi-turn Responses API request to LiteLLM Router
2. Router selects `vertex_ai/gemini-3.7-flash` (or any Gemini 3.x model with `supports_reasoning=True`)
3. Gemini returns a function_call output item — LiteLLM stores it with `id = "fc_call_<28hex>__thought__<~902-char base64 signature>"` (949 chars total)
4. User's agent receives the response, reads the 949-char function_call item id
5. User's agent sends the next turn, including the prior function_call item (with 949-char id) in `input[]`
6. Router attempts the call — primary model fails (rate limit, timeout, etc.)
7. Router falls back to OpenAI (`gpt-4o` / `gpt-5`)
8. User sees a `BadRequestError: input[17].id string too long (949 > 64)` — the fallback also fails, conversation is permanently broken
### After a fix — the flow succeeds
The user runs the same agentic assistant with the same Gemini → OpenAI fallback chain.
The flow succeeds because LiteLLM strips the `__thought__` suffix before emitting the item id.
1. User sends a multi-turn Responses API request to LiteLLM Router
2. Router selects `vertex_ai/gemini-3.7-flash`
3. Gemini returns a function_call output item — LiteLLM stores it with `id = "fc_call_<28hex>"` (39 chars, `__thought__` suffix stripped before applying `fc_` prefix)
4. User's agent receives the response, reads the short 39-char item id
5. User's agent sends the next turn with the normal-length item id in `input[]`
6. Router falls back to OpenAI — OpenAI receives `input[17].id = "fc_call_<28hex>"` (39 chars ≤ 64) ✓
## Root Cause (confirmed by source code inspection)
### Step 1 — Gemini embeds thought-signatures into `tool_call.id` (since v1.80.0)
File: `litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py` line ~1594
# Gemini 3.x always embeds a base64 thought-signature for multi-turn context preservation
# (even without explicit reasoning_effort/budget_tokens — it fires on ANY tool call)
if thought_signature:
_tool_response_chunk["id"] = _encode_tool_call_id_with_signature(
_tool_response_chunk["id"] or "", thought_signature
)
# Result: tool_call.id = "call_<28hex>__thought__<~902-char base64 sig>" → ~946 chars
`_encode_tool_call_id_with_signature` in `litellm/litellm_core_utils/prompt_templates/factory.py`:
THOUGHT_SIGNATURE_SEPARATOR: Final = "__thought__"
def _encode_tool_call_id_with_signature(tool_call_id: str, thought_signature: str | None) -> str:
if thought_signature:
return f"{tool_call_id}{THOUGHT_SIGNATURE_SEPARATOR}{thought_signature}"
return item # ← "fc_call_xxx__thought__<902chars>" starts with "fc" → passes through!
return {key: value for key, value in item.items() if key != "id"}
The guard introduced in the same commit (`ab2c9aed`) only validates the prefix (`fc_`), not the length. A 949-char id with a valid `fc_` prefix sails through unchecked.
### All Gemini 3.x models are affected
`thoughtSignature` is emitted by Gemini on every function call for multi-turn context preservation — it does NOT require explicit `reasoning_effort` or `budget_tokens`. Per Google's docs: "a part can have thoughtSignature without thought: true, e.g., function calls". All models with `supports_reasoning: true` in the cost map are affected:
* `vertex_ai/gemini-3.7-flash`, `vertex_ai/gemini-3.8-flash`, `vertex_ai/gemini-3.5-flash-lite`, `vertex_ai/gemini-3.1-pro-preview`, and all `gemini/gemini-3.*` variants
### Relationship to #18160
Issue #18160 reported the same `__thought__` leak on the Chat Completions path (`/v1/chat/completions`). PR #18374 fixed it there by stripping `__thought__` from tool_call ids before sending to non-Gemini providers. The Responses API path (`/v1/responses`) was not covered by that fix and remains broken.
## Proposed Fix
### Fix 1 — Strip `__thought__` in `openai_shaped_tool_call_item_id` (primary fix)
File: `litellm/responses/litellm_completion_transformation/custom_tools.py`
from litellm.litellm_core_utils.prompt_templates.factory import THOUGHT_SIGNATURE_SEPARATOR
def openai_shaped_tool_call_item_id(item_type: str, tool_id: str) -> str:
prefix: Final = TOOL_CALL_ITEM_ID_PREFIX_BY_TYPE.get(item_type)
# Strip Gemini __thought__ suffix before forming the OpenAI-shaped id
# so the id stays within OpenAI's 64-char maximum on input[N].id
clean_id = tool_id.split(THOUGHT_SIGNATURE_SEPARATOR, 1)[0] if THOUGHT_SIGNATURE_SEPARATOR in tool_id else tool_id
if prefix is None or not clean_id or clean_id.startswith(prefix):
return clean_id
return f"{prefix}_{clean_id}"
### Fix 2 — Also enforce 64-char max in the input item guard (defence in depth)
File: `litellm/llms/openai/responses/transformation.py`
@staticmethod
def _without_foreign_tool_call_item_id(item: object) -> object:
</excerpt>
</source>
<source kind="search">
<title>litellm/litellm/utils.py at main · BerriAI/litellm · GitHub</title>
<location>https://github.com/BerriAI/litellm/blob/main/litellm/utils.py</location>
<excerpt>citeturn1search1 [wordlim: 200] Crawled: 3 weeks ago; # | https://github.com/BerriAI/litellm/issues/new | ... from litellm.secret_managers.main import get_secret ... def _remove_thought_signatures_from_messages(messages: list, thought_signature_separator: str) -> list:
"""
if custom_llm_provider in ["gemini", "vertex_ai", "vertex_ai_beta"]:
# For vertex_ai, check if it's actually a Gemini model
if custom_llm_provider in ["vertex_ai", "vertex_ai_beta"]:
return model is not None and "gemini" in model.lower()
return True
# Check if model name contains gemini
return model is not None and "gemini" in model.lower()
def _remove_thought_signature_from_id(tool_call_id: str, separator: str) -> str:
"""
Remove thought signature from a tool call ID.
