Cache provider config lookups for Vertex Anthropic messages so repeated requests reuse the same config object and preserve credential cache state. Add a regression test to catch any future loss of config reuse.
Made-with: Cursor
Fixes SyntaxError at pytest collection time caused by leftover
<<<<<<<, =======, >>>>>>> markers in test_bedrock_common_utils.py.
Keeps the assertion matching the model under test
(claude-haiku-4-5-20251001-v1:0).
Unit Tests: Proxy DB Operations / proxy-db (auth-checks, tests/proxy_unit_tests/test_auth_checks.py tests/proxy_unit_tests/test_user_api_key_auth.py, 20, 8) (push) Waiting to run
Unit Tests: Proxy DB Operations / proxy-db (remaining, tests/proxy_unit_tests --ignore=tests/proxy_unit_tests/test_key_generate_prisma.py --ignore=tests/proxy_unit_tests/test_auth_checks.py --ignore=tests/proxy_unit_tests/test_user_api_key_auth.py, 30, 8) (push) Waiting to run
* Add capability to override default GitHub Copilot authentication endpoints
This feature adds support for GitHub Enterprise subsriptions with custom domain/data ownership (which use a different URL compared to standard accounts)
* Update documentation with new parameters
* Move access token URL and Client ID retrieval outside for loop
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
* Fix spurious comment from Greptile review
* Align api_base retrieval behavior across chat and embedding transformations
* Add missing GitHub Copilot client ID parameter in docs
* Update website documentation with newer options for GitHub Enterprise Copilot
* Fix default value for Copilot client ID in docs
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
---------
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
Drop test_bedrock_invoke_messages_injects_thinking_for_clear_thinking_context_management.
Its assertion 'interleaved-thinking-2025-05-14' in betas cannot hold because
anthropic_beta_headers_config.json maps that header to null for the bedrock
provider, so filter_and_transform_beta_headers drops it from the auto-added
beta set before anthropic_beta is written to the request.
The adjacent test_bedrock_invoke_messages_skips_thinking_injection_when_already_enabled
already covers the inverse behavior for the same model, so no coverage is lost.
Bedrock rejects clear_thinking_20251015 unless thinking is enabled or adaptive.
Inject minimal extended thinking and interleaved-thinking beta when Claude Code
sends context_management without thinking. Adds unit tests.
Made-with: Cursor
* feat(proxy): add NO_OPENAPI env var to disable /openapi.json endpoint (#25696)
* feat(proxy): add NO_OPENAPI env var to disable /openapi.json endpoint - Fixes#25538
* test(proxy): add tests for _get_openapi_url
---------
Co-authored-by: Progressive-engg <lov.kumari55@gmail.com>
* feat(prometheus): add api_provider label to spend metric (#25693)
* feat(prometheus): add api_provider label to spend metric
Add `api_provider` to `litellm_spend_metric` labels so users can
build Grafana dashboards that break down spend by cloud provider
(e.g. bedrock, anthropic, openai, azure, vertex_ai).
The `api_provider` label already exists in UserAPIKeyLabelValues and
is populated from `standard_logging_payload["custom_llm_provider"]`,
but was not included in the spend metric's label list.
* add api_provider to requests metric + add test
Address review feedback:
- Add api_provider to litellm_requests_metric too (same call-site as
spend metric, keeps label sets in sync)
- Add test_api_provider_in_spend_and_requests_metrics following the
existing pattern in test_prometheus_labels.py
* fix: ensure `litellm_metadata` is attached to `pre_call` guardrail to align with `post_call` guardrail (#25641)
* fix: ensure `litellm_metadata` is attached to pre_call to align with post_call
* refactor: remove unused BaseTranslation._ensure_litellm_metadata
* refactor: module level imports for ensure_litellm_metadata and CodeQL
* fix: update based off of Codex comment
* revert: undo usage of `_guardrail_litellm_metadata`
* feat: add pricing entry for openrouter/google/gemini-3.1-flash-lite-preview (#25610)
* fix(bedrock): skip synthetic tool injection for json_object with no schema (#25740)
When response_format={"type": "json_object"} is sent without a JSON
schema, _create_json_tool_call_for_response_format builds a tool with an
empty schema (properties: {}). The model follows the empty schema and
returns {} instead of the actual JSON the caller asked for.
This patch:
- Skips synthetic json_tool_call injection when no schema is provided.
The model already returns JSON when the prompt asks for it.
- Fixes finish_reason: after _filter_json_mode_tools strips all
synthetic tool calls, finish_reason stays "tool_calls" instead of
"stop". Callers (like the OpenAI SDK) misinterpret this as a pending
tool invocation.
json_schema requests with an explicit schema are unchanged.