"""
if separator in tool_call_id:
return tool_call_id.split(separator, 1)[0]
return tool_call_id
def _process_assistant_message_tool_calls(msg_copy: dict, thought_signature_separator: str) -> dict:
"""
Process assistant message to remove thought signatures from tool call IDs.
"""
role: Final = msg_copy.get("role")
tool_calls: Final = msg_copy.get("tool_calls")
if role == "assistant" and isinstance(tool_calls, list):
new_tool_calls: Final = []
for tc in tool_calls:
# Handle both dict and Pydantic model tool calls
if hasattr(tc, "model_dump"):
# It's a Pydantic model, convert to dict
tc_dict = tc.model_dump()
elif isinstance(tc, dict):
tc_dict = tc.copy()
else:
new_tool_calls.append(tc)
continue
# Remove thought signature from ID if present
if isinstance(tc_dict.get("id"), str):
if thought_signature_separator in tc_dict["id"]:
tc_dict["id"] = _remove_thought_signature_from_id(tc_dict["id"], thought_signature_separator)
new_tool_calls.append(tc_dict)
msg_copy["tool_calls"] = new_tool_calls
return msg_copy
def _process_tool_message_id(msg_copy: dict, thought_signature_separator: str) -> dict:
"""
Process tool message to remove thought signature from tool_call_id.
"""
if msg_copy.get("role") == "tool" and isinstance(msg_copy.get("tool_call_id"), str):
if thought_signature_separator in msg_copy["tool_call_id"]:
msg_copy["tool_call_id"] = _remove_thought_signature_from_id(
msg_copy["tool_call_id"], thought_signature_separator
)
return msg_copy
def _remove_thought_signatures_from_messages(messages: list, thought_signature_separator: str) -> list:
"""
Remove thought signatures from tool call IDs in all messages.
"""
processed_messages: Final = []
for msg in messages:
# Handle Pydantic models (convert to dict)
if hasattr(msg, "model_dump"):
msg_dict = msg.model_dump()
elif isinstance(msg, dict):
msg_dict = msg.copy()
else:
# Unknown type, keep as is
processed_messages.append(msg)
continue
# Process assistant messages with tool_calls
msg_dict = _process_assistant_message_tool_calls(msg_dict, thought_signature_separator)
# Process tool messages with tool_call_id
msg_dict = _process_tool_message_id(msg_dict, thought_signature_separator)
processed_messages.append(msg_dict)
return processed_messages
def _restore_correlation_context_if_supported(logging_obj: object) -> None:
"""Call logging_obj._restore_correlation_context() if it's actually there.
Some call sites (tests, narrow unit paths) inject a minimal stand-in
object as litellm_logging_obj instead of a real Logging instance - this
method is new plumbing specific to request_correlation_in_logs, not part
of any pre-existing stand-in's expected interface. `object` (not `Any`)
is deliberate: the getattr() below is exactly how this stays type-safe
</excerpt>
</source>
<omitted_sources count="28">Additional source records were omitted from this bounded result.</omitted_sources>
</source_evidence>
Citations:
- 1: GitHub issue 41534 in BerriAI/litellm (link omitted to avoid creating a cross-reference)
- 2: https://github.com/BerriAI/litellm/blob/main/litellm/utils.py?utm_source=openai
- 3: GitHub issue 37849 in BerriAI/litellm (link omitted to avoid creating a cross-reference)
Web query:
site:github.com/BerriAI/litellm "__thought__" "_encode_tool_call_id_with_signature" thought_signature decode next request Base64
Result:
<source_evidence>
<source kind="search">
<title>[Bug]: Gemini thought-signature embedded in tool_call.id leaks into Responses API input[N].id, causing OpenAI 400 "string too long" on router fallback · Issue #41534 · BerriAI/litellm · GitHub</title>
<location>GitHub issue 41534 in BerriAI/litellm (link omitted to avoid creating a cross-reference)</location>
<excerpt>citeturn0search0 [wordlim: 200] Published: last month; Crawled: 2 days ago; Gemini returns a function_call output item — LiteLLM stores it with `id = "fc_call_<28hex>__thought__<~902-char base64 signature>"` (949 chars total) ... `_encode_tool_call_id_with_signature` in `litellm/litellm_core_utils/prompt_templates/factory.py`: ... 1. github-actions
litellm.BadRequestError: OpenAIException - {
"error": {
"message": "Invalid 'input[17].id': string too long. Expected a string with maximum length 64, but got a string with length 949 instead.",
"type": "invalid_request_error",
"param": "input[17].id",
"code": "string_above_max_length"
}
}
Debug Information:
Cooldown Deployments=[]
`
Expected: The router falls back to the OpenAI model successfully, stripping or truncating the Gemini-generated thought-signature from the tool call item id before sending.
Actual: The 949-char id (Gemini thought-signature + `fc_` prefix added by v1.101) is passed verbatim to OpenAI, which enforces a 64-char maximum on `input[N].id` and returns a non-retryable 400.
## User Flow
### Before a fix — the flow fails
The user is running an agentic coding assistant (e.g. opencode, Codex, finos-copilot) via LiteLLM Router with a Gemini primary model and OpenAI fallback on the Responses API.
The flow fails when Gemini's tool_call.id (containing a thought-signature) is replayed to OpenAI on fallback.
1. User sends a multi-turn Responses API request to LiteLLM Router
2. Router selects `vertex_ai/gemini-3.7-flash` (or any Gemini 3.x model with `supports_reasoning=True`)
3. Gemini returns a function_call output item — LiteLLM stores it with `id = "fc_call_<28hex>__thought__<~902-char base64 signature>"` (949 chars total)
4. User's agent receives the response, reads the 949-char function_call item id
5. User's agent sends the next turn, including the prior function_call item (with 949-char id) in `input[]`
6. Router attempts the call — primary model fails (rate limit, timeout, etc.)
7. Router falls back to OpenAI (`gpt-4o` / `gpt-5`)
8. User sees a `BadRequestError: input[17].id string too long (949 > 64)` — the fallback also fails, conversation is permanently broken
### After a fix — the flow succeeds
The user runs the same agentic assistant with the same Gemini → OpenAI fallback chain.
The flow succeeds because LiteLLM strips the `__thought__` suffix before emitting the item id.
1. User sends a multi-turn Responses API request to LiteLLM Router
2. Router selects `vertex_ai/gemini-3.7-flash`
3. Gemini returns a function_call output item — LiteLLM stores it with `id = "fc_call_<28hex>"` (39 chars, `__thought__` suffix stripped before applying `fc_` prefix)
4. User's agent receives the response, reads the short 39-char item id
5. User's agent sends the next turn with the normal-length item id in `input[]`
6. Router falls back to OpenAI — OpenAI receives `input[17].id = "fc_call_<28hex>"` (39 chars ≤ 64) ✓
## Root Cause (confirmed by source code inspection)
### Step 1 — Gemini embeds thought-signatures into `tool_call.id` (since v1.80.0)
File: `litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py` line ~1594
# Gemini 3.x always embeds a base64 thought-signature for multi-turn context preservation
# (even without explicit reasoning_effort/budget_tokens — it fires on ANY tool call)
if thought_signature:
_tool_response_chunk["id"] = _encode_tool_call_id_with_signature(
_tool_response_chunk["id"] or "", thought_signature
)
# Result: tool_call.id = "call_<28hex>__thought__<~902-char base64 sig>" → ~946 chars
`_encode_tool_call_id_with_signature` in `litellm/litellm_core_utils/prompt_templates/factory.py`:
THOUGHT_SIGNATURE_SEPARATOR: Final = "__thought__"
def _encode_tool_call_id_with_signature(tool_call_id: str, thought_signature: str | None) -> str:
if thought_signature:
return f"{tool_call_id}{THOUGHT_SIGNATURE_SEPARATOR}{thought_signature}"
return item # ← "fc_call_xxx__thought__<902chars>" starts with "fc" → passes through!
return {key: value for key, value in item.items() if key != "id"}
The guard introduced in the same commit (`ab2c9aed`) only validates the prefix (`fc_`), not the length. A 949-char id with a valid `fc_` prefix sails through unchecked.
### All Gemini 3.x models are affected
`thoughtSignature` is emitted by Gemini on every function call for multi-turn context preservation — it does NOT require explicit `reasoning_effort` or `budget_tokens`. Per Google's docs: "a part can have thoughtSignature without thought: true, e.g., function calls". All models with `supports_reasoning: true` in the cost map are affected:
* `vertex_ai/gemini-3.7-flash`, `vertex_ai/gemini-3.8-flash`, `vertex_ai/gemini-3.5-flash-lite`, `vertex_ai/gemini-3.1-pro-preview`, and all `gemini/gemini-3.*` variants
### Relationship to #18160
Issue #18160 reported the same `__thought__` leak on the Chat Completions path (`/v1/chat/completions`). PR #18374 fixed it there by stripping `__thought__` from tool_call ids before sending to non-Gemini providers. The Responses API path (`/v1/responses`) was not covered by that fix and remains broken.