Co-authored-by: Claude <noreply@anthropic.com>
* fix(utils): allowed_openai_params must not forward unset params as None
`_apply_openai_param_overrides` iterated `allowed_openai_params` and
unconditionally wrote `optional_params[param] = non_default_params.pop(param, None)`
for each entry. If the caller listed a param name but did not actually
send that param in the request, the pop returned `None` and `None` was
still written to `optional_params`. The openai SDK then rejected it as
a top-level kwarg:
AsyncCompletions.create() got an unexpected keyword argument 'enable_thinking'
Reproducer (from #25697):
allowed_openai_params = ["chat_template_kwargs", "enable_thinking"]
body = {"chat_template_kwargs": {"enable_thinking": False}}
Here `enable_thinking` is only present nested inside
`chat_template_kwargs`, so the helper should forward
`chat_template_kwargs` and leave `enable_thinking` alone. Instead it
wrote `optional_params["enable_thinking"] = None`.
Fix: only forward a param if it was actually present in
`non_default_params`. Behavior is unchanged for the happy path (param
sent → still forwarded), and the explicit `None` leakage is gone.
Adds a regression test exercising the helper in isolation so the test
does not depend on any provider-specific `map_openai_params` plumbing.
Fixes#25697
---------
Co-authored-by: lovek629 <59618812+lovek629@users.noreply.github.com>
Co-authored-by: Progressive-engg <lov.kumari55@gmail.com>
Co-authored-by: Ori Kotek <ori.k@codium.ai>
Co-authored-by: Alexander Grattan <51346343+agrattan0820@users.noreply.github.com>
Co-authored-by: Mohana Siddhartha Chivukula <103447836+iamsiddhu3007@users.noreply.github.com>
Co-authored-by: Amiram Mizne <amiramm@users.noreply.github.com>
Co-authored-by: Claude <noreply@anthropic.com>
* fix(ollama): propagate done_reason='length' as finish_reason for max_tokens truncation
Ollama returns done_reason='length' when a response is cut off by num_predict
(the max_tokens limit). Previously, non-streaming responses hardcoded
finish_reason='stop', and streaming used chunk.get('done_reason', 'stop')
which also defaulted to 'stop' when done_reason was absent.
This meant callers (e.g. the Anthropic pass-through adapter, which maps
OpenAI 'length' -> Anthropic 'max_tokens') could never detect truncation,
making stop_reason always appear as 'end_turn' even for cut-off responses.
Fix: read done_reason from the response JSON in the non-streaming path and
use `chunk.get('done_reason') or 'stop'` in the streaming path, so Ollama's
actual done_reason passes through to the caller unchanged.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* Update test_ollama_chat_transformation.py
* Update litellm/llms/ollama/chat/transformation.py
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
---------
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
Co-authored-by: greptile-apps[bot] <165735046+greptile-apps[bot]@users.noreply.github.com>
The Vertex AI count-tokens endpoint rejects model names that include
version suffixes (@default, @20251001, etc.) with:
"claude-sonnet-4-6@default is not supported for token counting"
The same model without the suffix ("claude-sonnet-4-6") works correctly.
Strip @suffix from both the model parameter and request_data["model"]
in handle_count_tokens_request before sending to the API.
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Bedrock /v1/messages streams can report cache tokens only on message_start while message_delta carries only uncached input tokens. Merge cache fields onto the final delta usage and clamp negative text-token remainders in cost calc to keep usage/cost consistent.
Made-with: Cursor
MaskedHTTPStatusError constructs a new httpx.Response from the original
error. Two bugs surfaced under real HTTP error responses:
1. The new Response was created without request=, so response.request
raised RuntimeError("The .request property has not been set.") for
any downstream caller (e.g. exception_mapping_utils) that inspected it.
2. The decoded response bytes were passed together with the original
Content-Encoding header. On construction httpx tried to decompress
the already-decoded bytes and raised httpx.DecodingError
("Error -3 while decompressing data: incorrect header check").
Set response.request to the masked Request and strip Content-Encoding
(and the now-stale Content-Length) before rebuilding the Response.
URL/message masking is unchanged; the new request carries the already
masked URL.
Also update test_logging_key_masking_gemini: the security commit
25f93bed91 moved Gemini API keys from ?key=... URL params to the
x-goog-api-key header, so api_base no longer contains the key.
* [Test] Add Azure async chat completion timeout test. WIP
* Capture TTFT for /v1/messages streaming responses
The pass-through streaming path for /v1/messages (Anthropic, Bedrock,
Vertex AI, Azure AI, Minimax) logged completion_start_time only after
the entire stream finished. async_success_handler then fell back to
end_time, making TTFT equal to total duration or null in the UI and
Prometheus.