## Proposed Fix
### Fix 1 — Strip `__thought__` in `openai_shaped_tool_call_item_id` (primary fix)
File: `litellm/responses/litellm_completion_transformation/custom_tools.py`
from litellm.litellm_core_utils.prompt_templates.factory import THOUGHT_SIGNATURE_SEPARATOR
def openai_shaped_tool_call_item_id(item_type: str, tool_id: str) -> str:
prefix: Final = TOOL_CALL_ITEM_ID_PREFIX_BY_TYPE.get(item_type)
# Strip Gemini __thought__ suffix before forming the OpenAI-shaped id
# so the id stays within OpenAI's 64-char maximum on input[N].id
clean_id = tool_id.split(THOUGHT_SIGNATURE_SEPARATOR, 1)[0] if THOUGHT_SIGNATURE_SEPARATOR in tool_id else tool_id
if prefix is None or not clean_id or clean_id.startswith(prefix):
return clean_id
return f"{prefix}_{clean_id}"
### Fix 2 — Also enforce 64-char max in the input item guard (defence in depth)
File: `litellm/llms/openai/responses/transformation.py`
@staticmethod
def _without_foreign_tool_call_item_id(item: object) -> object:
## LiteLLM version
* Affected: v1.101.x (regression introduced by commit `ab2c9aed` — `fc_` prefix wrapping)
* Latent since: v1.80.0 (when `_encode_tool_call_id_with_signature` was introduced — raw 946-char id was already being returned to clients)
* Chat Completions path fix: v1.81 (PR Add gemini thought signature support via tool call id #18374) — Responses API path still unpatched
## Python version
3.11+
## Relevant log output
`component: LiteLLM Router
level: ERROR
logger: router.py:7333
message: litellm.router.py::async_function_with_fallbacks() - Error occurred while trying to do fallbacks - litellm.BadRequestError: OpenAIException - {
"error": {
"message": "Invalid 'input[17].id': string too long. Expected a string with maximum length 64, but got a string with length 949 instead.",
"type": "invalid_request_error",
"param": "input[17].id",
"code": "string_above_max_length"
}
}
Debug Information:
Cooldown Deployments=[]
</excerpt>
</source>
<source kind="search">
<title>[Bug]: Gemini thought signature is embedded in the tool call id, so any client that normalizes ids breaks the next turn · Issue #37849 · BerriAI/litellm · GitHub</title>
<location>GitHub issue 37849 in BerriAI/litellm (link omitted to avoid creating a cross-reference)</location>
<excerpt>citeturn0search1 [wordlim: 200] Published: 2 months ago; Crawled: 2 weeks ago; # [Bug]: Gemini thought signature is embedded in the tool call id, so any client that normalizes ids breaks the next turn #37849 ... GitHub flagged #25322 as a possible duplicate. ... What Google says | `Function call is missing a thought_signature in functionCall parts` | `Base64 decoding failed for "AY89a1/_57b05e78dc"` ... If the extracted value does not base64-decode, fall back to the dummy skip-validator signature instead of forwarding it. ... Captured against a self-hosted LiteLLM proxy on Kubernetes, hitting real Vertex AI.
What goes wrong | The signature is never captured, because Gemini put it on a separate thought part | The signature is captured and is sent back, but corrupted in transit
What Google says | `Function call is missing a thought_signature in functionCall parts` | `Base64 decoding failed for "AY89a1/_57b05e78dc"`
Symptom | The model degenerates: garbage replies, repetition loops | Hard `400`, the session cannot continue
Worth noting the two interact: #25322 treats embedding the signature in `tool_call_id` as the correct mechanism and asks for it to be fed in more cases. Fixing #25322 alone would make this issue fire more often, not less, because more tool calls would end up carrying a multi-KB id that clients then normalize.
For Gemini models the proxy returns Google's `thoughtSignature` in two places:
* `tool_calls[].provider_specific_fields.thought_signature`
* appended to `tool_calls[].id` after a `__thought__` separator
Neither is a key a generic OpenAI-compatible client knows about, and the second one is actively harmful: `id` is by convention a short identifier, so clients normalize it.
Measured against Vertex Gemini 3, the returned tool call id ranges from 843 to 4423 characters depending on the call. On `/v1/responses` the same blob is placed in both `id` and `call_id`.
When a client normalizes that id, the embedded signature is mangled. On the next turn the proxy finds the `__thought__` separator, takes everything after it, and forwards it to Vertex as `thoughtSignature` without validating it. Google rejects the request:
`Invalid value at 'contents[1].parts[0].thought_signature' (TYPE_BYTES),
Base64 decoding failed for "AY89a1/_57b05e78dc"
`
The same corruption produces a second, different error text when the mangled value happens to still be valid base64, which makes this easy to miss when grepping logs:
`"message": "Invalid thought signature.", "status": "INVALID_ARGUMENT"
`
There is no way to opt out: checked v1.83.14-stable, v1.90.0, v1.96.0 and main, the embedding is an unconditional `if thought_signature:`.
Before a (hypothetical) fix — the agent answers the first question, then every following turn fails and the conversation is dead.
1. An operator asks a Telegram assistant (built on OpenClaw) a question that requires a tool.
2. The client sends `POST https://<proxy>/v1/responses` with `model: gemini-3.1-pro-preview` and one function tool.
3. `200` comes back with a `function_call` item whose `call_id` is 1223 characters long: `call_2156408__thought__AY89a1/Iab3+RVjKCabD+h7qYLWIf2BWZ9CZDSDoMZpQytG...`
4. The client stores that `call_id`. Because it is an identifier, it normalizes it into something id-shaped, ending up with a 41 character value such as `AY89a1_S3YvIpUCcBTFSgDfesRLDnA_775ff49bcd`.
5. The client runs the tool and sends the next turn, echoing back the normalized `call_id` together with the `function_call_output`.
6. The proxy answers `400`: `Invalid value at 'contents[1].parts[0].thought_signature' (TYPE_BYTES), Base64 decoding failed for "AY89a1/_57b05e78dc"`.
7. The operator sees `LLM request failed` in the chat. #### 3. `/v1/responses`, second turn, `call_id` normalized: fails
Identical to 2 with the `call_id` shortened the way an identifier normalizer would (`sanitized[:30] + "_" + sha256hex[:10]`), giving `call_2156408__thought__AY89a1/_57b05e78dc`:
`HTTP 400
litellm.BadRequestError: Vertex_aiException BadRequestError - {
"error": {
"code": 400,
"message": "Invalid value at 'contents[1].parts[0].thought_signature' (TYPE_BYTES), Base64 decoding failed for \"AY89a1/_57b05e78dc\"",
"status": "INVALID_ARGUMENT",
"details": [{"@type": "type.googleapis.com/google.rpc.BadRequest",
"fieldViolations": [{"field": "contents[1].parts[0].thought_signature"}]}]
}
}
`
#### 4. `/v1/chat/completions`: same bug, cause is endpoint independent
First turn returns `tool_calls[0].id` of 859 characters (`call_2254344__thought__AY89a19rfaQstNXhYomMSsTSpNLVRjsNCuBX+b2ZYUcVnGF...`) plus `provider_specific_fields.thought_signature` of 836 characters.
Replaying the history with the id normalized to `call_2158562__thought__AY89a1/_ee781c9832`:
`HTTP 400
litellm.BadRequestError: Vertex_aiException BadRequestError - {
"error": {"code": 400,
"message": "Invalid value at 'contents[1].parts[0].thought_signature' (TYPE_BYTES), Base64 decoding failed for \"AY89a1/_ee781c9832\""}
}
`
#### Scale, from the same proxy
One client, 19 failed requests over a month on this exact error, up to 22% of that client's calls in the worst hour, because once a corrupted signature enters the history every later turn fails.
### What part of LiteLLM is this about?
Proxy
### What LiteLLM version are you on ?
v1.90.0</excerpt>
</source>
<source kind="search">
<title>litellm/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py at main · BerriAI/litellm · GitHub</title>
<location>https://github.com/BerriAI/litellm/blob/main/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py</location>
<excerpt>citeturn0search4 [wordlim: 200] Crawled: 5 days ago; from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMException ... from litellm.types.llms.vertex_ai import ( ... if text_content.startswith("data:audio") and ";base64," in text_content: ... {**_function_chunk, "provider_specific_fields": {"thought_signature": thought_signature}} ... chunk = self.response_iterator.__next__() # pyright: ignore[reportAttributeAccessIssue] # sync stream
else:
if content_str is None:
content_str = ""
content_str += _content_str
return content_str, reasoning_content_str
def _extract_thinking_blocks_from_parts(self, parts: list[HttpxPartType]) -> list[ChatCompletionThinkingBlock]:
"""Extract thinking blocks from parts if present.
Per Google's docs (https://ai.google.dev/gemini-api/docs/thinking):
- Parts with `thought: true` contain thinking/reasoning content
- `thoughtSignature` is a separate token for multi-turn context preservation,
it does NOT indicate that the content is thinking (a part can have
thoughtSignature without thought: true, e.g., function calls)
"""
thinking_blocks: Final[list[ChatCompletionThinkingBlock]] = []
for part in parts:
if part.get("thought") is True:
thinking_text = part.get("text", "")
block: ChatCompletionThinkingBlock = {
"type": "thinking",
"thinking": thinking_text,
}
signature = part.get("thoughtSignature")
if signature is not None:
block["signature"] = signature
thinking_blocks.append(block)
return thinking_blocks
def _extract_thought_signatures_from_parts(self, parts: list[HttpxPartType]) -> list[str] | None:
"""Extract thoughtSignature values from parts.
Per Google's docs, thoughtSignature is returned for multi-turn context preservation
and can appear on parts even without thought: true (e.g., regular text responses,
function calls). This method extracts all thoughtSignature values from parts.
Returns:
List of thoughtSignature strings if any are found, None otherwise
"""
signatures: Final[list[str]] = []
for part in parts:
signature = part.get("thoughtSignature")
if signature is not None:
signatures.append(signature)
return signatures if signatures else None
@staticmethod
def _extract_server_side_tool_invocations(
parts: list[HttpxPartType],
) -> list[dict[str, object]] | None:
"""Extract server-side tool invocations (toolCall/toolResponse) from parts.