Record the timestamp of the first chunk in async_sse_wrapper and
propagate it to model_call_details before the logging handler runs,
so gen_ai.response.time_to_first_token reflects the real first-chunk
latency.
Fixes#25598
* [Refactor] Implement timeout resolution logic in completion function
add fetch ``request_timeout`` from litellm_settings
* remove stale test case
* remove extra print statement
* default request timeout value in constants to 600s to match timeout defaults handled in the proxy
* fix request timeout if using default value from constants.py
* update code structure, test cases
* only override if the global timeout sets timeout to 6000s
* update code structure, move hard coded values to const and make the reslve function readable by moving fallback logic to a seperate function
* modify default timeout values, replacing hard coded ones with default values defined
---------
Co-authored-by: harish876 <harishgokul01@gmail.com>
Co-authored-by: Joaquin Hui Gomez <joaquinhuigomez@users.noreply.github.com>
Tighten validation of request body parameters in the proxy routing
layer. Use context variables for internal call state management
instead of passing flags through request kwargs. Clean up metadata
handling at the proxy boundary.
Add 50 tests across 3 files covering the new MaskedHTTPStatusError,
safe response helpers, _redact_string in error paths, Gemini
interactions x-goog-api-key header auth, and RAG ingestion header
usage.
Fix missing early-validation for Gemini API key in _get_token_and_url()
which caused TypeError when key was None (headers got None value).
Harmonize error messages between the two validation sites.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Gemini API keys embedded in URLs as ?key= query parameters leak through
httpx error tracebacks, which are then captured by traceback.format_exc()
and forwarded to logging callbacks, Slack/Teams alerts, and HTTP client
responses.
Short-term: all httpx.HTTPStatusError handlers now raise
MaskedHTTPStatusError(...) from None, which masks the URL and breaks
exception chaining so the original error never appears in tracebacks.
Long-term: moved all Gemini/Vertex URL constructions from ?key={api_key}
to x-goog-api-key header (Google's documented auth method), so the key
is never in the URL at all. WebSocket realtime is the only exception
since WS clients cannot use custom headers.
Additionally hardened all outbound credential paths:
- WebSocket close reasons now pass through _redact_string()
- Callback pipeline (failure_handler) redacts traceback_exception and
error_str before forwarding to integrations (Langfuse, Datadog, etc.)
- Slack/Teams alert messages redacted in send_llm_exception_alert,
ProxyLogging.failure_handler, and post_call_failure_hook
- HTTP error responses in proxy SSE and health endpoints redacted
- Exception messages in exception_mapping_utils redacted
- print_verbose() stdout output redacted when set_verbose=True
- HTTPHandler.put() now has MaskedHTTPStatusError (was missing)
Vertex AI rejects requests containing both search tools (googleSearch,
enterpriseWebSearch, urlContext) and function declarations with error:
'Multiple tools are supported only when they are all search tools.'
When _merge_tools_from_deployment() combines deployment-level search
tools with user-request function tools (e.g. via MCP), the mixed tool
list causes a 400 error. This fix detects the conflict in _map_function()
and drops search tools, keeping function declarations.
Non-search tools like code_execution and computerUse are preserved.
Fixes#23337
* fix: emit input_json_delta for tool args bundled in first streaming chunk
Some providers (xAI, Gemini) include tool_call function arguments in the
same streaming chunk as the function name/id. The AnthropicStreamWrapper
was discarding the trigger chunk entirely when starting a new content
block, which silently dropped the input_json_delta carrying tool
arguments. This caused tool_use blocks to arrive with empty input {}.
Now queue the processed_chunk after content_block_start when it carries
non-empty input_json_delta data. Backward compatible: providers that send
empty arguments in the first chunk (OpenAI-style) are unaffected since
the condition checks for truthy partial_json.
* test: add tests for input_json_delta emission on bundled tool args
Covers the fix for providers (xAI, Gemini) that bundle tool_call
arguments in the same streaming chunk as the function name/id.
Verifies the AnthropicStreamWrapper emits input_json_delta after
content_block_start, and that empty-arg chunks (OpenAI-style) are
unaffected.
* style: apply Black formatting to streaming_iterator.py
* fix: mirror input_json_delta fix to sync __next__ and add sync tests
* test: make no_extra_delta tests assert explicitly instead of passing silently
- Omit messages whose list content is empty after stripping thinking blocks
- Retry only on HTTP 400 plus invalid-signature body match
- Return response inline from retry loop; drop unreachable None guard
- Tests: thinking-only turn dropped, non-400 no retry
Made-with: Cursor
Strip thinking blocks from the request body and retry once when Anthropic returns an invalid thinking signature error (e.g. after credential or deployment change). Applies to all BaseAnthropicMessagesConfig providers (direct Anthropic, Bedrock, Vertex, Azure AI).
Made-with: Cursor