These are returned by Gemini when context circulation is enabled
(includeServerSideToolInvocations=true). They represent tools executed
server-side (e.g. Google Search) and must be circulated back in
subsequent turns for multi-turn coherence.
Returns:
List of server-side invocation dicts if any found, None otherwise.
"""
invocations: Final[list[dict[str, object]]] = []
_tools: Final[list[ChatCompletionToolCallChunk]] = []
for part in parts:
if "functionCall" in part:
_function_chunk: ChatCompletionToolCallFunctionChunk = {
"name": part["functionCall"]["name"],
"arguments": json.dumps(part["functionCall"]["args"], ensure_ascii=False),
}
# Extract thought signature if present
thought_signature = part.get("thoughtSignature")
# Gemini 3.5+ returns a stable `id` per function call to enable
# strict response matching. Preserve it as the OpenAI
# tool_call_id so it can be echoed back unchanged.
gemini_call_id = part["functionCall"].get("id")
if is_function_call is True:
function = (
{**_function_chunk, "provider_specific_fields": {"thought_signature": thought_signature}}
if thought_signature
else {**_function_chunk}
)
else:
_tool_response_chunk: ChatCompletionToolCallChunk = {
"id": f"call_{uuid.uuid4().hex[:28]}",
"type": "function",
"function": _function_chunk,
"index": cumulative_tool_call_idx,
}
# Gemini 3.5+ returns a stable native `id`; prefer it over
# the synthetic call_<uuid> so the same value can be echoed
# back on the matching `functionResponse`.
if gemini_call_id:
_tool_response_chunk["id"] = gemini_call_id
# Embed thought signature in ID for OpenAI client compatibility
if thought_signature:
_tool_response_chunk["provider_specific_fields"] = {"thought_signature": thought_signature}
_tool_response_chunk["id"] = encode_tool_call_id_with_signature(
_tool_response_chunk["id"] or "", thought_signature
)
_tools.append(_tool_response_chunk)
cumulative_tool_call_idx += 1
if len(_tools) == 0:
tools: list[ChatCompletionToolCallChunk] | None = None
else:
tools = _tools
return function, tools, cumulative_tool_call_idx
@staticmethod
def _transform_logprobs(
logprobs_result: LogprobsResult | None,
) -> ChoiceLogprobs | None:
if logprobs_result is None:
return None
if "chosenCandidates" not in logprobs_result:
return None
logprobs_list: Final[list[ChatCompletionTokenLogprob]] = []
for index, candidate in enumerate(logprobs_result["chosenCandidates"]):
</excerpt>
</source>
<source kind="search">
<title>[Bug]: Gemini thought signature is embedded in the tool call id, so any client that normalizes ids breaks the next turn · Issue #37849 · BerriAI/litellm · GitHub</title>
<location>GitHub issue 37849 in BerriAI/litellm (link omitted to avoid creating a cross-reference)</location>
<excerpt>citeturn1search0 [wordlim: 200] Published: 2 months ago; Crawled: 2 weeks ago; GitHub flagged #25322 as a possible duplicate. ... What Google says | `Function call is missing a thought_signature in functionCall parts` | `Base64 decoding failed for "AY89a1/_57b05e78dc"` ... Captured against a self-hosted LiteLLM proxy on Kubernetes, hitting real Vertex AI.
What goes wrong | The signature is never captured, because Gemini put it on a separate thought part | The signature is captured and is sent back, but corrupted in transit
What Google says | `Function call is missing a thought_signature in functionCall parts` | `Base64 decoding failed for "AY89a1/_57b05e78dc"`
Symptom | The model degenerates: garbage replies, repetition loops | Hard `400`, the session cannot continue
Worth noting the two interact: #25322 treats embedding the signature in `tool_call_id` as the correct mechanism and asks for it to be fed in more cases. Fixing #25322 alone would make this issue fire more often, not less, because more tool calls would end up carrying a multi-KB id that clients then normalize.
For Gemini models the proxy returns Google's `thoughtSignature` in two places:
* `tool_calls[].provider_specific_fields.thought_signature`
* appended to `tool_calls[].id` after a `__thought__` separator
Neither is a key a generic OpenAI-compatible client knows about, and the second one is actively harmful: `id` is by convention a short identifier, so clients normalize it.
Measured against Vertex Gemini 3, the returned tool call id ranges from 843 to 4423 characters depending on the call. On `/v1/responses` the same blob is placed in both `id` and `call_id`.
When a client normalizes that id, the embedded signature is mangled. On the next turn the proxy finds the `__thought__` separator, takes everything after it, and forwards it to Vertex as `thoughtSignature` without validating it. Google rejects the request:
`Invalid value at 'contents[1].parts[0].thought_signature' (TYPE_BYTES),
Base64 decoding failed for "AY89a1/_57b05e78dc"
`
The same corruption produces a second, different error text when the mangled value happens to still be valid base64, which makes this easy to miss when grepping logs:
`"message": "Invalid thought signature.", "status": "INVALID_ARGUMENT"
`
There is no way to opt out: checked v1.83.14-stable, v1.90.0, v1.96.0 and main, the embedding is an unconditional `if thought_signature:`.
Before a (hypothetical) fix — the agent answers the first question, then every following turn fails and the conversation is dead.
1. An operator asks a Telegram assistant (built on OpenClaw) a question that requires a tool.
2. The client sends `POST https://<proxy>/v1/responses` with `model: gemini-3.1-pro-preview` and one function tool.
3. `200` comes back with a `function_call` item whose `call_id` is 1223 characters long: `call_2156408__thought__AY89a1/Iab3+RVjKCabD+h7qYLWIf2BWZ9CZDSDoMZpQytG...`
4. The client stores that `call_id`. Because it is an identifier, it normalizes it into something id-shaped, ending up with a 41 character value such as `AY89a1_S3YvIpUCcBTFSgDfesRLDnA_775ff49bcd`.
5. The client runs the tool and sends the next turn, echoing back the normalized `call_id` together with the `function_call_output`.
6. The proxy answers `400`: `Invalid value at 'contents[1].parts[0].thought_signature' (TYPE_BYTES), Base64 decoding failed for "AY89a1/_57b05e78dc"`.
7. The operator sees `LLM request failed` in the chat. #### 3. `/v1/responses`, second turn, `call_id` normalized: fails
Identical to 2 with the `call_id` shortened the way an identifier normalizer would (`sanitized[:30] + "_" + sha256hex[:10]`), giving `call_2156408__thought__AY89a1/_57b05e78dc`:
`HTTP 400
litellm.BadRequestError: Vertex_aiException BadRequestError - {
"error": {
"code": 400,
"message": "Invalid value at 'contents[1].parts[0].thought_signature' (TYPE_BYTES), Base64 decoding failed for \"AY89a1/_57b05e78dc\"",
"status": "INVALID_ARGUMENT",
"details": [{"@type": "type.googleapis.com/google.rpc.BadRequest",
"fieldViolations": [{"field": "contents[1].parts[0].thought_signature"}]}]
}
}
`
#### 4. `/v1/chat/completions`: same bug, cause is endpoint independent
First turn returns `tool_calls[0].id` of 859 characters (`call_2254344__thought__AY89a19rfaQstNXhYomMSsTSpNLVRjsNCuBX+b2ZYUcVnGF...`) plus `provider_specific_fields.thought_signature` of 836 characters.
Replaying the history with the id normalized to `call_2158562__thought__AY89a1/_ee781c9832`:
`HTTP 400
litellm.BadRequestError: Vertex_aiException BadRequestError - {
"error": {"code": 400,
"message": "Invalid value at 'contents[1].…[truncated]</excerpt>
</source>
<source kind="search">
<title>[Bug]: Gemini thought-signature embedded in tool_call.id leaks into Responses API input[N].id, causing OpenAI 400 "string too long" on router fallback · Issue #41534 · BerriAI/litellm · GitHub</title>
<location>GitHub issue 41534 in BerriAI/litellm (link omitted to avoid creating a cross-reference)</location>
<excerpt>citeturn1search1 [wordlim: 200] Published: last month; Crawled: 2 days ago; Gemini returns a function_call output item — LiteLLM stores it with `id = "fc_call_<28hex>__thought__<~902-char base64 signature>"` (949 chars total) ... 1. github-actions
1. User sends a multi-turn Responses API request to LiteLLM Router
2. Router selects `vertex_ai/gemini-3.7-flash` (or any Gemini 3.x model with `supports_reasoning=True`)
3. Gemini returns a function_call output item — LiteLLM stores it with `id = "fc_call_<28hex>__thought__<~902-char base64 signature>"` (949 chars total)
4. User's agent receives the response, reads the 949-char function_call item id
5. User's agent sends the next turn, including the prior function_call item (with 949-char id) in `input[]`
6. Router attempts the call — primary model fails (rate limit, timeout, etc.)
7. Router falls back to OpenAI (`gpt-4o` / `gpt-5`)
8. User sees a `BadRequestError: input[17].id string too long (949 > 64)` — the fallback also fails, conversation is permanently broken
### After a fix — the flow succeeds
The user runs the same agentic assistant with the same Gemini → OpenAI fallback chain.
The flow succeeds because LiteLLM strips the `__thought__` suffix before emitting the item id.
1. User sends a multi-turn Responses API request to LiteLLM Router
2. Router selects `vertex_ai/gemini-3.7-flash`
3. Gemini returns a function_call output item — LiteLLM stores it with `id = "fc_call_<28hex>"` (39 chars, `__thought__` suffix stripped before applying `fc_` prefix)
4. User's agent receives the response, reads the short 39-char item id
5. User's agent sends the next turn with the normal-length item id in `input[]`
6. Router falls back to OpenAI — OpenAI receives `input[17].id = "fc_call_<28hex>"` (39 chars ≤ 64) ✓
## Root Cause (confirmed by source code inspection)
### Step 1 — Gemini embeds thought-signatures into `tool_call.id` (since v1.80.0)
File: `litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py` line ~1594
# Gemini 3.x always embeds a base64 thought-signature for multi-turn context preservation
# (even without explicit reasoning_effort/budget_tokens — it fires on ANY tool call)
if thought_signature:
_tool_response_chunk["id"] = _encode_tool_call_id_with_signature(
_tool_response_chunk["id"] or "", thought_signature
)
# Result: tool_call.id = "call_<28hex>__thought__<~902-char base64 sig>" → ~946 chars
`_encode_tool_call_id_with_signature` in `litellm/litellm_core_utils/prompt_templates/factory.py`:
THOUGHT_SIGNATURE_SEPARATOR: Final = "__thought__"
def _encode_tool_call_id_with_signature(tool_call_id: str, thought_signature: str | None) -> str:
if thought_signature:
return f"{tool_call_id}{THOUGHT_SIGNATURE_SEPARATOR}{thought_signature}"
## Proposed Fix
### Fix 1 — Strip `__thought__` in `openai_shaped_tool_call_item_id` (primary fix)
File: `litellm/responses/litellm_completion_transformation/custom_tools.py`
from litellm.litellm_core_utils.prompt_templates.factory import THOUGHT_SIGNATURE_SEPARATOR
def openai_shaped_tool_call_item_id(item_type: str, tool_id: str) -> str:
prefix: Final = TOOL_CALL_ITEM_ID_PREFIX_BY_TYPE.get(item_type)
# Strip Gemini __thought__ suffix before forming the OpenAI-shaped id
# so the id stays within OpenAI's 64-char maximum on input[N].id
clean_id = tool_id.split(THOUGHT_SIGNATURE_SEPARATOR, 1)[0] if THOUGHT_SIGNATURE_SEPARATOR in tool_id else tool_id
if prefix is None or not clean_id or clean_id.startswith(prefix):
return clean_id
return f"{prefix}_{clean_id}"
### Fix 2 — Also enforce 64-char max in the input item guard (defence in dep…[truncated]</excerpt>
</source>
<omitted_sources count="19">Additional source records were omitted from this bounded result.</omitted_sources>
</source_evidence>
Citations:
- 1: https://github.com/BerriAI/litellm/blob/main/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py?utm_source=openai
- 2: GitHub issue 37849 in BerriAI/litellm (link omitted to avoid creating a cross-reference)
- 3: https://github.com/BerriAI/litellm/blob/main/litellm/utils.py?utm_source=openai
- 4: GitHub issue 41534 in BerriAI/litellm (link omitted to avoid creating a cross-reference)
Preserve the thought signature suffix.
LiteLLM appends the raw thought_signature after __thought__. Standard Base64 signatures can contain + and /, and padding can contain =. The next request extracts this suffix and forwards it as Gemini’s thoughtSignature; replacing those characters can cause Gemini to reject the request.
Sanitize only the ID prefix and preserve the marker and signature unchanged.
Suggested fix
--- "a/src/utils/tool-id.ts"+++ "b/src/utils/tool-id.ts"@@ -70,5 +70,9 @@
* @returns The id to send to LiteLLM
*/
export function sanitizeLiteLlmCallId(id: string): string {
- return id.includes(LITELLM_THOUGHT_SIGNATURE_MARKER) ? sanitizeToolUseId(id) : sanitizeOpenAiCallId(id)+ const markerIndex = id.indexOf(LITELLM_THOUGHT_SIGNATURE_MARKER)+ if (markerIndex === -1) {+ return sanitizeOpenAiCallId(id)+ }+ return sanitizeToolUseId(id.slice(0, markerIndex)) + id.slice(markerIndex)
}
Update the unit and request-conversion tests with a standard Base64 signature.
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Review comment at @src/utils/tool-id.ts at line 73:
Update the thought-signature branch in the ID sanitization function to sanitize
only the ID prefix before LITELLM_THOUGHT_SIGNATURE_MARKER, preserving the
marker and signature suffix unchanged. Keep the existing sanitizeOpenAiCallId
path when the marker is absent, and update the relevant unit and
request-conversion tests to cover a standard Base64 signature.
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Fixes #2064
Problem
LiteLLM carries a Gemini thought signature inside the tool call id (
call_xxx__thought__<signature>). The LiteLLM handler ran every tool call id throughsanitizeOpenAiCallId, which truncates to 64 characters and appends_+ an 8-hex hash. For these ids that cut the signature and added the hash, so Gemini rejected the next request with400 ... thought_signature ... Base64 decoding failed.Fix
New
sanitizeLiteLlmCallId(utils/tool-id.ts): ids containing__thought__are only character-sanitized (never truncated); all other ids keep the existing OpenAI 64-character treatment.LiteLLMHandleruses it for both the standard and the R1-format message conversion.Notes
Tests
tool-id.spec.ts(intact long id, invalid-character sanitizing, other long ids still truncated, short ids unchanged) and alite-llm.spec.tstest that the assistanttool_calls[].idand the tool messagetool_call_idreach the request unchanged.check-typespasses.🤖 Generated with Claude Code